Phylactery 19

This is Phylactery 19, continuing from session 17 which is still wrapping up, with nothing actionable required right now. I could quietly orient myself by checking the project memory and RECORD while waiting.

I should start by locating the project memory file and folder, then check the Phylactery project's RECORD.md for context before planning further.

Despite increasing the block size to 4096 frames, he's still hitting stuttering issues. I should check RECORD.md section 85 and look into the engine's web audio implementation for clues.

I'm questioning whether the stutter is truly caused by ScriptProcessorNode block replay, since the benchmark shows 4096 frames with no lateness yet he still hears it. I'm considering other possible causes, like the engine's own streaming decoder underrunning its ring buffer during file playback.

Actually, since web has no real threading, the resource manager's job thread for async streamed decoding wouldn't exist there, so I'm wondering how miniaudio handles page decoding in single-threaded mode -- whether unready pages cause silence gaps rather than the described repeating behavior. Since the symptom is "stutters and repeats portions," that pattern fits ScriptProcessorNode replay much better than a silent gap from a stalled decoder.

I'm also considering that Chrome's ScriptProcessorNode double-buffers, so if the JS callback misses its deadline, it plays whatever stale buffer was filled two periods ago -- which is exactly the known repeat glitch. There's also the possibility that newer miniaudio builds use AudioWorklet instead, which needs SharedArrayBuffer and COOP/COEP headers, or that the repeats actually stem from the game's own audio code retriggering sounds too rapidly, making them sound like stutters.

I'm wondering whether the live site is actually serving the newly deployed engine build, since the HTML referencing the hashed engine filename could be cached even if the engine file itself isn't. I also need to figure out if he's testing on the website or the desktop build, since desktop uses WASAPI with its own audio thread and would stutter differently than the browser's ScriptProcessorNode setup.

I suspect the audio commands lock a mutex that the game thread also holds during heavy work, so if the game thread blocks, the audio thread stalls and underruns, causing glitches on either platform. Rather than ask him for more details right away, I should check this mechanically myself by digging into the engine's audio code to find where that locking happens.

Reading 17's audio work: it attributed the stutter to late browser audio blocks and only adjusted block size. Now I'll dig into the engine's web audio path for other causes of sound repetition.

I'm thinking through how miniaudio decodes OGG data in memory—each sound play initializes a decoder over the compressed buffer, and the actual Vorbis decoding happens inside the audio callback itself, streaming via stb_vorbis.

Beyond that, I'm considering whether ScriptProcessorNode timing is the real culprit versus something environment-specific on his machine, and whether miniaudio's web backend actually respects the configured period size for the buffer. The earlier benchmark showing late callbacks dropping from 44 to 0 suggests that config change did take effect.

I'm also second-guessing whether the live site is actually running the updated engine build, or if a cached loader/service worker is serving an older version. Beyond cache, there's a separate possibility: main-thread stalls from the site's own rendering work (since the game runs embedded inside the site's host renderer) could be stealing CPU and delaying audio callbacks regardless of buffer size.

Actually, I recall now: when the main thread is late, Process reads the output buffer at the current double-buffer index before it's been refilled, meaning it contains stale data from two periods back — which produces the repeat rather than silence.

Then once the read/write index wraps around and the buffer is ready to swap, the audio thread tries to acquire the process event lock via a non-blocking tryLock, since it can't risk blocking if the main thread is busy handling previous requests.

If it fails to acquire the lock, the main thread is clearly overloaded, so the audio thread just zeroes out the output buffer itself rather than swapping. Otherwise, if there's a valid execution context, it posts a cross-thread task to fire the process event with the current double-buffer index and then swaps the buffers -- meaning if things are running late, the same zeroed buffer effectively gets reused instead of advancing.

I'm wondering whether Chrome's actual implementation behaves differently from this simplified understanding, and whether Firefox's ScriptProcessorNode handles lateness by outputting silence and letting latency grow rather than repeating audio. That makes me question what's really causing the reported repetition artifact rather than simple dropouts.

This loop processes audio in chunks sized to the intermediary buffer, feeding each chunk through the playback callback and copying it into the output buffer until the full frame count is handled. That logic seems fine, so the "repeat" issue must be coming from somewhere else entirely.

Maybe it's in the game's audio layer system -- something with set_pos/seek being called repeatedly, or how layered sounds handle looping versus one-shot playback, or the bitcrush/sample-rate-division defaults making audio gritty rather than actually repeating. I'm also wondering whether the decoder wrapping or FX voice node could be involved, and whether this issue even reproduces on desktop or is specific to one platform.

I don't actually know where he tested this -- if the previous fix was web-only and he ran it on desktop instead, the fix simply wouldn't apply. Earlier rounds were judged on the live site, but it's unclear what platform this current round used, so I need to check the transcript for mentions of platform, and also consider whether desktop's threading model -- miniaudio mixing on its own thread while the decoder reads from the main thread -- could introduce a synchronization issue that the web fix never touched.

What catches my attention now is the per-frame DSP tweaking for 3D-positioned sound effects -- gain and reverb send values gliding from current to target each frame. If that voice FX pipeline includes any kind of delay line, like a Haas-width or pre-delay effect, and its delay time is being changed per-frame without proper interpolation, that would produce exactly the kind of stutter-and-repeat artifact being described, since a jumping read position in a modulated delay line repeats fragments of audio.

The width effect could rely on an interaural-time-difference delay that shifts as the source moves, and if that shift jumps instead of gliding, it'd cause clicks and repeated-sound artifacts. I also realize the earlier benchmark only measured mixing cost between FX and plain voices -- it never actually checked for audible glitches, so that doesn't rule this out. I want to dig into how the voice FX is actually implemented.

The FX delay's slew limiting is fine and not the issue. Now I'm looking at DSPDataSource's bit/sample-rate crushing — with sr_div=3 doing sample-and-hold every 3 samples, I suspect the bug lies in how chunk boundaries interact with that hold logic when the decoder feeds data in pieces.

I'm thinking about whether a "frames read < requested" mishandling, combined with the block size change from 2048 to 4096, could cause repeated chunks through miniaudio's data source and resampler pipeline.

I'm considering whether the MA_AT_END handling in the decoder could cause repeated frames, but that seems unlikely to be the real culprit. More likely, miniaudio's Vorbis decoding path handles memory-based streaming differently than file-based reads, which could explain the repeating audio segments.

I should check the game's audio.lua for any seek or set_pos logic—since the user describes stuttering and repeated portions of sound, this smells like a voice being restarted via a seek call. I want to grep the codebase for sound_handle_seek, set_pos, or start_sec to confirm.

I've ruled out the 3D voice effect and the bitcrush wrapper as causes—both work cleanly. Now checking the game's sound code for logic that restarts or seeks a playing sound, and confirming which build the issue was heard on.

I'm looking at the doppler effect calculation, computing a pitch factor clamped between 0.5 and 2 based on listener and source velocities, then applying it with sound_handle_set_pitch only when the change exceeds a small threshold to avoid redundant updates. I want to check how this maps to miniaudio's pitch-setting function, then continue reading further down in the file.

Now I'm examining how FX voices handle their start offset through sound_play_fx seeking into the decoder, and considering whether the web build runs without threading since sounds decode fully into memory per-voice rather than through the resource manager's job system. I'm also wondering whether the site renderer's own audio (unlock sounds, site radio) routes through the same engine music path using a data-source-backed sound, and whether there could be a sample-rate mismatch between the web AudioContext and the engine's expected rate causing issues.

But that leads me to miniaudio's webaudio device init, which creates the AudioContext using the requested sample rate (or default if none given), so the engine should just match the device's rate — that's fine. The real puzzle is reconciling the bench showing zero late callbacks at buffer size 4096 during the fight scene with Seventeen still perceiving a stutter: either his actual machine stalls longer than what the headless bench captured, the cause isn't callback lateness at all, or he's running a stale cached build. The site now exposes a `?perf` flag that could show in real time whether a "replayed" indicator turns red exactly when he hears the stutter — the most direct way to correlate perceived stutter with actual late audio callbacks, though I want to exhaust mechanical checks first since his test loop is a scarce resource that shouldn't be spent on things a machine could catch automatically.

Now I'm recalling the actual Chromium source for ScriptProcessorHandler::Process to verify whether a buffer not finished before playback catches up to it actually gets replayed.

Updating buffer index logic...

Writing cross-thread event dispatch logic...

So the key insight is that the audio thread swaps buffers on a fixed schedule regardless of whether the main thread finished processing, and if the JS callback hasn't completed by the time that buffer is needed again, the audio thread just plays stale content from the previous cycle.

With BufferSize at 4096 frames and 48kHz, that gives roughly 85ms of slack for the main thread to fill the buffer before it's needed again, though the actual render quantum is 128 frames per Process call.

I'm also thinking about task scheduling: the posted task runs on the main thread's event loop, interleaved with requestAnimationFrame callbacks, so as long as frame work stays under the buffer's time budget it should be fine. Then I trace deeper into how the WASM audio callback chain works — from the WebAudio onaudioprocess handler down through the miniaudio playback callback into the engine's PCM frame reader — and whether this ever runs concurrently with the main game loop when using emscripten's async main loop.

I also wonder if Asyncify could be a factor, since calling back into wasm mid-unwind during an async sleep could cause issues, though that's probably not what's happening here. Beyond that, I'm questioning whether miniaudio's web backend period-size rounding could be contributing, or whether the "late" measurement itself is misleading — callbacks could look on-time relative to each other while still drifting from the actual audio clock due to a constant offset.

If the delay between posting and execution consistently exceeds the period, I'm reasoning that the audio thread would read a buffer slot before it's refilled, playing stale content, while the late fill only lands in time for the next pass through that buffer — effectively causing a steady-state repeat every two periods rather than random late callbacks.

uantum. And crucially the read index doesn't advance when the lock fails, so playback just stalls and resumes at the same spot — meaning fill(A) can overwrite a1 mid-buffer while A is still being played, producing a mix of repeats, skips, and silence gaps. That messiness matches "stutter and repeats portions," but if the delay is uniform, the gap between callbacks stays near P, so audio_stats wouldn't flag it as "late" — a real blind spot in the measurement.

I'm trying to figure out whether the main thread could consistently delay posting by more than 85ms; if it's just running normal ~30ms frames and idling, the delay should stay under 30ms, so it'd only become a persistent problem if the main thread were saturated at a low frame rate, where the event task has to wait for a gap between rAFs, bounding the delay to roughly one frame duration.

A better measurement is to compare AudioProcessingEvent.playbackTime against audioContext.currentTime inside the callback itself — if currentTime exceeds playbackTime, the buffer is already late and will be skipped or replayed, giving a direct lateness margin rather than inferring it from timing gaps.

I can estimate expected deadlines by tracking the first callback's audio time and extrapolating by buffer period, so that comparing currentTime against that deadline reveals if a callback ran late, independent of main-thread timing. The tricky part is that when the audio thread blocks on a failed lock, it outputs silence but the audio clock keeps advancing, so the buffer schedule itself slips relative to real time—making this measurable but subtle to interpret correctly.

A cleaner approach might be to actually capture the output samples themselves, routing the ScriptProcessorNode to a recorder alongside the engine's intended mix, then diffing the two to directly catch repeated or silent regions in what the speaker would actually hear.

Before building that, though, it's worth checking cheaper things first: whether the live site is actually serving the updated engine build, whether a stale service worker is caching an old version, and whether the game's site package was rebuilt with the new engine at all.

Actually let me check if 17 actually heard the stutter by looking at the session transcript tail — I'll grep the jsonl session logs for recent mentions of testing after the deploy.

I want to check item 17's discussion response around 16:15-16:18 to see what other hypotheses were offered there, so I'll look at the assistant texts from that window.

17 claims the engine had no 3D sound voices before that deploy, and the stutter report came right after the engine deploy at 14:37. That timing is a strong clue — even though 17 said the mix cost was unchanged, something else must've shifted with that deploy, possibly GPU-related.

Actually, I wonder if the FX voices are the real cause through an actual bug rather than cost — specifically how miniaudio's node graph handles a node with multiple output buses (dry to endpoint, wet to reverb). miniaudio caches a node's output once processed so subsequent reads of other output buses reuse that cache, but there's a caveat: the cached data size is capped by a default frame limit per bus, which could be the source of the glitch.

I'm now thinking through the actual read order: the endpoint pulls from the voice's dry bus and the reverb's output, while the reverb itself pulls the voice's wet bus separately, and the chunk sizes for these reads might not line up. If the cache-cap chunking differs between these reads, I'm trying to figure out whether miniaudio re-serves cached data correctly or risks double-advancing the source and causing skips.

So the processing only happens once per pull cycle on output bus 0, caching the result, then subsequent output buses just read from that cache until the last bus triggers clearing it. This implies output buses must be read in order, which matters for the dry/wet split scenario — if the endpoint reads bus 0 and the reverb reads bus 1, the order they're each processed in determines correctness.

I should check the actual vendored miniaudio source to see how ma_node_read_pcm_frames handles the per-bus cache, since the cache cap is fixed at 480 frames per bus regardless of period size, meaning ordering bugs would show up independent of block size.

Desktop uses FX voices too (since round 67), and he's tuned sounds there extensively since round 68b without reporting repeats, suggesting the node graph itself works fine there. One difference: desktop's WASAPI period is roughly 480 frames, matching the cache cap exactly, while the web's 2048/4096-frame reads don't -- that's a plausible block-size-dependent trigger worth digging into in how ma_engine_read_pcm_frames pulls from the node graph.

The timing points to the reverb-splitting engine from change 17, and since desktop's small audio blocks never stuttered, I'll check whether miniaudio's node graph mishandles a two-way voice split when blocks are large.

I'm tracing how miniaudio's multi-output cache works: a node with two output buses processes once per period, caching up to 480 frames per bus, and uses per-bus "has_read" flags to decide whether to pull fresh data or reuse the cached buffer when a bus is read.

So I'm realizing the bug: the endpoint drains one attachment's bus fully across the whole requested frame count before moving to the next, so repeated reads of bus 0 keep retriggering fresh processing and overwriting bus 1's cached wet data — meaning the reverb bus's output gets stomped before it's ever consumed.

When the reverb node finally reads bus 1, it sees the flag flipped true again and triggers another full process call on the voice, advancing its decoder source further and consuming audio the dry path never played. That explains the symptom: the dry signal jumps forward (skipping consumed frames) while the wet path hears a stale repeated chunk followed by fresh ones — stutters and repeats.

I'm now tracing how frame counts actually propagate: ma_engine_read_pcm_frames calls into the node graph's endpoint read, which is a passthrough that requests the full device callback's frame count directly from its input bus. On desktop, that period size is typically ~480 frames at 48kHz, matching the cache cap exactly — so each period naturally becomes one chunk and the bug never shows. But on the web, callback buffer sizes of 2048 or 4096 frames vastly exceed 480, triggering the multiple-read bug within a single callback. I'm also checking whether global/local time tracking in ma_node_read_pcm_frames could mask this, but the has-read flag mechanism doesn't reference time at all, so it wouldn't help.

I'm also considering the reverb node — it's a continuous 1-in-1-out processor, not a passthrough, so it also goes through this same caching path when the endpoint reads it in sub-480-frame chunks.

For VoiceFx with its two output buses: the dry bus consumes the source in 480-frame chunks, but the reverb reading bus 1 would pull from cached wet data that may be misaligned once frame counts exceed 480, since the two buses aren't being read in lockstep.

Then it skips roughly 75ms before playing another 85ms chunk, producing that stuttering, repeating artifact from the reverb echoing cached chunks. I'm now wondering whether this is really always audible, or only under certain conditions — maybe it depends on whether the wet bus gets attached to the reverb at all when wet is zero for a voice. I should check whether the attachment happens unconditionally for every FX voice, since that would mean both buses get read each period regardless, driving the source consumption rate.

If it doubles for every FX voice, then every sound would be chopped and sped up, which seems way too noticeable to match "sometimes, not all sounds." So the explanation must hinge on ordering — whether the reverb node sits at the front or back of the endpoint's input bus list, since that determines whether the dry voice gets fully consumed before the reverb reads the same bus a second time.

Then the dry bus gets read with its flag false, serving stale cached data misaligned in time, while the next chunk flips the flag and reprocesses fresh — so the source is effectively consumed twice per period, which should break every FX voice when frame counts exceed 480. But that can't be right since not all 3D sounds sound broken — he specifically says it's just stutter and repeated portions, not universal doubling, so maybe short sounds behave differently and I need to recheck the actual frame counts the engine reads.

I'm trying to pin down what processingSizeInFrames actually gets set to inside ma_engine_init, and whether it's tied directly to the period size or configured independently through the node graph config — I'm not fully certain of the exact field name or default in this miniaudio version. I also want to check whether the web backend's ScriptProcessor callback delivers the full buffer or chunks it through an intermediary buffer of a different size, and whether that size could explain the discrepancy, plus whether WASAPI's typical 10ms shared-mode period (480 frames at 48kHz) lines up suspiciously well with the cache's frame cap — which would explain why it works fine on some devices and not others.

This makes me realize I should test this more mechanically: render the same audio with FX voices using small 480-frame reads versus large 4096-frame reads and compare the outputs directly, since that would reveal whether the bug is really about buffer sizing. I also want to re-verify my claim that the source gets consumed twice by tracing through the node's slow-path read logic, where it resets all output buses' "has_read" flags before processing a batch of frames and only marks the current bus as read afterward — if multiple buses pull from the same source during one processing round, that's where the double-consumption would occur.

I should check if ma_engine sets processingSizeInFrames with a periodSizeInFrames of 4096 — if not, the graph just processes the whole buffer in one pass. This also explains why the earlier cost measurement missed the issue: before the engine deploy, voices without FX bypassed the node-splitting path entirely, so the bug only appeared once FX voices were introduced at 14:37.

Thinking through why not all sounds are affected: with F=2048, the dry path plays 2048 frames then skips 1568 frames in a repeating pattern, which should sound like a choppy forward-skip. But for short percussive sounds with sharp attacks under 40ms, that skip might fall outside the audible window and go unnoticed.

For sustained sounds like loops, whooshes, the beam, or lift hum, that same stutter would be clearly audible. The reverb's cached-chunk misalignment could also cause it to replay portions of a stale dry chunk, which fits a "repeats portions" description better than a pure skip. A looping voice might maintain continuity overall but still sound chopped because its source consumption rate doubles.

Since every sound plays as an engine FX voice via sound_play_fx, all voices should attach to the wet bus the same way -- so the "not all sounds" distinction likely comes down to transient versus sustained sound types rather than which voices get FX treatment. I want to check whether the wet bus attachment order differs across voices, so I should keep reading through the rest of sound_play_ex.

I think I found the bug: our dual-output node only works correctly when audio blocks stay under 480 frames, but web blocks are 2048 (or 4096 after change 17), causing the dry/reverb reads to skip ahead and replay cached audio—explaining both the glitch and its timing with the 3D-voice deploy. I'll confirm this in the code now.

In version 0.11.23, since processingSizeInFrames matches the device's period exactly, the cache size equals the period too—so each read spans a full cache window, meaning the multi-output node gets processed once per period cleanly: the first bus read marks has_read true, and the second bus just pulls from cache without issue. That alignment is why the bug wouldn't surface in this case.

But I'm second-guessing this for the web backend. The agent/noDevice mode might set periodSizeInFrames to 0, which would make processingSizeInFrames 0 while the cache stays at 480 frames—and if audio rendering reads in 800-frame chunks, that mismatch could break FX voices in rendered video audio. That's a separate concern worth flagging, though not the user's current issue.

I want to trace how the webaudio backend's onaudioprocess handler actually calls the playback callback—specifically whether the frame count passed in always matches processingSizeInFrames, or if it can vary based on the intermediary buffer size. If the data callback gets called with a frame count that doesn't match the node graph's expected processing size, that misalignment could be exactly where the caching bug creeps in.

I'm tracing through how the node graph reads frames: it always requests exactly the processing size P from the cache, and the endpoint node is a passthrough that reads its input bus for P frames too. The first attachment on that input bus reads directly up to P frames, and since engine sound nodes have no input buses, they hit the fast path and pull straight from their underlying data source for up to P frames.

This all seems consistent for 0.11.23, so my hypothesis about a mismatch between cache size and processing size is probably wrong. I need to check the web backend specifically to see if it reports a different processing size than what the node graph assumes.

I'm checking whether cached buffer views could become stale if wasm memory grows, but the length-zero check for detached views already covers that case properly.

Shifting focus to the deploy timeline: the issue seems tied to when FX voices and reverb were added to the site engine. Before that, the game used plain voices without spatialization, so maybe the new FX voice + reverb path is broken specifically in the web build — possibly something in how the reverb node handles frame processing or stack allocation defaults that doesn't translate correctly to the web backend.

Now I'm examining the voice FX node's processing flags — since it's not marked continuous, when a sound ends there's no tail flushing, but that's likely fine. What catches my attention is that the VoiceFx process function seems to use the output frame count to index into the input buffer without checking whether the input count is actually smaller, which could mean it's reading uninitialized or stale data when the input produces fewer frames than requested.

Pitch changes and resampling in the sound node seem fine on desktop, as does doppler-driven pitch modulation. Stepping back, I think timing is the strongest discriminator for isolating the real issue here.

Checking the timeline: the game had sound on the site since round 78/session 15, and session 16 covered web performance work. In session 17's first message he mentioned load times being slow, and the stutter report came at 15:02, after a 14:37 deploy — I'm trying to pin down whether that deploy is what introduced the regression.

The 14:37 deploy also shipped packs and async shader compilation alongside whatever else, and packs seem to delay when sounds load — around 50 sound slots were getting loaded in a single frame instead of being spread out (now throttled to 3 per 50ms). Since I can't directly hear the browser's audio output, I'm thinking I need to build a measurement harness that captures what's actually produced and compares it against the engine's intended mix to detect skips or repeats.

I'm looking at how webbench runs headless Chrome via puppeteer/CDP, and I realize I could inject JS to tap into miniaudio's ScriptProcessorNode directly — connecting it to an AudioWorkletNode recorder that posts sample blocks back to the main thread, capturing exactly what gets played without any loss in the message queue.

A simpler approach: wrap onaudioprocess to capture the engine's mixed output per callback with a sequence number, then detect replay bugs by checking if any non-silent block of samples exactly repeats an earlier block two buffer-lengths back — that directly flags the ScriptProcessor duplicating output.

If stutter shows up in this reconstructed engine-intended stream, the bug lives in the engine's mixing; if it only appears in the actual captured audio output, it's a delivery/timing issue from ScriptProcessor lateness. Detecting stutter automatically in the engine's intended mix is harder in general, but could be made tractable by rendering a known reference sound and diffing against its expected decode.

A cleaner isolated test would be a minimal page that plays a single sustained FX voice through the web engine, captures the output, and compares it to the plain decoded sound file — if discontinuities appear only on web and not desktop, that confirms an engine-side bug specific to the web build. Though using real in-game scripted sound events is complicated since the web bench runs in real-time, so sound-start timing relative to audio frames won't be deterministic the same way desktop is.

Let me reconsider other web-specific possibilities: the resource manager job processing looks fine, and the sound data pointer living in wasm heap isn't an issue either. I'm wondering whether there are two separate engine instances — the site renderer and the game itself — each potentially running their own miniaudio device, AudioContext, and ScriptProcessorNode on the main thread, though that doesn't seem to be the root cause. I should also check whether the site's own radio feature interacts with audio playback in a way that could introduce glitches.

The webbench numbers suggest the main thread mixing is fast enough on its own, yet the user still hears glitches, so the quickest way to get decisive data would be to check the live audio stats while actually reproducing the issue. Rather than relying on manual reporting, I could run the capture experiment myself in headless Chrome using webbench with a real audio graph, since disabling audio output still pulls samples at real-time rate and would let me test this directly.

I'd tap the output with an AudioWorklet recorder, save it to WAV, and detect replays while also logging the engine's per-callback blocks, then compare the actual audio against the intended signal for discontinuities rather than needing to listen myself. The deeper question is whether Chrome's ScriptProcessorNode timing with a 4096 buffer size, firing roughly every 85ms, could be missing its deadline on a heavier or weaker device than the one used in benchmarking, since that's the main variable not yet accounted for.

I'm recalling that "his admin device" likely refers to his desktop PC browsing with an admin cookie, since site redesign judging happens on the live site. I want to look at webbench's existing capabilities to extend it for this capture experiment, and also sanity-check an alternative hypothesis around the reverb node's bus wiring and how miniaudio's sound attachment flags interact with threading — noting that on web there's no race since everything runs on the main thread, whereas on desktop miniaudio handles that via lock-free lists.

Tracing the cleanup path, uninitializing the sound detaches it from VoiceFx first, then uninitializing the FX node detaches it from the endpoint/reverb — that ordering seems fine, and any cut reverb/delay tail on sound completion is negligible (under a millisecond). I'm also checking whether `ma_sound_is_playing` could falsely report false before playback actually starts, but that traces back to node state correctly. Now I'm considering another web-specific angle involving looping behavior and loop_end handling as a potential culprit.

Checking whether voice slot reuse or frame-restart logic could explain the stutter, I realize those bugs would show up on desktop too, and he never complained of repeats there — so this looks web-only. Comparing his session logs, the "stuttering" complaint seems to have started appearing only after he began listening carefully on the site, and later sessions pin it down specifically as "stutter and repeats portions of the sound" during playback, distinct from general frame slowness. I'm now looking at what's actually different between plain and FX voices on the web, since CPU cost measured the same for both.

I'm wondering if the real issue isn't buffer size at all but something deeper in Chrome's WebAudio threading: the ScriptProcessor's bufferSize might get clamped or rounded internally rather than respecting the configured 4096, though a benchmark seems to confirm 4096 is actually applied. More likely, the repeats come from a locking issue — while the main thread holds a buffer lock during the onaudioprocess callback (which takes 2.5-3ms average but spikes to 6-10ms), the audio thread's own processing cycle every 128 frames could get blocked and cause exactly this kind of glitch.

I'm trying to recall the actual Chromium source for ScriptProcessorHandler to confirm this theory — specifically whether a failed TryLock causes it to zero out the output buffer and skip the index instead of waiting, which would explain the symptom precisely.

I think the newer implementation only holds the lock briefly during buffer copies, not while the JS callback runs, so lock contention alone shouldn't cause the delay he's seeing. That makes me focus on the real deadline: the main-thread task that copies back the output buffer must finish within one period (85ms) of being posted, so I'm wondering what could make that task run long on his machine specifically—maybe GC pauses or something about how FireProcessEvent gets scheduled on the main thread.

One possibility is Chrome's scheduler deprioritizing the audio task because the page is busy with heavy rAF callbacks—if a game frame eats most of the 16.7ms budget, the scheduler's "rendering starvation prevention" or compositor-priority policies could push normal-priority tasks like audio processing back by several frames, especially during active input like scrolling or mouse movement. Still, with ~4-5ms of idle time per frame, that alone seems like it shouldn't cause major starvation.

The benchmark's measured "late callback" gaps (≤1.25x the period) suggest no actual scheduling delay in this specific test, meaning there should theoretically be no replays. If the user is still hearing repeats, either the benchmark environment differs from their real setup, or what they're perceiving as "repeats" isn't a block replay at all. I think the most reliable path forward is building a ground-truth capture system directly in the web bench—recording the actual audio output via an AudioWorklet in the same context alongside the engine's internal block stream, so I can directly detect block replays and compare plain versus FX-processed voices spectrally to isolate where any artifact originates.

I keep going back and forth on whether the engine's graph processing could itself be producing broken audio through a buffer-size mismatch between the node cache and what's being pulled each period, tracing through how miniaudio's device init sets the processing buffer size, how the webaudio backend's period size compares to desktop, and how multi-output nodes like the reverb bus get read exactly once per period without duplication or skips. After reasoning through various scenarios involving sound restarts, resampling, and decoder sample rates, I conclude that theorizing further isn't productive—I need to actually extend the web bench with dual output taps (one capturing real browser output, one capturing the engine's internal stream) so I can directly measure for repeated blocks, misalignment, or dropped frames, while also considering whether a similar engine-level test could be run on desktop in agent mode to confirm or rule out a buffer-size bug separate from the web issue. I want to check exactly where the processing buffer size gets set during device initialization to confirm my assumption about the web's period size.

I'm tracing how the intermediary buffer capacity gets set from the period size, noting that if it's zero it falls back to a default period size in milliseconds when both device init parameters are unset.

Pre-17, periodSizeInFrames was 0 so intermediaryBufferCap defaulted to 480 frames at 48kHz, matching the engine's processing chunk size and node caches consistently against the web ScriptProcessor's 2048-frame blocks. After the change setting periodSizeInFrames to 4096, everything scales consistently to 4096 instead, so that line of reasoning about an inconsistency doesn't actually hold up — I need to look elsewhere, maybe at how callbacks and period_ms are tracked in the audio stats and how the fixed-size wrapper split the 2048 block into smaller chunks before the change.

Maybe the real culprit is audio.lua's voice update: targets are set every frame and the fx gain glides linearly across each block. On web, blocks span 4096 frames (85ms), so even though the main thread updates targets ~5 times per block, only the latest snapshot at callback time gets used — but the slew rate over that much longer block could cause audible lag or mismatched timing compared to native's shorter blocks.

Doppler pitch changes applied once per block would also produce stepwise jumps rather than smooth glides, but that's a pitch artifact, not repeating audio. The more likely explanation for "stutters and repeats portions" is that some event is retriggering the same sound from the start multiple times in quick succession — essentially a machine-gun restart — and this could be frame-rate dependent, meaning the web's lower or more variable frame rate causes the triggering condition to fire repeatedly in a way that doesn't happen at native frame rates.

I'm also considering the voice-limiting logic in audio.lua, where loop voices beyond a `max` count stay registered but silent, and wondering if one of these silent voices could get started partway through when another voice frees up, producing a jarring mid-sample restart. Another possibility is that asset packs load late on the web, so sounds might begin playing before they're fully ready, causing truncated or repeated playback until the full buffer is available.

Wait, that stutter would've been audible on desktop too during tuning, unless the deployed site's sound_picks.lua differs from what's on his desktop — maybe the deploy process regenerates it separately, introducing a mismatch.

I'm also considering delayed layer scheduling — timers using the delay/fade-in fields — and whether web frame hitches could cause delayed plays to fire together or out of order, though that seems more like timing drift than true repeats. I think the best approach now is to just capture ground truth, so let me look at the webbench tool.

The node graph checks out at 4096, ruling out that theory. I'll stop guessing and capture ground truth instead—tapping what the browser actually plays versus what the engine mixed to spot the repeats directly, starting with the bench tooling.

I'm planning how to capture audio output for analysis—injecting JS to wrap the audio processing callback so it copies output buffer channels into a tracked array alongside timing data like performance.now() and playback time, so I can inspect what the engine actually produced.

I'm weighing recorder options: an AudioWorklet or a secondary ScriptProcessorNode to tap exact PCM data, versus lossy alternatives like MediaRecorder with opus encoding. I need to check whether AudioWorklet's secure-context requirement is satisfied under file:// in Chrome, and whether a ScriptProcessor recorder's input buffer stays reliable if the main thread falls behind.

For the audio-thread recorder, memory for a 40-second capture at 48kHz stereo is small, so I can keep both channels. I'll transfer the captured samples to Node as base64 chunks, then in Python align the recorded stream to the engine's 4096-frame blocks by finding the first non-silent block as an offset anchor, then walk through and match each output segment against engine blocks using exact equality since the tap point is before any destination processing.

This lets me detect anomalies like repeated blocks, skips, or silence — and I realize Chrome's double-buffering means a late block would actually replay content from two periods earlier, showing up as a repeat of index k-2. If delivery turns out clean but the audio still stutters, the bug must be in the engine's mixing itself, which I could test separately by comparing a sustained FX voice's output against an independently decoded reference. But the delivery capture is the most direct starting point, and I need to make sure the benchmark setup actually mirrors the conditions he's experiencing.

Let me check whether WEBBENCH_SITE=1 runs the setup "as hosted," meaning the site renderer plus the game, and look into build.py to confirm what that flag actually does.

I'm adding a WEBBENCH_TAP mode to run.mjs that waits for audio initialization then injects a script to capture miniaudio device data directly from the running page.

Still writing capture script...

I'm wondering whether a zero-output AudioWorkletNode gets pulled automatically in Chrome, or whether I need to give it a silent output connected to destination to guarantee it's processed each render quantum.

Checking the data volume: posting every 128-frame quantum works out to roughly 375 messages/sec, which over a 40s run totals around 15MB — manageable for the main thread to buffer. I also confirm wrapping the existing `onaudioprocess` handler is safe since the bench already does this, and copying the float buffers per block is trivial overhead, so at the end I just need to serialize the collected data to base64 for output.

I'm concatenating the engine blocks' L/R Float32Arrays alongside metadata and the output stream indexed by currentFrame, then transferring it via CDP with returnByValue — since 15MB base64 could balloon to ~20MB, I'll chunk the transfer (e.g. 4MB pieces) to stay safe with websocket limits. For analysis, I plan to verify currentFrame continuity in the output stream and then hash 128-frame sub-chunks of each 4096-frame engine block to map them against each output quantum.

I'm checking whether the matched (block, offset) sequence over time advances monotonically by one quantum each step — backward jumps would indicate replay, and quanta with no matching block (that aren't silent) would indicate something unexpected. I'm also considering whether Chrome's ScriptProcessor copy-out and 128-frame alignment guarantees exact byte correspondence, and whether lock contention could cause unexpected silent quanta.

Silent quanta (all zeros) will match everything trivially, so I'm treating those as unknown rather than forcing a match. Then separately, I need to figure out whether the engine's own audio content stutters, which is harder to verify without a model of expected output, so I'll start by reviewing the bench results and audio logs, making sure bench.lua's warm-up and measurement windows give enough representative sound activity, and maybe timestamp sound events for better correlation.

I'll gate this addition behind WEBBENCH_TAP=1 in run.mjs, writing output to a scratch tap directory, and I want the engine blocks to record timing so they align with BENCH frame logs. Since the audio device might exist before the key event (created at init but suspended), I'll poll for its existence before installing the tap rather than waiting for the key—polling each second until the device appears, which works fine given AudioWorklet's async module loading. I'm also confirming that file:// URLs count as secure contexts in Chrome, so AudioWorklet should function without issues, and I'll proceed to implement this.

Now I'll write the capture: a tap in the web bench that records sample-by-sample what Chrome sends to the speakers, plus every block the engine mixed, so repeated, skipped, or silent pieces are clearly visible.

After playback finishes, I want to dump the captured tap data to disk, pulling the buffered window data and writing it out as separate float and JSON metadata files for both the engine and output streams, with each block interleaving left and right channel frames.

For the out stream specifically, I'm tracking frame index, sample count, and a hasData flag alongside the interleaved audio. To transfer the raw bytes efficiently, I'm base64-encoding the data in chunks via btoa and fetching it in 3MB slices rather than one giant payload, and I need to make sure I stop the recording flag before dumping so the buffer doesn't keep growing mid-transfer.

For tap.js, since it's evaluated with awaitPromise, I need the file to be an expression resolving to a Promise, so I'll wrap it as an async IIFE.

The context being suspended is fine since addModule works regardless, and the worklet just won't run until resumed. Now I'm sketching out tap_check.py: it'll load the metadata and the raw engine/output float buffers, split the engine data into blocks, and verify the output's frame continuity against the engine blocks using a hash table for matching.

For each engine block I'll index small chunks of L/R data keyed by their byte content, skipping silent ones, then for every output quantum look up matches to figure out which engine position it came from—flagging anything unmatched as unknown. The real goal is comparing the expected sequential position against the actual output index to detect anomalies like skipped content, repeated blocks, or inserted silence, which I can track by watching how the "lag" (difference between expected and actual position) shifts over time rather than staying constant.

I also want to compare each block's callback timestamp against when it actually gets scheduled to play, so I can diagnose whether late callbacks are causing playback delays. Time to write the actual script implementing this analysis.

I should build the hosted bench page with WEBBENCH_SITE=1 and run it with WEBBENCH_TAP=1, making sure the engine build reflects the recent 4096 change, and check that no other session is using the bench concurrently.

While that process runs, I can simultaneously check the engine's audio mix by playing game sounds in desktop agent mode and pulling the mix in chunks. But I realize agent mode's processing size is 0 with a 480 cache, so pulling 4096-frame chunks would trigger a multi-output caching mismatch -- this is an actual bug in agent-mode audio rendering that affects videos from agent runs.

Let me check the audio_render code around line 28019 to confirm the chunk sizes involved.

I'm tracing through how miniaudio handles frame counts in agent mode: pulls request 800 frames but processing size and node cache sit at 480, so when VoiceFx has two outputs needing more than the cache holds, the source ends up getting consumed by both buses. I want to double-check this against how the endpoint passthrough actually reads from the input bus and how attachments pull from VoiceFx's first bus.

Working through the simulation, the first bus consumes input in two rounds (480 then 320 frames) to fill its 800-frame request, which leaves bus 1's cache holding only the leftover 320 frames from the second round. When the reverb then tries to read bus 1, it gets that stale 320-frame cache first, then has to pull more input to catch up -- meaning the underlying voice data effectively advances faster than it should, which explains why the voice sounds sped-up and choppy specifically in agent renders.

This doesn't touch the web device path since there the processing size equals the period size, but it's a real bug in audio_render that's worth flagging even though it's not what the user originally complained about. I want to double check whether the web engine's processing size is really locked at 4096 frames, and trace back through how periodSizeInFrames and periodSizeInMilliseconds interact in ma_device_init, especially since the defaulting behavior changed in commit 17 -- I need to verify whether before that change the processing size and block size were still consistent with each other.

The reported numbers (2048-frame blocks at 42.7ms) suggest the intermediary buffer cap may have been 2048 at that point rather than what I'd been assuming. I'll hold off on further speculation and wait for the benchmark results to clarify this.

While I wait for the notification, I'm also considering an independent way to check the content side — if the delivery pipeline turns out clean but repeats are still heard, the artifact must be in the engine's mix itself, so I could capture that tap stream as a WAV and inspect it directly.

A decisive test would be to play a single known sound alone with static gain, capture the engine's output, and cross-correlate it against the decoded source file — any misalignment would expose skips or repeats. Doing this would need some way to trigger it from the bench page, maybe through Lua changes or a JS-to-Lua call, but I'm not sure that path exists, so I'll hold off and wait for the results first.

I'll wait for the bash background notification instead, and meanwhile check the has_read init flag in miniaudio to confirm the agent-mode bug.

I'm thinking through whether the has_read mechanism could break if a new FX voice is added mid-period, checking that both the reverb and endpoint buses still get read exactly once via the state-by-time-range check.

For finished voices, I trace through the frame counts: zero frames in means zero out, cache stays empty, and both the endpoint and reverb correctly handle the zero-frame case without issue. The cross-period ordering looks fine too since both nodes remain started, so I'm ready to move on to benchmarking.

I'm puzzled that the tap install loop never logged a successful "tap on" message, suggesting every attempt returned "no device," yet the audio check afterward somehow found device 0 — so the device must only materialize under some other condition I haven't pinned down yet.

Likely an exception is being swallowed silently inside the evaluated script, probably because `audioWorklet` isn't available in that context, causing the whole setup to fail and retry endlessly without ever connecting — meaning `window.__TAP` exists but `pack()` is undefined, producing "nothing recorded." I should add exception logging to the run script, guard against re-wrapping with a tried flag, and rerun to confirm.

I'm considering whether AudioWorklet might not work for file:// pages, which would require a fallback like a ScriptProcessorNode-based recorder tapping into the same audio buffer used by the miniaudio script node.

A better alternative might be using MediaStreamAudioDestinationNode with MediaRecorder's 'audio/webm;codecs=pcm' format, which Chrome supports and should give lossless float32 PCM audio. I could then decode the webm with ffmpeg to get exact raw float samples, since the context rate should stay bit-exact throughout. I think I should just check the actual error first before going further down this path.

The blob worklet is failing because of the opaque origin for blob:null, which likely isn't compatible with worklet loading in this context. I'm thinking I could try using a data: URL instead for the module script, since those might be permitted for worklet loading even under file:// origins.

Separately, I'm also noticing a worst-case gap of 104ms between callbacks, which falls short of the 1.25x period threshold for being flagged "late," so it probably doesn't trigger a replay — but I should double check that reasoning. Let me switch to testing with a data: URL.

I'm wondering about the "context running" state flagged at tap time versus key time—maybe that's an autoplay policy artifact. Checking the quanta math: 481 engine blocks times 32 quanta gives 15392, but output shows 15187, so there's a discrepancy to dig into.

The bench shows only 3 small lag events, each a +1 quantum shift representing a brief 2.7ms silence from a failed TryLock on the audio thread—essentially minor clicks rather than repeats or skips, with no replays or unknown gaps. I'm now double-checking what the "-152 to -151" lag transition actually represents.

But something's puzzling me about the callback spacing data: a 288ms max gap between callbacks on the audio clock spans more than 3 render blocks, yet no replays were detected, which seems contradictory unless currentTime readings are stale or cached, or unless Chrome queues multiple callback tasks that fire back-to-back after a main-thread stall without actually causing audio replay.

Looking at timing, that 288ms gap likely occurs during startup, probably from the boot sequence blocking synchronously while the context is already running, which also explains some of the silent quanta appearing early on. With no repeats detected, I'm now double-checking the engine's own mixing logic, the buffer sizes matching at 4096, and whether there's an issue in the endpoint read or processing size handling within the VoiceFx and reverb node.

Since the engine content itself seems correct, any repeats would have to come from the game logic rather than the audio pipeline. I could try comparing against a desktop mix of the same events, or run an autocorrelation analysis on the recorded wav to detect repeated segments, though that's likely to be noisy given non-periodic content. It's probably more useful to step back and focus on what's actually different between the bench setup and the real-world experience -- starting with the fact that the bench runs the game standalone rather than embedded within the site's renderer.

On the live site, the game runs inside the Anchor-based site renderer alongside the site's own Lua frame, meaning the main thread handles both the site's work and the game's work each frame -- more main-thread load means more chance of a late audio block, though with a 4096-frame buffer giving about 85ms of slack, the site's extra work would need to be pretty severe to cause an underrun. I'm also considering hardware differences: his real machine with Chrome, extensions, and background processes, versus the bench's fake audio output driven by a timer rather than real WASAPI hardware timing with its own buffer size.

I'm also wondering if his AudioContext sample rate differs from the bench's 48kHz -- if his hardware runs at 44.1kHz, the engine would resample 48k audio assets at runtime, which is extra work not present in the fake-device bench. And I should check whether the ending sequence, not just the fight sequence, was covered by our benchmark run.

Thinking about the bench's conditions more: it has all files loaded into the filesystem upfront, unlike the live site where sound packs may load late or stream in. So the bench showing clean delivery doesn't rule out the live site stalling or repeating sounds due to that difference -- he mentioned the fix from build 17 didn't resolve the stuttering, and I should check whether the perf overlay's audio replay indicator (which flashes red on repeats) actually lit up for him or not.

Now I'm working out exactly when Chrome considers an audio block "replayed" -- it posts the cross-thread event for the buffer that just finished being read and played, meaning JS has one full period to fill that buffer's output before it's needed next. If a lock contention happens between JS writing and the audio thread reading, the audio thread falls back to outputting silence instead of waiting.

I'm tracing through the case where the main thread's handling of fire(A) finishes after T+P: by then the audio thread has already started reading A's stale content again, causing an 85ms replay, until the mid-flight swap under lock updates it with fresh data partway through. I'm defining the callback gap as the delay between when fire(A) and fire(B) actually complete relative to T and T+P, since a replay only occurs when that delay crosses certain thresholds.

I realize audio_stats' "very late" flag (gap > 2P) isn't equivalent to a true replay condition — if callbacks are already consistently late, an additional small delay can push them past the replay threshold while audio_stats still only reports it as "late," not "very late." That means audio_stats undercounts the real problem, which is why my own tap measurement is more trustworthy — and in this run it confirms no replays despite audio_stats logging one "late" callback. To actually diagnose the issue on his end, I think the best path is to just ask him to report the ?perf numbers directly from the live site.

I could log sound playback calls with timestamps and names, then scan for the same slot firing repeatedly in a short window — that retriggering pattern would look like a stutter. Some slots have a gap setting, so if a slot fires on every tick, like an impact sound triggered per hit across many entities, that could be the culprit.

Then again, maybe the web's lower frame rate forces multiple game ticks per rendered frame to catch up, bunching several plays into the same audio moment — though that would cause overlap and loudness rather than stutter. Unless it's a timing quantization issue: the audio engine mixes blocks of samples at fixed intervals, so any sound triggered mid-period actually starts at the next block boundary, effectively snapping all playback starts onto a coarser timing grid.

This explains loop/retrigger effects like beams or lifts stuttering, and why desktop's 10ms period makes quantization negligible while the web's larger blocks make it worse — especially since the change to 4096 at step 17 worsened the 85 ms clumping. I'm now questioning whether sound starts are truly quantized to the block boundary: the game calls sound_play on each rAF frame, but the actual mix only happens when the next onaudioprocess callback fires, processing frames from the start of that buffer.

All sounds triggered between two callbacks get glued to that same buffer start, so yes — classic start-time quantization from ScriptProcessor mixing without proper scheduling. The game's own 16ms frame rate adds a tiny bit of quantization too, but that's negligible next to 85ms. I'm weighing whether this matches the bug report's phrasing: rapid retriggers (like a beam's zap sound or a lift's per-object cue) spaced evenly at, say, 20Hz would get unevenly bunched into blocks — some blocks getting two starts, others one — creating an irregular rhythm, though that reads more like stutter than literal "repeated portions."

Without access to the live site I can't confirm whether ?perf actually shows audio replays, so I should think about what fix would address all the web-specific causes at once: moving the mix off the main thread into an AudioWorklet. That raises the question of whether SharedArrayBuffer (and thus COOP/COEP headers) is strictly necessary, or whether I could instead have the main thread mix ahead by 100-150ms and stream Float32Array chunks to the worklet via postMessage without SAB.

If the worklet underruns, it should output silence rather than repeat a buffer — this avoids the "replay" artifact, though it's still a dropout. Mixing in small chunks tied to how much the worklet has actually consumed could give finer start-time granularity, but since the main thread only runs per rAF (~16ms), the practical start-time resolution is capped around a frame plus whatever latency buffer I choose to maintain.

This whole approach relies on AudioWorklet being available in a secure context, which should work fine on https without needing COOP/COEP since postMessage with transferable ArrayBuffers doesn't require SharedArrayBuffer, and the worklet module can load from a blob URL fine under a real origin. Implementing this in the engine would mean adding a custom web backend — possibly using miniaudio's custom backend support, or running the engine with noDevice=TRUE and driving PCM frame reads manually.

I'm sketching how that manual pump would work: initialize the engine with no device, fixed channel count and sample rate matching an AudioContext created at 48000 Hz, then on the JS side set up an AudioContext with a worklet that queues Float32Array chunks and reports consumption back to the main thread. Each frame, the main loop would calculate how many frames to mix ahead based on consumed frames plus a lead buffer, then pull exactly that many frames from the engine to keep the worklet fed without underruns.

The real tradeoff is latency versus stall tolerance — a larger lead buffer survives longer main-thread stalls but adds constant delay, though it's still better than the old ScriptProcessor approach since underruns just produce silence gaps rather than audio replays, and timing stays consistent since mixing happens every frame instead of in large blocks. I'm also considering that stalls affecting audio production also affect game logic, so there may be ways to exploit that overlap to recover more gracefully.

I need to be honest though: my bench test shows no replays at 4096, yet he still hears the problem live. I don't know if that's because the real site stalls more, or because something else like start quantization or content is causing it — adding a perf diagnostic line would clarify this, but since his testing time is limited, I want to gather as much evidence mechanically as I can without needing to run on his actual machine, which is tricky since I can't easily replicate the live site's conditions headlessly.

What I could do instead is check whether rapidly retriggered sounds — like per-letter landing cues, hit sounds, or layered gore effects — are getting quantized into the same audio buffer on the web, since timing gaps that would normally be 40ms apart on desktop could collapse to near-zero on the web and produce the stutter/repeat effect he's describing. I want to log play timestamps per slot in the bench to look for retriggers happening faster than roughly 100ms and see if those map onto the audio clump boundaries.

The real fix is an AudioWorklet queue, making the web behave like desktop: sounds trigger at exact frame times with constant latency, mixing happens in small per-frame chunks, and stalls become brief silences instead of replays.

But I should also check whether per-block parameter updates could cause distinct artifacts even with the queue fix — like doppler pitch being set per frame but applied per 85ms block, causing audible pitch steps that sound warbly or stuttery for a moving sound source.

Before building anything, I want to quantify this quantization/retrigger hypothesis against the bench data. Since there's no JS hook into wasm sound plays, I'm thinking of having the game's audio code log play events through print in bench mode so I can diagnose timing directly, even though printing per play adds overhead.

On a deterministic desktop agent run I could log A.play calls with game time and look for same-slot pairs firing within 10-85ms of each other, since on the web those pairs collapse to either 0 or 85ms apart. I'm fairly convinced now that the correct fix is worklet-queue delivery in the engine, though that's a significant change to code the live site depends on.

Given the owner's rule that local work is free but deploys need approval, I'm also considering a lighter fix within the existing ScriptProcessor model: schedule sound starts with sample-accurate offsets using the engine's start-time API, computing the offset from the gap between the call time and the last callback mapped into frames. This would make latency constant (one block plus double buffer) while preserving relative timing between sounds.

This increases average latency by roughly half a block, around 43ms at 4096 or 21ms at 2048, but it still doesn't address replays under stalls. A worklet-queue approach would fix both issues with a smaller lead time, though stalls exceeding that lead would create silence gaps. I'm also considering a miniaudio voice FX node with per-frame mixing, reading variable frame counts while keeping the graph's processing size fixed.

The node graph actually has a processing cache built in for handling variable reads, which is the key insight: in no-device mode, setting periodSizeInFrames to something like 480 would make processingSizeInFrames match, so the graph reads in consistent 480-frame chunks internally even when the engine requests 800 frames at once, using the cache to buffer the remainder for the next call. This same fix should resolve the agent-mode inconsistency bug too, since the endpoint always reads in fixed 480-frame chunks regardless of the external read size.

This confirms the root cause: with processingSizeInFrames at 0, the endpoint reads 800 frames at once but VoiceFx caches in 480-frame blocks, causing FX voices to break in agent renders. The fix is setting cfg.periodSizeInFrames to 480 in agent mode — but I need to check whether this affects desktop recordings too, since .apr playback uses a real device and stays consistent, while video export via audio_render of agent runs/replays could be rendering broken FX audio into published replay videos on the site.

I should treat this as a separate side finding to verify with a cheap test later — render a sustained FX voice to WAV and compare against expected output to confirm my read on the miniaudio multi-output cache. For now I need to refocus on the main decision: figuring out which theory is more likely the owner's actual issue, and whether I can discriminate between them without his input, though a sample-accurate timing test isn't available to me.

I could instead search the rendered audio for "repeat" artifacts — looking for identical sound onsets spaced near 4096 frames apart, which would be the signature of block-quantized start times. Since play starts are quantized to block boundaries, any repeated play of the same sound in a different block should show up as an offset of some multiple of 4096 frames, so I'm thinking about using autocorrelation at lag 4096 versus nearby lags to detect a sharp peak confirming that quantization.

But if audio.lua applies random pitch variation to each play, the copies wouldn't correlate well, so this detection approach might be unreliable. I think the most decisive evidence would really come from checking the perf audio line directly during a stutter, though I could still design the worklet-queue fix to be robust regardless. I'm sketching out the web-only engine changes needed: initializing the audio engine with no device, fixed channels and sample rate, and a small processing block size, plus EM_JS bindings to create the AudioContext on the JS side.

Still writing worklet setup...

I can estimate consumed frames either from periodic worklet messages or by computing elapsed time against the sample rate, accounting for underrun frames. The key question is choosing the lead L: if a stall lasts longer than L, listeners hear a gap, so the latency target needs to balance responsiveness against stall tolerance.

Comparing against a ScriptProcessor approach with block size B, that method tolerates stalls up to roughly B-2B but incurs higher latency (~1.5-2B plus hardware delay), whereas the queue approach achieves similar stall tolerance (~85ms) at lower latency and preserves exact start timing without quantization. I'm also noting that background-tab throttling of rAF will drain the queue and cause silence, which is acceptable since the game pauses and goes silent off-screen anyway.

On stalls during the main loop (like loading spikes), the queue will produce audio gaps unlike desktop where an independent audio thread keeps sustained sounds playing—ScriptProcessor would be worse, replaying stale buffers. I'm considering an adaptive lead mechanism, like a jitter buffer, that increases the lead time if underruns are detected, though this could be added later; also noting that mixing ahead means new sounds always start after the queued lead L, giving constant latency (versus desktop's 10-20ms), and that I need to handle cleanup/stats tracking for underruns and pump callbacks.

I need to preserve the audio_stats field names the site's ?perf code relies on, while updating semantics (e.g. "very_late" might now represent underrun counts), and update audio_try_unlock to resume our own AudioContext instead of miniaudio's device. Other systems—music via ma_engine, video elements, site radio via HTML audio—should be unaffected, and agent mode stays desktop-only; for memory, I'll transfer Float32Array copies to the worklet without needing SharedArrayBuffer.

One risk is AudioWorklet availability on older browsers or non-secure contexts like file:// pages (our bench page), which would need a ScriptProcessor fallback; I also need to check whether the site's CSP headers would block a data: URL for the worklet module. Given the scope of this work, I'm weighing whether to check in with the owner first, since he's shown he prefers discussing issues before diving into a fix.

Given CLAUDE.md's "Act, Don't Ask" guidance for reversible local work, I'll build the fix locally, verify it with the tap that proves no replays and preserved start timing, then ask before deploying. I want to confirm the queue approach covers all plausible causes on his setup — replays, start quantization, and per-block parameter steps — though I should also consider whether he might actually be hearing the issue on desktop too, which isn't something this fix addresses.

I'm weighing the quantization theory against the bench measurements: at 2048 blocks there were 44 late callbacks and roughly 1 very late one in a 30-second run, while at 4096 there were zero replays but quantization jitter got worse (85ms). If his live experience stays the same after switching to 4096, that points either to the site having more stalls than the bench, or to quantization being the real culprit — but the queue fix resolves both cases regardless. I don't think I need to cross-check the quantization theory against the bench data for same-slot double plays since the fix already covers it, but I'm also considering whether there's a cheaper partial fix worth trying first.

Before committing to the AudioWorklet queue approach, I want to check whether miniaudio's own webaudio backend ever gets instantiated on the web build, or if any other engine code touches the device object directly — things like audio_try_unlock calling ma_device_start or a window.miniaudio.unlock handler — so I'll grep for those references. Then I'll lay out the plan: confirm the bench tap is clean, implement the noDevice engine with an AudioWorklet queue and per-frame pump with a fallback path if AudioWorklet isn't available, then verify by rebuilding and re-running the bench — keeping in mind the tap script hooks into miniaudio's scriptProcessor node, so it'll need to be adapted to tap the new worklet node instead.

After that I need to connect the worklet node to the tap recorder and confirm there are no replays or unknown artifacts, check underrun counts, verify start latency is stable, and measure the per-frame mix cost. I also want to fix the agent-mode processing size for FX voices in audio_render, testing an FX voice against a plain voice to confirm. Then update the `?perf` stats line to match the new audio_stats fields, and handle the page comparison from the redesign ticket, though deployment still needs the owner's approval. Given the owner explicitly said the previous fix didn't work and expects this resolved, I have standing permission to make this engine-level change locally, so I'll proceed and just give him a brief update since it's been a while. First step is digging into the web audio unlock code and how audio_stats is read by the site.

The bench confirms the 4096 fix works there, but the live-site stutter likely comes from main-thread delays and 85 ms quantization of sound starts, not the block size itself. I'll fix both by having the engine mix small chunks per frame and feed them to an AudioWorklet, which preserves start timing and turns stalls into brief silence instead of repeats—building that now.

I need to find where audio_needs_unlock gets set, and check whether sound_cleanup_finished is called per frame in the web main loop iteration.

I'm thinking about where in the frame loop the audio pump should happen—pumping at the start means sounds triggered that frame get delayed a frame, while pumping at the end after update/draw means the pump timing is pushed back by however long the frame takes, so the queue's lead time needs to account for that either way.

I'm placing the pump call right after the fixed update loop and before render, alongside the worklet's consumed counter. I'm also confirming the site and game host share the same engine main loop, just running different Lua content, so the hook point stays consistent. Now I'm sketching the web-side C code for this integration.

Writing EM_JS bindings...

Defining the AudioWorklet processor logic...

I'm tracking underrun transitions only when the audio actually goes dry, posting consumed/under/underEvents stats periodically, and gating counting until playback has actually started. I'm also considering whether to fade out the last samples before silence to avoid an audible click on underrun rather than cutting abruptly.

For the queue depth estimate on the main thread, I want to correct for the delay between worklet reports: since the worklet message (consumed count plus frame timestamp) arrives with some lag, I should extrapolate the current consumed position using elapsed context time since that report, rather than trusting the stale value directly—otherwise the queue estimate overshoots and risks under-filling the buffer.

I'm thinking through lead time tradeoffs: a 100ms lead roughly matches current ScriptProcessor latency, but the measured gaps suggest something adaptive would work better — starting around 60ms lead and bumping it up quickly after underruns (to avoid clicks), while decaying back down slowly during quiet periods.

Increasing lead just delays new sounds slightly without glitching, while decreasing it drains naturally — so a long loading hitch would trigger a quick lead bump and recover cleanly. I'm also realizing that batching sound starts within each fixed-step update frame gives much tighter timing quantization (~7-17ms) than the current 85ms chunking.

Mixing cost per frame looks cheap, roughly 1ms, so it shouldn't strain the main thread. I'm now considering setting a smaller processing block size (256 frames) in the no-device web engine config so reads get chunked by the graph's own processing cache, weighing the overhead of more frequent node-processing calls against the latency benefit.

Deciding between 256 and 480 frames for the processing cache — 480 matches typical desktop device periods at 48kHz for consistency, while 256 would give finer granularity for FX parameter glides, though those only update per pump anyway. I'll go with 480 to mirror desktop behavior.

For sample rate, I'll create the AudioContext first with the default hardware rate, then read ctx.sampleRate to initialize ma_engine with noDevice mode — preserving parity with the old device-rate behavior, even though the context starts suspended until user gesture unlock, same as miniaudio's prior approach. If AudioWorklet isn't available, I'll fall back to the old miniaudio device path; since engine init is synchronous but module loading is async, I need to handle the window before the module resolves, and if loading fails entirely, consider implementing a custom ScriptProcessorNode fallback that pulls audio directly.

But now I'm worried about what happens when the main loop pauses while off-screen — if the engine's main loop stops, the queue drains and causes silence, whereas the old ScriptProcessor approach kept mixing independently of the main loop, so sounds would continue playing even when paused. That's a real behavior change: previously playing sounds would now cut off abruptly when the site pauses the main loop, instead of finishing naturally.

Thinking about whether this matters for background radio music — if radio plays through the engine's music channels and the tab becomes hidden, rAF stops and the audio queue drains, meaning the radio would now go silent on a hidden tab, which seems like a regression compared to the old behavior where background audio kept playing.

Building the new web audio path, which mixes audio each frame and posts it to an AudioWorklet queue. First I'm checking what else uses the engine's audio, like the radio, to make sure a hidden tab doesn't silence it before writing the code.

The radio is just a YouTube iframe embed, not engine audio, so hidden tabs aren't an issue there. I need to be careful only to touch health.lua since other sessions have uncommitted work elsewhere, and I'm reworking the audio_stats fields—callbacks for pumps, frames mixed, lead time in ms, and distinguishing late versus very_late events as underrun indicators.

I'll add new fields like underruns, underrun_ms, lead_ms, queue_ms_min, and a path label distinguishing worklet versus script processor, updating health.lua to display them when present—keeping very_late pinned at 0 for the worklet path since replays aren't possible there.

Now I'm shifting to the engine code itself, checking how anchor.c handles EM_JS conventions and the unlock flow where input events trigger ma_engine_start to resume the AudioContext. For the new worklet-based path I need web_aud_resume() instead, and since noDevice engines make ma_engine_start a no-op, I'll swap it in during audio_try_unlock when running on the worklet path. I'm also restructuring the init code to call web_aud_open(), which creates a suspended AudioContext and loads the worklet, returning its sample rate.

If a sample rate comes back I'll configure the engine with noDevice mode, stereo, that sample rate, and a small period size, then mark web_aud_on based on whether ma_engine_init succeeds, falling back to the old path otherwise. I'm reasoning through ma_engine_init's noDevice behavior and planning a fallback where, if the worklet fails to load asynchronously, JS switches to a ScriptProcessorNode reading from a queue, with both outputs sharing a common queue interface -- the worklet keeping its own queue via message port, the script processor pulling from a JS array instead. Now I'm sketching the C-side pump function that runs each frame, starting to track timing and lead frames.

I'm mapping out the pump logic: get the queued estimate from JS (returning -1 if not running), compute how many frames are needed to top off the lead, round and cap that amount, then read PCM frames from the engine into a buffer and push them to JS while updating stats. I'm also considering edge cases -- what happens while the audio context is suspended before unlock, where queued returns -1 and mixing stalls, versus the old path where the device itself was stopped, so behavior is similar; and what happens when the tab is hidden and requestAnimationFrame stops, draining the worklet to silence, where I don't want underrun counts to spike once the tab becomes visible again since the worklet currently counts underrun frames continuously once started.

I'm deciding to only count underrun transition events rather than raw frame counts, and to have the main thread ignore underrun events that occur while the document is hidden -- it'll track deltas from the worklet's counters and only add to a "heard underruns" tally when the page is visible, accepting a small imprecision at the hidden/visible boundary.

Now I'm working out the adaptive lead scheme: the main thread's pump interval is roughly 16.7ms (or ~7ms at high refresh rates), so the base lead needs to exceed that plus jitter -- I'm picking a 50ms starting lead with a ceiling around 160ms, and on each underrun I'll bump the lead up by about 25ms, deriving the stall size from the gap between successive pump calls.

Comparing a fixed ~70ms latency against an adaptive approach, I think adaptive with a higher ceiling is better since the user's real complaint was audio repeats, not silence -- gaps feel more acceptable than replays, so I'll start the lead at 60ms, add 30ms per underrun up to a 200ms cap, and decay it slowly (2ms per clean second, floor of 60ms) so latency stays elevated through bursts of load rather than oscillating. This also means new sounds will start roughly one lead-interval later than before, which is a reasonable tradeoff compared to the old 4096-sample ScriptProcessor approach.

I'm also working out how the queue estimate function should behave: it computes elapsed time since the last worklet report, estimates frames consumed by adding that elapsed amount to the last reported consumption, caps it at the total frames posted so it can't overshoot, and returns the remaining queued frames -- this stays correct even if the queue went dry mid-interval since the cap handles that case. I'm now considering how Chrome updates its audio context clock on the main thread and whether that introduces any timing skew I need to account for.

For the ScriptProcessor fallback path, consumption is tracked directly inside the processing callback on the main thread, so the queued estimate is just posted minus consumed, and the pump maintains lead time plus the fixed buffer size of 2048 frames. For transferring audio chunks to the worklet, I'm copying the relevant heap slice into its own buffer so it can be sent as a transferable ArrayBuffer via postMessage. I'm also checking whether loading the worklet module via a data URL will work under the site's Content Security Policy, since that's verified to work from file:// but might be blocked on https.

The radio turns out to be a YouTube embed rather than engine audio, so muting on tab hide is fine. I'll check the site for a Content-Security-Policy that might block a worklet module, then move on to writing the engine side.

I'm checking the CSP config at line 632 and realizing audio worklet module scripts fall under script-src, not worker-src, since worklets follow the script-src directive per spec — and since data: and blob: aren't whitelisted there, addModule calls using those URL schemes would get blocked.

'self' is allowed though, so the worklet would need to be served as an actual file from the site, which means figuring out how the engine's hashed JS/wasm files are deployed and whether a separate worklet file could be shipped alongside them through the same build pipeline.

The site's CSP restricts scripts to its own origin, so I can't load the worklet from a data: or blob: URL—it needs to be a file the site actually serves. I'll check how the engine files are deployed.

The CSP's script-src with `https:` allows any https-hosted script, so I could serve the worklet module as a same-origin file rather than data/blob URIs which are blocked. I'm thinking through shipping a separate "anchor-audio" file alongside the engine and having the engine derive its URL at load time via document.currentScript.

Falling back to a data: URI or main-thread ScriptProcessor queue won't satisfy the CSP on the live site and loses timing guarantees anyway, so the separate-file route is the only real option. I need to check how engine-boot.js and the build/deploy scripts name and serve files to figure out how the worklet file should be referenced.

I'm thinking through how the deploy pipeline's ASSET_VER hashing interacts with locateFile's name mapping, and whether adding a new worklet file means modifying deploy.sh versus instead having the site's Lua server serve the worklet code directly from an existing path to avoid touching the deploy pipeline at all.

Another option is pointing the worklet module at the engine's own hashed JS file directly, letting it run as an AudioWorklet module. Since that top-level emscripten code would crash in a worklet scope (no window/document), I'm considering whether a pre-js guard could detect AudioWorkletGlobalScope and register the processor early before the rest of the code throws, since registerProcessor might still succeed even if the module errors afterward.

But this is risky since the registration might race with the throw, and the "real" emscripten approach (ENVIRONMENT_IS_AUDIO_WORKLET) requires wasm workers and SharedArrayBuffer anyway. I'm also confirming that audioWorklet.addModule falls under script-src/script-src-elem for CSP purposes, and that an "https:" source won't match blob: or data: URLs, so that avenue is blocked too.

The cleanest fix seems to be adding a small static file to the deploy, something like a hashed or versioned worklet script served from the renderer directory, mirroring how other engine files like anchor.js are deployed. I need to check deploy.sh and the nginx location rules to see if static files are matched by hash patterns, since an unhashed file would need a version query parameter for cache-busting.

I'm considering naming the worklet file using a content hash so it matches the immutable URL pattern served from the render path, since the engine embeds the worklet source as a string and the deploy step would need to compute the hash of that source to write the correct filename.

Computing SHA synchronously in JS is awkward, so a simpler option is using a hardcoded version-based name like "anchor-a0d1001.js" baked into the engine as a constant, and bumping that name whenever the worklet code changes. That still leaves the question of where the worklet source itself should actually live as the single source of truth—likely a dedicated file in the engine repo.

For loading robustness, especially on file:// pages where CSP blocks most fetches, I'm leaning toward trying a data: URL embedding of the worklet source first since it works everywhere without restrictions, then falling back to a located file URL if that fails for other hosts.

I'm also considering whether I could reuse the engine script's own URL as the worklet module location, but that raises concerns about whether the main engine bundle (which references window/document) would even run safely inside the restricted AudioWorkletGlobalScope.

I'm wondering if Chrome's registerProcessor actually commits before module evaluation fully finishes, which makes the throw approach too fragile to rely on. Maybe instead of throwing, I should find a way to prevent the emscripten code from executing in the worklet context at all, perhaps through a post-processing step on the build output rather than a pre-js guard.

I'm considering whether wrapping the whole emitted script in a conditional block could mess with hoisting -- since sloppy-mode function declarations inside blocks follow odd Annex B semantics where they're hoisted within the block but only assigned to the outer variable once the declaration itself executes, which could break code that calls functions before their point of definition.

But the worklet environment parses as a module, meaning strict mode applies even to code that never runs -- and I'm not confident the emscripten output is fully strict-mode compatible, so this approach feels risky for a parse failure. Given that, adding an extra deployed file and adjusting the deploy script seems like the safer path forward.

The simplest version: build-web-engine.sh produces a standalone worklet script from a source file in the engine, deploy.sh hashes and uploads it as an immutable asset, and the EM_JS code resolves its URL via Module.locateFile so both the bench and the live site can fetch it correctly.

The build script could inject the escaped source via --pre-js so Module.__audioWorkletSrc holds it before Module is fully set up. Then the engine should try the data: URL first since it avoids a network request, falling back to the locateFile URL when CSP blocks it—though that fallback will still spit out a noisy CSP violation in the console.

I want to check whether locateFile actually remaps 'anchor-audio.js' to a distinct host-provided URL, since that would let me prefer the URL path when available instead of always trying data: first. I should check the locateFile implementation in engine-boot.js to see how it resolves this.

I'll deploy one extra content-hashed file alongside the engine rather than rewriting its JS, since that fits the existing nginx rule serving engine files. Now checking the boot loader's `locateFile`.

I realize naming the worklet file with the anchor-<hash>.js pattern is risky since the existing probe `ls -t anchor-*.js | head -1` could mistakenly pick it up as the engine file on non-engine deploys. I should use a distinct naming scheme like renderer-<hash>.js to avoid collisions with that detection logic.

Alternatively, I'm considering serving the worklet from a /static/ path instead, by committing it into the site repo's static folder so it gets deployed via the normal git push workflow, served with a day-long cache expiration.

Since the engine is shared across multiple hosts, the real design should let the host configure Module.audioWorkletURL, with the site's boot script pointing to the static file while other embeds fall back to the inline data: URL default. To avoid drift between the inline string and the static file, the single source of truth should be the engine build script, which generates both the pre-js worklet string and the standalone file, and deploy.sh then copies that generated file to the VPS.

For replays-player pages under /media/, I'm checking whether their CSP also blocks data: URLs — if so, the engine falls back to ScriptProcessor fed from a queue on the main thread, which still works for playback but loses the precise timing benefits of the worklet. I'm weighing whether that fallback alone (queue-based ScriptProcessor mixing) is sufficient without a true audio worklet, since it avoids 85ms quantization but may behave differently under stalls.

Actually, with the queue-fed ScriptProcessor, if the main thread is delayed Chrome still replays the stale buffer just like before, so stalls still cause replays — meaning the worklet really is necessary for stall-robustness. So I need to find the least invasive way to serve the worklet file: either a direct nginx location change (which requires a manual reload, probably not part of deploy.sh, so best avoided) or placing it under the existing /static/ alias if the work tree's static folder actually exists in the repo — I should check for that.

The /static/ directory is served from the site git repo, so I'd need to add the audio script there and push it to make it live. But pushing requires his approval for deployment, and I need to check whether the site repo's HEAD is in a clean, pushed state before touching it.

For the engine side, I'm working out where the worklet source should live — likely a single JS file in the engine repo — and how build-web-engine.sh should inject it into the output via a pre-js snippet that sets a property on the Module object without clobbering it, since emscripten places pre-js code right after Module is initialized.

I'm now settling on an approach: a canonical source file engine/src/web/audio_worklet.js that the build script turns into both a JSON-escaped pre-js snippet and a raw copy for static serving, with a check in deploy.sh --engine that fails loudly if the site's static copy ever drifts from the build output.

Rather than trying to version-bust the worklet URL with a content hash, I'll just lean on cache-control must-revalidate with ETags to handle updates. The engine itself should check for Module.audioWorkletURL and fall back to the data: URI if it's not set, with the site's engine-boot.js responsible for configuring that URL as part of its deploy-staged files — now I want to actually look at build-web-engine.sh to confirm how it's currently structured.

I'll commit the worklet into the site's static/ folder so engine-boot.js can serve it via URL, while pages without that CSP fall back to a data: URL. Now checking the web engine build script.

I'm deciding how to embed the worklet source into the build: generating a pre-js file that JSON-encodes the worklet script into a module property, using Python since the build script already relies on it, rather than embedding it directly via EM_JS.

For the site side, I need to make sure the static copy of the worklet file stays in sync with the engine's build output, and that deploy.sh warns if the static copy changes so it gets committed and pushed — but I'll defer that until the engine side is verified working. For URL resolution, I'm sketching how the engine JS picks an audio worklet URL, falling back to a static path on hosts where CSP blocks data: URLs.

Now I'm defining the AudioWorkletProcessor class itself, tracking a queue of interleaved stereo audio blocks, a read position, consumed frame count, and dry-run/started state, with a message handler distinguishing between incoming audio chunks and clear commands.

In the process method I'm filling the two-channel output buffer from the queue, periodically posting back stats like consumed frames, current frame, and dry-run counts every few quanta. I'm also weighing whether to fade audio at underrun boundaries to avoid clicks, though without lookahead a clean fade-out isn't really feasible once the queue runs dry.

Settling on decaying the last sample exponentially (~5ms to -60dB) on dryout and ramping back in over 128 samples on resume, which should be cheap and avoid harsh clicks. Now I'm writing the main-thread JS glue, starting with an EM_JS function to open the AudioContext and set up the audio graph object.

I need to think about autoplay policy here — a fresh AudioContext typically starts suspended until a user gesture, and miniaudio handled this by explicitly suspending at creation and resuming on start. I'm considering whether to mirror that explicit suspend/resume logic depending on whether autoplay happens to already be allowed.

To keep behavior consistent with before, where audio only began after the first user input unlocked it, I should explicitly suspend the context at creation and only resume it when the unlock routine runs on an input event, since that's a genuine user gesture Chrome will allow.

For the worklet mode, I'm setting up an output-only AudioWorkletNode connected to the destination with a message handler for reporting, and for the fallback mode I'm using a ScriptProcessor with zero inputs that copies queued audio data on each process callback. Now I'm working out the queued() function, estimating consumed samples based on elapsed context time and playback rate for the worklet path.

I'm also thinking through how the adaptive lead buffer should behave: starting from a base of 0.06 times the sample rate, growing by small increments after real audible dry runs up to a cap of 0.2 times the rate, and decaying back down if no audible run has occurred in the past two seconds.

Writing C pump function...

Since this reads via the node graph, leftover silence is handled automatically, which works fine for agent mode too. I'm now thinking about tracking stats per pump cycle—callbacks, frames, lead time, and underrun counts—plus noting which audio path is active (worklet, script fallback, or device) so I can distinguish heard dry-runs from undetectable late replays in script mode.

For exposing these counters to JS, I need an EM_JS getter that returns the stat values as doubles, plus a reset function to zero them out. I also want to handle the hidden-tab edge case: when the main loop pauses because the document isn't visible, any dry runs that occur during that gap shouldn't count as audible underruns, so the report handler needs to check document.hidden before attributing dry-run deltas to heard runs.

I should update the health.lua perf display to surface the new worklet stats -- gap count, gap time, lead time, max mix time, and voice count -- and double check nothing else depends on ma_engine_get_device that would break with noDevice mode.

For agent mode, I'm considering setting the periodSizeInFrames to 480 for the noDevice engine so the graph reads in matching chunks, but I need to verify this doesn't break the byte-compare replay test that checks captured frames match between the recording and playback instances — and I should confirm this bug actually manifests in agent mode before assuming the fix is needed.

I'm also checking desktop device mode, where the processing size tracks the device's period size and seems consistent already, plus confirming that MA_NO_DEVICE_IO isn't defined and that engine initialization with noDevice requires channels and sample rate to be explicitly set. Since agent mode doesn't apply to web builds, I don't need to worry about that interaction there.

Looking at `audio_try_unlock()` on web, I realize the resume call actually happens in the main loop via rAF, not inside the DOM event handler itself, since SDL queues web events for processing there — so the real reason this works despite not resuming synchronously inside the gesture is Chrome's sticky user activation, which lets `resume()` succeed on any later frame once the user has interacted with the page at all.

For robustness, miniaudio also installs its own fallback unlock listeners for touchend/click/keydown that retry resuming all devices, and I'm considering adding similar JS-side listeners that retry `ctx.resume()` on subsequent input if the engine flags that it still wants audio running but the context isn't yet in the running state.

I also need to account for the fact that `window.miniaudio` won't exist in worklet mode, so tap.js needs updating to support the worklet node path, tapping its output for recording and wrapping the engine's push calls to capture chunks per pump — I should grep the site to confirm nothing else references `window.miniaudio` before moving on to writing the worklet source file.

I've finalized the audio design: the engine mixes roughly one frame of audio per update and feeds it to an AudioWorklet (with a ScriptProcessor fallback) that plays from a buffered queue, fading to silence on stalls instead of replaying blocks. Now I'll implement the worklet first.

I need to remove that leftover empty conditional check that no longer does anything. For the fade-in logic, I'm making sure that whenever a quantum is dry (silent), fadeIn gets set to 128 so the next quantum with real data gradually ramps up instead of popping in abruptly—including the very first quantum of actual audio after the stream starts.

I'm figuring out where to place the EM_JS functions in the engine's C code, deciding to add a block near audio_data_callback and writing a pump function for the web audio output feature.

Writing the AudioContext setup and worklet fallback logic...

Still writing the audio fallback logic...

I'm wiring up event listeners to resume the audio context on first user interaction, then double-checking that my source string is safe to base64-encode — making sure it's pure ASCII or properly escaping it with encodeURIComponent before btoa.

Now I'm sketching the worklet's report handler, which tracks consumed samples and frame counts, distinguishing between "dry run" frames and actually audible frames based on whether the document is hidden, so I can detect real playback versus silent probing.

I'm also considering an edge case: if the shell pauses the main loop when the canvas loses focus (via emscripten_pause_main_loop), the pump that feeds the worklet stops entirely, which could affect how heard-frame counting behaves differently from simple tab-visibility hiding.

The old miniaudio path kept the ScriptProcessor running even while the game logic was paused, so sounds would keep playing — unlike my new approach, which would go silent when the queue drains. That's arguably more correct since the game is paused anyway, but I still need to make sure the dry-run counting doesn't incorrectly bump the lead during these pauses; maybe the pump should check the time gap since its last call and skip counting if it detects the main loop was paused.

So I'm tracking the last push time in JS, and treating a dry run as "heard" (and thus eligible to bump the lead) only when the page is visible and the gap since the last push is under a second — a longer gap just means the loop was paused, not glitching. Then the lead adaptation logic bumps the lead up quickly on heard glitches and slowly decays it back down otherwise, clamped between min/max bounds.

For the queued-frames calculation, I'm estimating how many frames have been consumed by combining a running consumed counter with an extrapolation from the current audio context time since the last report frame, clamping so it never exceeds what's been posted. The tricky part is that the report's frame count reflects the start of the processing quantum, so there's a slight timing offset I need to account for when extrapolating forward from currentTime.

I'm reasoning through creating a ScriptProcessor fallback node with appropriate channel counts, then starting to write the onaudioprocess callback...

Still writing audio callback...

Still writing EM_JS functions...

Writing pump stats logic...

I should add explicit fields to l_audio_stats for path, lead_ms, dry_runs, dry_ms, and queue_ms_min so the worklet path's "late"/"very_late" semantics stay meaningful—on the worklet path, late maps to dry_runs and very_late is always 0 since no block can be replayed, and the perf display should reflect that gap behavior. I'll also factor out a shared helper for the voices-counting loop instead of duplicating it from audio_data_callback.

For engine init on web, I'm branching based on whether web_aud_open succeeds: if it does, configure a no-device engine with stereo output at the detected sample rate and a 480-frame period size matching the audio graph's processing block, otherwise fall back to the normal data-callback path with a 4096-frame period; non-web builds just use the standard data-callback setup. I'm noting that in no-device mode the dataCallback field is irrelevant, and now I'm questioning whether 480 frames is the right period size.

At 44.1kHz, 480 frames is about 10.9ms, which seems fine. For unlocking audio, I'm writing a check that resumes the web audio context or starts the engine when a pending unlock exists and audio is initialized.

For shutdown I think ma_engine_uninit is sufficient, but I should consider adding a web_aud_close() to properly close the JS audio context, even though it's optional cleanup. More importantly, I realize there's a real bug: replay mode returns early before the normal update loop runs, so if I only place the audio pump call after the update loop, replays would never pump audio on the web and would play completely silent, since apr_replay_iteration triggers sound_play events that rely on the pump to actually mix them. I need to add the pump call in both places — right after apr_replay_iteration in the replay branch, and after the regular update loop — to ensure sound always gets processed.

Other call sites look safe since the pump runs every iteration regardless of fixed-step settings, and sound cleanup via ma_sound_is_playing still works correctly since our pump is what drives the mixing graph to completion. I also confirm that before the context is running or before the worklet module finishes loading asynchronously, queued() returns -1 so no mixing happens — behaving the same as a stopped device, which is the correct fallback. The audio context's sample rate (hardware-determined, e.g. 48000) naturally becomes the engine's sample rate, consistent with prior device-based behavior. Finally, I'm working out how the build script's generated pre-js file exposes the audio worklet source to the module.

Now I'm checking whether the site's own build script builds its own engine copy or just copies anchor.js/anchor.wasm from build-web-engine's output, and tracing how other consumers like the game packaging script and replay player pages would get the worklet — via data: URL normally, but falling back to ScriptProcessor if CSP blocks it, which should be acceptable behavior.

I can revisit setting the worklet URL for those pages later. Now I'm moving to write the actual C code, placing it right after the audio_data_callback definition.

I'm extracting the voice-counting logic into a shared helper function so both the audio callback and the web block can use it, then wiring it into audio_data_callback before the encoding step.

I'm confirming playing_sounds is declared before the stats struct that references it, so audio_st_count_voices should compile fine, and noting the dry-run count call in useScript looks correct too.

I'm checking the EM_JS byte-pointer arithmetic for the Float32Array slice (shifting by 2 to convert bytes to elements), and double-checking that quoted strings inside EM_JS blocks won't trip up the preprocessor's stringification.

Good, single quotes match existing EM_JS conventions. Now I need to handle the init, unlock, pump call sites, shutdown, and the audio_stats extension.

I'm confirming the replay-mode patch is correctly placed at the top of main_loop_iteration, and verifying there's only one emscripten_set_main_loop call so no separate boot path bypasses the fix.

Now I'm checking whether the shutdown code is shared with desktop via the ifdef, then expanding audio_stats with path, lead_ms, gap tracking, and queue_ms_min fields, mapping late to gaps and very_late to zero for the web path.

Good, the ordering checks out for web_aud_on, web_aud_stat, and audio_try_unlock relative to l_audio_stats. Now I should update the doc comment above l_audio_stats to reflect the new fields so the generated bindings documentation stays accurate.

I should check whether build-web-engine/ is tracked in git, since the commits mention it being rebuilt, and figure out if the generated file needs to be added or gitignored. Before running the web build, I want to make sure nothing else is using that directory right now, then kick off the build since it takes a minute or two.

Build succeeded, so now I need to update tap.js to work with the new path. Since the worklet mode transfers the audio buffer via postMessage, it gets detached after sending, so I need to hook web_aud_push to copy the buffer before it's posted rather than after.

I can wrap the port's postMessage method directly, capturing a copy of the data and timestamp before calling the original function — this works because the engine looks up postMessage on the port instance at call time, so our override takes effect. The output tap connects via the AudioWorkletNode to a recorder worklet loaded from a data URL.

For validating alignment, I'm reasoning through whether the engine's push quanta (always multiples of 128 frames) line up with the worklet's internal 128-frame read quanta, which depends on whether consumption starts aligned with the pushed chunks.

On underruns the output quantum can straddle a gap, splicing the tail of one push with the start of the next, which breaks simple 128-frame index matching after that point. A more robust check would track the engine's current frame position and, instead of assuming alignment, search a window around that position using a hash of a short sample window to relocate the match when direct comparison fails — or more simply, verify that if I strip out the silent/dry segments from the output, what remains equals the engine stream played in order.

During the dry period the engine position freezes, and when data resumes, the fade-in applies a decreasing gain over 128 samples, so I need to replicate that exact gain formula to detect the resumption point reliably.

Given how rare these gap events actually are, I'm thinking it's simpler to just track them directly through the worklet's reported counters rather than reconstructing the fade math from the output stream, though the quantum alignment after a partial dry period still complicates verifying strict ordering.

Still writing resync-detection logic...

Fading makes exact comparison unreliable, so I should only trust matches on non-faded regions: if the output quantum matches the engine buffer exactly, advance position normally; otherwise search nearby for a non-silent resync point using later samples in the quantum, and use that to re-derive position, logging the offset as a drift event.

Dry runs and repeats both show up as negative drift, but they're distinguishable: a dry run's mismatched samples will be near-silent or decaying rather than matching engine content, whereas a repeat's samples will match something the engine already produced earlier. I can use this to classify silent/decaying mismatches as dry runs versus actual repeats, and treat silence-matching-silence cases as a harmless dry run during quiet passages.

engine range only is skipped but actually contains sound.

I'm deciding to unify both paths into a single stream-based approach in tap_check.py, treating the engine data as one continuous stream regardless of whether it came from the worklet or the old 4096-block path. I'm sketching out the per-quantum sync logic that walks through output quantums and checks alignment against the engine stream position.

Still writing stream logic...

I need to verify candidates with full equality checks and handle the edge case where fade effects corrupt the tail comparison right after a dry run resumes mid-quantum—meaning the first quantum after resync might show no valid candidates, but the following quantum should resync properly since the fade clears by then.

I'm now planning to update tap.js to handle both the new and old engine paths, wrapping the message capture mechanism so timestamps align with output quanta for event logging regardless of block size.

The web engine builds successfully. I'm updating the tap to record each chunk the engine posts alongside the worklet's output, and adjusting the checker to compare the two streams sample by sample since queue gaps no longer align to 128-frame boundaries.

I should also adapt the audio clock check expression to surface Module.__aud state when present, since run.mjs reports audio status from window.miniaudio. Now I'm moving on to rewriting tap_check.py using the stream approach.

My current REPEAT vs GAP classification heuristic feels hacky—I should instead check whether the output stretch right before the resync actually matches engine content seen earlier, confirming a true repeat rather than just guessing from frame counts.

Working through the Chrome replay scenario: when a block replays, the output traces back to earlier engine content, causing a large negative jump, while a true gap shows up as a smaller jump paired with partial silence at the quantum's start. The distinguishing test is whether the full quantum matches engine content in sequence (indicating a repeat) versus only the tail matching after a silent or faded head (indicating a genuine gap).

Now I need to go fix that part of the code to reflect this distinction.

The old capture's format is incompatible with my current layout, so I'll skip reusing it and just validate the checker by running the new engine directly. I could also test REPEAT detection by running the bench against the old live engine to confirm the checker catches it.

Now I need to update run.mjs's audio print to include Module.__aud, then rebuild the bench page with the engine from build-web-engine and run it with tap.

The worklet path checks out: no gaps heard, lead held at 60ms, no repeat/skip/gap events, and mix timing stayed well within budget. The frame count difference matches the trailing queue exactly, confirming correctness. Now I'm puzzled by the 144 "unknown" quanta — these seem to be non-silent segments whose tail couldn't be matched, and I want to figure out why that's happening given there were no actual events in that stretch.

I think bitcrushed audio quantized to few distinct values could be fooling my silence/uniqueness check, but if that were happening mid-stream it should trigger a mismatch and produce a skip event on the next quantum — yet none appeared. So maybe the unknowns are clustered before synchronization even begins, rather than scattered through the middle.

Those 144 early quanta could be fade-in material or quiet/crushed sounds occurring before the first clean match — essentially before `pos` gets set. Rather than keep reasoning abstractly, I should just instrument the code to record and print where the unknown indices actually fall.

The new output runs the full 37.5s fight cleanly—no repeats, skips, or gaps—at a 60ms lead, with mix cost smoothed to 0.56ms/frame instead of spiking every 85ms. Before trusting the checker's results, I need to investigate 144 "unknown" quanta it flagged.

So the bitcrushed quiet sound at the start explains why the checker can't sync before the first sync point—those low-detail quanta are ambiguous but pass fine once compared in-order, and the 144 unknowns are just pre-sync segments rather than real errors. I need to adjust the checker logic to account for this.

I'll label unknowns with no position as "before sync" to make the report clearer, then test that the checker correctly flags injected REPEATs and GAPs by modifying a copy of the capture and running the checker against it in a temp directory.

The unknowns turn out to be quiet 8-bit-crushed audio before the checker locks on—too few distinct tail values to classify safely, but matching fine once locked. I'll label those as "before sync," then verify the checker correctly catches a planted repeat, skip, and gap.

I'm checking the REPEAT fault detected at 10.667s, where the output jumps back 170.7ms matching an 8192-frame replay, followed by a skip — confirming the replayed block offset lines up with the expected 8192-frame distance before resuming fresh content.

For the GAP fault at 21.355s, the detector misreports a 0.5ms "SKIP" when it should recognize a true silence gap, since the silent-branch logic holds position steady across zero-valued quanta instead of flagging the gap, so I need to trace frame-by-frame through the silent quanta to find where the classification goes wrong.

Really this is a 1000-frame gap, not a sync jump — I should think about it in terms of lag between output frame index and engine frame index rather than tracking raw positions. A GAP increases lag by its frame count, a REPEAT also increases lag since it replays earlier engine content, and a SKIP decreases lag; on resync I need to compute the new lag from the tail's output and engine indices and diff against the old lag to classify the event correctly.

Writing lag-tracking loop logic...

Still defining lag logic...

Checking the gap test case: at q8000 with 40 real samples plus zeros, the tail is silent so I continue without advancing lag, then at q8008 the new lag jumps by 1000, correctly flagging a 20.8ms gap since the head isn't engine audio. I'm now considering the tricky edge case where both the skipped region and engine output are silent simultaneously.

the repeat inserts those extra frames shows up as a net lag shift afterward, confirming the behavior. For SKIP, dropping frames produces a negative lag jump with the landing point beyond max_e, giving the 42.7ms skip detection. Time to implement this.

The checker correctly flagged the planted repeat and skip, but misclassified the 21 ms gap and the post-repeat return as extra skips. I'll rework it to track the offset between the played and engine streams, which should classify all three correctly.

The checker now correctly classifies all planted faults — the repeat, gap, and skip cases — with no false positives from the new engine's real capture. I'm double-checking the newline escaping in the heredoc, since I need `newline='\n'` to appear literally in the generated Python source rather than being interpreted by the shell.

Now I want to stress-test the new engine's robustness under stalls using the heavier WEBBENCH_ENDING scenario with its 50-100ms gaps, and compare against the old live engine on the same conditions to confirm it still repeats where the new one doesn't. I need to figure out which build folder the live engine (anchor-211a9c1c93) is deployed from so I can run WEBBENCH_ENGINE against it directly.

The checker now correctly flags all four planted faults and returns clean on the new engine's real capture. Next I'll run a harder test on the bench's worst stalls, first against the live 4096-block engine, then the new one.

The md5 prefix 211a9c1c93 matches the live engine hash, confirming the binary-safe redirection works correctly, so I can run the old engine with the ending and tap it through git show.

Interesting that on the live engine with 4096-sample blocks, both the fight and the ending ran with no replays — just one 2.7ms gap from lock contention. The ending showed a 142ms worst gap yet still no replay, which suggests Chrome's ScriptProcessor tolerated that gap without triggering a repeat, since the deadline window shifts between 85 and 170ms depending on phase with 85ms blocks.

So even on the bench, the live engine doesn't reproduce the repeats he's hearing on the live site, which means the cause is likely either his specific browser/site rendering conditions, the 85ms start-time quantization clumping, or something else in the content itself rather than a delivery timing issue. This makes me doubt the worklet fix actually targets his real problem, since the tap only measures delivery versus the engine's stream and can't see content-level causes. I should dig into the game's sound events — particularly slots with rapid retriggers governed by a "gap" parameter in sound_picks or audio.lua — to see if there's a content mechanism that would produce audible stutter/repeat on web but not desktop.

I'm now suspecting a voice-stealing/looping interaction: if the game checks whether a looping voice is "still playing" to decide whether to restart it, and the web engine's 85ms block latency means a just-started sound isn't registered as playing yet, the game could end up double-starting the same sound offset by a block — producing exactly the "repeated portions" effect he describes.

I should check whether `ma_sound_is_playing` returns true immediately on start (it does), but also look at whether the game logic instead reads the cursor/position to time loop crossfades. If so, since the cursor only advances in 4096-frame block jumps on the web rather than continuously, any logic waiting for "cursor near loop end" to trigger the next copy could fire late or multiple times — I want to grep audio.lua for cursor/time/at_end usage to confirm.

The bench never replays a block on the live engine—just one 2.7ms gap—so I can't reproduce the delivery fault there, meaning either his page stalls worse than the bench or the stutter comes from what the engine mixes. I'll check the game's sound logic for anything reading a playing voice's position or state, since on the web those update only once per 85ms block.

I'm checking that setting the fx on the handle returns whether the voice is still alive, since cleanup happens per frame based on playback state. For loops, if the underlying voice died, the loop's handle gets cleared so it can presumably be restarted later.

Content-wise desktop and web should behave the same except for block-quantized timing differences — delayed audibility, delayed one-shot endings, and pitch/dry-wet bus effects. I'm also considering whether the "max concurrent voices per slot" count behaves differently on web since voices might persist longer in game time there.

As for repeated loops like a hum or TV switching on/off rapidly, I can't find a content-level mechanism unique to the web beyond start-time quantization and the possibility of callback-gap replays. In the bench, the worst gaps (102ms and 142ms) sit right near the threshold where a replay becomes likely, and since his live site adds its own unmeasured per-frame work on top of real audio hardware, replays seem quite plausible there even though the bench itself can't reproduce them.

Rather than speculate further, I consider checking his machine directly via the ?perf line, but I can't access his browser that way. The Claude in Chrome tools could technically open his real browser to read perf numbers, but memory notes caution against driving his desktop or using browser tools for research without asking — doing this unprompted while he's working would be intrusive, so I rule it out.

The worklet path removes bot-specific latency issues and is verified clean in the bench, making it the right fix regardless. But I need to be honest that the bench never reproduced an actual repeat on the live engine, so I can't prove this caused his stutter — only the post-deploy ?perf data can confirm that, since repeats become impossible by construction with the new path. I consider whether I could still demonstrate the quantization hypothesis in the existing bench data by looking for cases where the same sound file started twice closer together than expected, and think about instrumenting the audio code to log play requests and timestamps to check for this pattern.

I weigh whether to build an A/B listening test with two WAVs — old path vs new path — so he can confirm by ear, but given how scarce his testing time is, it's probably better to just deploy the fix with his approval and let him validate it live on the site.

Instead I decide to quantify the jitter through play logging, since it's cheap and gives concrete numbers for the handover — something like "sounds start up to X ms off their time, and pairs of the same sound got pushed from N ms apart to 85 ms apart Y times during the fight." That data would make a solid case for why the 4096 change might have worsened things.

For implementation, I'm thinking of monkeypatching sound_play_fx inside the bench Lua to record timestamps, but I need a time source comparable to performance.now() from the engine callbacks. I'm unsure whether Lua's os.clock gives CPU time rather than wall time, so I want to check if there's a bound perf-time function like perf_time() or time_now() exposed in the engine API.

No delivery fault found on the live engine, so I'll measure timing drift instead—logging each play request against actual sound start on both engines to quantify how far the live engine shifts sound starts.

I'm checking how perf_time() maps to the browser's clock on web builds, tracing through SDL's performance counter implementation to confirm it ultimately derives from performance.now().

For benchmarking, my plan is to hook sound_play_fx and sound_play_handle to timestamp each request, then reason about when sounds actually become audible. On the old mixing path, the audible start depends on when the next callback fires after the request, introducing jitter up to the callback interval; I want to measure how often the resulting spacing between requests deviates meaningfully from the requested spacing. On the new path, since mixing happens right after each frame's update, the audible spacing should track the request spacing much more tightly, bounded by roughly one frame of slop.

I'm considering logging this directly from bench.lua, wrapping the sound-play calls globally when a bench logging flag is set, using tostring on the sound object as an identifier since filenames aren't passed through, and printing results as batched console lines that the harness can collect.

Since 'BENCH'-prefixed lines already get special treatment (echoed to stdout, collected into window.BENCH.lines), I should use a separate prefix like 'SNDLOG' to avoid noise, pulling those from the full console capture in run-log.json instead. Now I want to check bench.lua near the audio_switches section to see where best to hook in the wrapper.

I should also update aud_line to include the new fields like path, gaps, and queue_ms_min when they're present, and add the sound log wrapper into B.audio_switches via the bench_soundlog file.

I'm worried that if audio.lua captures sound_play_fx as a local upvalue at require time instead of looking it up globally each call, my wrapper replacement wouldn't actually take effect — need to check how boot.lua invokes it around line 27.

Now I'm planning the jitter analysis script, which takes the run log and tap metadata as input to model timing. For the device path, I'm working out that a request made at time r gets mixed in the first audio callback whose timestamp exceeds r.

I'm reasoning that in both the old callback path and the new worklet path, the audible start position equals the engine frame index of whichever chunk first gets mixed after the request, so the measurement focuses on computing start error relative to request time, then subtracting the median to isolate jitter from fixed latency, reporting percentile spreads and comparing paired requests sharing the same id.

I still need to confirm that SNDLOG's perf_time() clock shares an origin with performance.now() by checking whether SNDLOG timestamps land inside the corresponding chunk time ranges, then I can write up the final analysis.

I'm realizing both the live engine and new engine runs would write to the same tap/run-log locations if I'm not careful, so I need to keep their output paths separate—like bench-live.html vs bench-new.html—so one run doesn't overwrite the other's logs.

While that runs, I'll dig into the agent-mode multi-output issue — testing an agent instance playing an FX voice with reverb on a long sound, rendering to WAV, and comparing against a plain voice. I want to check how audio_render is invoked from Lua and reason through agent mode's frame processing size to confirm the claim.

With node cache defaulting to 480 but agent_audio_tick reading 800 frames, the FX bus ends up reading in uneven chunks (480 then 320), which can cause a second processing round that over-consumes the source — meaning FX voices may play too fast or get chopped in agent mode, impacting both agent-rendered audio and sound_cleanup timing.

To confirm this, I'm planning a small Lua test: load a long sound asset, play it through sound_play_fx with wet>0, and measure how many frames elapse before the voice reports as no longer alive versus the expected file duration, then compare against the same test using a plain sound_play_handle call to isolate whether the FX path specifically causes early termination.

I'm weighing whether to run this test inside the full lich-survivors game (slower but uses the actual engine) versus spinning up a minimal scratch game directory with a bare-bones main.lua just to init the engine — leaning toward just using `anchor drive start .` in the existing game since per-handle measurement won't be affected by other sounds playing in the background, and I need to avoid touching sound_picks.lua during this since I'm not driving the editor. I also realize I still need to rebuild the desktop engine binary since my anchor.c changes haven't been compiled in yet.

Since the current desktop exe predates my change, I'll use it first to confirm the bug exists, then apply the periodSizeInFrames=480 fix for agent mode, rebuild, and retest — while keeping an eye out for a possible LNK1104 conflict if another session's agent instance is still using that binary. Now I need to write the actual eval script for testing, which means picking a sufficiently long sound file from assets/sfx — probably just by comparing file sizes rather than bothering with ffprobe.

Found a potential bug in miniaudio: agent instances mix 800 frames against a 480-frame node cache, which could speed up 3D voices with reverb sends and corrupt rendered replay audio. I'm setting up a headless test with a long sound to verify this.

I notice there's an anchor.exe process running (pid 21628) that doesn't match the stale pid in agent.pid (15760) — it might belong to another session or his running game, so I shouldn't kill it. I'm wondering if `anchor drive start .` will conflict with that stale pid, though the drive tool likely handles stale entries gracefully, and I'm second-guessing whether running a full game agent instance alongside his own session is wise.

Rather than risk the full game directory, I think a minimal scratch setup using the playground template would be safer — I just need to figure out what `anchor drive start` requires, basically a dir with a main.lua that initializes the engine. I'm sketching a Lua eval script that loads a sound file by absolute path and plays it either as an effect with dry/wet parameters or as a plain handle, then loops for a few frames to let it run.

Now I'm double-checking the exact API names — whether `sound_handle_is_playing` and `sound_handle_set_fx` actually exist and what they return, so I should look these up in the quick API reference.

There's no sound_handle_is_playing function available, so I'm checking whether sound_handle_set_fx could work instead for a plain voice, and considering whether setting wet to 0 would still leave the bus routed through the reverb.

I'm sketching a test plan that loads a sound file and measures playback duration by stepping the engine and checking audio_stats().voices_now until it hits zero, comparing an FX-routed voice against a plain voice. But I realize audio_stats's voices_now is only updated through the device callback or web pump, which doesn't run in agent mode -- so this approach won't actually work without a real audio callback firing.

Since voices_now stays at zero in agent mode, I need a different way to detect whether a voice is still alive -- maybe checking if sound_handle_set_volume or sound_handle_set_fx fail silently on a dead handle, or return some alive/dead signal. I should look at how sound_handle_set_fx is implemented to see if it exposes that.

I'm thinking the control test should compare FX voice duration against the expected 25.58s — a much shorter duration would confirm the bug, so no separate plain control is needed. I also want to check whether the playground agent pulls audio each frame via its tick function.

I should wait for the bench to finish before running the agent, since running them concurrently could skew the stall measurements the bench is tracking, even though the agent's workload itself is light.

The live version looks clean, just two tiny 2.7ms gaps with no repeats. The new version has a 16ms gap near the ending where a 77ms pump gap exceeded the 60ms lead threshold, preceded by three unknown quanta during what's likely a fade-out being misclassified as non-engine sound — I should bump the lead to 90ms to account for this fade scenario.

The old 4096-block path tolerated stalls of 85-170ms without producing replays, meaning the new 60ms-lead approach is actually less tolerant to stalls than the old method, trading lower latency for occasional audible gaps. Comparing latencies: the old path averaged roughly 128ms (85-170ms range from block boundary waiting plus double-buffering), so a 100ms lead would still beat that average while covering stalls up to ~85-100ms. I'm leaning toward setting the minimum lead to 100ms, though I recall a note that the 4096 change added "43ms more latency" with an unclear answer on whether that's perceptible.

I'm reconsidering a minimum lead of 80ms with bumps up to 200ms on repeated stalls, but running the numbers on the 77ms gap I observed shows only 3ms of margin at that level — too tight. Bumping to 90ms minimum (max 250ms) gives better safety margin while still beating the old path's average latency, and since stalls stem from main-thread blocking, a deeper queue is really the only fix short of mixing off-thread. I'm also noticing the jitter logging timestamps look like epoch time rather than performance.now, which would explain the absurd millisecond values I'm seeing.

I realize perf_time() is actually returning epoch-based seconds, not performance.now()-style values, so I need performance.timeOrigin to properly align these timestamps with the chunk times captured in the tap, since estimating the offset indirectly would mask the very jitter I'm trying to measure.

Rather than rerunning the whole benchmark with timeOrigin added to the tap metadata, I can just modify the bench page's log function to prefix performance.now() onto SNDLOG lines directly when it prints them — since Module.print happens synchronously during the Lua call, this should give me an accurate, exact timestamp alignment.

Rerunning both benches costs about seven minutes, but it's worth it since this gives concrete evidence for the 85ms quantization explanation I'd want to present — especially now that the live engine bench shows no actual replays. While that runs, I can also do the agent-mode test, and separately think about how the checker classifies the fade-out quanta near the end of decaying tails as UNKNOWN.

I should merge those unknown quanta immediately preceding a GAP event into the fade classification — simplest fix is to strip UNKNOWN events within the 8 quanta before a GAP. My plan: bump the engine's leadMin/max to 90/250ms and rebuild, append performance.now() to the SNDLOG call, update start_jitter to read the fourth field when present, and adjust tap_check to fold fade unknowns into the GAP. Then kick off both benches in the background and run the agent test in parallel.

I'm bumping the minimum lead to 90ms since the 77ms ending stall ate through the old 60ms buffer, causing a brief fade-gap. I also need to fix the play-log, which was timestamping from the Unix epoch instead of page load—switching to the page clock and rerunning both engines.

The variable name f gets reused later in the loop, but since it's reassigned sequentially that's fine. Now I'll rerun both benchmarks in the background.

While I wait, I should prepare the agent-mode test script but hold off running it until the bench finishes so I don't perturb timing. I'm thinking about where to place the eval script for the playground game and whether the playground agent instance records replays separately.

I need to remember to call record_stop() first, since capture runs don't record for screenshot-only agent runs. I'm writing a Lua script that loads the cave ambience sound file and plays it at two wet/dry mix levels, stepping the engine frame by frame to measure how long each setting takes to drain versus the known 25.58s file length, converting frame counts to seconds assuming 60Hz stepping. I'm also second-guessing whether an absolute file path will work with the engine's file layer, which I'll just test and see.

I'm writing the agent-mode test while the benches run—it plays a 25.6 s sound as a 3D voice through the reverb and measures its duration. It's set to run after the benches finish so it doesn't skew their timing.

I need to copy the audio worklet file into the static directory and point engine-boot.js's Module config at it by adding an audioWorkletURL property alongside the existing locateFile setup.

I also need to think about caching — since `/static/` uses a 1-day expires with must-revalidate, browsers could keep a stale worklet file for up to a day without checking for updates. A versioned query string would fix this, but engine-boot.js doesn't currently have a mechanism to compute that hash for static assets, only for the ASSET_VER entries tied to root-served files, so I need a simpler approach for this case.

I'm adding an `AWV` placeholder version token to the worklet URL in engine-boot.js that deploy.sh's sed step replaces with an md5 of the static worklet file, mirroring the existing pattern. I'm also planning a deploy.sh check comparing static/anchor-audio-worklet.js against the engine build's embedded copy, failing loudly with a message to copy, commit, and push if they diverge, since the site serves the static copy and any mismatch would cause confusing behavior differences.

I'm checking other consumers of the worklet: the replays-player pages have their own boot script and may not get CSP headers under /media/, so they likely fall back to ScriptProcessor if blocked—worth noting but out of scope. The game host runs inside the same engine instance so it's covered automatically, and I'm also considering whether the headless Chrome page-comparison probe needs to account for this.

Since audio doesn't affect pixel comparisons, that check should be unaffected aside from a possible console warning. I'll go edit engine-boot.js's Module config and health.lua now, being careful to only commit my own files given other sessions have unrelated uncommitted changes in the repo.

I'll have `engine-boot.js` reference the worklet URL with a content-hash version from `deploy.sh`, and make `deploy.sh --engine` block deployment if the site copy doesn't match the engine build, while adding the new audio line under `?perf`.

I'm verifying the relative paths in deploy.sh work correctly from the renderer directory, tracing how each one resolves up to the Anchor engine build and static asset folders.

The escaped period in the Python string shouldn't be an issue since it's inside a non-raw triple-quoted string within a bash heredoc that doesn't process backslashes, and the test passed with matching grep results. I want to double-check that the existing sed command around line 322 wasn't accidentally altered by my edits.

Confirming the exit trap properly releases the deploy lock since it's acquired earlier, and tracing the fallback chain if the static file hasn't been pushed yet—module load fails, falls back to a data URI, gets blocked by CSP, and ultimately lands on the ScriptProcessor fallback gracefully.

Now checking the perf line update in health.lua.

The deploy.sh edit holds up, releasing the deploy lock on failed checks via the exit trap, and the engine gracefully falls back to a data URL then the ScriptProcessor queue if the static file is missing. Next I'll check the `?perf` line.

I realize queue_ms_min gets reset to 0 at the very first pump since the queue starts empty, so the minimum ends up permanently stuck at zero. I need to fix this by skipping the min update when the posted queue length is zero, only tracking it once there's actually something queued.

The currently running bench uses the older web build, so that 0-min reading is expected from the first pump and not a real bug. I'll rebuild after this run finishes, since the bench page already embedded the engine at build time — rebuilding now won't affect the live run, but the next build script in the chain will pick up the new engine when it executes.

While the bench chain is still running, I'm also considering the desktop engine fix—setting periodSizeInFrames=480 in the noDevice agent mode path of anchor.c. That file compiles into both builds, but the agent block only activates when agent_mode is true, so editing it shouldn't affect the web build; still, I need to be careful about modifying the source while the bench is actively running.

Afterward my plan is: confirm the bug with the old desktop exe running fx_len.lua, rebuild desktop with the fix, then rerun fx_len.lua. But I'm worried that pid 21628's anchor.exe might be the same binary I'd be rebuilding—if so, rebuilding would fail with a linker error while it's running. I need to check what binary that pid is actually using before proceeding.

Good, the anchor process confirms it's not running. Now I'm reconsidering the fix in anchor.c's agent-mode config—adding a periodSizeInFrames setting of 480, but I need to verify this actually resolves the frame-size mismatch in the no-device mode's PCM reading logic.

The caching behavior checks out correctly for both the node graph reads and VoiceFx processing. The only concern is whether this introduces a slight timing shift for sound start alignment, but since the max delay is under 10ms, it's negligible and acceptable—I'll proceed with editing the source now, holding off on building until the change is confirmed.

That file modification warning is probably from my own edits. I'll wait for the current bench run to finish, then rebuild the web engine with the queue min fix, run fx_len against the old executable, rebuild the desktop version, and rerun fx_len again.

I'm seeing concrete evidence now: the live engine with 4096 blocks shows a start error spread of 97.9 ms, and among 15 same-sound pairs under 150 ms apart, 9 had their spacing shifted by over 20 ms — one pair asked 25.9 ms apart was heard 85.3 ms apart, a clearly audible "repeat" artifact.

The ending case is even more striking: a rhythm asked at 145, 133, 85, 61, 48 ms intervals was heard as 170, 85, 170, 0, 85 ms — the rhythm turns into a stutter. Switching to per-frame pumps, the new spread drops to 42.7 ms, but that's still larger than expected for a one-frame error budget; I suspect it's coming from variance in the queued-duration estimate that determines where sounds get scheduled relative to the mix buffer, especially around stalls like the 83 ms pump gap near the end.

So the delta between hearing and requesting works out to roughly the lead time plus whatever error exists in the queue depth estimate at the moment of the pump, since the pump is designed to maintain depth at the lead target. That error comes from how the worklet reports consumed frames and the resolution of messaging delay — I could tighten this by having the worklet report every quantum instead of every 8, or by deriving consumed frames directly from the context's currentTime rather than relying on worklet messages.

My estimate adds a constant ~128-frame (2.7ms) overestimate from how I'm combining consumed frames with the frame offset, but that's a fixed bias, not the source of variance. The real variability likely comes from currentTime's granularity — the sample frame counter on the main thread only updates at render quantum boundaries, and with the fake/disabled audio output device used in some environments, the underlying audio callback may fire in bursts of 10-20ms rather than smooth 2.7ms increments, which would explain the jitter.

This matches the jitter pattern I'm seeing in the benchmark (±20ms range), consistent with device buffer sizes like WASAPI's 10ms. Comparing paths: the new timing approach shows much tighter jitter (p5-p95 of 28ms, with none of the same-sound test pairs shifting by more than 20ms) versus the old path's 77ms spread and 9 of 15 pairs shifting significantly — so the new approach is clearly more stable, though I'm considering whether anchoring to performance.now() with a smoothed mapping could reduce jitter even further.

That 28 ms p5-p95 still seems a bit large though. Let me actually plot the distribution of start errors for the new path before settling on a fix — I'll print a histogram of the error values.

The new output tightens timing error dramatically and eliminates the 9/15 cases of audibly shifted repeat spacing seen on the live engine. I'll now dig into where the remaining ±20 ms spread in the new path comes from before considering this finished.

Looking at the new mix wait times, p50 is 7 ms and p95 22 ms, well within a 16 ms frame. The error drifts slowly—like the -20 ms shift seen around 41-44 s—which reflects the queue depth wandering shallower rather than any sudden glitch, and such slow drifts aren't perceptible as stutter since relative timing stays smooth.

Checking same-sound pairs confirms zero change, so the drift likely comes from the queue being pulled lower, possibly tied to the fake device's burst clock, but that's fine. Now I'm satisfied with this and want to rebuild the web engine with the queue-min fix, run a quick sanity check with just the fight scenario, then do the final bench on the completed build—I'll handle desktop work first, then rebuild web for the final bench. I also need to test agent-mode FX voice with the old executable before building desktop with the fix and retesting.

The new output cuts mix wait times to 7ms median (22ms p95) versus 46ms/84ms on the live engine, with spacing between neighbouring sounds preserved despite some queue-depth drift. Next I'll run the agent-mode test on the current desktop build without the fix.

Interesting — the FX voice ran its full 25.58s without over-consumption, so my earlier theory about the agent-mode multi-output bug seems off. I need to reconsider the noDevice mode path in ma_engine_init, specifically how processingSizeInFrames gets set from the engine config's periodSizeInFrames when device I/O is skipped.

Tracing through the attachment order: the voice attaches to the endpoint first, then the reverb gets created and attaches afterward — if new attachments go to the head of the list, the reverb ends up read before the voice dry signal. Following the read cascade, the reverb's cache size forces it to pull from the voice bus in two rounds (480 then 320 frames) to satisfy an 800-frame request from the endpoint, which could explain where the extra consumption is coming from.

But working out the math, this predicts the source advancing 1280 frames per 800 pulled, which would shorten the measured duration far below the 25.58s actually observed — so this model must be wrong somewhere. I'm wondering if the ma_sound node itself has some time-tracking or end-of-stream behavior that prevents it from over-producing frames, or if I'm misreading the cache/round logic in the output-bus-has-read check.

Possibly the wet path content is off even though duration is fine, but since the measurement doesn't actually prove a bug, I'm inclined to revert the agent-mode change rather than touch something that isn't demonstrably broken—though I should consider if it's truly harmless since it could subtly affect chunking/latency.

Re-examining the web path's fixed processing size of 480 frames: it keeps the graph processing in consistent chunks regardless of how pump reads vary, matching the desktop's tested period size, so keeping 480 is the safer, already-validated choice.

The comment I wrote about outputs being read once per pass is an unverified claim — I should reword it to a neutral rationale about the graph processing in 480-frame passes rather than asserting behavior I haven't confirmed. I'm reverting the agent change.

The 3D voice played its full 25.58s on the old build, so the agent-mode issue I'd predicted doesn't actually occur. I'm reverting that change and adjusting the web init comment to stop claiming the problem exists.

I should avoid committing workflow/claude/CLAUDE.md and skills/msg/SKILL.md since someone else modified those. I also need to make sure the desktop build still compiles given the audio-related changes in anchor.c, so I should run build.bat, and since this is an engine change I should also run the replay check even though it's audio-only.

I should also run `anchor check` since bench.lua changed, and verify whether build.bat regenerates the docs (ENGINE_BINDINGS.md / anchor.meta.lua) the way build.sh does on Linux — I'll just run build.bat and see what happens.

Replay check passes, but I'm wondering whether the build regenerated the docs since the audio_stats comment changed — that depends on whether build.bat does the same doc generation as build.sh does on Linux. I should check git status in Anchor to see if docs changed.

The docs regenerated fine — shader-prelude.json's small diff is just the expected derived hash change from the wasm rebuild, nothing concerning. Now I want to run the final bench on the web build covering fight plus ending with tap and soundlog to confirm the stats line holds and nothing regressed, then update the audio docs.

I should check ENGINE_API.md for audio_stats details and search the docs for any mention of ScriptProcessor or the 4096 buffer size to make sure the web audio path description stays accurate.

Desktop build is clean, the replay check passes (160/160 frames identical, correct seek), and bindings docs are regenerated. Now running the final bench on the web build while checking the engine docs for mentions of the old web audio path.

I'm also thinking about other consumers of the web engine, like the replays-player pages and the package-web-game.sh scripts, and whether the site's /media/ location block sets the CSP header — if it does, the replays player would fall back to the ScriptProcessor-from-queue path, still functional under stalls.

Setting Module.audioWorkletURL in the replays player shell would be an improvement, but it's out of scope since the fallback is graceful. I want to actually verify this fallback path works, so I'm devising a bench variant that forces the worklet to fail (by pointing audioWorkletURL at a blocked/nonexistent file) to confirm it correctly drops to useScript — though I need to account for the pre-js inside anchor.js setting the embedded worklet src after the page's Module object is defined.

I can override this in preRun, which runs before main() and thus before the audio engine initializes: setting Module.anchorAudioWorkletSrc to an empty string there would cause web_aud_open's data-URL check to fail and fall back to useScript. I'll add this toggle to build.py and queue a fight-only bench with this fallback mode once the current run finishes, since it's worth confirming given the live replays player's usage.

I'll add a bench switch that forces the ScriptProcessor fallback path (used by pages like the replay players that don't name the worklet file) so it gets tested directly instead of assumed.

The assertion failure traces back to how backslashes get halved, and since the write happens afterward, the file never gets created. Since the page's JS is embedded as a plain string literal rather than an f-string, I need another way to inject the placeholder value—maybe by writing a small separate script tag before the main one that sets a flag the main script can read, since the preRun file writes happen too early for direct substitution.

Editing build.py won't affect the already-running chain since the page was built before run.mjs started. The script mode's tap.js wraps the audio queue push and routes the processor into the recorder, which looks fine. I'll let the bench finish, then run a faster fallback test covering just the fight without the ending.

Checking the final build results: no dropped events, zero gaps despite that 78ms stall near the end being absorbed by the queue buffer, and average mix time holding at 0.54ms. The start error spread of 55ms (from -25 to +7) suggests a slow drift in queue depth, and I'm wondering if that traces back to the lead adaptation logic.

Thinking through the mechanism: the pump only runs periodically, and during heavier CPU load the pump frequency drops, so between pumps the queue drains. Sounds requested right after a pump get mixed at the next pump, but their heard time equals the stream time at that pump plus the lead -- so the error should really just reflect pump delay plus a constant lead, meaning the spread should roughly track frame time rather than drift. The -20ms drift implies the actual depth after topping up was running lower than the intended lead, so the estimate must be diverging from the real depth somehow.

This isn't actually relevant to stutter since the relative spacing between nearby sounds stays intact, so I'll leave this alone and move to running the fallback script-mode test on just the fight portion to save time, double-checking that the tap wrapping still works correctly.

The final build runs clean from fight through ending with no repeats, skips, or gaps—the 78 ms ending stall was covered with 18.7 ms still queued, and the mix averages 0.54 ms/frame. Next I'll run the forced-fallback test on the fight section only.

I'm realizing that the ScriptProcessor fallback's 1024-frame buffers (21.3 ms) explain the repeated "42.7 ms" jumps as Chrome replaying a stale buffer from a delayed main-thread callback — the onaudioprocess handler stays cheap since it just copies from the queue, but the small buffer size leaves little tolerance for timing drift.

Switching to a 4096-frame buffer in the fallback would restore ~85 ms of delivery tolerance like the live engine, at the cost of extra latency (85-170 ms), which is acceptable since this is fallback-only. But I need to make sure the queue holds enough frames when the 4096-sample burst fires — the pump's lead time of 90 ms (4320 frames) barely covers a 4096 burst, so I should bump the lead specifically in script-processor mode to lead + bufferSize to avoid underruns right before each burst.

The SKIP events near the REPEATs are just Chrome replay artifacts from partial overwrites and aren't a real concern since this is fallback-only. I'll set up the script processor with a 4096-frame buffer, and adjust the queued-frames calculation to subtract the buffer size in script mode so the pump compensates by keeping more queued — this only touches the script-processor path, leaving the worklet branch untouched, so I only need to rerun the fallback test, not the full worklet regression.

I found that the forced fallback's 1024-frame ScriptProcessor buffers caused Chrome to replay a buffer four times, same issue as before. I'll switch the fallback back to 4096-frame buffers, which held up in testing, and keep an extra buffer queued to avoid short-queue pulls.

With 4096-sample buffers, I'm seeing only about 441 engine chunks pushed, since the pump only mixes once the queued amount drops below the lead threshold, which happens right after the SP consumes a full 4096-sample block — so the quantization reverts back to 85ms steps because the queue estimate jumps in 4096-sample increments between callbacks.

I need a better fix: estimate consumption continuously using the audio clock rather than discretely at callback time, since the SP's buffer actually plays out smoothly over the period even though the queue only updates in bursts. I should extrapolate consumed position using ctx.currentTime since the last callback, factoring in that onaudioprocess fires one buffer ahead of what's actually playing, so the real question is when the audio position corresponding to a newly posted sound will actually reach the speakers.

This virtual consumed value rises smoothly within each period then jumps continuously at the next callback, so queued = posted minus virtual_consumed stays near the target lead. Checking the underrun condition, the actual buffered frames at callback time comes out to about the lead value (4320 frames), just barely clearing the required 4096 with a thin 224-frame margin.

To be safer, I want to pad the script-mode lead by subtracting an extra buffer (around 2048 frames) from queued, since the ScriptProcessor's inherent latency is already high.

Writing elapsed-time tracking logic...

With a zero spConsumed edge case resolved, I should rebuild and rerun the fallback test to validate the queue estimate against actual underrun behavior.

That fix backfired—the fallback queue estimate only updated once per full 4096-frame buffer, so mixing reverted to 85 ms bursts and start spacing broke again (requested 42.7 ms apart, actual 85.3). I'll correct the estimate to advance with the audio clock between buffers so mixing stays per-frame.

The fallback mode works fine without repeats or skips, though it has ten tiny 2.7ms gaps from Chrome's ScriptProcessor lock contention, matching the silence pattern seen on the live engine. Mixing stays per-frame across 3534 chunks, and while the start error spread is a bit larger, there are no duplicate-sound pairs to compare against — overall acceptable for a fallback. I'm noting those 2.7ms gaps register as zero gaps heard in script mode.

Since the worklet path itself wasn't touched by my EM_JS changes, I should re-verify it on the final build with a quick fight-only run while I write up the notes in the background.

The fallback path is clean now—no repeats or skips, continuous frame mixing across 3534 chunks, with only ten 2.7 ms gaps matching the ScriptProcessor's known behavior from the live engine. I'm re-running the worklet path on this final build in the background while writing up the results.

While the benchmark runs, I'm thinking about whether to preemptively run the page comparison from REDESIGN 37, since it's required before an engine deploy — I should check what that doc says about how the comparison works, possibly loading the site in headless Chrome against the local build.

Wait — CLAUDE.md explicitly warns never to load the site in a headless browser since his download manager intercepts binaries. The comparison must use a safer harness, which is what he did last time, so I'll read REDESIGN.md section 37 to understand the actual procedure before deciding whether to run it now or wait for his go-ahead.

The page comparison runs desktop builds through the pages_probe tool to check rendering, but my change is web-only audio work that only adds fields to audio_stats on desktop without touching rendering at all. Still, the procedure calls for running every check regardless.

I should go ahead and run it mechanically to have results ready — comparing today's desktop build against the live engine's pin, which looks like it might already exist in anchor-pins since a prior run covered the corresponding web build.

Running the standard pixel-comparison check across all 11 site pages on desktop builds, even though my change only affects web audio, since this is required for every engine deploy.

I need to build the pin for the live engine using build_pin.py, but since 211a9c1c93 isn't in pins.tsv yet, I'm checking whether _pin_commit can search git history to find the matching wasm hash.

Let me run build_pin.py on the new commit to create a worktree and build it, then figure out how to run the pages probe against both engines — I should check how instance 17 invoked the renderer with ANCHOR_ENGINE_BIN set, so I'll grep their transcript for "pages_probe" usage.

I want to see the full command used for the second page comparison, eng2, so let me print that out completely.

I should also try building the pin at commit 6659d99 using build_pin.py, but that requires compiling a desktop exe in a worktree, which takes several minutes. Since the benchmark is running concurrently in Chrome, the MSVC compile could load the CPU and skew the bench's timing, so I need to weigh that tradeoff.

This run shows just one small 8 ms gap from an 88 ms stall exceeding the 90 ms lead, while the old engine at buffer size 4096 rode through larger 106-142 ms gaps without any audible dropouts in these same tests.

The trade-off becomes whether to raise the minimum lead to around 120 ms, matching the old path's latency range of 85-170 ms, but start timing stays accurate regardless of lead in the new approach—the old 2048 buffer averaged only ~64 ms latency. For a click-triggered sound like beam firing, 120 ms is noticeably sluggish compared to desktop's 20-30 ms, so I'm weighing whether to instead address the root stall at the climax (six objects dropping plus a death sequence) or dynamically raise the lead just before a known heavy moment, perhaps via a scripted cue signaling the engine to brace for it.

Live site conditions could introduce extra stalls, pushing lead times up and increasing perceived latency further. The real fix would be decoupling via an AudioWorklet-based mixer with SharedArrayBuffer command passing, but that requires COOP/COEP headers that break embeds, so that's off the table for now.

Settling on a min lead of 120 ms and max of 300 ms, decaying by 5 ms/sec after 3 seconds clean, so a bump to 150 would settle back to 120 in about 9 seconds. I want to double check the latency math for the old scheduled-source path holds up — tracing through how the double-buffered callback timing determines when a mixed block actually starts playing relative to when it was requested.

Working out that old latency averages around 85-170 ms depending on callback timing, while the new approach with a 120 ms minimum lead plus at most one frame of wait lands around 120-136 ms — comparable to the old average and never worse than its worst case. I'll finalize leadMin at 120 and leadMax at 300, update the comments, rebuild the web engine, and rerun the fight/ending benchmark once more to confirm there are no audio gaps.

Final build check holds up with no repeats, skips, or spacing issues across all 13 pairs, except one 8 ms fade-gap in the ending from a stall slightly outrunning the 90 ms lead. I'll bump the minimum lead to 120 ms (max 300) to cover it, then run one more full test.

While I wait, I'll draft RECORD.md round 87 for lich-survivors and the REDESIGN.md section 50 along with memory updates, then hold off committing until the bench confirms. I'm noting that round 86 belonged to session 18's Steam work, making this round 87, and I want to document the new tools added like tap.js, tap_check.py, start_jitter.py, and the WEBBENCH_TAP/WEBBENCH_SOU variables.

I'm also checking whether the game's site package needs changes — since audio.lua doesn't touch engine internals, and bench.lua changes are inert unless bench_on is set, the game package shouldn't need re-syncing for this deploy. For the deploy plan itself, I need his approval to commit the static audio-worklet and engine-boot files, deploy.sh, health.lua, and REDESIGN.md to the site repo, but first I should check what's already unpushed since other sessions may have pending commits.

I'll run a comparison between the old and new desktop builds, and figure out whether deploy.sh needs the --engine --data flags again since the engine build should produce the same output from the same source. Let me draft the record text now, then do the page comparison once the bench run finishes to avoid CPU contention.

The site repo is clean, nothing unpushed, and those untracked audio files in lich-survivors aren't mine to touch. I should draft the round 87 record before the Anchor port section, using placeholders for the final lead-120 numbers until the bench results come in.

No events in the end, with the 88ms stall covered by the remaining 40ms, and spacing between identical sounds preserved untouched. The start error spread is just isolated outliers from pump gaps, so that's fine. I'm now worried I need to double-check that the static copy of the worklet file matches the latest build, since I may have copied it before making later changes to the source worklet JS.

I'll verify with cmp that the worklet hasn't actually diverged since my copy—the fallback changes were in anchor.c's EM_JS, not the worklet file itself. Next I need to build pin 211a9c1c93 (desktop build of Anchor 6659d99) and compare it across the 11 pages, running build_pin which could take 5-10 minutes, and checking whether rv._pin_commit correctly finds that commit.

At a 120 ms lead, the fight and ending showed no events, with the 88 ms ending stall fully covered by the 40 ms queue and no spacing changes across any of the 14 same-sound pairs. Next, I'll build the desktop twin of the live engine and run the 11-page comparison on both engines.

All 11 runs confirm the same pattern on each engine, with the old path being nil and the new path showing "device" in the desktop agent. I also want to run anchor check for lich-survivors since bench.lua changed, and verify the site's renderer picked up my health.lua edit correctly during the page comparison.

For health.lua specifically, I should syntax-check it directly using the bundled lua54 binary to attempt loading the file and confirm there are no parse errors.

Build is clean with 0 errors, so now I'm preparing to log RECORD 87 and REDESIGN 50 and commit the anchor repo changes, covering the engine source, audio worklet script, build script, and the generated build artifacts.

I'll decide whether to track or ignore the regenerated pre-js file (leaning toward tracking anchor-audio-worklet.js for cross-machine consistency), and need to include docs and the anchor.meta.lua, while excluding files owned by others. For lich-survivors I'm gathering the bench and webbench tooling plus RECORD.md, skipping the gitignored scratch and unrelated untracked audio files, and for site I'm including the static worklet script, renderer health/tooling files, deploy script, and REDESIGN notes.

Committing is fine since it's local, reversible work matching prior session conventions — I'll use the standard Co-Authored-By attribution and match each repo's commit message style, including a new RECORD 87 entry for lich-survivors written in that dense, factual voice. I also need to update memory files: add a Phylactery 19 entry to the game project memory, note REDESIGN 50 in the site memory, and refresh the MEMORY.md index line for Phylactery's "NEXT" status.

I need to check whether section 49 is the last section in REDESIGN.md before appending section 50, or if there's more content after it.

I'm looking at the commits now and checking whether engine/engine has a .gitignore that should exclude the build-web-engine/audio-worklet-pre.js file, rather than adding that file directly.

I should check whether deploy.sh refuses a dirty tree and whether it has an --allow-dirty flag, since other sessions' modified files might block deployment — I'll need to figure that out at deploy time. I should also update my memory for the project_lich_dream_game.

I need to add a Phylactery 19 entry to MEMORY.md near the "Audio stutter, measured" entry, and update the index line's description to mention session 19. I'm also considering whether the engine's web audio change is worth a brief line in project_anchor3.md, so I'll check the end of that file and keep any additions short.

I need to confirm with him before deploying — push the site and run deploy.sh with the engine flag since the change affects the engine, not just data sync, though I should watch for a dirty tree since another session left uncommitted changes to media-meta.json and the tweet fetcher script. I should also flag that I wasn't able to measure his live page directly.

I need to push the site repo first, checking if only my commit is unpushed via origin/main..HEAD, then run deploy.sh with the engine flag. I should also check how deploy.sh handles a dirty tree, since other sessions' uncommitted files might trigger a refusal unless I pass --allow-dirty.

I'm checking the "prod" remote status — my commit is one ahead, and the dirty files are non-code tool scripts and a metadata JSON, so the dirty check should pass, though I need to verify untracked JS files under the replays-player media directory aren't caught by the check.

Before pushing with `git push prod main`, I recall a memory about a new push hook and deploy.sh ordering/locks from the site redesign notes, so I want to pull up that memory to confirm the correct order before triggering the VPS hook.

I'm recalling that the pre-push hook likely handles replay syncing to R2, so pushing to prod should trigger that automatically. For the actual deploy step, I need to run the deploy script with the right flags—following the pattern from before where both --engine and --data were used together.

I'm noticing the deployed anchor.wasm hash doesn't match my committed build — the file sizes differ between the earlier commit and what the rebuild produced, so something changed between my build and the deploy, possibly from the fallback edits I made afterward.

Deployed successfully — the site now runs engine `anchor-62002214dd.wasm` with JS `anchor-999b885e98.js`, and server checks passed. Since the deploy rebuilt the engine, I'll verify this build matches what I committed and benchmarked.

The build is deterministic, confirmed identical, and the bench's final run after the lead change used the committed wasm 62002214dd. Now I want to verify engine-boot.js references the worklet URL with the correct stamped version, and that the live engine JS actually contains the worklet path logic.

Confirmed the deployed wasm matches the committed/benched build (62002214dd) exactly, and the live loader correctly passes the versioned worklet URL while the engine JS reflects the new output code.

The grep confirms it's present, just on a minified line. I should update the memory and REDESIGN records to reflect the deploy, though I need to think about whether the REDESIGN.md commit needs pushing now or can wait for the next session's push.

I'll commit and push this doc update now since pushing is harmless and was already part of what he approved — updating RECORD 87 and REDESIGN 50 from "not deployed" to reflect the actual deployed state.

Everything's live — the loader's hashed filename matches the deployed file, and the engine JS reflects the latest output. I'll log this deploy in RECORD, REDESIGN, and memory now.

The user is just connecting, nothing needed from me here—I should just acknowledge briefly and mark the remaining plan steps as complete since the deploy and verify work is done.

He just said "Connecting." so I should give a quick status update — the new web audio work is deployed and ready for him to test, so I'll point him toward what to check.

I'm thinking about why dropping to 30fps makes the game look slower rather than just choppier—likely because the fixed timestep update rate interacts with the render rate in a way that affects perceived motion speed when vsync kicks in.

Then I turn to the actual profiling data: draw calls take about 6ms/frame, updates about 5.7ms (with snakes and physics being notable costs), and there are occasional long frames around 27.8ms where most of the time is spent in Lua execution. I'm examining where those slow frames come from to figure out what's worth optimizing.

I should check RECORD 81 and 84, which document prior web performance sessions, along with the CLAUDE.md performance notes, to understand what's already been tried. Given the user's RTX 2060 and Chrome setup, intermittent frame drops to 30-45fps would naturally occur if frame times spike to 17-25ms on a 60Hz display, even without a hard vsync-style halving.

My plan: first orient by reading the existing performance records and bench tooling to see where frame time goes — GPU draw submission, snake updates, or rendering. Then I'll profile uncapped in the browser to isolate the costliest sections, and optimize those hot paths carefully while verifying behavior stays identical via digest checks.

The constraint that both builds must stay bit-exact matters since the web build uses a Lua port of three.js for rendering — any optimization has to preserve game logic exactly, which the digests will confirm.

I'll set up my task list now and start reading the record and perf notes.

Other sessions already pushed rounds 88-92 plus a Steam build, and the owner considers the game done — shipping involves both a site pack deploy and a Steam build, which needs his approval. I want to check if another session is currently active on lich-survivors by looking at git status and the latest commit timestamp.

Looking back at the round 81 "not done" list, I'm noting the remaining optimization targets: draw calls still cost 3.4ms from re-sending uniforms per material across roughly 185 draws, and the snake tube builds cost 2.6ms/tick since 6 tubes with 72 rings each get rebuilt every tick in Lua.

I should also check whether frame drops cause a visible slowdown in game speed, not just stutter. I need to trace how the fixed-timestep loop handles dt clamping — if MAX_UPDATES caps ticks per frame, dropping to low fps could trigger a genuine slowdown rather than just choppiness, so I want to verify how the site's game_host drives the hosted game's update calls relative to its own tick rate.

My plan is to profile the game as hosted with WEBBENCH_SITE=1 uncapped, using the existing profiler sections to isolate frame costs, then target the biggest contributors while keeping game logic and visuals exactly identical. I want to check the project's CLAUDE.md and the digest/frame-comparison tooling (port_digest, digest_run.lua, frames_probe.lua, cmp_frames.py) along with prior stutter investigation notes before running the benchmark.

Before kicking off a bench run, I should check whether another session is already editing the game or running benchmarks, since that could conflict with my changes or skew results — worth checking git status and any active sessions first.

Good, only untracked sfx files show up, so it's safe to proceed. I'll kick off the baseline bench in the background while I read through the port files, the draw path in main.lua and three/renderer.lua, and the tube-building logic in snakes.lua.

I'll pick up from the current game since other sessions already shipped rounds 88–92 this morning. I'm kicking off a baseline web benchmark in the background while I review the render and update paths.

I should look at the renderer file in full, since the draw loop re-sends uniforms per material across roughly 185 draws costing 3.4ms, plus check the snake tube-building code and the main loop.

Looking at this Lua port of three.js's renderer, I'm thinking about caching the shadow pass for static cave casters separately from dynamic ones like snakes, the lich, and rocks — rendering static geometry into a cached depth map once and blitting it each frame, then drawing only the moving casters on top.

For this to work I need to check whether the directional light follows the player camera, since that would move the shadow camera and invalidate the cache. I'm also estimating draw call overhead — with roughly 185 draws per frame and several Lua-to-C uniform calls each (receiveShadow, modelViewMatrix, normalMatrix, modelMatrix), that could add up to a few milliseconds, but I really need actual profiler data rather than guesses. I want to dig into prof.lua and bench.lua to see how to enable fine-grained profiling in the web benchmark and understand the output format, focusing on long-frame counts (p99) rather than averages since dropped frames happen only occasionally.

I'm looking back at the bench data defining "long" frames as those over 25ms, and reviewing the earlier frame timing breakdown showing average frame costs, Lua time, and the raf callback percentile stats across the sampled callbacks.

I want to wait for the baseline to finish, then check if bench.lua supports a finer-grained profiling mode to get a per-draw breakdown of where time is going.

While the baseline runs, I'm reviewing the profiler and bench code, noting it flags frames over 25ms as "long" and has an optional fine mode that times each draw's parts. I'll check how fine mode gets enabled in the web bench.

The game runs physics at 60Hz with a max of 8 ticks per frame, so at 30fps it should just tick twice per draw without changing speed—unless there's a dt clamp I'm missing. I'm checking the engine's MAX_UPDATES logic and the PHYSICS_RATE constant to see if dt gets forcibly clamped to 1/120 under certain conditions.

I'll check the engine's and host's catch-up limits, since a clamp there could explain the perceived slowdown even though 2 ticks/frame at 30fps should match the normal speed.

With the cap set at 83 ms, a normal 30fps frame still gets 4 physics steps in without any slowdown, so the game runs at real-time speed except when frame times exceed that threshold, at which point it visibly slows down.

Thinking through where the 30fps drops actually come from on a 165Hz display: since draws only happen once accumulated render-lag crosses the 16.67ms threshold, the effective frame interval snaps to multiples of the 6.06ms vsync period, so frame work in the 25-30ms range produces an effective 33fps, matching the "long" frames seen in the benchmark data around 27.8ms each.

I'm also wondering whether the site itself adds per-frame overhead when hosting the game — if the site redraws its surrounding page content each frame, that could be a separate cost worth isolating from the game's own update/draw time, though the actual request is focused on optimizing the game rather than the site.

Let me go check the baseline output now.

The clamps aren't the issue—frame times of 18–30ms are capping the game at roughly 33–41 fps on his 60Hz draw cap, which explains the perceived speed change. Now checking the baseline.

In my headless Chrome baseline at 60Hz pacing, the fight scene runs around 10.3ms per frame with Lua split between draw and update work, giving p90 of 21ms and p99 of 24ms with 14 long frames, and the ending sequence shows spikes up to 75ms. On his actual live site, extra site work plus his browser's extensions compound on top, with Lua already busy 62% of the time.

Reducing Lua cost directly cuts frame time. The biggest cost buckets are the world pass draw call at 4.66ms (mostly inside r3.draw at 3.27ms), snakes at 2.44ms per tick, fx at 0.81ms, and a tail of smaller systems like powers, gore, lich, warp, bloom, and audio.

My main target is r3.draw's 3.27ms across roughly 185 draws — I want a finer profile breaking down program binds, mesh binds, camera/object uniforms, state changes, and the actual draw call, which I can get faster by running the native desktop agent build with the benchmark flag enabled rather than rebuilding the 130MB web page each time, even though native GL timings don't map 1:1 to WebGL's more expensive validated calls through ANGLE — so cutting the raw number of GL calls matters more than the per-call cost on the web.

Now I want to check whether gpu_set caches uniform values to skip redundant GL calls, so I'm digging into the engine's gpu_set and gpu_draw implementation to see how it handles uniform locations and per-program caching.

The baseline runs at 60 fps, but Lua eats 10.3 ms/frame (5.5 drawing, 4.8 updating), with 14 frames over 25 ms and 52–75 ms spikes near the end—mostly from world draws (3.3 ms), snake ticks (2.4 ms), shadows (1.0 ms), effects (0.8 ms), and scene traversal (0.9 ms). I'll now dig into how the engine handles uniforms and draw calls to figure out what's safe to cut.

I'm tracing how gpu_set avoids redundant updates by comparing values, how uniforms get resolved from Lua names, and how table data is pulled via repeated raw reads for matrices. I'm also checking how recording state gets flagged before replay calls and confirming each uniform call still requires binding the program first.

I realize glUseProgram gets called repeatedly even when the program hasn't changed, and I'm wondering whether emscripten's GL emulation caches this or forwards straight to WebGL, which would make redundant calls costly. Caching the current program engine-side seems viable but risky since multiple code paths call glUseProgram independently, so I'd need a global tracking variable updated consistently everywhere. Since this touches shared engine code affecting the live site, I want to quantify the actual WebGL call volume per frame before deciding whether to pursue this change, so I'm going to check how gpu_draw handles state changes per draw call.

Each uniform set triggers a redundant `glUseProgram` call, which is costly on web since every GL call crosses the WebGL boundary. I'll check `gpu_draw` next to see how much GL state it resends per draw.

I'm noticing that gpu_draw_core redundantly resets every GL state each draw call—program, texture units, vertex attributes, VAO—adding up to roughly 40 calls per draw across 185 draws, or about 7400 WebGL calls per frame. That's a lot of overhead given how expensive each WebGL call is in Chrome.

So I'm thinking a state-caching layer could help: track current blend, depth, cull, program, texture bindings, and VAO, only emitting GL calls when something actually changes. The tricky part is correctly invalidating that cache whenever other code paths—2D layer rendering, blits, clears, mipmap generation—touch GL state outside the gpu draw path.

I'm realizing the invalidation surface is large since clear, blit, mipmap, mesh updates, and texture uploads all mutate GL state the draw cache depends on, so a blanket "mark dirty everywhere except draw/set" rule would be fragile and error-prone. A cleaner approach is to pin invalidation to a few well-defined entry points like gpu_end_core and gpu_clear_core rather than scattering it across every function.

Before committing to this optimization, I want to check whether the GL-call overhead is even significant — profiling draw sections against other engine sections on web would tell me if the risk is worth taking, with frame comparisons as the safety net for catching any missed invalidation bugs.

If quantized at 1ms, summing over many calls should still give roughly unbiased estimates if phase noise is random. But I'm now second-guessing whether perf_time on web is actually 1ms resolution — performance.now() is typically much finer (microseconds) depending on cross-origin isolation, so I should double check against the bench's actual observed timing values before assuming coarse quantization.

For more reliable analysis, profiling natively on the desktop with finer timer resolution would give cleaner results than trusting the web-based measurements.

I'm weighing a better test: empirically measure GL call overhead on the web by implementing state caching and comparing draw timings before and after. I'm ranking candidate optimizations by expected gain, starting with eliminating redundant state changes in the engine's draw call and caching the current shader program to avoid unnecessary glUseProgram calls — both targeting the roughly 10ms/frame Lua and GPU overhead budget on web, with the engine-level change carrying more risk and needing careful verification against frame comparisons.

On the Lua side, I'm examining the per-draw overhead in r3.draw — key lookups, program lookups, filling the draw-state table, setting four object uniforms as 16-number tables read through C, computing the object's world matrix determinant, and the profiling calls that add small function-call overhead even when disabled. For shadows, I'm considering whether static cave geometry and casters that don't move each frame could have their shadow rendering cached instead of redrawn every frame, which means figuring out exactly what in the scene casts shadows.

I'm now working through whether I could render the static casters once into a cached depth buffer, then blit that into the shadow map each frame and only draw dynamic casters on top — checking whether the engine's blit function supports depth alongside color, since the shadow map relies on RGBA-packed depth in the color buffer plus a real depth buffer for testing. The tricky part is confirming that interleaving static and dynamic draws versus drawing static first then dynamic produces identical results under a LEQUAL depth test, since the final depth at each pixel should just be the minimum across all casters regardless of draw order.

I also need to check whether the shadow camera is actually static, since directional lights that follow the player would invalidate the whole caching approach.

Separately, I'm looking at the scene graph traversal cost — recomputing matrix_world for every object each frame plus frustum culling and sorting — and whether disabling matrixAutoUpdate for static objects would actually change behavior or just skip redundant work.

For dirty tracking to be exact, inputs would need to be unchanged since position/rotation/scale are mutated directly on Vector3/Quaternion fields, so the only reliable approach is comparing the ~10 raw numbers against cached values rather than trying to track mutation flags — that comparison is cheaper than recomputing the full matrix composition anyway.

For snakes at 2.44ms/tick, the tube geometry gets rebuilt across 6 tubes × 72 rings every single tick, and while caching the ground height grid would save ~30% it would introduce sub-millimeter positional drift, so that's off the table — I need to find redundant computation elsewhere in that code instead. The remaining systems (fx, powers, gore, lich, warp, cave) take smaller shares of time. On the GC side, heap usage swings between 83 and 127 MB with a roughly 19ms atomic pause every 15-25 seconds, so cutting allocations would reduce that pressure.

Now I'm looking at where garbage actually comes from — mostly per-frame tables — and since the incremental collector in auto mode spreads its work across sections, any GC pause gets attributed to whatever section happened to be allocating at the time, so less garbage overall means less GC time everywhere. The post-processing passes (warp+view, bloom+final) are each fairly small contributors. What's interesting is frames where total frame time is long but Lua time is short, implying 12ms or so going to something outside Lua — likely GPU rendering or browser compositing, especially if the canvas is being composited at a much higher display resolution than the game's internal render target.

My plan is to first fine-profile natively using the desktop agent to break down Lua hotspots inside draw, snakes, and fx sections, since Lua has no built-in profiler but the fine-grained PROF sections already exist for this. I'll run the desktop bench with PROF.fine enabled, stepping through fixed-step frames to get a clean read on Lua costs isolated from browser-side overhead.

I found that `gpu_draw` redundantly resends full GL state on every draw, resulting in thousands of costly WebGL calls per frame—a likely engine-side bottleneck. Next I'll run a fine-grained profile of the Lua side natively for speed.

I'm digging into the desktop native frame breakdown, noting snakes taking 1.885ms per tick dominated by tube rebuilds, and draw calls costing 2.10ms with mesh buffer uploads pushing 135k floats per frame straight from Lua tables.

The biggest costs are the tube build, buffer uploads via gpu_mesh_update (where iterating per-float through Lua's rawgeti is roughly 1ms natively but 2-3x slower on web), and material uniform updates. I'm now tracing which specific buffers contribute the 135k floats — instanced flake matrices, FX points and ribbons, and shadow-pass instance matrices that seem to re-upload the same flake data under a different material name.

I'm wondering if the engine has a dedicated binary gpu buffer path that bypasses the slow Lua-table rawgeti iteration, since that per-float loop could add several milliseconds on web if each access costs 30-50ns there versus 10-20ns natively.

One optimization is to only upload the sub-range of live particles instead of the whole fixed-size array, which would require an engine-side change to gpu_mesh_update so it accepts a first/count range and uses glBufferSubData for a partial update.

For the Snakes tube-building cost, I'm checking whether the 1.66ms native cost can be shifted to a C-side gpu_tube ring-building routine instead of Lua, as long as it produces identical floats.

For the material uniform refresh cost (0.55ms, 47 refreshes/frame), I'm thinking about caching uniform values per program/material pair so repeated writes get skipped when nothing has actually changed, since the engine already does value comparisons at the C level.

Next I'm checking r3.shadow's 0.66ms cost from walking casters and drawing shadows to see if that work is cacheable, then moving to r3.project and r3.matrices (0.39 + 0.35ms) for scene graph updates and culling, where matrix update caching looks like an exact win. After that there's fx, powers, gore, and lich costs to look at too.

Comparing web vs native r3.draw timings (3.27 vs 2.10ms, roughly 1.55x) versus snakes (2.44 vs 1.89, 1.3x), it's clear r3.draw is disproportionately costly on web — pointing to GL call overhead and table reads as the culprit, alongside GC pressure. The biggest wins look like partial-range mesh updates with a faster table read path, plus state caching in the GPU layer.

Since engine changes affect the shared site engine used across all pages, and the Steam desktop build also inherits these engine optimizations, I should check what buffer facilities the engine API already exposes before building anything new.

Profiling shows the tube rebuild (1.66ms) and vertex buffer uploads (1.19ms, 135k floats read from Lua tables) as the biggest per-frame costs, with material uniforms, shadow pass, and scene matrices rounding out the rest. I'll now check what buffer and tube APIs the engine exposes before deciding on fixes.

I'm checking whether gpu_mesh_update's offset parameter already allows partial updates without allocating new sub-tables each frame, since passing a smaller table for the sub-array would otherwise cost extra allocation.

I'm considering whether recording behavior differs between web, desktop dev runs, and Steam/store builds, but deciding this distinction isn't relevant to the current concern.

Switching gears, I'm examining table reads in gpu_data_arg: rawgeti is already the fast C API path, but on wasm each element lookup might cost 15-25 ns, which for 135k elements could mean roughly 3 ms — a surprisingly large chunk of the web draw time. I'm weighing options to cut this cost: uploading fewer floats by only syncing changed ranges, hacking direct access into Lua's table internals for speed at the cost of fragility, or having Lua write straight into a C-side gpu_buffer so the upload becomes a simple memcpy, though writing via a userdata metamethod from Lua is itself slower than table writes.

The real bottleneck seems to be reading the Lua tables themselves rather than the upload step, since even dirty-range tracking still requires scanning all values to detect what changed. Dead or unchanged particle slots (like inactive fx points, ribbons, flakes, or warp streaks) might stay zero between frames, but confirming that still means reading everything — so I need a way to skip reading unchanged data entirely rather than just skipping the upload.

I'm breaking down which uploaders cost the most: flake instance matrices, fx points/ribbons, warp streaks, gore splats, and depth matrices, which together sum to roughly 1 ms, plus mesh draw overhead from per-attribute update calls across ~185 draws adding another chunk. I want to check fx.lua next to see how point arrays are sized and whether draw ranges are used to avoid touching unused slots.

I've traced the 135k floats/frame to `gpu_mesh_update`'s element-by-element Lua table reads across the leak flakes, effect points/ribbons, warp streaks, and gore buffers. Now checking `fx.lua` to see if these buffers are sending more data than actually gets rendered.

I'm looking at the fixed costs of the sprite pools—motes, shards, and puffs total 2600 particles—and noticing that every tick marks position, color, and size as needing updates regardless of how many particles are actually alive, which forces an upload of around 20,800 floats per frame even for dead particles with zero size and alpha.

Then I'm checking ribbons: 240 ribbons with 4 vertices each mean 960 vertices across 25 floats and 8 attributes get rewritten entirely every tick, pushing roughly 24,000 floats per frame whether or not any ribbon is actually alive. Chunks add another 2560 floats from set_matrix_at calls on all 160 instances.

Adding up these effects pools puts total fx uploads around 47k floats out of 135k per frame, so I'm thinking through optimizations that preserve identical visual output—specifically tracking live slot ranges so dead, already-zeroed data doesn't need re-upload, and using partial update ranges on the GPU buffers to only push the dirty subset of the array each frame.

I'm weighing an engine-level change to gpu_mesh_update that accepts a source offset and count, letting it read only a sub-range of the Lua table directly rather than requiring a Lua-side copy loop, since copying in Lua before upload would actually cost more than the current full-table read. But the real bottleneck might be the Lua iteration itself, since writing particle and ribbon updates per tick involves tens of thousands of individual field assignments.

I'm thinking through whether dead particles and ribbons could be skipped once their state is already zeroed out rather than rewritten every tick, but that requires tracking liveness without changing the exact ordering and spawn behavior the simulation depends on. I'm also noting that dead ribbons still rewrite several constant fields each tick even though those values don't change while dead, which is another spot where skipping redundant writes could help if done carefully.

The risk is a buggy owner still holding a dead ribbon reference and mutating b.p0/p1 after release — but since width and alpha are already zero for dead ribbons, any such writes would remain visually invisible, so the rendered output stays identical regardless.

On the upload side, I'm thinking about the fact that currently every ribbon's full attribute set gets re-uploaded each frame; adding dirty-range tracking per fx/warp/leak/gore system to only upload changed vertex ranges could substantially cut that bandwidth.

This is still behavior-identical: the game logic doesn't change (no RNG or state effects), and visually the pixels match since dead slots stay invisible and live slots get written the same way — the main risk is subtle partial-write edge cases needing careful implementation.

Next I want to look at the snakes build code, since it's the largest single native Lua cost per tick at 1.66ms.

The effects module resends its entire pools each tick (~47k of 135k floats), but dead slots draw nothing since size/width/alpha are zero, so skipping them won't change rendering. Next I'll look at the snakes' tube build, the biggest single cost.

I'm looking at the per-snake build cost across 72 rings, noting that ground sampling for cave.ground_y is the expensive part of the loop—roughly 30% of build time—while the tube geometry generation and eye placement are relatively cheap. I want to dig into optimizing that ground height lookup specifically.

Caching won't help since each ring's (x,z) position shifts every tick as the trail interpolates along its arc length—since snakes keep moving continuously during the benchmark, there's no reuse of exact coordinate pairs across ticks. So I need to actually examine how cave.ground_y is implemented to find savings elsewhere.

The ground height calculation is already using the engine's noise functions efficiently, so I'm estimating roughly 1700 native calls per tick across six snakes, which comes out to a negligible 0.3ms—well within budget.

Breaking down the snake build cost further, each ring involves smoothstep computations, trail walks, radius calculations, spine copies, and the GPU tube call, with additional overhead when burn/glow effects are active and writing to the aBurn buffer. Micro-optimizing this Lua logic would only yield 20-40% gains and risks breaking bit-exact parity with the three.js build, so I'd rather leave the math alone and focus on reducing call overhead through local caching instead.

Stepping back, I want to rank the biggest wins with the least risk: the top priority is cutting upload volume across fx pools, ribbons, chunks, warp streaks, leak flakes, and gore, since that directly reduces both C table-read time and WebGL bufferSubData calls—potentially saving 1-2ms on the web build.

Next is engine draw-state caching, which could cut WebGL calls by roughly 70%, though I can't measure that gain natively since it's web-specific; that would need an engine change plus a site deploy to verify, but both the hosted web game and Steam build would benefit. After that come matrix update caching, shadow pass caching for static casters, and tightening up material uniform writes for redundant work, followed by the snake build micro-optimizations and reducing per-frame garbage to cut down GC pauses.

The long frame outliers matter most for how the game feels at 30fps—I'm looking at frames exceeding 25ms, with 14 such frames in a 30-second bench fight and even worse 52-75ms spikes near the ending, likely tied to the roughly 19ms GC atomic step that fires every 15-25 seconds despite incremental AUTO mode being enabled.

Comparing the bench's long frames against normal ones (14.4ms vs 10.2ms) shows about +4ms from Lua and +6ms from elsewhere, and some of these are probably GC steps since Lua's incremental collection scales with allocation—a big allocation burst triggers a bigger step, so cutting garbage should shrink those spikes. I want to measure the actual allocation rate by stopping GC, stepping through frames, and checking how much collectgarbage('count') increases per tick.

The ground_y noise already runs in C, so per-ring Lua bookkeeping is the main overhead and optimizing it will only yield modest gains. I'll now measure per-frame garbage generation, since GC steps are a likely cause of occasional long frames.

I'm estimating the garbage generation rate at about 1.9 MB/s, which isn't huge relative to an 80 MB live heap, and working through how Lua 5.4's incremental GC pacing (based on the pause multiplier defaulting to 200%) would trigger the next cycle around 160 MB.

I'm also wondering what's actually filling that 80 MB — possibly static geometry stored as Lua tables of floats, where each number takes 16 bytes as a TValue, meaning 5 million floats would account for it. If that static data could be released or converted after upload to the GPU, the live heap would shrink and GC cycles would run faster and less frequently, since marking cost scales with the number of entries the collector has to traverse even for simple numeric arrays.

Releasing those arrays feels risky though, since other systems like nav mesh building or raycasts might depend on reading them later, so I'll hold off and look at heap composition more carefully if GC proves to be a real bottleneck.

Given the priority is the web build and Steam, I'm laying out a phase of game-side Lua fixes that ship without touching the engine: skipping redundant writes to dead particle slots, avoiding re-uploads when effects data hasn't changed, trimming similar waste in streaks, flakes, gore splats, and dust uploaders, skipping matrix recomputation for static objects, cutting per-draw Lua overhead, checking for redundant material uniform writes, and reviewing whether static shadow casters and lights can skip redundant shadow pass work.

For the engine-side phase, I'm considering caching GL state and uniform program binds to avoid redundant calls, plus adding partial-range table uploads to the mesh update function if the game-side fixes need it -- this would require another engine deploy, which should be fine since one just went out for audio.

For verification, I need to check how the digest system in tools/digest_run.lua runs game-logic digests to confirm identical state, and use tools/frames_probe.lua with tools/cmp_frames.py to do frame-by-frame visual comparisons with a fixed seed, making sure to cover effect-heavy moments and key scenes like the ending and the lens — since none of my changes alter actual drawn data, just skip redundant work, the frames should come out bit-exact.

The ~32 KB/frame of garbage isn't the main issue—I'll focus on reducing redundant work: on the game side, dead particle slots, unnecessary matrix recomputation, per-draw Lua overhead, and shadow caster caching; on the engine side, cutting redundant GL state resends in `gpu_draw`. Since every change must keep game state and rendered frames byte-identical, I'm checking the verification tools first.

I'm checking whether replay recording is disabled on web builds versus desktop or Steam builds — since there's no explicit opt-out in the init table, the Steam/desktop version might be recording replays for players, which could have a CPU cost.

I want to measure this properly: benchmark desktop performance with recording on vs off, verify the web engine's recording hooks early-out cheaply when not recording, and check the Steam packaging code to confirm whether it leaves recording enabled by default. If the Steam build does record, that's a meaningful disk write cost for real players, so I need to trace exactly where recording gets triggered automatically.

I'll test recording behavior on desktop and check if the web or Steam builds record too—since nothing in `main.lua` disables it by default, Steam players' copies may be recording as well.

So recording behavior differs by context: web builds never record, shipped Steam builds only record on the owner's machine via the ANCHOR_WORKSPACE flag, and desktop dev/testing runs record freely. I'm also noting that gpu_precision(14) in boot.lua rounds per-frame float data like matrix uniforms and dynamic attributes.

This runs on every platform including the web, even though recording never happens there -- so the cost is wasted and it subtly alters what's drawn by truncating float mantissas. Disabling it on web would shift sub-pixel precision rather than game state, likely imperceptibly, but I estimate the rounding costs roughly 0.3-0.5ms per frame across the float buffer, which seems worth verifying. I should check the early-out logic in the apr_gpu_rec hooks to confirm they skip work properly when not recording.

So recording is disabled on web and for Steam players, but gpu_precision is still active globally, rounding a large batch of floats per frame whenever data flows through gpu_fbuf—covering table uploads and matrix operations in gpu_set.

Since this rounding serves no purpose when nothing is recording, disabling it there would actually restore the original three.js full-precision reference behavior from before precision was enabled on 2026-10-03, with only sub-mm pixel differences at edges. I want to measure actual costs: compare desktop runs with recording on vs off, and gpu_precision at 14 vs 0, to get real frame-cost numbers rather than assumptions.

I'm setting up three bench variants — recording on, recording off, and recording off plus gpu_precision(0) — each running thousands of frames via engine_step loops, and queuing them to run sequentially in the background since each takes roughly 1.5-2 minutes.

Recording only happens locally (desktop runs), not on the site or in Steam builds, yet `boot.lua`'s `gpu_precision(14)` call rounds every per-frame float for compression purposes even when nothing is recording. I'll measure three native cases—recording on, recording off, and recording off without the rounding—to quantify that wasted cost.

While that runs, I want to check cmp_frames.py and digest_run.lua to understand the verification flow, plus revisit the renderer modules like core.lua's update_matrix_world and the materials write_uniforms logic.

Since matrix caching is already handled, the recursive matrix cost per draw likely comes from traversing the hundreds of scene objects doing comparisons. For each draw call there's also the model-view multiply plus normal matrix computation, which involves setting from the matrix, inverting, and transposing.

The normal matrix gets recomputed every frame for every draw since the camera moves (following the player), so even static objects get a fresh model-view matrix — this seems unavoidable. I'll hold off on conclusions until I see the actual recording measurements, and in the meantime check how materials.lua handles write_uniforms.

I'm realizing that write_uniforms wastes a lot of work setting uniforms that got optimized out of the compiled shader program — each one is a failed lookup, and with dozens of material refreshes per frame times dozens of uniform sets, that's a significant number of wasted calls.

The fix is to cache per-program which uniform names actually exist (via a one-time query of the program's uniform list or by remembering failed gpu_set lookups), then skip setting absent uniforms entirely in the Lua set function — though I need to be careful with array-indexed uniform names like light array entries, since those need matching against the real uniform table format.

Since gpu_set returning false has no side effects, skipping those calls is safe. Beyond that, I'm considering a lightweight Lua-side cache of last-sent scalar values per (program, uniform) to avoid redundant C calls for unchanged values, while leaving vector/matrix comparisons alone since comparing those in Lua isn't obviously cheaper.

The risk is that this cache only stays accurate if all uniform sets for a given program go through the renderer's own `set` function — if another module calls gpu_set directly on the same shared program object, the Lua cache could go stale. I'll scope the cache strictly to programs managed by the renderer's own set path to avoid that hazard.

Now let me check the results of the recording benchmark.

I'll cache which uniform names are absent per program so those lookups get skipped entirely, cutting unnecessary calls while preserving behavior. Now checking the recording measurements.

Looking at the desktop test results, recording adds about 2.25ms of draw time per frame, bumping overall frame time by roughly 27% in his native dev runs—though that overhead doesn't apply on the site or Steam build. Meanwhile, toggling gpu_precision off barely changes anything.

So the recording overhead is isolated to desktop dev workflows and isn't the cause of his drops on the site, since the site never records. I'll report this finding rather than change anything about recording, and move forward to set up a verification baseline—capturing frames before any optimizations and checking how the frame comparison and digest tools work.

Measurements show recording adds ~2.5 ms/frame (+27%), but since site and Steam builds never record, that's not what's causing the site's 30 fps drop—and the 14-bit rounding overhead is negligible, so I'll leave it as is. Now moving on to optimizations, starting with capturing a baseline of frames and game-state digests for comparison.

I'm planning a verification suite with digest checks comparing the LICH_ROOM three.js-comparable path against the default GAME.step mode, using a fixed seed and periodic port_digest calls to catch divergence across runs.

For visual coverage, I want to extend frames_probe into a richer snapshot script capturing more frames throughout the scenario, including the ending and effects-heavy moments, with LICH_SEED=7 for determinism and engine_snapshot at scale 2 producing 1280x720 images for each frame.

Still writing frame-stepping script...

I also want a digest version of the same scenario taking state digests every 100 ticks, and plan to run both before and after making changes to compare baselines. One worry is whether rendering itself can mutate game state—things like camera smoothing or render_acc timing—since draws happen based on dt from wall-clock accumulation, which could introduce nondeterminism I need to watch for.

I'm setting up two before/after checks: a fixed-seed run sampling 47 frames across the fight and ending, plus the same run dumping full game-state digests every 100 ticks. Every optimization must keep both byte-identical.

I'm comparing two runs of the same baseline and noticing frame differences around t4200 and t4440, right at the ending visuals — suggesting some nondeterminism in that segment, possibly from a time-based or wall-clock-driven effect rather than true logic differences. I want to verify whether the digests actually match or diverge to confirm this.

I ran the baseline twice and found 2 of 49 frames differ between identical runs, both near the ending, meaning something there isn't deterministic. I'll investigate this before trusting the comparison.

I notice the egg's rocking varies slightly between identical runs, which suggests the physics isn't fully deterministic — maybe tied to wall-clock timing rather than ticks. I'm wondering if the coroutine that preps the egg's physics each step is being time-sliced by real-world time, which would explain the inconsistency.

Since the digests still match despite this, the nondeterminism seems to be pre-existing and outside what I'm trying to verify. Rather than just writing it off as noise, I want to check how eggphys actually steps its physics to see if there's a real cause I can pin down.

The egg rocks slightly differently between two identical runs, meaning the egg physics depends on something beyond game ticks—likely randomness I'm not changing but which obscures the check for those two frames. I'll dig into the cause.

I'm realizing the egg's physics only updates at engine-step marks, since it's tied to the fixed 120Hz engine update rather than GAME.step, so in my frame-stepping mode it effectively advances once per 60 game ticks rather than continuously — meaning the physics is decoupled from the finer game-tick granularity I was assuming.

This makes me suspect Box3D itself could be a source of nondeterminism between runs — possibly from multithreaded contact solving, pointer-keyed hash iteration order, or tiny float summation differences that get amplified by chaotic dynamics, rather than anything in my own code. I'll treat the egg's bounding box near the hatch sequence as an expected zone of divergence and compare each candidate run against both baseline runs, flagging any differences outside that region as meaningful.

Now I'm moving into implementation. First I want to make the renderer skip setting uniforms that don't exist in a given shader program, using a per-program cache. Then for the fx system, I want dead sprite slots, ribbons, and chunk pools to skip redundant work and only upload GPU buffer data when something actually changed — since dead slots are already zeroed, skipping them shouldn't alter pixel output, and for live particles I'm considering partial buffer updates over just the changed range instead of re-uploading everything.

For something like the motes pool with 1600 particles, since live ones tend to cluster in a contiguous ring segment near recent spawns, a ranged upload covering just that segment could avoid uploading the full array every frame, though this would require adding range support to the engine's buffer update function.

Beyond ranged uploads, I'm listing the rest of the optimization queue: caching GPU draw state and shader program binding, then deploying the engine changes (needs approval, plus the Steam build), shadow pass caching based on whether casters or lights actually moved, some snake-specific micro-optimizations, and finally micro-optimizing the Lua render walk for projection and painter's-algorithm sorting. Starting with the first item — skipping redundant uniform sets in the renderer when values haven't changed.

Writing uniform-caching logic...

The per-object uniforms like receiveShadow and the matrices also flow through this same skip logic, so programs missing them just get bypassed after the first attempt. I also notice write_lights rebuilds strings like 'directionalLights[i].' on every refresh call, which creates minor garbage that could be precomputed, though it's a small concern.

I'll cache those index strings in a lookup table instead. There's also unnecessary allocation happening in common() when no color is set, and similar eager computation for map transforms even when the uniform ends up unused -- restructuring to check absence first would be more invasive, so I'll measure impact after the absent-skip fix before going further and then move on to editing renderer.lua.

Digests match exactly, and the small per-frame difference is confined to the egg's hatching physics, which jitters slightly on its own clock even in unmodified runs—so that's known pre-existing noise, not a regression. I'll start the renderer optimizations now, beginning with skipping uniforms the program doesn't actually use.

I'm realizing programs are lightuserdata (pointers), so a weak-keyed cache keyed on them won't garbage collect, but there's a subtler risk: if a program is destroyed and its address gets reused by a new program with different uniforms, stale cache entries could point to the wrong data.

To be safe, I should key the absent-uniform cache by the string `key` already computed in `draw()` rather than by the raw pointer, since that key uniquely identifies the compiled program and is immune to pointer reuse.

I should check whether other code paths call self._set with a different _prog without updating _absent_cur, since _set is only used in draw() currently -- I'll search for other "_set(" usages and "._prog" references, like in post.lua, to make sure nothing else touches the renderer state this way.

I'm designing the sprite pool so dead slots avoid redundant writes by tracking which ones already have their size and alpha zeroed out, only updating the arrays for slots that actually changed state.

But I realize there's a subtlety: skipping an upload based on whether anything was written this tick only works if the GPU buffer was last synced after the previous write. Since multiple ticks can occur per draw call, I need to make sure `needs_update` correctly reflects whether the arrays have diverged from what's currently on the GPU, not just whether this particular tick touched anything.

I'm also checking that only pool.write touches the sa/ca arrays, so skipping redundant zero-writes for already-dead slots won't cause divergence. And since the float attribute arrays start zeroed, initializing dead_ok to false just means the first write redundantly re-zeros already-zero values, which still matches the original behavior and correctly triggers the initial upload.

Thinking further about performance, I'm considering tracking an idle flag at the pool level so an entire tick can skip the per-slot loop when there's nothing live and nothing to spawn — spawn() would clear this flag, and a full pass with no live particles found would set it. This stays exact because once idle, every slot is already zeroed and nothing changes until a new particle spawns.

But then I realize the pool's particle array (P) might be exposed externally, so other code could potentially mutate particle life directly without going through spawn(), which would break the idle-flag assumption. I should check whether sprite_pool's P array is accessed elsewhere, like other fx modules, before relying on this optimization.

The absent-uniform skip is now wired in with no other renderer code writing uniforms directly. Next I'll check who else uses `sprite_pool` and reaches into its particles before modifying how it writes.

I'm checking whether a caller holding a stale reference to a dead particle could bypass spawn() and revive it directly just by setting p.life on that reference.

Those pools belong to other modules, and checking the fx.lua callers confirms the mote/shard/puff return values aren't being stored and mutated elsewhere, so the idle-flag approach should be safe for sprite pools — I just need to refine it a bit more.

Weighing the costs, looping all slots each tick without the idle flag adds real overhead (~0.15-0.2ms for 4000 slots), so I'll stick with the idle flag cleared on spawn, since there's no evidence of revival-by-reference bypassing it externally. Next I want to tackle the upload cost by implementing dirty range tracking so I'm not uploading all 8 floats per particle every tick.

I'm considering whether float attribute arrays should be GPU buffers instead of Lua tables, but indexing GPU buffers via metamethods from Lua is too slow for writes, so tables stay. I'm thinking through adding a new engine function that lets a sub-range of a table be uploaded to the GPU at a given offset, with backward compatibility in mind—but I realize the old engine currently live would ignore the extra range arguments and just upload the whole table at the wrong offset, which would break things.

To avoid that, I'll use a distinct function name like `gpu_mesh_update_range` so the game can detect support by checking if it exists, falling back to a full upload otherwise. The existing recording function already supports offset-based writes, so that part is fine, and I can apply the same dead/live skip logic to the 240 ribbons, writing only live ribbon vertices and uploading just the affected range.

For the 160 instanced chunks, I need to compute per-attribute byte ranges from vertex index ranges since each attribute has a different item size, and skip redundant zero-writes for already-dead chunks. Since multiple ticks can happen per frame, I'll accumulate the dirty range as a min/max union until the renderer uploads and clears it, and I'm designing a generic `mark(lo, hi)` method on BufferAttribute to track this.

I need the renderer's upload logic to distinguish full updates (plain `needs_update = true`) from ranged updates (set via `mark()`), since I can't intercept a plain field assignment. So I'll keep them as separate flags: `needs_update` triggers a full upload and clears any pending range, while `range_lo`/`range_hi` alone trigger a partial upload using a ranged GPU update function if available, falling back to a full upload otherwise.

I also need the size-mismatch path to clear any pending range, and I should double check correctness depends entirely on marking every write — for the sprite pool this means every slot touched in `pool.write` must get marked, otherwise unmarked regions would go stale on the GPU.

Since effects like warp streaks, leak flakes, gore splats, and cave dust keep pools active constantly during fights, I decide to implement ranged updates properly: a Lua-side marking mechanism plus an engine-side `gpu_mesh_update_range` function, falling back to full uploads on older engines. Now I'm writing the core BufferAttribute mark function, looking at how `T.float_attr` is structured to hook the range tracking in.

Since all revivals go through `spawn()`, I'll have pools write dead slots once then sit idle, skipping idle pools entirely and sending only changed slot ranges via a new `gpu_mesh_update_range` engine call (with a fallback for engines that lack it). Starting by reading `BufferAttribute` in `core.lua`.

I'm adding a `mark` method to BufferAttribute that tracks a dirty range by element indices, and planning a `gpu_mesh_update_range` engine function that writes a partial slice of floats to a GPU buffer at a given offset.

Writing C implementation for update range...

I'm checking that the core update function behaves correctly when writing partway into a buffer, and making sure the recording path handles both table and GpuBuffer data sources consistently with precision conversion applied for table inputs.

Now I need to find where the Lua binding registers this function so I can add the new partial-update version alongside it.

I'll add a `mark(lo, hi)` method alongside the unused `update_range` field, and implement `gpu_mesh_update_range` in the engine to read and send only the specified float range with the same 14-bit rounding and recording behavior as a full update.

I'll implement the range update for tables only, since buffer uploads are handled elsewhere via full memcpy. For buffer inputs, I'll just route through the plain data copy path instead of adding special handling.

I'm designing a static GL state cache for the draw engine, tracking blend modes, depth/cull settings, color masks, polygon offset, and bound program/textures to avoid redundant GPU state changes.

The tricky part is figuring out when to invalidate this cache—clearly at the end of frame reset, clear, blit, texture binds, and other GL-touching calls—but I'm wary of missing a spot. Rather than enumerating every call site, I'm considering treating the cache as valid only during an unbroken run of gpu_draw/gpu_set calls, flipping a validity flag at the end of gpu_draw_core and clearing it whenever something outside that sequence touches GL state.

A cleaner solution might be a shadow-state layer that intercepts every GL state call engine-wide through macros, so the cache stays consistent as long as nothing bypasses it—though I need to check whether SDL, ImGui, or other libraries ever touch GL state directly, which could desync the cache.

Redefining glEnable this way differs across platforms: on desktop with glad it's already a macro I can redirect, while on Emscripten it's a real function, so I'd need to define the cache wrapper before the macro to avoid infinite recursion. This still feels like a large, intrusive change across a 30k-line engine where the 2D renderer likely already manages its own per-batch state.

Beyond glEnable/glDisable, the same caching would need to extend to blend functions, depth/cull settings, color masks, texture/buffer bindings, and program usage — and subtle cases like glBlendFunc versus glBlendFuncSeparate updating a shared cache correctly. There's also a correctness risk: deleting a texture implicitly unbinds it from its unit, so if a new texture reuses that same id, the cache could get out of sync with the real GL state.

I'm estimating this is a significant amount of risky work spanning the whole site, so I want to weigh the payoff by roughly counting GL calls per draw — state toggles, program binding, and per-texture-unit activate/bind pairs across typical three.js materials with several samplers.

Adding attribute and VAO bind counts, plus draw and cleanup calls, I'm landing at roughly 50-60 GL calls per draw, which across 185 draws comes out to about 10,000 WebGL calls per frame — likely 2-5ms of overhead, making this probably the single biggest web-specific performance cost.

A better approach is caching a VAO per (mesh, program) pair so attribute setup only happens once per combination, invalidating the cache when the mesh's attribute layout changes via a version counter.

Since meshes are typically drawn by only a handful of programs (main pass plus shadow/depth passes), this collapses roughly 25 calls per draw down into a single glBindVertexArray call — a big win with low risk, since VAO state is self-contained and doesn't interact with other GL state, aside from edge cases like constant attribute values set via glVertexAttrib4f for missing attributes.

I also need to account for global context state that stays separate from VAO state — the code that sets default attribute values when a program expects an attribute the mesh doesn't provide still needs to run each draw since it's cheap and rare. Right now there's a single shared VAO being rebound and respecified every draw, which won't be needed once meshes have their own VAOs, but I should be careful with the 2D renderer since it likely manages its own VAO; keeping the unbind-to-zero call at the end is safer even if it costs one extra call.

The index buffer is part of VAO state too, bound via glBindBuffer with ELEMENT_ARRAY_BUFFER — since the VAO references the buffer object rather than its contents, updating data via glBufferData on the same buffer doesn't require a version bump, but creating a new index buffer after the VAO already exists does require bumping the mesh version. Similarly, adding a new vertex attribute or changing its component count/divisor needs to trigger a version bump, while just updating buffer data through glBufferData does not since the VAO binding itself hasn't changed.

There's a trickier issue on the program side: VAOs are keyed by program pointer, but GL program names can be reused after deletion, so a stale VAO could end up associated with a completely different program that has different attribute locations. I need to use a stable unique identifier instead of the raw GL name — checking whether GpuProgram's recording id (rid) is guaranteed unique across the program's lifetime, or whether I need to introduce a separate serial number assigned at creation time.

I'm settling on adding a global serial counter to GpuProgram, and making sure mesh destruction also cleans up its associated VAOs. For the state caching problem across the ~14 state calls and textures, I'm trying to figure out how to detect when something outside gpu_draw has touched GL state between calls — a global "foreign epoch" counter feels fragile since it requires enumerating every code path that touches GL, and the 2D renderer also mutates blend state, so I'm now tracing through how the frame is actually structured (shadow/world/post passes via gpu_* calls, then gpu_end, then the 2D layer render) to find a cleaner boundary.

Rather than instrumenting every 2D code path individually, I'm leaning toward invalidating the cache centrally at key chokepoints — gpu_end_core, clear, blit, texture operations, target/program creation and destruction, mesh binding — so I don't have to track every possible source of foreign GL state changes.

I'm also tracing whether layer functions get interleaved with gpu draws — things like reading a layer's texture or rendering the HUD layer before the main render pass, and how the final composite/present step interacts with that ordering.

A more robust fix would be a global state epoch counter, bumped whenever the 2D renderer flushes a batch or whenever gpu state-changing calls (clear, blit, end, texture ops) happen. The gpu_draw cache would record the epoch it's valid for, forcing a full resend if the epoch changes — the only risk being some engine path that mutates GL state without incrementing it.

I need to narrow down where this could happen between two gpu_draw calls — mainly gpu bindings (I'll audit that section around lines 12700-14800) and 2D rendering paths through layer_render and its flush functions. I'll search for every call site touching glUseProgram, glEnable, glBlend*, glDepth*, glCullFace, glFrontFace, glColorMask, glPolygonOffset, glActiveTexture, and glBindTexture to map out the structure.

I've traced the engine's GL usage and found each `gpu_draw` re-specifies all vertex attributes and state, totaling ~50 calls per draw (~10,000/frame). I'll cache a vertex-array object per mesh/program rebuilt only on layout change, but first I'm checking other places the engine changes GL state to scope how far to extend caching.

Since GL calls are scattered across the engine, a global state cache feels risky, so I'm leaning toward two safer approaches: a per-(mesh, program) VAO cache that's self-contained, and a more localized state cache inside gpu_draw_core that's only valid across consecutive gpu_draw calls, tracked via some kind of touch counter or invalidation trick to detect when other GL state changes happen in between.

I'm weighing whether to detect state changes by actually reading GL state, but glGet calls are too slow and not reliable even with client-side caching in WebGL. Instead I'm considering having the renderer explicitly signal when a batch of draws starts and ends, then auditing the handful of other GPU functions (clear, blit, mipmap generation, bind) to see which ones need to invalidate the cache since they can touch masks, bindings, or active texture units.

But this explicit-toggle approach is fragile since any other code path calling draw functions while the cache is active could desync, so I'm thinking about simplifying by invalidating whenever any GPU binding function outside the small whitelist (draw, set, viewport, bind) runs, which covers roughly thirty functions, plus making sure the 2D renderer path also triggers invalidation even if it runs without explicitly ending the GPU batch.

I want to locate the central 2D batch flush function and add a dirty-state call there to be safe. Weighing cost versus benefit: with per-draw state diffing, consecutive draws should need only 2-4 state calls instead of 14+, and texture binding can skip redundant activate/bind calls when the same texture is already bound on a unit.

Rough estimate: VAO caching drops attribute calls from ~25 to 2, state caching from ~14 to ~3, and textures from 2×units+1 down to just the changed ones — roughly 50 calls per draw shrinking to ~10, an 80% cut in WebGL calls. Before committing further, I should measure actual GL call cost with a microbenchmark, but the clear first win is implementing VAO caching and measuring draw performance before and after.

I'll start with the Lua-side changes for absent uniforms and fx pools, then build the VAO cache with ranges, measure on the web, and only then decide on the state cache. To write the VAO cache I need to check the GpuMesh struct and GpuProgram definitions.

A global GL state cache is too risky given ~300 state-change sites, so I'll focus on the self-contained vertex-array cache, which eliminates about 25 of the ~50 calls per draw. I'll now look at the program and mesh structs.

I'm designing a small VAO cache for GpuMesh, storing a fixed set of program-serial-to-VAO mappings along with a version counter that increments whenever attributes or the index buffer change, and giving GpuProgram its own serial ID so cached VAOs can be invalidated correctly.

Writing VAO lookup and build logic...

Writing VAO attribute binding logic...

I also need to call glVertexAttrib4f each draw for missing attributes to match old behavior exactly, and preserve the post-draw unbinding sequence—rebinding VAO 0 and clearing the element array buffer binding the same way the original code did.

Since my VAO approach stores element bindings per-mesh, I need to check whether unbinding to VAO 0 after drawing produces the same observable effect as the old code's explicit glBindBuffer(ELEMENT_ARRAY_BUFFER, 0) call, especially since the 2D renderer may rely on VAO 0 directly.

Also, I don't need the global enabled-attribute mask anymore since each per-mesh VAO tracks its own state independently — I confirmed gpu_vao isn't referenced elsewhere besides the draw call. Now I'm checking where mesh version numbers get bumped, particularly around attribute core updates when a new attribute is added or its components/divisor change.

Re-specifying an attribute with identical comps/divisor via glBufferData on the same VBO keeps the VAO valid, and creating a new index buffer also bumps the version. The replay player reuses the same draw/mesh functions so behavior stays consistent, and mesh free properly deletes the VAOs (ignoring WebGL context-loss edge cases). I also note that since program serials are unique, destroying and recreating a program invalidates any VAO slot tied to the old serial, forcing a rebuild — which is correct behavior.

A mesh drawn by many different programs would thrash VAO rebuilds each draw, but that's no worse than the old cost model, so it's acceptable. Pending async programs are fine too since attribute locations are only known after linking, and VAO construction happens later in the draw core once the program is confirmed ready. I'll hold off on a smaller caching optimization for now and just implement the VAO approach first, then measure its impact on the web build before writing the actual code.

I'll implement the vertex-array cache: each mesh stores up to four built-once vertex arrays keyed by program serial, rebuilt when the mesh's version bumps (attribute changes or index buffer creation), while constant attribute values still get set per draw as before.

Using calloc confirms the VAOs start zeroed, which is good. I'm checking whether the replay player's path creates meshes through the same gpu_mesh_new function, which it does, and verifying that vertex array binding gets properly reset to 0 after draws and buffer setup since ARRAY_BUFFER binding is global state rather than VAO-local.

Tracing through gpu_mesh_update_core and gpu_mesh_index_core, I confirm they only touch global buffer bindings or VAO 0 temporarily, so per-mesh VAOs stay undisturbed, and the old global gpu_vao reference has been fully removed. The 2D renderer also still unbinds VAO to 0 at the end just like before, so that behavior is unchanged.

I plan to build both the desktop and web engines after finishing the engine-side changes, then verify compilation and measure performance on web with the Lua changes applied. Next up is updating core.lua's BufferAttribute mark logic and the renderer's ranged update path.

Vertex arrays get freed with their mesh and `calloc` zero-inits each new mesh. On the Lua side, `BufferAttribute:mark(lo, hi)` tracks changed ranges so the renderer only uploads that range when `gpu_mesh_update_range` is available, otherwise it uploads everything.

I'm checking that the captured global reference is still valid at load time in the new engine, and confirming the game's sandboxed environment inherits engine globals through its __index metatable. I also want to check other places that rely on `a.needs_update` to trigger uploads, like gpu_mesh_of.

Now I'm verifying that index buffer updates and dirty geometry recreation correctly reset sizes and force a full attribute upload, and that an uncreated GPU buffer on first draw also triggers a full upload. Moving to the fx pools, I'm rewriting sprite_pool.write to skip dead/idle slots while tracking dirty ranges for efficient writes...

Still writing pool logic...

My idle flag check works correctly: it's only set when every dead slot has already been marked as written-dead in that pass, which matches the original behavior since zero live particles implies full coverage. I'm also confirming that both versions upload the full arrays every frame regardless, and the initial dead_w state correctly defaults to writing zeros across the whole range on the first pass, mirroring the original's startup behavior.

For the spectrum allocation, I want to avoid the per-tick garbage from allocating a new color table each time — I'll write a local variant that reuses a single table for the same arithmetic, since FX.spectrum is shared elsewhere and shouldn't be touched globally.

For ribbons, I'm checking that a dead ribbon correctly collapses to zero width and zero alpha even though its other fields (position, color, hue, style) stay stale between ticks — since both width and alpha hit zero, the ribbon stays invisible regardless of those unchanged fields, matching the original visual behavior.

I want to confirm this by checking the ribbon shader itself: the quad width comes from the on/off flag times the ribbon width, and alpha is additive, so a zero width should mean no fragments get drawn at all — though I should double check there isn't some minimum-width fallback in the style logic, and I'll let the frame comparison catch it if there is.

I'm also tracking which ribbons get skipped versus written each tick — dead ones can skip array updates, but live and held ribbons (regardless of remaining life) still get their vertex ranges written every tick.

One more concern: idle detection for the whole pool should only trigger when nothing is on and everything's dead, but I need to make sure release properly clears state so a ribbon doesn't linger as live after being released, and I should check whether any external code mutates a ribbon's held or life fields directly outside the normal spawn/release API, since that could break my idle-skipping assumption.

Ranged uploads are done. Next I'm rewriting the effect pools to skip dead slots, idle until spawn, and track changes precisely—starting by checking if any module revives a ribbon manually instead of via `spawn()`.

Held ribbons get updated every tick while active, but once released, external changes wouldn't matter since they're no longer written. The reference in beam.lua reading ribbon data looks fine, and the per-tick vector references between ribbons are also safe. Now I'm ready to write the updated fx.lua code.

Held ribbons get updated externally each tick while active, and are only read (not written) after release, so a one-time write to a dead ribbon is safe since it renders nothing at zero width/alpha. I'll now rewrite the three pools.

I'm checking whether the goto jumps into the scope of a local variable, since Lua forbids jumping into a block where a local would be in scope—but since the label sits at the end of the block after the locals are declared, this should be valid.

I'm also verifying edge cases around life decrementing: a ribbon whose life crosses from positive to zero within the same tick still gets correctly marked dead in my version, matching the original's behavior.

Spawn and release both correctly flip idle to false since they modify live entries. I'm leaving the spectrum pool alone for now and turning to the chunks pool, checking how set_matrix_at writes into the instance matrix array and confirming chunks use frustum_culled=false so dead entries just get zeroed out each tick without needing special-case tracking.

The ribbon rewrite checks out—the `goto` correctly lands after the per-ribbon locals, and a dying ribbon still gets written dead that tick. Moving on to the 160 snake-chunk instances with the same pattern next.

I confirm the chunk pool initializes all matrices to zero, meaning dead_w starts entirely true as expected. Now I'm checking whether the chunk mesh's shadow pass, which draws instances with a depth material, could interact with this in some unexpected way.

Next I want to track down the other big instance-matrix uploaders flagged in the fine profile — leak flakes, gore splats, blood points, warp streaks, and cave dust — so I'll go look at leak.lua, gore.lua, and warp.lua to see how those instance buffers are built and updated.

I confirmed the chunk pool initializes correctly, with all 160 matrices zeroed before the first upload. Next, I'll check the other major senders: the leak's flakes, gore, warp streaks, and cave dust.

I'm noticing a leak with dying flakes—the update loop uploads the full instance matrix and color every tick regardless of whether any live flakes changed, so I need to track the actual range of live flakes written and skip the upload entirely if none exist, rather than running an idle check every frame.

For gore decals, the dirty-flag-triggers-full-upload pattern can become per-pool lo/hi marks over just the written indices.

For blood particles, the idle flag already exists, but when active it writes all four attributes for every slot—dead ones get zeroed matrices each tick just like the sprite pool. I'm working out how to track a "dead_w" flag that correctly captures both the steady-state dead branch and the moment a particle's life hits zero mid-update from a ground hit, since both cases write the zero matrix but through different code paths.

I also need to handle the edge case where life decays to zero without a ground hit—the original code still writes a full visible size that tick before going dark, so my dead_w tracking has to treat that as a live write, not a dead one.

The dust, warp streaks, petals, shards, lens cone, and remaining gore pieces either already get full uploads each tick or are small enough not to matter much, so I'll leave those alone for now. Once the blood and gore decal marks are applied, I'll rerun the fine-grained profile to see how the upload counts shift.

I found similar inefficiencies in the leak flakes, gore splats, and blood effects—all resending unchanged data unnecessarily—and I'll apply the same mark-what-you-wrote fix to each. The cave dust genuinely updates fully every tick, so I'll leave it unchanged.

The blood arrays start zeroed out and nothing else writes to them, so this initialization matches the original behavior. Now I'm moving on to building the engines and running verification along with a fine profile pass.

I need to build both the desktop and web engines, then verify digests and frame output against baseline using the new desktop engine, keeping in mind the Lua changes also need to work on the old engine's fallback full-upload path — both the ranged update and fallback must produce identical frames.

I'm starting fresh by writing every slot dead, so the first pass sends the whole pool like before. Now I'll build the desktop engine with the vertex-array cache and ranged upload, then verify digests and frames against the baseline.

The digests are identical, and only frames t4200 and t4440 differ -- the known nondeterministic egg frames. I want to verify these fall within the egg bbox region and build a better comparison that treats a frame as OK if it matches either baseline, or if its diff stays within the known noise bbox.

Digests match, with 47 of 49 frames byte-identical; the two differing frames are hatching-egg frames that also vary between baseline runs. I'll update the compare script so a frame passes if it matches either baseline or its differences stay within the egg's known noise box.

I should run a desktop profile with recording off to check floats_up and the mesh/draw calls, then move on to the web benchmark once the web engine build is ready.

The float count dropped significantly from 135k to 61k per frame, and mesh and draw timings improved too, bringing the frame mean down from 9.49 to 8.49ms. But I'm noticing some inconsistency — gore and snake timings actually got slightly worse between runs, which could be due to variation in spawn timing rather than a real regression.

Since the bench uses a fixed seed, this shouldn't be noise from game logic, so it's likely wall-clock variance or CPU frequency scaling during the run. The extra bdead_w table lookup I added shouldn't account for such a large time increase given how few slots are involved, so I'll hold off worrying about it until I can rerun the benchmark multiple times. I still need to track down where the remaining 61k floats are coming from since my current grep only surfaced the smaller contributors.

Per-frame floats dropped from 135k to 61k with effects tick down to 0.10 ms and buffer uploads to 0.59 ms, though gore ticked up slightly (0.30 to 0.45 ms), which I'll check against noise. Now digging into what's making up the remaining 61k floats.

I'm looking at the remaining cost breakdown for warp streaks and snake body rendering in the shadow pass, checking where the mesh uploads and GPU buffer memcpy times are being spent.

For the warp streaks, the whole mesh gets marked dirty and all attributes get flagged for update even if only a few streak ranges changed—I want to check warp.lua to see if I can scope the update to just the changed ranges.

I'm figuring out how to map each streak's vertex range per chunk and mark the dirty lo/hi bounds for the position, previous/next, parameter, and fade attributes so only the touched vertex ranges get updated.

I should also consider marking the puffs similarly, tracking lo/hi over both dead and live writes for those smaller buffers.

The per-vertex burn attribute table only updates each tick for snakes actively charring or glowing, which is fine since it's limited to that state. With the VAO-based attribute call reductions done at the engine level, I want to rerun the web benchmark to check the impact, but first I need to confirm the web engine build is current after my rebuild.

Actually, since the bench page bundles the game files at build time, I need to rebuild the bench page itself, not just the engine, before running it.

Lua's busy time improved nicely and draw/fx costs dropped too, but oddly the long-frame count jumped from 14 to 92, with p90/p99 latencies also creeping up slightly. The world draw count dipped a bit as well, so something's causing more frame hitches despite the lower average costs.

I suspect this could be GPU-side stalls — maybe the VAO change or the buffered glBufferSubData calls with offsets are causing implicit sync stalls, or it could just be run-to-run noise since the bench is running on his working machine. I should rerun the old baseline and the new build back-to-back sequentially to separate real regressions from noise.

Lua time and drawing both improved (615→513ms and 5.5→4.6ms), but slow frames over 25ms jumped from 14 to 92, suggesting something else degraded or the machine got busier. I'll re-run the baseline and this page back to back to isolate the cause.

While those runs finish, I'm thinking through possible GPU-side causes, like whether partial versus whole-buffer glBufferSubData calls behave differently under ANGLE's D3D11 backend in terms of triggering UpdateSubresource or staging copies.

I also wonder about VAO handling per mesh on ANGLE/D3D11, and whether updating a buffer bound in a VAO forces re-translation of vertex data, though float data should go through directly—plus there's the possibility some of the slowdown is just noise from his machine's background activity.

While waiting on the data, I want to look at deterministic Lua-side optimizations next—starting with the shadow pass's 0.95ms cost, so I should check cave.lua to see what's being drawn there and whether the shadow camera is static or tracking something like the sun.

I'm separating the scene into static shadow casters (cave geometry, walls, the lid) versus dynamic ones (snakes, lich parts, gore pieces, physics objects), thinking through a caching strategy where the static set's visibility can be computed once and reused since the sun and shadow camera never move.

But I realize the egg shell isn't truly static since it's parented to a group that rocks during the ending, so I need to verify exactly which objects have the cast flag set in the export rather than assuming. For the caching mechanism, I'm reasoning that rendering the static casters into a cached depth target once, then compositing it with the dynamic casters each frame via depth testing, should produce an identical result to rendering everything together, since depth packing is a deterministic function of fragment depth and ties between casters at the same depth would pack to the same bytes anyway.

I also need to check whether gpu_blit actually copies the depth buffer along with color, since that's required for this caching trick to work — the blit call needs a mask parameter that includes depth, and I'm not yet sure the engine supports that.

Rather than trying to detect staticness dynamically each frame, the simpler path is to explicitly designate the cave statics (excluding anything under the egg group or the lid) as the static casters, then verify they truly never move or get hidden — e.g. checking whether the ending's fade-to-color effect hides objects outright (which would invalidate cached shadows) versus just changing material color (which wouldn't affect depth-only shadow passes).

I should also account for material-dependent shadow properties like alpha maps, alpha_test, and side, since texture swaps could alter the depth output even if geometry stays static — these need to be part of any snapshot comparison. Then I'm weighing whether the gain is worth it: of the 0.95ms total shadow cost, how much comes from the static cave geometry (which could be numerous large draws) versus the dynamic objects like the snakes.

I want to actually measure this rather than guess — I'll instrument the renderer's draw call to count objects per name during the shadow pass, but I should do that after the current benchmark finishes so I don't skew the timing. Then I'll move on to checking what gpu_blit's depth capability looks like.

Since the sun and shadow camera are static, the cave's unmoving casters produce the same depth map every frame, so I can cache it once and only redraw moving objects on top. I'm checking whether `gpu_blit` supports copying depth buffers to make this work.

I'm checking whether gpu_blit's 'both' mode (color+depth) was supported in the older live engine version, since if it wasn't, an old build calling that function would silently skip the operation.

I confirmed `gpu_blit` supports combined color/depth copying ('both', nearest). Now I'll verify the live site engine also supports this, since without it the copy would silently fall back to colour only.

Since 'both' has existed since October 1, 2026, the live engine should already have it. I'll wait for the rerun results to confirm.

The long-frame counts are noisy between runs, likely due to background PC activity, but the Lua busy time is the reliable signal: baseline runs around 608-612 ms/s versus o2's 498-514 ms/s, roughly 100 ms/s less, about a 17% reduction per frame at 60fps. Since headless Chrome paces consistently at 60fps, I'm treating the occasional long frames as environmental noise rather than a real regression.

The remaining ~14ms gap in long frames (Lua at 10-14ms, total frame at 26-28ms) could come from GPU work, browser compositing, or JS garbage collection on the webbench page itself, which handles large base64 strings. Without CDP tracing to inspect GPU/V8 activity during those frames, it's hard to pin down the exact cause, though it's possible headless Chrome's fake vsync is causing frames to occasionally skip an interval.

On his 165 Hz screen, the engine's 60 Hz draw cap likely produces an uneven pattern where draws happen roughly every third rAF, since the world only redraws once per game tick while skipped frames just reshow the prior layers. This alternating pattern between 2-3 rAFs per draw could be exactly what's causing the perceived drops to 30 fps that he's complaining about.

Real play likely has more concurrent snakes and powers firing than our bench's 6-wave scenario, so reducing per-frame Lua work still matters most. But I also need to check the site's own rendering overhead while hosting the game — if the site redraws its whole page every frame even during gameplay, that could add significant cost beyond the game itself, worth investigating after the game-side optimizations.

Next steps I'm planning: cache shadows for static casters, measure how shadow draw time splits between static and dynamic objects, apply micro-optimizations to the snake build logic, and cache more engine state to cut further WebGL calls — since GPU draw calls happen synchronously within Lua's execution and do count toward the Lua busy metric, so VAO caching and upload batching already dropped one draw call from 3.27ms to 2.34ms. I'll start with analyzing the shadow pass, counting draws per object and timing static versus dynamic separately.

Long-frame counts are too noisy to trust (just background load), but Lua time dropped consistently from ~608-612ms/s to ~498-514ms/s, a solid 17% improvement. Now I'll check the shadow pass by counting which casters it draws each frame.

Since objects don't have names, I'll classify them instead by whether they're static or dynamic—tracking each caster's matrix_world across 60 frames to see if it stays unchanged and visible. I'll also tally draw counts and vertex counts per group, updating the probe to record per-object identity, counts, vertex totals, matrix changes, and whether it belongs to the cave's static group.

Only 12 static draws exist compared to 54 moving casters contributing 862k indices per frame, so the moving elements—snakes with dense ring geometry and the lich's many large body parts—dominate the shadow pass cost. Caching the static casters would only shave off roughly 12 of 66 draws, so maybe a 20% reduction at best.

GPU-wise, 922k indices in a shadow map that size is trivial for an RTX 2060, so the real bottleneck is CPU per-draw overhead across 186 draws per frame, working out to about 12.6 microseconds each when broken into mesh upload, camera/material uniforms, state changes, and the actual GPU draw call. I'm noticing that my earlier optimization to skip absent uniforms didn't actually move the needle on the camera-material uniform cost—it's basically unchanged, which is confusing since I expected it to help.

So I'm realizing the real cost isn't the C-side calls at all, it's the Lua-side value computation and set() calls happening per refresh—around 2800 set calls across 47 refreshes, each involving closure calls, table lookups, and value construction. I'm considering caching a uniform "version" per material+program pair to skip redundant writes, but since material values can change unpredictably (animations, color shifts), that would require tracking every write, which isn't trivial without a full value comparison.

I'm also noting that refreshes happen whenever consecutive draws switch materials, and since GL programs persist their uniform state, a material only needs rewriting if another material using the same program overwrote those uniforms in between—so the C side already skips unchanged values, but the Lua side still pays the cost of generating them, including calls like maps_defaults that mostly set defaults that get skipped early anyway. That overhead alone could account for a chunk of the 0.56ms, with the rest coming from actual value computation.

I'm weighing whether to restructure write_uniforms to check presence before computing values, but that gets ugly since presence is only known lazily after the first failed set call. A bigger architectural fix—caching a full "draw record" per object+material pair—seems too complex for now, so I'm instead breaking down the overall frame budget: draw pass costs (world rendering, shadows, projection, matrices), bloom/final compositing, warp/view, and update costs like snake simulation, to see where the real savings are.

Snakes stand out as the single largest cost at 2.5ms/tick, about 27% of total Lua time, with build() itself taking 1.66ms natively across roughly 6 builds per frame. I want to instrument build() with finer profiling sections—covering the RK/GL loop, trail walk, burn accumulation, tube geometry generation, and eye placement—to see which part is actually expensive, and since the game already has fine-grained profiling sections, adding more should be safe.

Caching static shadow draws won't help much since moving casters dominate the index count, so I'm skipping that optimization. Instead, I'll focus on the snakes, now the top cost at 2.5 ms/tick on web, and time their per-snake build steps.

The spine loop takes 1.156ms of 1.465ms total (79%) for 5.5 builds per frame, so each build of 72 rings costs 0.21ms, roughly 2.9µs per ring natively. That per-ring work includes copying UU, walking the trail, computing radius, applying a lift factor based on smoothstep curves and sag, resetting SH, checking effects, and setting position against the ground height.

The ground height function combines cave terrain noise (a few fbm/value-noise C calls plus sqrt/exp) with a bump sample using several min/max lookups, likely costing around 2µs, with the underlying noise octaves running fast in C via corner-hash evaluations. I consider whether exact (x,z) memoization could help, but since ring positions shift continuously along the trail as the snake moves forward each tick, there's no exact position reuse possible between ticks.

Inlining ground's call chain could preserve bit-identical results since the operation order stays the same, but function call overhead in Lua is only around 30-50ns each, so even inlining 8 nested calls saves maybe 0.3µs out of 2.9µs total — not a huge win. Looking elsewhere in the ring loop, there's a cluster of vector set/copy method calls, an F32 conversion, two smoothstep calls, and a sink_depth call that itself invokes smoothstep, each adding its own overhead.

I suspect the real bottleneck is the noise computation itself, so let me actually measure it directly by timing 100k calls to ground_y natively rather than theorizing.

The spine loop dominates the snake cost (1.16 of 1.47 ms, ~2.9 µs per ring), mostly from the floor-height function and ~10 Lua helpers per ring. I'll time the floor height calculation on its own to see if it's the main contributor.

ground_y costs roughly 0.74 µs natively, with sampling adding another 0.3 µs, so only about 1 µs of the 2.9 µs total is accounted for—the remaining 1.9 µs must be Lua overhead in the ring loop itself. With 72 rings and about 5.5 builds per frame, that's roughly 400 rings per frame costing 1.16 ms total, so I need to figure out exactly where to cut, starting with ground_y.

Reimplementing ground_y in C would require matching the exact arithmetic—same fbm/value noise calls, same js_exp, same pow, same smoothstep formula—to produce identical results, but since this is game-specific logic baked into the engine, that's not the right place to optimize. Better to focus on Lua overhead in the loop instead: I can restructure to reduce function calls while keeping identical arithmetic, like writing directly to vector fields instead of calling a setter method.

Writing direct field copies...

This should save maybe 30% of the spine loop time, bringing native down to around 0.35ms (0.45ms web)—worth doing carefully. I also notice some per-snake constant values like the sag smoothstep and sink_depth that get recomputed every iteration but can be hoisted outside the loop since they're pure functions evaluating to the same result each time.

Looking at the ground() function, I can keep it as a closure combining cave height with bump sampling rather than inlining further. For the per-ring lift calculation, I realize the smoothstep term only depends on the ring's SA value, which is constant per ring—so I can precompute a lookup table once at load time instead of recalculating it every frame.

Now I'm restructuring the main ring loop, keeping the leak-dependent calculations conditional on s.leak > 0 as before, then rewriting the coil placement branch with sag computed from a smoothstep on s.sag.

Still writing placement logic...

I should double-check how Vector3:copy behaves before finalizing this.

The sink_depth value should stay constant per ring unless fx callbacks like lift or shift mutate s.sink or s.sag mid-loop, which I should check — same concern applies to s.sag. I want to verify this by checking the Vector3 set/copy implementations in math.lua.

Most of each ring's cost is Lua overhead from repeated method calls and helpers that recompute values unnecessarily per ring or per snake. I'll rewrite the ring loop with fewer calls while preserving the same arithmetic, after confirming `Vector3:set`/`copy` are simple field writes.

I notice the lift calculation uses smoothstep to blend the raise animation with the sag effect. Now I want to check whether any fx callbacks in the powers modules modify s.sag or s.sink during the loop, so I should grep for fx tables with lift/shift/place functions that reference the snake state.

Good, confirming the fx table field is only mutated inside fx tick callbacks, not during build, and that sag/sink stay untouched there. Now I need to look at the actual current spine loop code around lines 770-805 to rewrite it properly.

I'm checking the order of multiplication in the lift calculation to confirm it matches the left-to-right evaluation Lua would use for the raise, smoothstep, scale, and sag terms.

The math function substitutions at boot don't affect this since smoothstep only uses clamping and arithmetic, no transcendentals. I should run verify now, then move on to checking the other Lua hot spots—powers, gore, lich, warp, projection, matrices, and per-call draw overhead.

Now I'm looking at the renderer's per-draw path, checking costs like mesh rendering, camera matrix uploads, state changes, and object uniform updates to see where time is going in render_object's matrix work.

Including the shadow pass, total draw-related time comes out to roughly 2.2ms across 186 draws, around 12 microseconds per draw natively -- that's heavy for Lua, so I break it down into mesh (3.2µs), uniforms (3.0µs), state (0.9µs), gpu_draw (0.9µs), with the remainder in matrix multiply and normal matrix computation.

I'm digging into gpu_mesh_of, which loops over each geometry attribute and uploads it, and I'm checking whether computing array length via `#a.array` is fast or slow depending on how Lua's table array part is sized.

Actually I realize the fine-mode profiler itself is adding overhead -- each perf_time call for QueryPerformanceCounter costs something, and with six calls per draw that's inflating the measured numbers by roughly a microsecond. Rather than keep micro-analyzing timing noise, I should shift focus toward finding redundant per-draw work that could be restructured away.

For static objects, gpu_mesh_of re-checks every attribute each draw even when nothing changed -- caching the attribute list per geometry and inlining the dirty check would cut function-call overhead, though the gain is modest. I'm also looking at the four uniform sets happening per draw, like receiveShadow, to see which could be skipped or batched.

Batching draws is beyond scope here, and the lich's many parts mean 186 draws can't easily be merged without reworking rendering. Since the complaint is really about frame drops rather than averages, I should focus on the spikes — the long 26-28ms frames where Lua only takes 10-14ms — and investigate what's eating the rest (GC, GPU process, compositor) using Chrome tracing, setting up a capture in run.mjs with the devtools timeline categories.

Rather than full tracing, maybe simpler to measure per-rAF-callback timing directly: I already have callback duration stats (p50 1.5, p90 10.4, p99 15.6 ms) and gap-between-callbacks stats (p50 7.6, p90 10.6, p99 16.7 ms). The gaps being larger than the callback work itself suggests the delay isn't in JS/wasm execution but something external — likely GPU backpressure delaying the compositor, or V8 GC pauses.

I'm wary that the bench page's GC load might be an artifact from the large base64 strings it retains, not something that happens on the live site, so I should focus on robust wins like reducing Lua execution time. I also want to think about GPU cost per frame — the low-res world render plus post-processing passes (bloom, warp, view, final composite), especially since the final output gets upscaled to a high-refresh big monitor.

The best path forward is to actually trace it: I'll add a tracing mode to run.mjs that starts Chrome tracing after warm-up, captures ten seconds of data, and writes it to a trace.json file. Then I'll analyze it with a script that looks at long frames and task durations on the main render thread and GPU thread, plus V8 GC events, to pin down what's actually causing the slowdowns.

The snake change checks out—digests and frames all match—but the real issue is frame drops, not Lua averages; long frames run 26–28ms while the game's Lua only accounts for 10–14ms of that. I'm adding a Chrome trace to the bench to see what's consuming the rest (GC, GPU process, compositor).

Mapping out the timing: page load around 0s, boot takes 1-2s, warm-up gives 8s of game time, so the measured span runs 8-38s wall clock. Tracing from 20s to 32s should capture the fight waves between 7.5 and 34.5 game-seconds. I'm also considering adding a marker for long frames, having the bench page call a timing mark on each animation frame callback.

I'm writing an analysis script that loads the trace, identifies the renderer main thread and GPU thread, then lists top-level tasks, rAF callback durations, and GC events with their durations, flagging frame intervals over 25ms alongside whatever else was running in the main thread or GPU during those gaps.

I'm looking at the trace data over the 12-second window: rAF callback gaps are tight (p99 15.3ms, nothing over 25ms), firing roughly every 7.6ms at p50, while the game only draws when render_lag crosses the 1/60s threshold. I'm now checking the game's own frame-timing measurements to see how many long frames occurred using the 1ms-resolution perf timer.

Those 19 "long" frames at ~26ms in the game's measurement don't match any corresponding rAF gap over 25ms in the trace, so I realize this is an artifact of the 60Hz cap: when the render accumulator misses a slot, the draw gets pushed to the next rAF, making intervals alternate between 2 and 3 rAF ticks (roughly 15ms vs 23ms) — that's pacing jitter from the cap logic itself, not actual CPU work, and GC overhead in the trace is negligible at only 19ms over 12 seconds. Translating this to his 165Hz setup, the 60Hz cap would produce mostly 3-rAF draw intervals (~18ms, ~55fps), with occasional slips to 4 or 5 rAFs (24ms/41fps or 30ms/33fps) whenever per-frame work exceeds budget, so the real question becomes how much total main-thread time his Lua execution plus the site's own per-frame work actually consumes.

I need to actually measure the site's own update/draw cost while the game is hosted inside it, since that's currently unmeasured — checking whether the site has built-in perf timing hooks and looking for existing probe scripts like boot_probe.lua that could let me run the site renderer with the game loaded to capture those numbers.

The trace shows the real bottleneck isn't game work (frames, GC, and callbacks all stayed well within budget) but frame slot slippage from the 60Hz cap misaligned with the 165Hz display, plus unmeasured site overhead around the embedded game. I'll now check how session 17 drove the site with the game loaded.

I'm thinking about how to measure the site's own per-frame cost separately from the hosted game, looking at where the game host's update and draw calls sit within the renderer's main loop so I can isolate timing for each.

I want to measure how much time the site itself spends versus the game's own update/draw calls, so I'm planning to wrap the site's update and draw functions along with the game's instance-level env.update and env.draw to compare their timings in a desktop agent instance.

I'm planning a probe that starts the site agent, waits for the game to boot, then kicks off the walk-in by simulating the first click, using the host debug environment to set powers and waves similar to the bench setup, then wrapping the update/draw calls for both the site and the game to measure performance over N frames.

Since the engine runs at a fixed 120Hz step with each engine_step producing one rendered frame and two updates, I'm monkey-patching the global `update`/`draw` functions to accumulate timing, and doing the same for the game environment's `update`/`draw` fields, since both are looked up by name/field each call so wrapping should work reliably. The site-only cost is then just total minus game cost.

I'll run the site in an agent with the game hosted, play through the bench's fight, and measure the site's frame time separately from the game's to isolate how much overhead the site itself adds.

The site's own rendering overhead is tiny, around 0.74ms per frame natively—negligible compared to the game itself. I also realize the site renderer is syncing an old copy of the game package, so those numbers reflect stale code rather than my changes, meaning the game logic itself is clearly the real bottleneck to keep optimizing.

Looking at the breakdown, AW plus UPDATE totals roughly 8.2ms web, with snakes at 2.2ms dominated by per-ring ground() calls across 400 rings—caching isn't viable since each ring interpolates to a unique position along the trail. For draw overhead at 2.3ms web, the cost comes from uniform updates, material refresh, and state string parsing (things like 'back'/'ccw'/blend names going through strcmp chains in the C-side gpu_draw function), so engine-side optimization of those string lookups looks like the next target.

I'm thinking a numeric-enum API instead of string-based options could save 1-2µs per draw, roughly 0.2-0.4ms across all draws. The bigger suspect is GL state redundancy—around 14 state calls per draw could cost 5-8µs each on web, so I want to test this empirically by building a variant that naively skips redundant state calls (ignoring correctness for now) just to measure the potential savings, then implement a proper invalidation-safe cache if the delta proves significant.

Since there's no central hook to detect every possible GL state change, I'm trying to reason through what could actually happen between two consecutive draw calls in the real frame sequence—checking whether operations like mesh updates, viewport changes, binds, or clears would actually invalidate the cached state or not.

Since layer_render likely executes GL draws immediately, I realize state gets set before gpu passes, so I need a validity flag: if unknown, resend all state and cache it, otherwise just send diffs. I'm invalidating this flag on gpu_end, clear, blit, texture operations, and target creation/destroy, since those all touch bindings.

For program/uniform handling, I shouldn't invalidate on glUseProgram changes but instead update the cached program id directly in gpu_uniform_gl. I also need to invalidate at the start of the 2D batch flush function and at the start of each frame in the main loop, since that covers swap buffers and other cross-frame GL state changes -- then I need to trace which 2D code paths could run between two gpu_draw calls without hitting one of these invalidation points.

Those paths include layer rendering and flush functions, font atlas texture uploads during text drawing, video frame texture pumping, and layer texture queries that might trigger renders -- each needs its own invalidation point since they call GL directly. The texture-unit cache is the riskiest part since glBindTexture gets called everywhere, so I'm considering skipping that cache entirely and only caching a small fixed set of bindings instead.

Before committing to any invalidation scheme, I want actual numbers: I could build a quick unshipped experiment with a naive state cache (skip identical state everywhere, reset only at frame boundaries) plus texture caching, then measure draw performance on the web even if rendering breaks visibly -- the cost estimate would still be roughly valid. An even safer approach is just counting GL calls per frame by wrapping the WebGL context methods in the benchmark, without touching the actual caching logic at all.

Counting calls per frame by type -- matrix uploads, enable/disable, bindTexture, activeTexture, useProgram, drawElements, bufferSubData -- would give a clear breakdown of where the overhead is concentrated, and I could follow up with a rough timing pass using performance.now() around the wrapped calls, accounting for wrapper overhead. Starting with the call counts first makes sense before worrying about precise timing.

The game itself is the main cost, since the site adds negligible overhead natively. To quantify the WebGL call overhead on the web, I'm injecting a wrapper into the bench page to count every WebGL call per frame by type and track time spent in each.

I'm thinking about how emscripten exposes GLctx as a global, which means if it's a classic script, that variable would attach to window, and that matters for how I'd intercept calls like drawElements on the instance itself.

Also, the 600 frame counter runs on rAF ticks which fire much faster than the game's actual 60fps draws in headless mode, so I should report based on elapsed time instead and derive drawn-frame counts from that rather than raw tick counts.

The script edit didn't take effect, but I can still work with the raw numbers: 600 animation frames at roughly 130 Hz headless rAF rate, meaning I need to scale by about 2.17x to get per-60Hz-game-frame figures. Per animation frame that's 2863 GL calls costing 0.85 ms, measured via performance.now wrapping each call, though I should double check the timer's actual resolution in this environment.

Scaling up, per game frame that's roughly 6200 GL calls and 1.85 ms spent inside WebGL. useProgram shows 558 calls per anim frame — likely from a uniform-setting helper calling glUseProgram on every uniform update rather than batching it. I'm also noticing getParameter, disable, and bindVertexArray all showing matching counts around 191.7 per anim frame, which lines up with roughly 95.6 draws per frame (drawElements + drawArrays + instanced draws) — suggesting bindVertexArray and disable each fire twice per draw, and getParameter once per draw, which is suspicious and worth tracing back to wherever the emscripten GL emulation layer is injecting that call.

Counting shows about 2,860 WebGL calls per browser frame, dominated by redundant `useProgram` rebinds (558), resent fixed state, and ~192 `getParameter` calls. I'll now track down what's triggering the `getParameter` calls.

I'm trying to figure out which emscripten GL library function might be calling getParameter per draw, scanning through candidates like glDisableVertexAttribArray or glDrawElements for client-side array handling.

With `FULL_ES3=1`, emscripten emulates client-side vertex arrays, so glDrawElements checks whether an element buffer is bound. I recall now that glBindVertexArray tracks the current element array buffer by calling getParameter with GL_ELEMENT_ARRAY_BUFFER_BINDING, which explains the per-call cost I measured (~0.34 µs).

Counting shows getParameter happens once per bindVertexArray at a 1:1 ratio, confirming that's the source of overhead. I'm wondering if I can avoid the extra bind-to-zero call after each draw by leaving the last VAO bound, since the old code explicitly unbinds via glBindVertexArray(0) after every gpu_draw.

But that risks corrupting state elsewhere — if other code like the 2D renderer assumes VAO 0 is bound and sets attribute pointers directly rather than binding its own VAO first, leaving our mesh VAO active would cause it to write into our VAO's state instead of a neutral one. So I need to check whether gpu_mesh_index_core and the 2D renderer always bind their own VAO before touching attribute state, since gpu_end_core's unconditional unbind suggests that assumption is baked in.

There's also the risk of layer_render running mid-draw without an intervening gpu_end — if some code calls the 2D layer renderer between gpu draws, and that renderer uses VAO 0 implicitly through client arrays rather than binding its own VAO, it could end up writing into our still-bound VAO and corrupt it.

Checking: most 2D draw paths do bind their own VAO before setting up attribute pointers, so leaving ours bound should generally be safe, but there are too many call sites to audit exhaustively. Given the cost of unbinding is negligible (under a fraction of a millisecond per frame), it's safer to just keep unbinding rather than risk this edge case.

The bigger overhead sources are elsewhere: repeated useProgram calls, state toggling (enable/disable/masks/funcs), texture binding, and uniform updates each add up to roughly 1.85 ms per game frame when combined, with the C-side shadow cache already skipping redundant uniform writes where possible.

So realistic savings from caching the GL program and state look modest—maybe 0.4-0.6 ms for state caching and another 0.2-0.3 ms from avoiding redundant glUseProgram calls, which is simple enough to implement safely by tracking the currently bound program and skipping the call when it's already active.

But this gets tricky since glUseProgram is called in 43 places throughout the engine, including the 2D renderer, so invalidating the cache correctly whenever other code touches the GL state is risky. I'm checking whether emscripten's WebGL shim already tracks the current program internally, but it doesn't skip redundant calls—it just stores the value, so I'd need to implement the skip-logic myself in the engine code rather than relying on emscripten's library.

I think the safer approach is to macro-wrap glUseProgram across the whole codebase so every call routes through a tracker that records the current program ID before calling the real function, letting gpu_uniform_gl skip redundant calls by comparing against that tracked value—as long as nothing else bypasses the macro, like glDeleteProgram affecting the currently bound program.

I need to also wrap glDeleteProgram: if the deleted id matches the tracked current program, I should reset the tracker to unknown, since deleting the active program and later reusing that same id could otherwise cause my tracker to wrongly skip rebinding. Checking emscripten's ID allocation, its counter-based scheme for program IDs only increments and doesn't reuse freed slots, so that particular collision risk doesn't apply there, though I still want to confirm desktop GL drivers behave the same way.

Extending this macro approach across the whole engine's cached GL state seems feasible: wrapping every state-changing call (bind, delete, activate) for textures, buffers, VAOs, and framebuffers so the shadow cache stays consistent, with deletion macros clearing any matching cache entries across all bound units. The remaining risk is calls that bypass the macros entirely, like external helpers (SDL/emscripten_webgl texture uploads) that call glBindTexture directly without going through my wrapped functions.

I'm specifically worried about the video pump code, which uses JS to call GLctx.bindTexture and texImage2D directly, bypassing my cache macros and leaving stale state behind. The fix would be to invalidate the texture cache at frame start and after any video pump call, and more broadly I need to grep for all EM_JS blocks touching GLctx so I can invalidate the cache after each of them too.

Given the moderate expected gain here, I think it's better to start with cheaper, safer wins first: caching glUseProgram calls via wrapper macros (engine-wide, saves roughly 1200 redundant calls per frame), and adding a validity flag to the gpu_draw_core state cache that gets invalidated by any macro-tracked state change.

I'm realizing a cleaner approach: increment a global state epoch counter whenever any GL state-setting function is called outside gpu_draw_core, then have gpu_draw_core compare its stored epoch after each of its own calls. If the epoch changed, it knows to resend state fully — a simple, precise invalidation scheme. Wrapping the relevant functions...

Wait, there's a problem: if glUseProgram is macro'd to bump the epoch every time a uniform is set between draws, that would invalidate the draw cache constantly, defeating its purpose. I need to track program state separately from the general epoch to avoid this conflict.

I'm verifying other GL state paths too: sampler bindings just store unit indices without actual GL calls, mesh updates bind array buffers outside the cache, and clear/blit operations correctly bump the epoch since they're conservative about state changes.

I should check if any emscripten JS-side calls touch GL state directly, bypassing the macros — that's the one risk to this epoch design. The approach is sound as long as all C-side state calls route through macros, JS-side changes bump the epoch, and no other library touches GL state mid-frame.

Writing epoch tracking code...

I also need raw (unwrapped) versions of these functions for use inside gpu_draw_core itself, defined before the macros take over, so the core rendering path calls the real GL functions directly without triggering the epoch bump. Then I'm listing which state-setting calls gpu_draw_core actually caches and needs wrapped: glEnable, glDisable, glBlendFuncSeparate, glBlendFunc...

Continuing through the list -- glBlendEquationSeparate, glDepthFunc, glCullFace, glColorMask, texture binding and program calls -- while noting that viewport, scissor, and pixel storage calls aren't cached and don't need wrapping. I'm also checking where the GL headers get included so the macros land after all GL includes but before any code that calls them, confirming glad.c is compiled separately so these macros won't interfere with it. For glDeleteTextures specifically, the wrapper just needs to bump the epoch counter to invalidate the texture cache.

Now I'm thinking about any direct GLctx calls via EM_JS, planning to bump the epoch after those too -- like after video_pump_all or web image texture creation -- so I need to grep for GLctx usage in anchor.c.

The per-draw getParameter call is cheap and the post-draw unbind is needed for the 2D renderer, so I'll leave those alone. I'll focus on cutting redundant useProgram/state calls by tracking a GL state "epoch" via wrapped calls, and now I'm checking for any direct GL calls in the engine's JS that would bypass those wrappers.

I'm tracking down the single JS call site that binds a texture, called each frame from the C video pump loop, and planning to bump the epoch counter right after that loop finishes rather than inside the JS binding itself. I also need to check how SDL's swap-window call on desktop fits into this timing.

Checking the include order, I'm placing new GL state wrapper macros right after the GL header block, confirming other included libraries don't reference GL symbols so there's no conflict. Since desktop's glad already defines glEnable as an object-like macro, I need to undef it first before redefining it, then I'm writing the static tracking variables and inline wrapper functions for the GL state cache.

Writing GL wrapper functions...

I'm reasoning that glUseProgram doesn't need an epoch bump since gl_cur_prog tracking handles it independently and accurately. Then I'm considering which other state calls like glBindVertexArray or framebuffer binding aren't cached and don't need wrapping either.

Now I'm working through the per-texture-unit cache: each unit tracks a target/id pair alongside the active unit, valid while the epoch stays unchanged. I'm noting that binding a different target on the same unit coexists with existing bindings rather than overwriting them, so the cache needs separate entries per (unit, target) combination across 2D, 3D, and cube map targets.

I'm also considering the final `glActiveTexture(GL_TEXTURE0)` reset at the end of draw calls, which should only execute if the cache doesn't already show unit 0 active, plus sketching out the broader state cache struct with an epoch field and fields for blend, depth, cull, and color mask state.

The cache logic checks if the epoch matches the current GL epoch to determine validity—if not, it invalidates every field with sentinel values so no stale comparison passes. Then for each state category like blend, it only issues the actual GL enable/disable and parameter calls when the cached value differs from what's needed, updating the cache afterward.

I'm also noting a subtlety in the old semantics: when blend is disabled, the original code never updated the blend function values, so the cache correctly retains stale-but-valid function state. I'm tracing the same pattern through depth test, depth mask, cull face, front face, and color mask—most follow enable/set-or-disable, but a few (depth mask, front face, color mask) are always applied regardless of a toggle flag.

Now I'm working through program binding: I want to avoid redundant glUseProgram calls by checking a cached current-program value before issuing the call, relying on a tracked macro version of glUseProgram so the cache stays accurate across the engine, including inside the uniform-setting path. I'm double-checking that nothing outside the macro (like JS-side code) calls useProgram directly, which would desync the cache.

The remaining concern is WebGL context loss — if that happens, GL state resets but my cached program id wouldn't know, causing a skipped useProgram call. I'm deciding this is an edge case the engine probably doesn't handle robustly anyway, since a context loss would also wipe out textures and buffers, so I'll let it be.

I need to bump the gl_epoch counter inside video_pump_all since it changes active texture and binding on unit 0, meaning this should happen each frame in the main loop. Then I'm revising the texture-binding loop in gpu_draw_core to cache per-unit state and skip redundant bind calls, mirroring the earlier per-slot comparisons I added for buffers and programs.

I also need to double check a subtle case: if a texture is deleted and its id gets reused by a newly created texture, the cache must still be invalidated correctly since deletion already triggers an epoch bump through the macro, and texture ids from glGenTextures remain stable otherwise, so this edge case should already be handled.

Wait, there's a desktop-only issue: enabling GL_PROGRAM_POINT_SIZE for point-mode draws goes through the tracked macro, so it would bump the epoch and invalidate the cache on every single points draw. Since that state isn't part of the cached set anyway, I should call the raw GL enable there instead to avoid unnecessary invalidation.

I'm tracing through each gpu function to verify cache validity: draw core, uniform setting, mesh updates, viewport, framebuffer binding, and clear all use unwrapped calls or only touch state outside the cache, so the epoch stays correct — except clear, which correctly bumps epoch when it touches color/depth masks, and blit, which correctly bumps epoch when disabling scissor test.

Now I'm checking the 2D renderer and mipmap paths similarly, then auditing whether any engine code mutates cached GL state through calls that bypass the wrapped functions — like pixel storage or the video pump's texture binding — to make sure nothing slips past the epoch tracking undetected.

SDL's web swap is a no-op and emscripten's client-side array emulation touches uncached VAO state, so desktop glad builds and web builds both look safe. I'm now estimating the actual savings per animation frame from the disable/enable call counts I gathered earlier.

Tallying up roughly 1800 redundant calls per animation frame against 2863 total, caching should cut that to around 300, saving about 1500 calls per anim frame — roughly 3200 per game frame, translating to a meaningful ~0.5-0.7ms improvement per frame on web given emscripten's call overhead. That's a worthwhile optimization, so I'll go ahead and write it up.

I found that only the web video pump touches GL directly in JS, so I'll bump the epoch after it while routing all C-side GL calls through wrappers that track the current program and cache fixed/texture state tied to that epoch.

Since gpu_clear_core touches color/depth mask and enable state, wrapping it with an epoch bump means the next draw conservatively resends everything rather than me trying to update the cache precisely — with ~6 clears per frame that's roughly 120 extra calls, which is acceptable overhead. I'll write the wrapper block now.

Now I need to update the state and texture handling in gpu_draw_core, adjust useProgram in gpu_uniform_gl, and bump the epoch in video_pump_all. I'm setting up blend state toggling based on draw flags...

Replacing the texture binding loop with cache logic keyed against GPU_MAX_UNITS, so I avoid redundant glActiveTexture/glBindTexture calls when the same texture is already bound to a unit.

I'm verifying the sentinel logic in gpu_ds_forget: the memset to 0xFF followed by explicit field overrides for depth_mask and epoch looks correct, and texture ids using 0xFFFFFFFF as a sentinel should never collide with a valid id.

I'm also checking that gpu_ds's zero-initialized epoch versus gl_epoch starting at 1 forces the first draw to always forget, which is the intended behavior. For texture targets beyond the three cached slots (like 2D_ARRAY mapping into the cube slot), I'm reasoning through whether a stale entry could cause an incorrect skip, and since texture ids are unique per object, mismatched targets sharing a slot should still compare correctly and bind as needed.

Then I'm making sure gpu_end_core's GL calls route through the tracked macros so epoch increments and shader program binds are properly recorded, and noting that the video pump paths (both web and desktop libav) need the epoch bump added after their draw loops.

Desktop MSVC build looks clean with no macro redefinition warnings. I'm checking whether glad's `glEnable` macro expansion interacts correctly with my wrapper function, since the `#undef` happens after the macro-expanded call inside the body, so it should resolve to `glad_glEnable` correctly.

Now I need to run the replay check and verify digest plus frame output on this desktop build, since GL state caching could affect rendering and frames must match exactly, then later compare pages across both engines.

The churn cost dropped from 1.711 to 1.067 ms/frame clean, about a 38% improvement in the replay test's own frame cost. Now I'll build the web engine and run the web benchmark with GL counting alongside the normal benchmark.

WebGL calls per animation frame dropped 58% and the time spent inside those calls nearly halved, but Lua busy time stayed roughly flat compared to before — which is odd since the savings should have shown up there too, so I'm trying to reconcile where that expected drop went.

My suspicion is the wrapper's own performance.now() overhead is masking most of the real savings, since actual per-call WebGL state-change cost in Chrome is tiny (~0.1-0.2µs), putting the true savings around 0.36ms per frame — well within the ±0.2ms noise floor of these measurements. I'm also noting that Chrome's command buffer architecture means most of the reduced call overhead actually benefits GPU process time and frame pacing rather than main-thread Lua time, which explains why the measured DRAW cost barely moved. Now I'm breaking down the remaining Lua frame costs on the web build.

Looking at snakes specifically, my spine loop rewrite gave a modest 0.3ms improvement from the earlier version but didn't close the gap with native. Digging into what else the per-snake update loop does — gradient sampling calls, distance math, and especially a trail insert at the front of the table — I suspect that insert is the real culprit, since shifting up to 600 trail entries per snake per tick is O(n) and adds up to thousands of element moves per tick across all snakes.

Looking at the native breakdown, the trail insert itself is cheap (~0.022 ms/frame), so that's not actually the bottleneck. The remaining cost after build (1.47 of 1.71 ms) is spread across the ring loop — ground sampling, ring radius/lift math, state resets, and the spine copy to C. Ground sampling looks like the biggest remaining target, so I'm considering ways to speed up the ground_y plus bump sample calculation.

It calls into several C functions per ring (noise_fbm3 up to twice, noise_value3, sqrt, exp, smoothstep), and that exp call for the mound falloff is the double-double precision version which is noticeably slower than a normal exp — but I can't swap it out without breaking bit-exactness with the reference three.js build. I'm also considering whether ground results could be cached across ticks for repeated trail positions, but since positions shift every tick that seems unlikely to help unless rings land on the same interpolation fractions as before.

Rings are anchored to a moving head position along a fixed world-space trail, so their arc-length offsets shift every tick — nothing to reuse there. Reducing ring count or precision would change visible behavior, so that's off the table too, and detecting unchanged snake inputs (to skip rebuilding) seems too complex to be worth it; the snakes seem close to a practical performance floor given the current logic, so I should look elsewhere.

Now I'm digging into the draw call costs: about 2.28ms for roughly 190 draws, averaging ~12µs each. On the Lua side there's matrix multiplication for the model view, normal matrix computation (set, invert, transpose), then the draw call itself does cached key lookups, program lookups, draw range checks, mesh attribute loops, uniform updates, and state table writes — plus a determinant calculation and a C-side option parser that does many field lookups and string comparisons per draw. I'm identifying specific micro-optimizations here, starting with how the native draw function parses its options table field by field.

Timing the native code shows the draw call itself (0.166ms for 186 draws, ~0.9µs each) is already small, so there's limited gain there on web even accounting for overhead. The bigger cost is the camera uniform/material refresh step at 0.56ms native across 47 refreshes (~12µs each) — that looks like the main target, followed by mesh setup at 0.59ms and smaller contributions from object uniforms and state writes. I'm now looking at what exactly the material refresh writes per call to see where it can be trimmed.

Even with my caching, the value expressions for uniforms like color conversions still get computed even when the cache skips the write, so I want to check how `col()` and related helper functions in materials.lua are implemented to see if they allocate or return reused tables.

WebGL calls per frame dropped 58% (2,860→1,205), with useProgram calls down from 558 to 19 and texture binds from 394 to 76. Next I'll look into the material uniform refresh helpers, the biggest remaining per-draw Lua cost at ~12 µs each, 47 times a frame.

These optimizations already happened at round 64, so I'm now tracing the cost of transform updates and material refresh — each uniform set involves a closure call, array lookup, and a hash lookup by name in the GPU uniform table, all fairly cheap but adding up across 50-80 calls.

Caching last-written values per material could help, but the bigger constraint is that sort order comes from three.js and can't be changed. So I'm stepping back to look at overall web frame time: Lua now takes 8.4 ms per frame (down from 10.3), with draw and update taking the biggest chunks, followed by shadows, projection, and matrices, then a long tail of smaller systems each contributing modestly.

That's an 18% reduction in Lua time plus big cuts to WebGL calls and uploads — solid progress, but the remaining items are all individually small, so I need to think about what else might yield meaningful payoff.

Looking at the projection and matrix-update walks, I notice the lights collection does a full separate scene traversal every frame using traverse_visible, duplicating work already done in the main projection walk. Since both walks respect the same visibility semantics, I'm thinking about merging light collection directly into the main traversal — checking if a node is a light during that single walk instead of doing a second full pass, while making sure parent-before-child traversal order is preserved.

Similarly, the shadow pass walks the scene yet again to gather casters with their own frustum checks; this is also a candidate for merging into the main traversal, collecting cast-shadow-eligible objects in traversal order during the first walk rather than re-traversing. The ordering must still match exactly since the shadow pass happens after projection in the same frame.

That means roughly two of the four per-frame traversals can be eliminated, saving maybe 0.2-0.3 ms on web — though the matrix-world update traversal must stay separate since it has to run before projection. I'm also noting that render() may handle multiple scenes (world, viewmodel, warp) and the post passes are fullscreen quad renders, so I should check if those add further traversal overhead.

Beyond that, I'm weighing other leads: snake-like dependency chains in the update order, the per-frame garbage (32 KB) whose GC cost is already folded into the measured sections rather than isolated, and the small engine-side costs of the 2D layer pipeline (present, hud). Since Lua lacks built-in allocation profiling, tracking down the exact garbage sources would need a custom sampling hook, which I'll set aside for now and focus instead on the post-processing passes (warp/view, bloom/final), which cost about 0.5 ms on web mostly from Lua-side setup of a handful of full-screen draws.

Now I'm turning to merging the traversal walks — comparing collect(), project(), and shadow_pass to confirm they all respect the same visibility-gating logic (skip subtree if an object isn't visible), so that lights and shadow casters can be gathered in the same pre-order pass as the main render walk rather than separate traversals, since the traversal order and visibility checks already line up between them.

I'm confirming shadow casters should be collected before the frustum check (so culling doesn't affect which objects cast shadows), and that shadow_pass still loops per-light over the shared caster list rather than re-walking the scene. I also need to check if shadow_pass is called elsewhere as a public method that could be affected by this refactor.

I'll merge the lights and shadow-caster collection into the existing draw-list walk, since it already visits objects in the same order with the same visibility rules—cutting two redundant scene graph traversals per render.

I'm confirming that shadow_pass correctly receives the light casters, and since the shadow-related module-level state gets cleared and repopulated right before shadow_pass runs within the same render call, there's no cross-scene contamination even if render() handles multiple scenes per frame.

The traversal order between collecting lights and projecting objects stays the same as before, and the visibility checks match too, so I'm confident that part's unaffected. The remaining optimization is the depth-program key strings being built via per-draw concatenation (66 times a frame) — I should precompute these as constants for the default case and cache them on custom materials for the custom case, mirroring the existing key_of caching in draw(). Once that's done I'll run a quick native profile before testing on web.

I'm considering the matrix updates across the scene graph and the per-object frustum culling cost in the projection pass, wondering if a sampling-based profiler using a debug hook would give clearer timing data than manual sections.

I'll write a scratch script that sets a count-based instruction hook, captures the current executing line via debug.getinfo, runs the fight benchmark for several hundred frames, then aggregates and prints the hottest lines.

Digests and frames both check out. Next, I'll attach a count hook to the Lua interpreter to sample which source line runs every few thousand instructions during the fight, building a statistical profile of where Lua time is spent.

Looking at instruction-count sampling by VM instructions, the matrix multiply function stands out at 12.1% of all Lua instructions — it's called heavily from render_object and update_matrix_world, with 64 multiplies and 48 adds per call adding up fast across hundreds of draws.

Other hotspots include warp.update's streak vertex-write loop (6.8%), renderer.lua's draw function (4.9%), the bilinear sampling and floor/route closures in snakes.lua, ground_y in cave.lua, frustum culling and the walk closure in renderer.lua, and determinant/compose/invert calls in math.lua. Overall, matrix math clearly dominates the renderer's cost — multiply, determinant, normal-matrix computation, and compose together account for a large chunk of total instructions. I'm now tracing where multiply_matrices gets called each frame, starting with render_object.

For optimizations, I'm considering caching model_view matrices for static shadow casters since the shadow camera doesn't move — if an object's world matrix and the shadow camera are both unchanged since the last computation, the multiply and normal matrix work could be skipped entirely.

The complication is that draw() reads the model_view_matrix straight off the object, which gets overwritten every frame by the main pass, so I'd need a separate cache (shadow model view and normal) and some way to feed those into draw() for the shadow pass specifically — doable but fiddly, and with only about a dozen static casters the payoff is modest.

I also want to check whether multiply_matrices itself is implemented efficiently, so I'm looking at the actual Lua code to see if it's using local variables for the matrix elements rather than table indexing, which would matter a lot for performance.

Beyond that, there's the per-draw determinant check for front_cw winding, which runs 190 times a frame — that's cacheable per object if matrix_world hasn't changed, but since draw gets called multiple times per object (shadow pass, main pass, transmission pass) I'd need to key the cache on a frame counter rather than recomputing or diffing all sixteen matrix elements, which would cost about as much as just computing the determinant.

I'm also noticing shadow pass draws recompute front_cw separately from the main pass, so caching across those two calls alone would cut determinant calls roughly in half. Similarly, the shadow pass computes the full normal matrix (set, invert, transpose) for every caster even though the depth shader likely has no normalMatrix uniform — meaning that work may be entirely wasted unless the main pass reuses it.

I wonder if that unused normal matrix computation could be skipped, but the main render pass overwrites model_view and normal matrices for every drawn object anyway—so the real risk is objects drawn only in the shadow pass that never get reprocessed in the main pass.

Actually, the shadow pass's draw() call still sets normalMatrix from the caller-computed value, so the fix needs restructuring: skip computing when the program lacks the uniform, but compute on demand once a program's requirement is learned, leaving trace mode untouched since it always needs full coverage. This should save around 66 normal matrix computations per frame, roughly 1.5% of Lua instructions — now moving on to the main pass.

Applying the same lazy logic for the normal matrix elsewhere: ShaderMaterials like fx points and ribbons probably don't use normalMatrix either, so I should make render_object only compute model_view eagerly, deferring normal_matrix calculation into draw() right before it's set, only when the program needs it. I need to check whether double-sided transparent objects drawn twice would compute it redundantly, and whether anything outside the renderer reads o.normal_matrix directly.

Profiling shows matrix math dominating Lua execution, with two wasteful patterns: normal matrices being computed for passes that never use them, and mirror-test determinants being recalculated redundantly per object. I'll restrict normal matrix computation to programs with the uniform and compute determinants once per render—first confirming nothing else depends on `normal_matrix`.

I should check for on_before_render hooks to see if they handle the normal matrix, then set up the draw function to apply receive_shadow and modelViewMatrix, computing the normal matrix only when the shader program actually declares that uniform.

I'll mark the normal matrix as stale whenever the model-view matrix is recomputed elsewhere (render_object, shadow pass, transmission), so it's only recalculated lazily when actually needed, with caching keyed off program-absence tracking and a per-frame determinant stamp.

One concern though: if an object's on_before_render hook mutates its own matrix_world mid-render (e.g. billboard tricks), the cached determinant from an earlier pass like shadows could go stale for the main pass, since update_matrix_world only runs once at render start. I need to check how on_before_render is actually used in the game to see if this is a real risk.

The scale_from_world flag triggers a matrix_world recompute inside on_before_render, but since this hook fires after render_object already multiplied model_view using the prior matrix_world, that update only matters if position, rotation, or scale changed between those two points.

Since matrix_world is already updated at the start of render via scene:update_matrix_world and nothing modifies the object's local transform during rendering, the recompute call is a safe no-op — matrix_world stays the same whether it runs before or after the hook. These particular objects likely don't cast shadows anyway, so timing differences across passes shouldn't matter.

I'm now planning the lazy normal matrix and determinant cache: render_object will compute model_view and mark the object's normal matrix as stale instead of eagerly recomputing it.

Setting stale flags across shadow and transmission passes...

The draw call will only compute and set the normal matrix lazily when the shader actually needs it, skipping that work otherwise unless we're in trace mode, where it's always computed. For the determinant cache, I'm tracking a render counter so each mesh only recalculates its matrix_world determinant once per render instead of every draw call, while non-mesh objects like points just default to false.

I need to verify this caching doesn't break anything when something outside the normal render() loop calls draw directly on an object whose matrix changed -- that would leave a stale cached determinant. I should check whether modules like post.lua or silhouette.lua call draw directly without going through render() first.

I confirmed the mid-render `update_matrix_world` call is a no-op since the world matrix is already current, so the per-render determinant cache stays valid. Now I'll check if anything calls `draw` outside `render()`, which could leave the cache stale.

I'm noticing silhouette.lua calls render_object directly outside the normal render() flow, used for the ending's silhouettes of lich parts, egg, and gore. I need to check whether these silhouettes render in the same frame after the world render (meaning the determinant cache for matrix_world stays valid) or whether they happen before/after matrices get updated independently, which would invalidate that cache assumption.

The safest fix is to only trust the determinant cache while inside an active render() call — I'll set a flag that's true during render() and false otherwise, so silhouette's external render_object calls always compute the determinant fresh instead of relying on a stale stamp. The normal matrix staleness flag can stay as-is since it's lazily recomputed on draw regardless of stamps, so I'll implement this render-scoped guard now.

The silhouette pass bypasses `render()` by calling `render_object` directly, so the determinant cache only applies within `render()` calls while everything else recomputes fresh — no guard is needed for the lazy normal matrix since it just recalculates on the next required draw.

I'm checking edge cases: if render() errors mid-way, the render flag could stay stuck true, causing stale state on the next call. I'm also considering objects that never get drawn — their stale flag defaults to nil, so draw would skip recomputation and just reuse whatever normal_matrix already exists.

I verify that `ab` is already defined earlier in draw before the uniform block runs, so absent-detection works correctly on first draw. The normal matrix computation stays consistent since it always derives from the same model_view, so there's no divergence — this all checks out, time to run verification.

I should double check whether my frame sampling actually captures the silhouette ending sequence, since those ticks might fall within the range but I'm not certain they're well represented. Also worth noting the inventory viewer with on_before_render isn't exercised since no viewer was opened during this run.

I should figure out if GAME exposes a function to open the inspect/viewer directly, so let me search for GAME. function definitions in game.lua.

I should add a viewer segment to v_run where I inspect each of the six objects with their poses, snapshot the frames, then close the inspection. I'll also insert a throw action for a floor item before the ending sequence, which already handles removing drops through remove_all.

For ticking through inspect mode, I need to call GAME.step so inspect.tick actually runs while the game is paused, then capture the snapshot — placing this segment right before remove_all around tick 2100, including digests if that mode is active.

Now I'm realizing the baseline needs regenerating against the original pre-change code, but my working tree only has the modified version — I'm considering using a separate git worktree checked out at HEAD to run the baseline from a clean copy of the game folder.

For the engine binary itself, I want baseline frames rendered with the pre-VAO engine rather than the current one, and I recall there's an existing desktop build pinned at the commit before the audio-only change — that build is rendering-identical to what the earlier base/base2 comparisons used, so I can reuse it as ANCHOR_ENGINE_BIN against the HEAD worktree to get a true apples-to-apples baseline for the extended scenario.

I'll set up a separate git worktree for HEAD so I can run it independently without disturbing the main checkout, keeping in mind that some untracked asset files (like newly added sound effects) might not carry over — that could cause harmless audio load warnings but shouldn't affect the RNG or frame digests, so I'll just try it and see.

I'll extend the frame scenario to include all six objects in the viewer plus thrown objects on the floor. Since this needs a fresh baseline, I'm setting up a worktree at HEAD and running it on the desktop twin of the live engine.

I'm planning verify2.sh to run two comparisons: a base run using the headgame directory with the pinned engine build, and a new run using the current lich-survivors game with the current engine build, both invoking v_run.lua copied into the game directory.

I'm checking the digest output format at tick 400 to understand the lich position values, then comparing base vs base2 runs for determinism before updating cmp.py to accept the new baseline argument.

This time base and base2 matched perfectly, but new1 diverges at t4320 and t4440 in egg-related regions. My noise table only tracked boxes for t4200/t4440 from the earlier run, and since the extended scenario shifted timing, the egg frames now land on different ticks than before, so I need to check whether these new diffs are actually legitimate.

To isolate engine versus Lua changes, I should run new1 a second time (new1b) and compare it against the original new1, and also test the HEAD game paired with the new engine separately — then look at the actual frames to understand what's visually different.

Both baselines match exactly, but the new code diverges at two late hatching-egg frames (t4320, t4440) — possibly the same wobble seen in earlier identical runs, but I need to confirm rather than assume. I'll rerun the new code twice more plus HEAD on the new engine to isolate whether the engine or the Lua changes caused the difference.

The fact that new1 and new1b diverge despite identical code and engine suggests the egg physics is nondeterministic across runs, likely tied to timing rather than my changes. Since base and base2 matched fully this time, the nondeterminism seems intermittent and possibly linked to something wall-clock-dependent, like the egg physics prepare coroutine.

In frames mode each mark triggers engine steps that drive the egg's motion, so the physics only advances during marks — which makes consistent nondeterminism odd unless Box3D's solver uses multithreaded contact solving that introduces floating-point order differences. Since this is pre-existing behavior unrelated to my changes, I want to isolate it by excluding the egg region from frame comparisons, so I'm updating the noise map to mask out the egg-region boxes at the relevant timestamps.

The t4440 diff bbox is large but that's just the hatch sequence with pieces flying apart, so it's expected. I'll document that these frames differ even between identical runs of the same code, then update the noise dictionary to include the v2 bounding boxes so those regions are treated as acceptable noise.

The differences between runs are just the egg physics' inherent run-to-run noise, not caused by my changes. I'll log these as known noise for the extended scenario and continue.

The viewer frames and thrown objects both check out. Let me re-sample to see how the hotspot distribution has updated after this round of optimization.

The matrix operations at math.lua:347 dropped their share while multiply_matrices held steady at 12.5%, so its relative weight actually increased now that the total shrank. I want to trace where these multiply_matrices calls originate, so I'm setting up a wrapper that counts calls by caller frame using debug.getinfo at level 3.

I'm seeing 247 calls per frame to update_matrix_world where changed objects recompute world transforms from parent times local, plus 68 calls to update_world_matrix triggered through recursive parent updates from game code like lich.lua calling get_world_position. I need to figure out which specific objects are flagged as having changed locals to trigger that heavier 247/frame cost.

The lich's skeletal hierarchy likely explains most of the 247 count — posing the skeleton every tick cascades transform changes down through many bone nodes, plus snake eyes and item/fx nodes adding more. The 68/frame update_world_matrix cost comes from an uncached recursive climb up the parent chain every single call — each ancestor's matrix gets recomposed and multiplied with no memoization, so repeated calls to get_world_position in game code (lich positions, etc.) keep redoing the same ancestor work redundantly.

Now I'm designing an exact caching scheme mirroring how update_matrix_world avoids redundant compose/multiply: give each object a local matrix version that bumps only when its compose inputs actually change, and a world version that bumps when its world matrix is actually recomputed differently. Then before multiplying parent-world × local-matrix, I check if the stored (parent world version, local version) key still matches — if so, skip the multiply entirely since the result can't have changed.

But I realize direct external writes to matrix_world or matrix (bypassing update functions, common when matrix_auto_update is false) would make cached keys stale, so I need to grep for direct matrix_world/matrix assignments in the codebase and make sure matrix_world_needs_update flags are treated as forcing a recompute. I'm also weighing whether the 247 multiplies per frame are even worth optimizing given their rough instruction cost.

I want to figure out exactly which objects are being recomputed every frame unnecessarily — maybe their values aren't actually changing but something is forcing matrix_world_needs_update anyway, possibly through parent propagation. The plan is to instrument update_matrix_world itself, wrapping the recursive call so I can count, per object, how often the needs_update-or-force branch triggers.

I've found the two main hotspots: `update_matrix_world` recomputing 247 world matrices per frame, and `update_world_matrix` adding 68 more by recomposing every ancestor's local matrix uncached. Now I'll identify which specific objects these are to determine what's safe to skip.

I'm tracing why the scene's world matrix flag gets set every frame — that single flag on the root scene is what forces all 63 meshes and 14 groups underneath to recompute their world matrices each frame, so I need to find what's triggering that flag.

Following the call chain, `update_world_matrix` walks upward and always calls `update_matrix`, which unconditionally sets `matrix_world_needs_update = true` on the scene whenever something requests a world position — like the lich head position lookup — so every such call dirties the whole scene and cascades a full recompute down to all 247 descendants, even though nothing actually moved. This matches standard three.js semantics of always marking the flag dirty, meaning the cascade is technically correct but computationally wasteful.

Since recomputing identical inputs always yields identical matrices (deterministic arithmetic), I can apply the same input-comparison caching here: only call `update_matrix()` when the compared inputs (_mc) have actually changed, so the needs_update flag only gets set when something truly moved. The world matrix multiplication itself doesn't need to change — it just needs to be gated so it's skipped when both parent and local matrices are unchanged, preserving exact correctness while eliminating the unnecessary cascade.

Since matrix_world won't get recomputed without the flag, update_matrix_world at render time won't touch already-current objects—that's fine normally. But if some code directly overwrites matrix_world on a child (expecting the original cascade to leave it alone since local matrix didn't change), my change actually preserves that direct write, whereas the original would've overwritten it via the parent×local cascade—so behavior diverges in that edge case.

I should check if any code in the port modules sets matrix_world directly through copy, multiply, compose, set, or elements assignment.

Found the culprit: world-position lookups force a root recompose that flags every object as changed, triggering 247 unnecessary matrix recalculations per frame. I'll skip recomposing when inputs are unchanged, but first need to verify no code writes world matrices out of band, which the full cascade was masking.

I'm checking whether cameras directly set their matrices—the shadow matrix and texture UV transforms don't write to an object's own matrix_world, so I want to look at the camera update_world_matrix override around lines 230-250 to see how camera matrices get computed.

Both lich.lua and lichmask.lua also call update_world_matrix, so I'm sketching how to implement it: optionally update the parent first, recompute the local matrix only if position/quaternion/scale changed, then either copy the parent's world matrix or multiply it with the local matrix depending on whether there's a parent. This still does a multiply per ancestor per call, but it avoids unnecessary dirty-flag propagation—I could further optimize by skipping multiplies when nothing changed using version tracking, but I'll focus on the flag fix first since that's causing the bigger cascade. I also need to double check the existing matrix_world_needs_update logic to make sure the fix doesn't conflict with it.

Tracing through the cases: if an ancestor's inputs changed and got eagerly recomputed with the flag set, the renderer's update_matrix_world will see that flag and correctly force children to recompute. Similarly if a parent changed earlier in the frame and was flagged, the child still gets forced to recompute even without its own flag. The only real behavioral difference from before is that the old code would recompute all world matrices when the root was spuriously flagged, but since the inputs are unchanged, the outputs are identical either way, so the fix should be exact.

One caveat I need to check: the scene root's own `matrix_world_needs_update` flag, set by update_matrix when the root composes with unchanged inputs, is now skipped — I should verify this doesn't matter. I also want to check whether update_matrix is called from other locations like camera code, so let me look further into core.lua around lines 230-250.

I'm checking that the quaternion callbacks stay consistent with how update_matrix_world computes things, and confirming that recomputing ancestor matrices in update_world_matrix produces identical results when inputs are unchanged. But I'm now noticing a subtler issue involving object flags that could break this assumption.

I'm also checking whether anything depends on update_world_matrix setting the parent's needs-update flag to force a later recompute—like code that moves an object then calls get_world_position, which recomposes and sets the flag. My change still triggers compose when inputs changed, so the flag gets set the same way. Now I'll verify this against the digests and frame data in the extended scenario.

The frame time without fine profiling dropped from 9.27 to 7.13 ms, with Lua update and draw costs each shrinking noticeably, bringing total Lua time down about 25%, and matrix work fell from 0.354 to 0.252. I'm now looking at what else to target next, with snake rendering at 1.61 ms standing out.

I'm scanning the remaining native costs: draw at 1.24, shadows at 0.46, gore at 0.38, projection at 0.36, powers at 0.29, matrices at 0.25, lich at 0.20, and warp at 0.19 — but warp's update function alone is eating 7% of Lua samples, which is surprising given it's just writing streak vertex data. I'm digging into why: each active streak recomputes its path points every tick via flings, so its vertex buffer has to be rewritten constantly even though only ~11 of 200 streaks are typically active at once. I'm trying to estimate whether the per-vertex float writes (position, color, etc.) at that streak count and point density actually justify a 7% sample share, since my rough math suggests it should be cheaper than that.

Shifting focus to the web build now — Lua execution there runs roughly 1.3x slower than native, so I want to re-run the web benchmark to get a current number rather than relying on the earlier 8.4ms/frame estimate. Beyond that, I'm weighing a few more potential optimizations: snakes consume 1.6ms natively and mostly can't skip work since fx-held snakes rebuild every tick; the 60Hz tick/draw cap means there's nothing to gain from the high-refresh monitor; and there may be overhead in the engine's Lua-to-C bridge when matrices get passed to gpu_set, since reading sixteen numbers out of a Lua table isn't free.

That matrix overhead turns out to be small in practice — maybe 0.18ms natively across all draws. Looking at the profiler output, write_uniforms isn't showing up as hot, but renderer.lua's draw, frustum check, and walk functions are taking a noticeable chunk. The frustum culling check in particular stands out at 3.4% since it recomputes sphere bounds and scale per object per render pass, which for static objects could potentially be cached if I can reliably detect when their world matrix hasn't changed.

I'm thinking a version counter on matrix_world updates could let me cache the world-space bounding sphere and skip redundant math — bumping the counter whenever update_matrix_world or update_world_matrix actually recomputes the matrix. The risk is any code path that mutates matrix_world without going through those two functions, which would cause stale culling results, but a grep confirms those are the only writers. The savings are modest, maybe 2% of total Lua time, but it's a cheap win.

Next I'm checking warp.update's 7% cost — whether "on" but static streaks get rewritten every tick even when their vertices haven't changed, and tracing which systems (flings vs others) actually own and move streak data each frame.

Frame time dropped 23% (9.27→7.13ms) and Lua time 25% (8.0→5.96ms); remaining hotspots are snakes, draws, shadow pass, gore, and scene walk. I'll also check the warp streaks' write loop, since the sampler shows it eating 7% of Lua time, to see if active streaks actually need updating every tick.

I'm estimating the per-tick cost of the streak vertex loop—with roughly 11 active streaks and 24 points each, that's around 7400 table writes per tick, which roughly matches the ~7% instruction share warp.update shows in the sample.

I should run the web benchmark next to see the current state, then plan next steps: frustum caching, snake behavior, and digging into gore update costs, since gore's native time is 0.38ms and I need to check what's happening in its decal and piece-handling loops.

Lua busy time on web dropped to 463ms/s from 608-615, about 7.7ms per frame instead of 10.2 — roughly a 25% improvement, with draw and update costs both falling to around 3.86ms. But the snakes benchmark on web is still at 2.24, barely moving despite the spine rewrite, while native is at 1.61 — I'm wondering why the web gap isn't closing much even though earlier runs showed some improvement from 2.53 to 2.23 after the rewrite.

The end-of-run spikes have shifted — still seeing spikes around 40.8s and 51.2s, though smaller than baseline, and notably the death spike at 65.8s is gone this time. I'm trying to pin down what's causing the 40.8s spike (likely the six objects dropping simultaneously after giving up, triggering landing spot search and physics body creation) and the 51s spike (possibly snake pool spawning or coil-start allocations) — these are brief 60ms hitches rather than sustained frame drops, so I need to think about whether the real "drops to 30fps" complaint is more about average frame cost and frame pacing on high-refresh displays rather than these isolated spikes.

Working through the rAF/physics tick interaction at 165Hz, I realize visual judder from running 60fps game logic on a 165Hz display is somewhat inherent, and that if any frame's work exceeds roughly one vsync budget it cascades into a missed frame and a momentary drop to ~40fps. To actually confirm where sustained 30fps drops happen, I need to measure per-frame Lua cost during the ending sequence specifically, since the bench currently only flags spikes above 50ms rather than showing the full frame time distribution there.

The ending likely runs consistently heavier — multiple coiled snakes with tube builds, silhouette passes, egg physics, gore — so I want a proper per-phase profile of that span rather than relying on spike detection. I'll write a probe that runs the bench up to the ending's start, resets profiling with coarse (non-fine) sections enabled, then steps through and prints the section breakdown for that phase.

Web Lua time dropped ~25% (608–615ms to 463ms/sec), with drawing and updating both improving to about 3.86ms per frame—the fight is in good shape now. The remaining spikes (58–62ms) likely come from the heavier ending sequence (snakes, silhouettes, gore, egg physics), so I'll profile that section next.

I'm breaking down the native frame times: update plus draw totals about 7.24ms/frame versus roughly 6ms in the fight scene, with snake coils, dropped items, lich ragdoll physics, and gore effects each contributing their own share. I'm also looking at where the big frame spikes occur natively, like the one over 25ms around the two-second mark after the probe starts.

Now I want to figure out what's causing those spikes, so I'm planning to modify the probe to snapshot per-frame timing deltas and print out the top contributing sections whenever a frame runs slow.

The ending runs heavier than the fight itself at 7.2ms/frame, mostly from the snake coils (2.0ms), dropped objects (0.48ms), lich (0.46ms), and gore (0.45ms), plus hitches of 32-47ms. I'll break down each hitch frame by section to find the cause.

The output got jammed onto one line but it's still readable. Looking at the slowest frame, it took 44 ms at frame 128, coinciding with a GC cycle that dropped the heap by 75 MB, with 38.1 ms spent in the update of items.

Two other slow frames at 32 ms and 39 ms also line up with GC completions, dropping the heap by 78 MB and 1 MB respectively, with time attributed to snakes and the ending sequence. So the biggest hitches are clearly GC cycles finishing — the heap oscillates between roughly 80 and 155 MB, building up garbage before a collection reclaims ~75 MB, and the atomic/sweep phase cost scales with how much garbage piled up, which explains the 23-38 ms native spikes (and worse on web after the multiplier).

To cut these spikes I'm thinking through two angles: reducing garbage generation so cycles happen less often, or shrinking the live heap so each atomic/mark phase has less to traverse. The baseline ~80 MB live heap is suspicious — if it's full of large Lua tables holding geometry arrays (mesh vertices as number arrays), those get fully traversed every mark phase even though numbers aren't collectable, and traversing millions of slots adds real time. Releasing that static geometry data after GPU upload, or storing it as binary strings instead of Lua tables, could meaningfully shrink the live heap and the per-cycle mark/atomic cost.

Beyond that, I'm also considering the atomic phase's re-traversal of "grayagain" tables — large dynamic arrays rewritten every frame (fx pools, ribbons, streaks) get flagged by write barriers and re-scanned atomically, which could account for a few extra milliseconds, though that alone doesn't explain the full 23-38ms native spikes. It's possible the incremental collector's debt-based step sizing is causing a burst of catch-up work when a large allocation spikes the debt, so I want to check the gcpace settings next.

I should try directly measuring: run a full collectgarbage('collect') at roughly 150MB heap (~75MB garbage) and time it, then inspect the heap composition to see how much is tables versus arrays, to isolate whether the cost is in marking, sweeping, or the atomic step itself.

If I sum array-part sizes across tables, 80 MB roughly matches 5 million numbers at 16 bytes each. I should hunt for the biggest tables and trace their reference paths to pinpoint things like cave geometry arrays.

The biggest slowdowns come from GC cycles freeing ~75MB at once (44ms/32ms) plus the ending's one-off steps (19-22ms), with the live heap sitting around 80MB that the collector must scan each cycle. I'll now dig into what's making up that 80MB.

The reachable tables only account for ~20MB out of the 80MB heap, so the rest must be strings, userdata, closures, or tables not caught by my traversal—maybe things referenced only from C, coroutine stacks, or the host environment.

Big static geometry data stored as strings in Lua would count toward the heap but are cheap for GC marking since strings are leaf objects marked in O(1). So the 1.24M table entries should only cost 5-10ms to traverse natively, meaning the real spike likely comes from the atomic phase—remarking upvalues and grayagain tables that got write-barriered during the cycle. But my changes should have reduced writes to the big dynamic arrays, which doesn't fully explain where the 23-38ms spike is coming from; maybe it's actually the sweep phase on 75MB of garbage.

Since Lua 5.4's incremental sweep does work proportional to allocation debt, a large allocation burst (like items dropping and allocating landing-spot tables) could create a big debt that gets swept in one chunk, lining up with that items frame showing 38.1ms and a 75MB heap drop. Rather than keep theorizing, I should just test by stopping the GC during the ending sequence to see if the spikes disappear—if they do, I can tune the incremental collector's pause and stepmul values or schedule explicit collections at safer moments, so I want to check the current gcpace.lua settings.

Most of the 80 MB heap turns out to be geometry blobs rather than reachable tables, so the mark walk itself isn't the bottleneck. I'll check the collector's configuration next and test whether the spikes disappear with collection paused.

I'm thinking about Lua's incremental GC settings—pause 200, stepmul 100, 8KB step size—and puzzling over why a 75 MB drop could happen in a single frame, since sweeping is supposed to be incremental and spread across multiple steps rather than freeing that much memory all at once.

That could happen if the freed memory is mostly a few large objects—big temporary strings or tables—getting collected together rather than many small allocations. I estimate the ending sequence accumulates roughly 40 MB of garbage over 20 seconds at normal allocation rates, plus occasional bursts, which doesn't fully explain the 75 MB heap drop from 155 to 80 MB. To confirm these spikes are GC-related, I want to try disabling collection during the ending sequence and see if the spikes at those two frames vanish while the heap keeps growing instead.

Disabling GC confirms those two spikes disappear entirely, leaving only the native step costs around 19-22.5 ms. So GC completion is the culprit behind the periodic 25-40 ms hitches, occurring roughly every 20-40 seconds, and I don't see any frames over 50 ms during the actual fight on web.

Now I'm digging into why the cycle's end is so costly — freeing 75 MB has to involve expensive atomic and sweep phases. I'm thinking through how Lua's incremental GC works: the atomic step handles remarking, propagating gray objects, clearing weak tables, and running finalizers, while the sweep phase processes garbage in increments bounded by stepsize and stepmul. With potentially millions of small objects in that 75 MB of garbage, the sweep work could be heavily front-loaded into a single costly step rather than evenly spread out.

Suspecting the real spike comes from atomic retraversing tables marked grayagain due to backward barriers — every dynamic array that got written to mid-cycle (fx pools, trails, warp chunks, ribbons, gore, dust) has to be rescanned slot by slot. Tallying up all those array slots across pools gets into the hundreds of thousands to over a million entries, which at native speed should only cost a few milliseconds, not the 25-38 ms being observed, so that math doesn't fully explain it yet.

I'm considering directly instrumenting the GC phases, stepping collectgarbage manually to time each basic step and isolate which phase spikes, though Lua doesn't expose which phase is active during a step. I also think about just reducing garbage generation so cycles happen far less often — at the current rate a cycle triggers roughly every 40 seconds, but cutting garbage to a quarter of that rate would stretch cycles out to every couple of minutes, making the spike's frequency much less painful even if I can't eliminate its cause.

Confirmed: stopping the collector removes both spikes, leaving only the ending's death/hatch steps (19–22 ms) — so cycle completion itself costs 25–40 ms every 20–40 s. Next, I'll time a full collection and count live objects to pinpoint what to cut.

A full collect with no garbage takes 3.5ms, while stepping manually in 1KB increments totals 17.9ms with the slowest steps near the end hitting 1.5-2.4ms during sweep of large objects—so no single manual step exceeds that. This makes me wonder how the automatic incremental collector's step sizing, governed by stepmul and stepsize parameters, compares.

But then I hit the real anomaly: 75MB freed in a single frame costing 25-38ms. If the heap grows a lot beyond the threshold before a step runs—say from a big allocation like reading a multi-megabyte file into a string—the debt calculation in Lua 5.4's incremental step logic could spike proportionally, causing the collector to do enough work in one shot to mark and sweep the entire cycle at once, producing exactly this kind of spike.

I'm wondering if large table rehashes during gameplay (growing arrays for geometry data like positions, normals, UVs) could trigger these debt spikes too, since a rehash can allocate megabytes at once. The incremental collector's catch-up behavior seems inherent to how debt is paced, so maybe the fix is to manually drive small fixed-budget GC steps per tick while keeping the automatic collector stopped, rather than relying on its built-in pacing.

So a manual pacer calling collectgarbage('step', 0) repeatedly — one basic step each with debt reset every time — could bound GC work per tick, stepping until a time budget or step count is reached.

Since perf_time on the web only has 1ms resolution, time budgets are too coarse, so I should use a step count instead. Looking at how a single step(0) call actually works in Lua 5.4's incremental stepper, each basic step ends up processing roughly 8KB worth of work scaled by the step multiplier, so I need to figure out how many of these steps are needed per frame given that games typically allocate something like 32-100KB of garbage per frame.

With a 1KB-step probe showing around 56,738 steps per full collection cycle, totaling about 17.9ms of work, spreading that over N seconds means the per-frame cost becomes negligible if N is reasonably large — say under 0.02ms/frame at N=20. This points toward a manual pacer that runs a fixed number of small steps each tick, finishing a cycle well before garbage accumulates and keeping the debt from ever building up.

But I'm also noting a snag: in the sweep phase, individual steps spiked to 1.5-2.4ms when freeing large strings or tables, which is still much better than the 25-38ms native spikes but could be worse on web (maybe 5ms). This mirrors earlier experiments where manual pacing had similar debt issues before switching to AUTO mode as the best-tested option.

Checking whether step(0) still functions correctly when the collector is stopped via collectgarbage('stop') — in 5.4 the internal GCSTEP temporarily clears the stop flag, runs the step, then restores it, so it should work fine even while stopped, and resetting debt to zero via setdebt is safe regardless of stop state. This leads me toward a manual pacing design: stop the collector at game start, then issue one step per tick.

To size the step count, I want it to scale with allocation since the last tick — base steps plus extra proportional to kilobytes allocated, aiming to mimic Lua's own stepmul-based policy where work rate tracks allocation rate. The key difference from Lua's automatic incremental stepping is that a manual approach risks big allocation bursts producing a huge step count in a single tick, reintroducing the very spike I'm trying to avoid.

To smooth that out, I'd cap the per-tick step budget (roughly 1.5ms of work) and carry any excess as backlog into future ticks, letting the heap grow a bit temporarily since memory headroom is ample. For deciding when to start a new collection cycle, rather than continuously stepping (which wastefully re-marks the live heap, as seen in r81), I should use a pause threshold similar to Lua's pause=200 — waiting until the heap reaches roughly double the live size before beginning the next cycle, with no stepping during that wait.

I want to verify this eliminates native GC frame spikes above ~2-3ms, and I need to track probe spikes, total GC cost per frame, and heap range to confirm. Since the game shares the site's Lua state, my new smooth pacing mode needs to properly handle pause/resume when the tab is hidden/visible, just like the manual mode did, restoring the auto collector appropriately — and I should account for bursty allocations like egg nest prep growing the heap, which the backlog mechanism should absorb.

Writing GC pacing module...

Now I'm working out the actual math behind Lua's incremental step sizing—how debt converts to step count via stepmul and WORK2MEM—since I need to calibrate my step-per-tick formula against Lua 5.4's real pacing behavior rather than guessing.

I'm thinking through a calibration approach: measure how many step(0) calls a full collection cycle takes at a representative heap size, then derive a steps-per-KB ratio so I can budget work per tick proportional to allocation, capped at a max per tick with backlog carried forward.

A simpler option occurs to me: just run a fixed number of steps per tick (say 40, costing ~0.15 ms) during an active cycle, letting the cycle finish in a few seconds, then pausing until the heap grows enough to warrant another pass — this keeps average GC overhead per frame quite low.

If allocation outpaces the cycle and the heap balloons beyond safe bounds, I'd bump up to a capped catch-up rate of steps per tick. I still need to actually measure the real cost and count of these steps against a live heap to calibrate these numbers properly.

Stepping manually confirms the full cycle costs 17.9 ms with no single step over 2.4 ms—the spikes trace to Lua's auto-pacing dumping accumulated collector debt into one frame after large allocations. I'll fix this by driving the collector myself with `collectgarbage('step', 0)`, which does exactly one basic step without paying debt, and now calibrate what that single step costs.

I'm estimating that a basic GC step does roughly 37 units of work per cycle, averaging about 0.3 ms with occasional spikes up to 2.27 ms, though the exact unit of "work" varies depending on object sizes involved.

Pacing-wise, running one step per tick means a cycle spans about 37 ticks, or 0.6 seconds, pausing afterward until the heap grows enough to trigger another cycle; even the heaviest step stays well under the problematic 25-38 ms spikes I'm seeing elsewhere. I'm considering tuning stepsize smaller to spread work into more, lighter steps, with the atomic phase likely responsible for the costliest single step.

I'm sketching a policy: keep the collector stopped and game-driven, start stepping when the heap crosses a pause threshold relative to the live set, run one step per tick normally, but ramp up to multiple steps per tick if the heap grows faster than the cycle can keep pace, and update the live count once a cycle completes.

This mirrors round 64's approach but swaps in a fixed step count instead of a time-budget based on the web's coarse clock. I'm recalling that round 84 found Lua's own pacing outperformed others once round 64's leak was fixed, cutting frame garbage from 232 KB to 40 KB, and I want my approach to produce zero GC-caused frames over roughly 5 ms. I also need to account for the game sharing the site's Lua state — when the game is paused, control reverts to Lua's auto collector, and the site's own allocations count toward the shared heap my pacer tracks; bursty allocations during the ending sequence (nest prep, egg geometry, item drops) should be fine since the catch-up logic absorbs temporary heap growth. I'm planning to implement this as a new 'SMOOTH' mode in gcpace.lua, set as default alongside the existing AUTO and manual modes.

Writing GC pacing logic...

Now I'm thinking through pause/resume semantics for the smooth mode — on resume, should it wait for the heap to grow before stepping again, or kick off a cleanup cycle immediately? I'm leaning toward starting a step cycle right away since it's cheap, but I need to check where GP.start actually gets called at boot and whether collectgarbage('step', 0) reliably signals cycle completion.

I confirmed the engine runs Lua 5.4, where the step function returns true once a cycle finishes, matching my earlier calibration of 37 steps. Now I'm wondering whether GC timing affects determinism at all, particularly through finalizers with side effects like GPU buffer cleanup, though those seem purely cosmetic rather than game-logic-relevant.

I'll go ahead and implement this with GC enabled and measure using the ending probe to confirm.

I found that a full collection cycle takes 37 steps averaging 0.3 ms each (11 ms total), coarser-grained than expected, so I'm setting the pacing mode to run one step per tick between cycles (triggered at 1.6× heap growth), scaling to three steps if the heap grows too fast, while still yielding to the collector when the game is hidden.

Good, the GC spikes are gone, leaving just the ending's own steps at 18.9 and 21.8 ms. Now I want to verify overall GC cost and heap behavior with SMOOTH enabled by running the fight bench and checking the gc section, heap range, and frames over 25ms, making sure the heap isn't growing unbounded—so I'll run the desktop bench without fine mode and inspect those sections.

The collector spikes at the end are now resolved—only the death (18.9 ms) and hatch (21.8 ms) steps remain over 25 ms. Next, I'll check the collector's steady-state cost and heap range throughout the fight.

GC overhead looks fine, averaging 0.018 ms/tick with heap usage between 73-134 MB. The recurring mystery is that long 55.2 ms frame at 27.2 seconds where Lua only accounts for 4.9 ms — something outside Lua, possibly the engine or driver, is eating the rest, and it shows up consistently across every desktop run at that same timestamp.

My best guess is a shader program compile triggered by a material variant combination that wasn't included in the warm-up precompile list, causing a synchronous compile hitch on first use. I want to instrument the program creation path to log when a new compiled program is created along with its timing, to confirm whether this frame coincides with a compile event.

GC overhead now averages 0.018ms/tick with heap holding steady at 73-134MB, but a recurring 55ms native spike at 27.2s (only 4.9ms Lua) remains unexplained from round 81. I'll check if it's a shader program compiling on first use that the intro sequence failed to warm up.

That 50ms frame at tick 4297 doesn't match a compile event, so I'm wondering what else in the engine could cause that spike — maybe a texture creation or render target resize, a sound decode, or a physics world step spike from bodies being created.

Since the Lua profiler sections only show 4.9ms, the cost is likely outside Lua entirely: engine physics step, audio, layer rendering, or a present/swap call that triggers a GPU sync stall. I should check whether there's an engine-level frame profiler or timing API exposed to Lua that could reveal this.

I don't have per-section engine timing, so I need to isolate what's Lua (update/draw) versus engine work like physics steps, input, and render flush, to figure out what's causing that 50ms outlier in physics3 specifically.

My plan is to wrap the global update and draw functions in Lua to time them directly, then compare against the total engine_step duration — the difference gives me the engine-internal cost (physics, present), which I can run against tick ~4297 to isolate the spike.

Since the engine lacks per-phase timing, I'll time the game's `update` and `draw` calls from Lua against the total engine step, treating the remainder as the engine's own work (physics, layers, present).

Tick 4290 shows 36ms spent in the engine itself, not Lua or physics. Since this happens deterministically at the same tick every run, it must be triggered by something game-specific at that point—maybe a texture load, a GPU swap stall, or some other resource event.

That 41 ms frame has 36 ms coming from inside the engine itself (not the game's Lua), consistently at the same tick, pointing to something in the game's state triggering it—likely the 3D physics world. I'll check what in the game is using it.

Since physics3_init only happens at the egg's end, that 36ms frame during the fight must come from something else in engine_step outside the Lua update/draw loop - possibly input/event pumping, or more likely audio mixing via agent_audio_tick reading PCM frames in agent mode.

Mixing itself shouldn't take 36ms unless many voices with FX and reverb are active simultaneously, but even then normal mix costs are only a few milliseconds. I'm also considering GPU stalls - the engine could be blocking on a swap buffer call even in agent mode, or stalling from a large buffer upload, or hitting a lazy shader compile/link that some drivers defer until first actual draw call.

Checking whether sound cleanup when many sounds end at once could cause this is cheap, so I rule it out, and layer rendering happens inside Lua draw, not the engine step - so I'm narrowing down to what's actually inside engine_step itself, specifically the present/swap call, and whether this spike shows up in web traces too (it doesn't seem to).

I'm also noting another isolated spike at tick 245 (startup, probably first-use allocation like lazy creation of something), which seems benign and one-off. Now I want to double-check that the GC pacing change doesn't affect determinism or logic unless there are finalizers or weak tables tied into logging.

The 3D physics world only shows up in the ending, so that fight frame isn't physics-related—it's specific to the hidden-window agent instance and doesn't affect the site. I'll note this and move on to verifying the collector change.

I'm cross-referencing the frame-time spikes near death and hatch events with the web run's timing, trying to pin down whether the 58-62ms spikes correlate with the item-drop moment that happened natively around the same point.

To isolate the cause, I think the ending's native hitches are tied to specific one-time events: the death sequence creating a ragdoll with gore, and the hatch sequence building shard geometry and physics bodies for the crack_egg effect. Rather than guessing, I should profile with fine-grained sections inside the tick loop, or just sample the whole ending run and let the ending-specific functions surface naturally.

Digests and frames all match, leaving only two ending-specific hitches—his death and the hatch sequence—that would cause noticeable 35–45 ms stalls on the web. I'll sample those moments to see what's causing them.

With the hook enabled, overhead swamps every frame so it's not useful for precise timing, but it does surface the overall hotspot: stack_lifts() at snakes.lua:684, with lines 698-708 handling the coiled snakes' stacking on the grid — roughly 16% of the ending's Lua time.

That's the coil stacking logic running every tick across 8 coiled snakes (72 rings each, 576 rings total needing grid hashing), plus coil.lua's coverage work. Let me dig into stack_lifts itself.

The profile covers the whole ending rather than isolating the two steps, but it shows `stack_lifts` in snakes.lua—which stacks coiled snakes every tick—accounts for about 16% of the Lua time. I'll read that function next.

I'm working out the spatial hash grid logic for stack lifts, figuring out how many neighboring cells each ring needs to query based on the collision radius.

The approach also recreates grid and list tables every tick, which generates unnecessary garbage, so I should reuse and clear these structures between ticks instead of reallocating. I'm also checking whether the order in which crossing updates get applied affects the final max-height value given float32 rounding, since the comparison uses a rounded running maximum against a double-precision candidate — but since float32 rounding is monotonic, the final result should converge to the same value regardless of processing order.

The set of neighbor pairs found only depends on whether each pair's distance is within the query radius, and since every neighbor's radius is bounded by R0, the query range Dl is always ≤ Rq, so the grid cell size I choose can't cause any pair within distance Dl to be missed.

That confirms I can optimize the implementation without changing correctness. The main costs are the nested loops over grid cells with repeated key-generation calls and lookups, so I'm planning to inline the key computation, precompute bounds once, and reuse grid and list tables with a generation counter to avoid reallocating memory each pass.

With insertion order preserved and queries unaffected by order, this stays correct while cutting allocation and function-call overhead — likely a meaningful speedup since this logic accounts for a notable share of runtime samples.

I'm also weighing whether switching from division to multiplication by the reciprocal in the grid-cell computation could change floor rounding and shift cell boundaries, so I need to verify that substitution preserves identical behavior before applying it.

I found that `stack_lifts` rebuilds its hash grid from scratch every tick, with costly cell key calls and lookups across ~576 rings—though its result is order-independent, so correctness won't be affected by changes. I'll rewrite it to reuse tables between ticks and compute cell keys inline, keeping the same division logic.

I'm checking that my local `best` variable tracks the running value correctly against Lf[i], matching the original's comparison logic, and now I'm considering whether stale list entries beyond the valid count could cause issues when iterating.

I'm confirming the grid key computation matches the original's bit-packing scheme exactly, including the order of operations and integer arithmetic guarantees from math.floor in Lua 5.4. I'm also verifying the coil stacking at the ending correctly affects digest calculations through spine positions.

The ending sequence's snake rendering dropped from 2.02-2.2 to 1.88 ms/tick, a modest gain. Now I want to dig into the two outlier one-off steps: 19ms at death and 22ms at the hatch, starting by tracing what happens in the death tick where a ragdoll gets created for the lich.

Since those frame indices are deterministic across runs, I'll arm the sampling hook only for a narrow window around each spike—frames 1503-1507 and 2046-2050—and collect the data separately for each.

Tick time in the ending dropped from 2.0–2.2ms to 1.88ms with everything still verifying, but two deterministic spikes remain at frames 1505 and 2048. I'll profile just those frames to find what's costing 19–22ms during the death and hatch events.

The death frame's cost is spread across many small calls -- gore bursts, bounding sphere computation, geometry creation -- with no single dominant hotspot, which makes it hard to optimize without touching the underlying logic.

For hatching, I'm seeing eggphys.lua line 101 taking a notable chunk, called during shard geometry construction, plus more bounding sphere computations triggered when the renderer first encounters the new shard geometries. I want to dig into what's happening around lines 60-110 and 390-430 in eggphys.lua to understand that cost better.

The death logic is spread across multiple causes with no single source, but the hatch effect is concentrated: 26% in one function at `eggphys.lua` line 101, called from the shard-geometry builder plus bounding spheres for shard meshes. I'll read that code next.

I'm noticing that gi_at allocates a fresh table on every call and runs per vertex across thousands of verts, which is eating 26% of the hatch frame alongside uv_at and acos calls. One fix would be to precompute the shard geometry ahead of time instead of doing it inline during the critical path.

But a better fix might be memoizing gi_at/uv_at results per shared vertex point, since each point gets reused across up to six triangles — caching by point identity (since the functions are pure given fixed inputs) could cut calls by 4-6x and eliminate the repeated table allocations entirely.

Rounding out the profile breakdown, the other hot spots are bounding sphere computation in core.lua, the shard geometry build, gi_grid construction, and ensure_array's per-float unpacking of position/gi buffers — all triggered when gi_grid gets built at the hatch moment inside crack_egg.

Memoizing gi_at/uv_at should cut roughly 25% off the hatch frame, bringing it from ~22ms down to ~15ms native, while precomputing would save more but carries more risk. The bounding sphere work is unavoidable since it's computed once per new geometry. The death frame is spread out enough that I'll skip optimizing it. Now I want to implement the memoization and check what gi_at actually returns.

I found that the color lookup is being recomputed per triangle corner even though shard points are shared across up to six triangles, rebuilding a fresh table each time unnecessarily. I'll cache the result per point instead, since the pure function always returns identical values—let me check how it returns its values first.

I'm checking whether the p tables could be affected elsewhere, and confirming that uv_at always returns a valid number so the nil-grid edge case is safe. Now I want to search for other shard functions, like the bottom and cap, to see if they rely on different vertex functions.

Beyond that, I need to verify the hatch frame comparison is actually trustworthy — the egg digest might be too lenient, so I should write a probe that hashes the shard geometry arrays (position, normal, uv, gi) directly at the hatch moment and compares old vs new code exactly. That means finding where the egg pieces and their geometry are stored, likely through eggphys, so I can locate the shard_geometry calls and hash their contents deterministically.

Since the hatch frames fall in the egg-noise box, a visual frame compare won't confirm the shards are unchanged. I'll instead directly compare shard geometry by hashing each piece's vertex arrays at the hatch, old code vs. new.

The shard geometry matches exactly between old and new code, so that's confirmed. I should rerun the general verification once more after the eggphys change and remeasure the web, then move on to deciding what to tackle next — I'm thinking about in_frustum caching with version stamps and moving gpu_draw opts parsing into C as potential targets.

The spike count dropped significantly on the web and heap usage improved too. Snakes and r3.draw still dominate the per-tick cost, so I want to dig deeper into what's making snakes expensive specifically.

Sampling natively, floor_at (ground_y + sample) accounts for roughly 10% of all Lua time, around 0.6ms of the snakes' 1.6ms. I could inline the bump-grid lookup function directly into sample to cut down on call overhead, keeping the bilinear interpolation math identical, which might shave off another 30% of sample's cost. Meanwhile ground_y involves three C calls plus some Lua math with smoothstep calls that could also be reduced since snakes mostly operate within a specific radius range.

Inlining the smoothstep (and its internal clamp call) should preserve the exact same math while cutting function call overhead, giving maybe a 20-30% reduction in ground_y's Lua cost -- small gains individually but worth doing since snakes dominate the profile. I'm now wondering if there's a bigger structural win elsewhere, like skipping the build step entirely for snakes that are waiting or held in some fx state rather than just optimizing the math inside build.

Performance's improved: slow ending frames dropped from four to one and heap usage is lower, though snakes still cost 2.2 ms/tick, with `ground_y`'s bilinear sampling making up ~10% of Lua runtime. I'll inline those cell reads and the `smoothstep` call to speed this up, first verifying `clamp`'s behavior to ensure the inline matches exactly.

I'm inlining the sampling function to compute bilinear interpolation coordinates and clamp grid indices within bounds.

I'm also checking that the smoothstep inline for the cave ground height computation preserves the same clamp-and-cubic-ease formula as the original, just expanded in place.

The second smoothstep term applies only when r exceeds 12.5, scaled by 1.4 — I'll inline that the same way, and leave the mound term in ground_y using exp(-r2/4.0) untouched. Now I'll apply both edits.

I'm confirming that boot.lua's math.exp override happens before cave.lua loads, so its local exp reference already points to js_exp unchanged. Now I'm checking the original smoothstep formula's normalization using a=3.5 and b=7.

The subtraction constant-folds to 3.5 identically, and floor_at relies on cave.ground_y which my edited function populates correctly, with ground_y_ref left untouched. I should verify digests and frames since ground height changes propagate to snakes and particles.

I should also check LICH_ROOM mode digests, since RECORD treats that as the standard comparison check and it exercises older code paths like respawn timers that my default-mode verify doesn't cover. Let me run digest_run with LICH_ROOM=1 to compare against the old engine.

I should also run the replay check and anchor check afterward. Also I need to think about the site-hosted path — since the game shares the site's Lua state, my gcpace changes (SMOOTH mode with collectgarbage stopped) affect the site's garbage collector too: when the game is visible it takes over stepping the collector manually, and GP.pause() restores the automatic collector when hidden.

The one concern is GP.start() triggering a full collectgarbage('collect') at boot — with the site's heap potentially 100-300MB from page content, this could add 10-60ms to the already ~0.6-1s boot time, which seems acceptable since the old manual mode did the same. I also need to confirm SMOOTH resume properly restarts stepping immediately, and consider how the site's own performance monitoring interacts with this.

To actually test the hosted path mechanically, I'd need to sync the new game files into the site's admin data directory using sync_lich_survivors.py, then run the site agent with the game loaded to probe costs and check for errors — though I should be careful since syncing modifies the site repo's working tree, which mirrors what an actual deploy would do.

I'd rather hold off on that sync until I'm finalizing things, though, since it's reversible anyway. For now, let me resample the current distribution to find the next optimization candidates.

The profile is flattening out, with multiply_matrices now around 10%, mostly from model_view and shadow draws plus the world matrix chain recomputing even when objects haven't moved, particularly from the lich's repeated lookups. I'm thinking about trimming the redundant multiplies in update_world_matrix's chain calls for things like the lich's head position.

To fix this I want to add version tracking: each object gets a world-version counter that bumps only when its world matrix actually changes, plus recorded "source versions" for its parent and local matrix so a call can detect nothing changed and skip recomputation entirely. This also means the render walk's forced cascade (where children recompute just because the parent recomputed) could skip work too, as long as I track the source versions carefully there as well.

I realize there's a flaw: the needs_update flag could be set externally (e.g. when matrix_auto_update is off and someone edits matrix directly), so my version counter wouldn't catch that change and I'd wrongly skip recomputation. I need to treat an externally-set needs_update as an always-recompute signal, reserving version-based skipping only for the parent-propagated force path.

Thinking through the force cascade more carefully, I realize that once I've eliminated spurious flagging, any remaining forced recompute down the chain is actually necessary since it stems from a real local change bumping the parent's version — so version skipping buys little in update_matrix_world itself, though it still helps in the broader world-matrix chain walk.

The real cost shows up with lich's bone lookup chain, nested four to five Group levels deep, called around 68 times a frame across roughly 14 chains during the game tick, before rendering, right after lich.pose() updates bones. Since poses change every tick, the chain recompute is genuinely necessary the first time, but repeated calls within the same tick — like head_pos being queried separately for audio, camera, etc. — end up recomputing the same chain redundantly after the first call already resolved it.

Caching with version checks could save maybe 50 multiplies per frame, only about 1-1.5% of total Lua cost, so it's marginal and somewhat risky to implement — I'll skip it for now.

The bigger target is warp.update at 7.7%, which has grown disproportionately. Looking at its loop over 200 streaks, off streaks that are already written do minimal work, but active streaks (~11 of them) each write K points times two vertices, meaning 14 writes plus lookups per point. If K is around 24, that's roughly 672 vertex writes per streak, totaling about 7.4k writes per tick — matching the 7.7% instruction share. I'm considering whether aPrev/aNext really need separate writes since they're just shifted copies of position.

These derived values are needed as separate shader attributes since I can't change the shader without risking visual differences, and the per-vertex overhead (index arithmetic plus the constant per-streak values like weight, amplitude, chroma, phase, fade) adds up to significant redundant writes — though the potential savings here look minor, so I'll leave warp alone too. Turning now to the renderer, draw is costing 5.7% and I want to check in_frustum next.

Looking at the full breakdown — _set 3.5%, walk 2.7%, gpu_mesh_of 1.9%, project/sort 1.6%, up 1.5%, shadow 1.1%, draw_state 1.0% — roughly 23% of total Lua time is per-object renderer overhead. Caching in_frustum results with a version check tied to matrix_world updates could shave another 2-3%, bringing my total gains to around 25-30% of Lua runtime so far. Given diminishing returns on further micro-optimizations, I'll pick a few of the cleanest remaining wins, starting with caching the object's world-space bounding sphere since it only changes when matrix_world or geometry bounds change.

I'm verifying that matrix_world is only ever recomputed in core.lua's two sites, so a version counter there would safely key the sphere cache — checking the ragdoll bone code confirms it only writes local transforms, not matrix_world directly, so the caching approach holds. Instanced mesh bounding spheres are already cached permanently after first computation, which I'll leave as-is since changing that behavior is out of scope. Now I'm tracing where in_frustum gets called — the main object walk and the shadow pass — and looking at its per-call cost: computing the sphere, doing the center transform, and extracting the max scale from the matrix.

The compute involves a sqrt and several squared-length calculations plus six plane tests, so caching the center and radius would skip most of that work. My plan is to add a version counter that increments whenever update_matrix_world or update_world_matrix actually recomputes the matrix, then in in_frustum check if the cached version matches before reusing the stored sphere data — falling back to a fresh computation if the version is missing or stale, since any object that's never been updated shouldn't use a cached value. I also need to make sure the bounding sphere's identity stays stable across recomputations, since if geometry mutates its bounding sphere table in place rather than replacing it, the identity check would never catch changes.

Since geometries with changing vertices tend to recompute their bounding sphere in place, I'll widen the cache key to also include the radius and center coordinates so stale data gets detected cheaply. The expected gain is modest, maybe 2% of total Lua time, but worth doing. I'm also noticing the world-space center gets computed twice — once in in_frustum and again during z-sorting in the walk/project pass — so I should reuse that result instead of redoing the transform, accounting for the fact that in_frustum applies a perspective divide while the sorting pass doesn't.

For affine matrices this divide is harmless since w resolves to exactly 1, but to be safe I'll just cache both versions of the coordinates separately keyed by the object's version number, alongside the bounding sphere fields and scaled radius. Now I'm implementing this caching scheme in renderer.lua, bumping version numbers in core.lua accordingly.

Performance is now solid. Next, I'll cut redundant work in culling/sorting by versioning each object's world matrix recompute in core.lua and caching the world-space bounding sphere so it's only recalculated when the matrix or sphere actually changes.

I'm verifying the matrix multiplication order matches the original for computing x, y, z, and w transforms, confirming my indices line up correctly. I also note the local `g` variable from the walk is still used later for `g.groups`.

Checking in_frustum against the original, the center/matrix_world extraction and radius-scaling formulas match. I'm reasoning through cache invalidation: version bumps correctly trigger recomputation even when matrix_world is set to the same value, objects never touched by update_matrix_world fall back to recompute since their version is nil, and InstancedMesh's bounding sphere caching and reused buffer table behave the same as before.

I'm estimating the per-draw overhead for the gpu_draw option parsing in the engine, since each call reads about 15 fields via lua_getfield lookups. Measured natively it's roughly 0.9 µs per draw across 186 draws (0.166 ms/frame total), which I'm using as a baseline to compare against the web timing.

Caching the opts table per material/side/orientation combo seems like it wouldn't actually save much, since material properties can change at runtime and the comparison cost would likely offset any savings — so I think the per-draw overhead here is just diminishing returns at this point. Instead I want to refocus on the snakes, since they're the biggest web cost at 2.2 ms/tick across 6-8 snakes and 72 rings each; I should re-measure the build function's per-ring cost natively now that spine has been inlined, to see where further gains are possible before moving on to finalize the GL call count check and the deploy question.

Spine time dropped to 0.886 ms, meaning each ring takes about 2.2 µs, with roughly 1 µs being ground-truth C/Lua overhead. I'm examining what's left in the per-ring cost, looking at the trail-walking loop that accumulates segment lengths until it reaches a target distance.

Since trail points are spaced about 1 cm and the snake length is 4.4 m, that's roughly 440 segments walked once per build cycle (since the index persists across rings), costing about 13 µs per build and 0.07 ms overall once scaled up — acceptable.

Checking the ground() function per ring, I trace through the floor lookup chain down to the noise and exponential calculations in C, wondering if skipping the Gaussian mound term for large radii would help, but realize the tail values are tiny but nonzero and would subtly change results, so I should leave it as is. I'm concluding the snake logic is about as optimized as it can get without deeper algorithmic changes, and the overall frame mean has improved from 9.27 to 6.88.

Now I'm moving to finalize the engine-side changes — the mesh update range, VAO cache, and GL state cache with epoch tracking — and need to verify these against a page comparison across the site's desktop builds, since several pages embed games that rely on the gpu layer differently (some use the 3D path instead). I want to run this comparison against the current build versus the pinned reference commit, and also check other games like SNKRX, megablock, and HYDANCER that run through the same engine build, since my VAO and state cache changes need to hold up correctly across all of them.

There's real risk here: a subtle bug in the state cache could silently break rendering in other games that share this engine. I'm considering grepping across the games directory for usages of gpu_draw to identify which titles are actually affected, then spot-checking frames from one like megablock against both the pinned and new engine builds, plus verifying the playground's 3D layer path still behaves correctly through the epoch-based state bumps. The real stakes are the live site, Phylactery, and whatever games come next that depend on this engine layer.

Spine loop time is now down to 0.89ms, and remaining overhead is just floor-level noise, so that's near its limit. Next I'll check the vertex-array and GL state caches in the engine, comparing the site's 11-page benchmarks against the live engine and listing other games using `gpu_draw`.

All 11 pages match across old and new engines, confirming the gpu_mesh_update_range difference is accounted for. Now I want to check megablock and the effects/charge-streaks code, which use gpu_draw, by running the agent on both engines for a fixed number of frames and comparing snapshots—though I need to confirm the scene is deterministic before trusting that comparison.

All 11 site pages render pixel-identical across old and new engines. Now I'll verify megablock and the charge-streaks effect, the two other projects using the GPU layer, render identically as well.

Both engines produce identical results on the megablock test with frames at 60-step intervals. I want to verify these frames aren't just blank black screens before trusting the comparison, so I'm checking the image stats for one of them.

Megablock has real visual content while charge-streaks is just a flat gray frame since that effect likely needs a key/mouse trigger, so it's not a meaningful test case—megablock is the better substantive gpu-layer test. Now I should finalize by documenting gpu_mesh_update_range in the engine API quick reference along with a note about the state cache, keeping in mind the bindings doc regenerates separately.

Beyond that: confirm the game's own anchor framework copy needs no changes, run `anchor check .` on the game to catch any LuaLS warnings like an unknown global for gpu_mesh_update_range, and verify the PROF fine-grained timing sections in the build path stay cheap enough to keep since they're just boolean-gated function calls with negligible overhead. Then I need to write RECORD.md for round 93 with bench numbers and verification method, commit the engine and game changes, and figure out what needs his approval before shipping, including the site's engine deploy with the new engine build.

I also want to make sure the game's sandboxed Lua environment exposes any new globals the engine introduced, so I should check how game_host sets up its environment table and whether it falls through to the global table via metatable indexing.

Megablock's frames show a real scene (1,615 colors) and match exactly, while charge-streaks render as flat grey since the effect waits for input, so that test isn't conclusive. Next I'll check if the site's sandbox exposes new engine globals like `gpu_mesh_update_range`.

The hosted environment falls back to the global table, so the new engine globals are available there, and static checking shows zero errors. I'm also thinking about how collectgarbage('stop') in smooth-pacing mode would affect the whole site's shared Lua state, since garbage collection would only run through the game's own ticks and heap growth thresholds, meaning the site's own allocations get counted against that same heap.

Given the site's content could push the live heap to something like 300 MB, the pause threshold would scale up accordingly, leading to longer collection cycles -- but that still seems fine both for memory limits and for the per-step cost at this scale, even accounting for mobile constraints since the game seems desktop-only here.

Now I want to actually verify this by syncing the game into the site's local admin copy and running the site agent to check for errors and frame cost, using the existing sync and pack scripts that update the site's tracked game data for deployment.

I'm weighing whether running the sync script locally without deploying would leave the site repo with uncommitted changes, and if so I'd want to revert those with git checkout afterward -- though first I should check whether the sync script itself does any committing.

Static check passed with 0 errors, but I need to sync the site's local copy of the game to test it as hosted. Let me check what the sync script touches.

Since the admin copy is gitignored and regenerated anyway, syncing it locally should be safe—the desktop's run-unlock.bat does this on every launch. I'll run the sync, then start the site agent hosting the game with my site_cost probe to measure site and game costs and confirm there are no errors.

The site's game copy is auto-generated and ignored by git, and it already gets re-synced on every desktop launch, so syncing locally won't affect the live site. I'll go ahead and sync it, then run the site using the hosted game and the bench fight, checking for errors and comparing the site's frame timing against the game's.

The numbers look suspiciously unchanged — 3.91/3.70 now versus 3.91/3.73 before, despite the sync reporting 12 copied files. I need to check whether that earlier measurement was actually taken before the sync happened.

My suspicion is the hosted site might be loading the game from the packed mirror in data/admin rather than directly from admin/, meaning the sync didn't actually update what's being run. I should run pack_games.py to regenerate that packed mirror (which is git-ignored and safe to regenerate) and re-measure to confirm.

The hosted numbers didn't change, indicating the site loads from the packed mirror rather than the synced folder—I'll run the pack step and re-measure.

In the hosted environment, game update and draw times improved substantially, cutting total game time by about 28%, with gcpace smoothness confirmed. But I realize this test used the packed mirror files, which are locally regenerated and gitignored, so I need to check whether the actual deploy would use these same packs.

The live site still has the old pack until I run deploy --data. Next I want to do a final web bench run plus a GL count for the record, then move to RECORD, docs, commits, and asking about shipping — and I should also remember to clean up the scratch worktree with git worktree remove once I'm done, keeping it around only until the final checks are complete.

Frame time dropped from 7.61 ms to 5.51 ms (−28%) with the new collector pacing live on the hosted game. Now running a final web benchmark through the fight and ending sequences, tracking WebGL call counts.

Final results look good: Lua time dropped 25% to 459ms/s, no frames exceed 50ms anymore, heap usage shrank to 70-111MB, and GL calls per frame fell from 2863 to 1182. I'm noting the GL wrapper adds some overhead to these numbers but it's acceptable, and now I should write up the record for round 93 along with the engine API quick-reference docs.

I need to verify both the web and desktop engine builds are current given recent state cache changes, then check the Anchor repo diff covering anchor.c, ENGINE_BINDINGS.md, and the regenerated meta.lua before updating the ENGINE_API_QUICK.md entry for gpu_mesh_update. After that I'll record round 93 in the game and prepare the commit spanning the renderer and various game modules plus the webbench tooling — I want to double check whether build.py's audio script changes were already committed in the earlier session. Then I need to think through the shipping steps, starting with the push.

Shipping still requires pushing the engine and deploying the site engine, packing and deploying the site game data, and uploading the Steam build via package.py and ship.py — all of which per CLAUDE.md need his explicit OK before going live, same as rounds 88-92. I'll check the steam-release runbook for the exact build upload command once he approves, then write up the docs, record the round, commit, and update memory.

Performance comparison shows big wins: Game Lua time dropped ~25% (459ms vs 608-615ms), no more frame spikes over 50ms, lower heap usage, and WebGL calls cut by more than half (1,182 vs 2,863). Now I'll document the new API call, log round 93 in RECORD.md, and commit.

The untracked round-2 folder and sound_rooms.lua belong to other sessions, so I'll leave those alone and not commit them; build.py is already committed with no changes. Now I should update ENGINE_API_QUICK.

I've confirmed the goto usage in fx.lua runs fine under Lua 5.4, and the per-tick timing checks out within expected bounds. I'm ready to commit the anchor engine file along with the web build artifacts and documentation.

Verifying the web build is current with the final anchor.c reveals no anchor.c changes since the last desktop/web build, so both are good to go. For the game commit, I'll include the port files, record, and webbench tooling, but leave untracked files like sound_rooms.lua alone. After updating memory, I'll remove the headgame worktree while preserving the scratch outputs.

I need to update project_lich_dream_game.md with the Phylactery 19 optimization summary and a MEMORY.md line, plus update project_anchor3.md noting the engine's GL state cache, VAO, and gpu_mesh_update_range work -- important context being that every GL state call must go through wrappers and JS-side GL changes need to bump gl_epoch. I'm also thinking about how to reference the verification method using a worktree of HEAD.

Then I want to clarify what "shipping" actually requires: based on past records, it looks like there are two independent deploy paths -- the site engine deploy (which rebuilds the web engine from Anchor source, affecting all pages) and the game package deploy (data/pack upload), since the new gpu_mesh_update_range is optional and falls back gracefully on the old engine. For Steam, shipping means running package.py and ship.py to cut a new BuildID and set it live for owners, though I should check the steam-release runbook for the exact build+upload command for the desktop novideo-steam engine build. Rather than guess, I'll just ask him whether he wants to ship to both the site and Steam, then follow the runbook accordingly -- but first I should get the memory updates done.

Both commits are in (Anchor 3eab972, game e860801). Now I'll update memory to note that future engine GL calls must go through the state wrappers.

I should ask the user about shipping next—whether to ship to the site (engine + game package) and also do a Steam build. Let me check the steam-release runbook for the specific ship.py commands so I can reference them concretely, then close out the task list with everything marked done.