Prime's ask (via the coordinator): investigate the "Parakeet skips
stretches of speech" finding, including other Parakeet weights.
Investigation only; nothing deployed.
Against ground truth (official SCOTUS transcript, Gutenberg #38916) the
drops are real: production v3 loses 140 / 66 clean words per transcript on
the two public files and ~50 on each private one (Whisper-referenced,
Canary-confirmed; adjudicator 129/129 correct on the calibration). Cause:
the v2/v3 0.6B weights collapse deep inside long full-attention windows;
the encoder output is degraded, the audio alone transcribes fine, and
1.1B TDT/RNNT/CTC and CTC-0.6B never do it. Decoding (CUDA graphs, greedy
variants, max_symbols, beam), slice length, local attention, loudness,
resampling and a noise floor do not fix it. Controls: A-vs-A, silence
positive control (>=15 words 36/36), null control, bootstrap floor.
Proposed patch 0002 re-transcribes >=3 s stretches where the audio holds
speech but no word came out (-80 to -90 % lost words on all four
recordings, lower WER, no invented text, +10 MiB) and adds an explicit
PARAKEET_MODEL_PATH with the loaded model recorded in JSON and ModelUsed.
Reviewed at high effort, all findings fixed; built and tested as
scriberr:local-blackwell-a353078-dropout2, not deployed.
scriberr-rebuild: --patches takes DIR[:DIR...]; embeds and seam-checks
both Parakeet scripts (seam-check --standard for the short-audio one).
Prime 2026-09-30: move scriberr to GPU 3 and extend the Jev endpoint to 32k tokens.
Scriberr holds 0 VRAM idle; verified a 20-min job on GPU 3 at 5,496 MiB. With GPU 1
freed, intern-decision's measured card peak at MAX_TOKENS=32768 is 15,220 MiB against
a 15,437 MiB budget (n=3, 1 and 16 questions); 32,769 tokens is refused 422 up front.
JevBench v1.2.16 via /v1/systemone unchanged: 202/231, 0 diffs vs the bench.
Deployed 2026-09-30 1211 PT by pointing SCRIBERR_IMAGE at the patched tag (.env backed up as .env.bak-20260930-pre-slicer1; rollback is the unpatched scriberr:local-blackwell). PrepareEnvironment rewrote the env's parakeet_transcribe_buffered.py from the embed (sha256 matches the patched source). One live run on GPU 1 beside intern-decision peaked at 5,496 MiB. Memory records the open Parakeet mid-chunk dropout finding and the held upstream PR.
Stopped (not removed) 2026-09-30 0135 PT. Scriberr's Parakeet path hardcodes
5-minute slices; attention memory is quadratic in slice length, so a long file
needs >6 GB and hit CUDA OOM with SemIf resident (~6.7 GB free). GPU 1 now
81,806 MiB used. Durable fix (shorter scriberr slices) deferred to later.
Blender is now a mandatory stage in draupnir's pipeline (Prime, 2026-09-28), and draupnir asked
for eight add-ons from extensions.blender.org: SurfacePsycho 0.10.4, CAD Sketcher 0.32.1,
3D-Print Toolbox 1.4.1, STEP Importer 1.2.1, Bool Tool 2.1.0, LoopTools 4.7.7, MeasureIt 1.8.4,
3MF Import/Export 2.7.7.
- stacks/blender/extensions.lock pins each by version and archive sha256.
- scripts/blender-extensions sync builds fv-ml1:/tank/blender-extensions/5.2/system with Blender's
own install-file, pre-warms and byte-compiles it, checks a read-only enable, then swaps it in.
It refuses while the GUI or a blender-run job holds the old directory.
- conf/scripts/startup/fleet_extensions.py enables every package in the System repo: in a timer
in the GUI (after the prefs load), and as --python ahead of the caller's args in
blender-run --extensions (a failed enable exits 1 before the caller's script).
- It also patches SurfacePsycho's sp_overwrite_segment_selection from eval() to literal_eval():
the eval walked past MCP safe mode (control: unpatched ran code, patched refuses).
- blender-run: --extensions (bind mounts via --mount so a missing source fails instead of being
created); USER/LOGNAME set, which CAD Sketcher's getpass needs.
- compose.yaml mounts the repo read-only and the hook into the GUI container. NOT yet deployed.
- scripts/blender-probes/extensions_acceptance.py: one operator run per add-on, safe-mode
compliant. Headless 8/9 online and with --network none; CAD Sketcher sketching is GUI-only.
A Python audit hook saw no network/process events (positive control fired).
The MCP server (mcp-for-blender 2.1.1, frozen requirements) runs inside the Blender
container. Its add-on is vendored at upstream 41a18432 (MIT) and started by a
startup hook. scripts/blender-mcp carries the stdio over ssh + docker exec, so the
add-on socket, which runs arbitrary Python with no auth, stays on the container's
localhost with no published port. It also runs there because viewport screenshots
need a filesystem shared by server and Blender. Telemetry is off and safe mode is
on. The hook also defaults Cycles to OptiX on GPU 3, because safe mode forbids
agents from touching preferences.
Verified end to end from nh3-dev: 36 tools; a GPU render of an agent-built scene;
a viewport screenshot; and safe mode refusing 'import os'. Blender left down
(on demand).
The shim holds 10.0.50.47/24, which gives two equal connected 10.0.50.0/24 routes,
and ens18's wins. Host-to-HA traffic therefore left via the macvlan parent and was
dropped. HA lost MQTT to the broker on this host on 2026-08-19, 2026-09-21 and
2026-09-25 (the last lasted two days). This adds an ifupdown if-up.d hook that
routes 10.0.50.46/32 via macvlan-shim; /etc/network/interfaces is not edited.
Verified: the route resolves via the shim, the host pings HA, HA reaches :1883,
and HA reconnected to the broker. Diagnosis by ha-dev.
Also corrects the zigbee2mqtt acceptance note, which had wrongly reported HA as
connected.
Requested by ha-dev; approved by Prime in this session. Radio: SLZB-MR1U chip 0
(EFR32MG21, EmberZNet 8.0.2) at tcp://10.0.90.10:6638, adapter ember. A fresh
network was formed on channel 25, PAN 0xCFF4. The broker is reached as mosquitto
user zigbee2mqtt, with HA discovery on homeassistant/. The frontend on :8099 is
token-protected.
State and the network key stay host-only in /opt/docker/data/zigbee2mqtt
(root 0700, restic). The repo carries only compose, .env.example and the README.
configuration.yaml refers to the secrets as !secret.yaml, and those references
survived Z2M's v4->v5 settings migration.
SemIf's shared scorer trims one token at the state boundary. When an object
state's last value ends in ')', ';' or '}', the JSON that follows re-merges two
tokens back, so score_shared refused the request with 422. The engine now wraps
semif_phase1.shared._state_prefix to keep only the tokens the full prompts
share. Each row scores the same token sequence; only the prefill/suffix split
moves.
Startup proves the fix is in effect, not just installed (heid bug hunt SKAL,
folded). It checks that the hook is callable and is what score_shared resolves,
that an ordinary state keeps upstream's whole prefix, and that a merge-prone
state scores through the shared path.
Real tokenizer: 154 states, 23 refused before and 0 after, with no ordinary or
authored144 prefix changed. Acceptance: 144/144 parity. Shared vs direct 71/72;
the miss is a bf16 tie that flipped across a plain restart (see README).
Against talk /face's guided pose (first paragraph 246 ms median), SemIf in
parallel adds 32 ms and SemIf-first adds 94 ms (n=72 each, noise floor 16.5 ms).
Removing the pose header saves only ~31 ms, and SemIf shares GPU 1 with the LLM.
Acceptable pose 67% vs 92% on clear-emotion lines, and the mood carried through
mundane follow-ups 7/15 vs 14/15. SemIf gestures far less (13% vs 58%).
README: rotations cost options^2 in suffix tokens, and /decide/shared returns
422 when an object state's last value ends in ) ; or }.
Harness consumer_fit.py runs a consumer's per-turn decisions over hand-labelled
cases, with rotations, a content-free null control and tagged positive controls.
Cicada: an input-only 'does this earn a visible reaction?' gate scored 30/31
with descriptive options and 19/31 with terse yes/no options. Scoped by
Cicada's 2026-09-20 ruling (affect is emitted once, no mood-ring classifier).
Wyrd: on 3 real seed graphs, the first place-change wording failed its
positive controls (1/6 moves). A location-anchored rewording scored 21/21, and
exit selection scored 18/21. Semif fits the choice, not writing the node.
Order averaging (Prime, after the 739aa03 spike):
- A decision may set orderings: rotations|all (all only for <= 4 options). Every
ordering goes to the engine in one shared batch.
- The reply keeps each native result and adds combined {probabilities (log-mean),
top, agreement, spread}.
- Through the service on SemIf's labelled sets (252 rows): 78.6% -> 88.1%
(group-bootstrap 95% CI +5.1..+14.3). Unanimous agreement is 94.5% accurate.
Fast kernels: flash-linear-attention 0.5.2 and causal-conv1d 1.7.0 are now the
default build. A/B on the empty GPU 3:
- parity with upstream went from 142/144 to 144/144;
- a ~2k-token /decide went from 169 to 92 ms server-side;
- short 3-rotation batches cost ~3-6 ms more.
triton builds a C shim at runtime, so the image carries gcc. Without it the
warm-up failed and startup failed closed.
Heid bug-hunt panel (4/4 arms, thread 01M3H3F4RR7XBP90KQ3A39H4SX), folded:
- Startup validation: VRAM cap 0 no longer means uncapped (C1); limits must be
>= 1 (S1); the token must be visible ASCII (S2); the calibration file must
exist and parse, with T in [0.05, 20] (S8, and C3's NaN leg).
- The body limit is checked before a chunk is kept, and a Unicode-digit
Content-Length no longer crashes (C2, S3).
- Failures while building the response now get the 500 envelope (C3).
- 429 busy past SEMIF_MAX_QUEUE requests in progress (C6).
- The engine releases memory on every non-validation failure, unchained after
gc; an empty OOM message is handled; 'out of memory' RuntimeErrors map to 503
(C4, C5, S9).
- The entry point forces HF_HUB_OFFLINE (S10). README wording fixed (S5, S6).
- New guard tests close the gaps the arms' mutation grids exposed: early stop of
the body read, a shared-route lock, calibration pass-through, the gc cycle,
the exact caps, TorchEngine.load's arch and device checks, and the offline
entry point.
86 tests.
Deployed on fv-ml1 GPU 1: parity 144/144, OOM and burst release verified, shared
capacity 63/51/26/16 rows at ~140/520/1960/3900 prefix tokens.
services/semif-serve is a FastAPI wrapper around SemIf's direct and shared torch
scorers (SemIf-OpenJev @ 23cf1f39, MIT). Upstream ships only a batch CLI. The
wrapper loads the pinned Qwen3.5-4B (851bf6e8, BF16) once from the offline HF
cache and returns SemIf's result dicts unchanged, with an optional per-workload
temperature-calibrated view. Contract: semif-serve.contract.md. Built with a
short contract, TDD (39 tests, fake engine and fake torch, no GPU) and a heid
bug-hunt panel (pending).
On the card:
- torch 2.10.0+cu128 with sm_120 kernels, which is SemIf's own stack;
- a hard 12 GiB VRAM cap.
Two defects surfaced only on the card, and each fix is covered by a test:
- 0.1.1: an OOM raised as a chained exception kept the failed request's tensors
alive (11.9 GiB after the 503). It is now raised unchained, after gc.
- 0.1.2: a large request left 12.6 GB reserved on the shared card. After each
call, reserved memory over the baseline + 512 MiB is now released.
Acceptance against SemIf's committed torch predictions (authored144):
- 142/144 same top choice; both misses are exact bf16 ties;
- 144/144 identical prompt hashes;
- deterministic A-vs-A;
- negative control 14/144;
- shared vs direct 72/72.
21 binary criteria over one state take 159 ms. The shared-mode capacity table
under the cap is in stacks/semif/README.md.
The Dockerfile installs dependencies from a manifest with the project version
blanked, so a version bump reuses the ~4 GB torch layer. Verified: 41 s rebuild,
dependency layer CACHED.
DNS: semif.fv.internal. Token: vault semif/api-token.
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