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Measured Data

The ledger: every number we’ve measured, with the conditions that pin it.

One row = one measurement — hardware, conditions, and date included, because a number without its conditions is how wrong advice spreads. The canonical article remains the record of truth; this ledger is its index. Machine-readable mirror: /data.json.

219 measurements · 39 dispatches · verified hardware only

Industrial AI

75 rows
What Does the Future Production Line Look Like? Full-Inspection Vision, Patrol Agents, and an On-Prem 27B LLM in One Discrete-Manufacturing Plant VERIFIED · LineWatch patrol system, two live production lines at a discrete-manufacturing plant (production run Aug–Sep 2026) · on-prem machine-room workstation, single RTX 4090D 24GB, Qwen-family 27B model, 4-bit quantized, served via vLLM Measured 2026-09-05
Subject Metric Value Conditions
LineWatch patrol agent (scheduled patrols) Scheduled patrol trips per day 3 trips/day Day-shift patrol, overnight dispatch, morning reconciliation; scored PDF per line per trip, roughly 5 minutes line-to-phone
LineWatch test agent (overnight research shift) Controlled measurement rounds per overnight shift 8 rounds First unsupervised overnight shift on the evening of 2026-09-05; one round per hour on the hour through the night; on both lines; in-container autonomous run
On-prem event judge (Qwen-family 27B, 4-bit, via vLLM on RTX 4090D 24GB) Test cases ruled correctly, night of 2026-09-05 5/5 Includes one correct ruling of 'uncertain' on insufficient evidence; measured after three rounds of role-manual iteration (~1.2K words per manual)
On-prem alert triage (same 27B deployment) Stress-test cases passed, night of 2026-09-05 5/5 Real fault not falsely downgraded; orphan alert kept as an independent case; device alert correctly exempt
On-prem text roles (summarizer, judge, triage) number fidelity Fabricated numbers found by automated per-number comparison against real patrol JSON 0 Automated check comparing every number the model writes against the source patrol JSON, 2026-09-05
No One on the Line Tonight: The Dress Rehearsal Before an AI Agent's First Unsupervised Night Shift VERIFIED · LineWatch patrol system, two live production lines at a discrete-manufacturing plant (production run Aug–Sep 2026) · rehearsal executed on the live lines over the encrypted read-only collection path Measured 2026-09-05
Subject Metric Value Conditions
Test agent remote dispatch (dress rehearsal, 2026-09-05) Ignition-confirmation latency after launch 25 s Both lines dispatched 18:09/18:10; process-alive probe confirms the research process is really alive, not a fake start
Test agent hour-boundary alignment (dress rehearsal) Error of second-round start versus 19:00:00, both lines same second 0 s Agent re-aligned itself to the next hour boundary after being woken early; both lines opened round two in the same second
Dress rehearsal versus formal overnight shift Rounds run (rehearsal override / formal default) 2 / 8 rounds Same unmodified in-container script; only a launcher knob shortened the rehearsal; formal launch takes the default with no parameter
Patrol agent day-shift patrol scores (2026-09-05) Line scores out of 100 (line one / line two) 90 / 100 Line-one deduction event: an early-afternoon spec change with a 25-minute detection gap; discovered, graded and delivered with no one on site
Headless sentinel (rehearsal window) First automatic bulletin delivered at 18:55 'Both lines OK, round 1 complete' — arrived on the manager's phone with nobody at a computer; must-send contract, silence is the alarm
19:00:00 Sharp, Two Lines, Zero-Second Error — and Not One Bit of AI in That Second VERIFIED · LineWatch patrol system, two live production lines at a discrete-manufacturing plant (production run Aug–Sep 2026) · deterministic in-container script; design parameters as deployed Measured 2026-09-05
Subject Metric Value Conditions
Hour-boundary alignment arithmetic (one line of Bash, in-container) Start error of round two versus 19:00:00 across both lines 0 s (now/3600+1)*3600 alignment to the next hour boundary; observed in the 2026-09-05 dress rehearsal, zero error on both lines
Dispatcher launch command (hard-coded) Remote command timeout 60 s SSH + container execution; no probabilistic component in the dispatch path
Ignition-confirmation guard Post-launch process-alive probe delay 25 s Added after a real fake-start lesson (a background command 'succeeded' while dying instantly on a missing directory)
Gate condition on database freshness Maximum database staleness that still allows launch 60 min WAL side-file must be considered alongside the main database file; a main-file-only check misjudges a producing line as stopped
Overnight rounds knob (launcher parameter) Default rounds / rehearsal override 8 / 2 rounds The formal overnight launch runs without the parameter and is byte-for-byte the original design; the rehearsal set it to 2
The Hardest Paper Came Back Blank Twice — and the Fix Was the Exam Rules, Not the Model VERIFIED · Changeover-daily pipeline (LineWatch family) on two live production lines at a discrete-manufacturing plant · on-prem Qwen-family 27B, 4-bit quantized, on one RTX 4090D 24GB under vLLM · incident 2026-09-10 night, fix and re-exam 2026-09-11 Measured 2026-09-11
Subject Metric Value Conditions
On-prem Qwen-family 27B (4-bit, single stream) — the invigilator library's constant Sustained decode rate on the RTX 4090D 13.4 tok/s vLLM, stability-first launch settings; derived from the inference server's 10-second-sampled logs across the seven exam windows of 2026-09-10/11; the start-time assertion divides max_tokens by this rate (×1.05 safety factor)
The twice-blank hardest window (283 machine-history rows), re-examined after the rule fix Generated tokens with finish=stop (wall time 484 s, config 12288/medium self-attested on the sheet) 5986 tokens the same paper first hit the 8192-token cap mid-thinking (≈8.7 K generated, body empty), then a config race handed the retry a 4096 pad (≈4 K generated, body empty); re-exam used less than half the pad
Changeover-daily generation budget peak, hardest report day of the four-window verification Tokens used of pad (wall time 842 s, finish=stop) 10881/12288 tokens 88.5% of pad — a 12% headroom; the daily tool's pad was subsequently raised to 16384 on a 1400 s clock (assertion floor ≈1282 s)
Three-way changeover judgment, same real production windows Latency: hard-coded path / local 27B per window / cloud model per six-day dataset 2.5 / 149–600 / 23.3 s / s / min verdict kept all three seats: judgment to code, exploration to cloud (7 anomalies code could not find), interpretation and writing to local with zero process data leaving the plant
KV-cache offload beyond on-card memory Decode rate with context pushed past VRAM 3.7 tok/s 48K context + CPU offload vs 13.4 in-card (a 73% loss); context window fixed at 38912 as the measured sweet spot; every generated token re-reads the KV notebook across a PCIe bridge roughly 40× narrower than on-card memory
The Whole Factory Had One AI Call — and 97% of Its Bill Was Door Fee VERIFIED · LineWatch patrol system on two live production lines at a discrete-manufacturing plant (production run Aug–Sep 2026) · audit covers the AI-summary lane's full production life 2026-09-05→09-11 (44 calls, transcript-measured) · rebuild and A/B on 2026-09-12, same model both sides Measured 2026-09-12
Subject Metric Value Conditions
The AI-summary lane's full production life under the agent-harness route (2026-09-05→09-11) Calls and total tokens, transcript-measured with a reproducible filter 44 / 2,239,178 calls / tokens trigger = verdict red or event-bearing; green and yellow shifts make zero calls; building week 09-05→09-08 = 24 calls / ~1.2M tokens (development traffic included); steady window 09-09→09-11 = 20 calls / ~1.04M (~52K per call, ~104K per event trip); 09-06 = zero calls (no event trip)
Night-patrol receipts, both lines (2026-09-11 ~20:58, event shift) Input / cache-read / output tokens, wall time, tool invocations 39,485 / 6,720 / 2,312 · 35 s · 0 and 39,556 / 6,720 / 4,940 · 72 s · 0 tokens · s · calls fields verbatim from the call transcripts; same frontier cloud model on both sides of the later A/B (re-verified call-by-call); thinking tokens 0; per-receipt totals 48,517 and 51,216
Door-fee share of input, agent-harness route Briefing tokens ÷ total input ≈97.5 % business payload ≈1K tokens (sanitized fact sheet) of ≈39.5K input; computed this episode from the 09-11 receipts; the tool catalog was carried on every call and invoked on none
Rebuilt direct route, same-model A/B (2026-09-12, same fact JSON as the 09-11 night shift) Per-call tokens and wall time, with the role manual mounted 2,030 · 7.7 s and 2,156 · 8.3 s tokens · s line one 1,650 in + 380 out; line two 1,735 in + 421 out; bare (no manual) run measured 1.4K; per call steady-state ~52K→~2.1K (−96%); per event trip ~104K→≈5K (−95%)
The digest role manual Size and revision chain 2,652 · v0.1→v0.2 bytes · commits v0.1 committed 2026-09-07 (cbab20f), v0.2 committed 2026-09-12 (7b0a9e4; one line + version bump, every revision diffable); one file serves the cloud production lane and the on-prem 27B experiment lane as the same system message, read fresh from disk each run
The Staircase: Four Steps for Every Job in the Plant, and the Top Step Stays Empty VERIFIED · LineWatch patrol system on two live production lines at a discrete-manufacturing plant · staircase assembled 2026-09-12 from the Episode-4 audit and the same-day lane rebuild · dual-lane comparison mornings 2026-09-11→09-13 (12 windows, two recipes) · ruling delivered 2026-09-13 · the rebuilt lane's first live event shift 2026-09-12 20:55, with the next morning's green shift as the zero-call control Measured 2026-09-13
Subject Metric Value Conditions
Dual-lane comparison, three mornings (2026-09-11→09-13) Windows completed, blanks, wall time — same sanitized fact sheet to both lanes 12 / 2 / 0 windows / cloud blanks / on-prem failures cloud lane ran a stress recipe (earlier-generation model, bare, 8,192 output pad) — not the production recipe (current generation, manual mounted, 4,096 pad); on-prem 27B with manual went 12-for-12 at 124–171 s; cloud completed windows at 20–27 s; both blanks stopped at exactly 8,192 output tokens (09-12 line two, 09-13 line one; an early log note misnamed one blank's line — the comparison reports are the authority, corrected in this record)
Cost of running both lanes per day Tokens per day, both lanes combined, by morning 13,196 / 18,089 / 18,522 tokens 09-11 / 09-12 / 09-13; local (on-prem) share 7,389 / 7,675 / 8,026 at zero API cost; cloud share 5,807 / 10,414 / 12,496; the two blank windows (8,192 each) were the largest single items; in+out as self-reported by each API
The rebuilt lane's first live event shift (2026-09-12 20:55) End-to-end chain time and per-line AI completion ≤3 min 35 s · both lines true s · boolean trip generated 20:55:47, service journal logged both lanes' AI calls complete at 20:59:22 (alert delivered, both PDFs rendered and sent); token-level receipt absent — the production path prints usage only on failure, so the cost anchor remains the A/B measurement (~2K per call); a one-line success-usage print awaits approval; next morning's 06:30 patrol was green on both lines — zero calls, reports rendered on schedule
The 09-13 ruling on the Step-3 contest Window extension, as landed in code 2026-09-13 → 2026-09-20 dates ruling: not either-or — both lanes stay, comparison continues; operator's reason paraphrased from the session record (the comparison costs almost nothing; let it keep running); landed as a one-line change to the comparison script's exit date, syntax-checked, delivered 09:20–09:35 CST per the session timeline
Episode 4's 500-token discrepancy, settled Steady-state total, re-added from the per-day table 1,035,772 tokens 207,528 + 416,208 + 412,036 over 09-09→09-11 (20 calls); the prose figure 1,036,272 was a transcription slip no combination of rows can produce; Episode 4's published text rounds to 'about 1.04M' and stands uncorrected on the site — the correction lives in this record
The Audit Came Home: 1.8 Billion Tokens, 95% Re-read — the Cure Was Forgetting VERIFIED · The operator's home fleet — a three-node private mesh (cloud services node + on-prem GPU workstation) · all evidence gathered on-box on 2026-09-12: thirty days of interactive coding-agent session transcripts aggregated per-session from usage fields, the chat gateway's own ledger read from its local store (2,564 metered calls since 2026-07-25), compaction applied the same day to the gateway's four largest sessions and verified at the wallet level Measured 2026-09-12
Subject Metric Value Conditions
The desk book — interactive coding-agent sessions, 30 days to 2026-09-12 Session count, cache-read share of tokens, top-ten concentration 85 · ~95% · 64 sessions · share · share ~1.8 billion tokens total in the window; top five sessions 39%; the plant's whole audited AI life (2,239,178 tokens, Episode 4) is about one part in a thousand of this book (computed); all top-ten sessions are multi-day relay jobs
Heaviest session and the counterfactual compaction Turns, total tokens, back-half re-reads before → after a fold at turn 300 667 · 197,060,000 · 144,730,000 → 12,810,000 · −91 turns · tokens · tokens · % counterfactual computed 2026-09-12 from the session's own per-turn usage; fold assumed ~35K of minutes; the back half was 73% of the session's total cost; house trigger line set at 300 turns or 250K of context
The chat gateway's heartbeat tax Low-value heartbeat polls and tokens they hauled 2,564 · 92 · 16,600,000 calls · polls · tokens gateway ledger since 2026-07-25 totals 263M tokens, cost-equivalent ~91.5M fresh-input at the cache list rate ~18.5%; each poll carries the full ~159K session history; the main session ran 118M tokens over nine days from birth on 09-03, 11.19M of them on audit morning; cache share 70–97%
The compaction, same day, gateway sessions Context watermarks before → after, per session; final sweep count 160,000/205,000 (78%) → 41,000 · 216,000→23,000 · 120,000→23,000 · 115,000→20,000 · 42 tokens · tokens · sessions one session already at 106% of the window when caught; each pressed session ended at a quarter or less of its pre-press size (computed); final sweep: 42 sessions, none over the 100,000 line; wallet-level receipt after compaction: 41K in, 4 out on the first real heartbeat
Estimated monthly savings at current intensity Tokens per month, all books, estimate 0.8–1.2 billion tokens/month (estimate) gateway compactions ~500M/month if periodic discipline holds (group sessions rebound); desk-side compaction discipline 300–700M at half-credit (law passed, first battle unfought); plant lane already rebuilt in Episode 4; ≈half of a ~2 billion monthly burn; cache priced at the list rate — actual bills depend on the plan in force
Can Simulation Fill Your Defect-Sample Gap? An Isaac Sim Route That Never Got Past the Install VERIFIED · Workstation 2-GPU rig (RTX 4090D 24GB + RTX A4000 16GB) — RTX present but never used by this line Measured 2026-08-30
Subject Metric Value Conditions
~/.ros/log Kit launch attempts Log files from 2025-12-18, all zero bytes 19 Real process IDs and timestamps, zero logged output — launches that died before boot; no session ran
Isaac Sim on workstation Installations present on disk 0 No application tree, no Omniverse packages, no isaacsim in any conda env; planning + troubleshooting documents only
Synthetic-data line under ~/tools SDG-side repositories staged 3 actor_sdg (generation scheduler), isaacsim.sensors.rtx (sensor sim), scene_blox (scene composition)
~/tensorboard monitoring directory Event files retained at inspection 0 Scaffolding stood up; nothing trained on this line
How Does a Vision Model Reach a Factory-Edge NPU? The ONNX→RKNN→RK3588 Route, Staged and Smoke-Tested VERIFIED · Workstation docker engine (2-GPU rig: RTX 4090D 24GB + RTX A4000 16GB) Measured 2026-08-30
Subject Metric Value Conditions
docker image rk3588-rknn-dev:20.04 Image size on disk 3.03 GB docker images; conversion + quantization environment
docker image rk3588-cross-compiler:20.04 Image size on disk 3.03 GB docker images; aarch64 application cross-compile
docker image rk3588-base-cross-compiler:20.04 Image size on disk 798 MB docker images; base layer for leaner build images
Rockchip edge staging (three images total) Combined disk footprint 6.86 GB Sum of the three image sizes above
rk3588-rknn-dev container (first-ever execution) Python version inside image 3.8.10 docker run --rm rk3588-rknn-dev:20.04 python3 -V; no prior containers from these images existed
Machine-wide conversion check .rknn artifacts on disk 0 find across home tree; the ONNX-to-RKNN leg has never been walked here
resnet18 ONNX export (conversion leg, station one) Exported model file size 46733662 bytes torch.onnx.export opset 12, dynamo=False (torch 2.9 default exporter requires onnxscript, absent); random weights
RKNN toolkit acquisition (2026-08-30 session) Routes tried / succeeded 6 / 0 git-https (TLS reset), Releases API (zero assets), tree API (no wheels), raw CDN (404), container PyPI (unreachable), domestic mirror (package absent)
anomalib_env (export side of the route) ONNX package version 1.19.1 pip list in conda env; openvino 2025.3.0 also present as parallel edge route
SAM3 on a Workstation: Docker Door, Source Door, and a Wire Into ComfyUI VERIFIED · Workstation 2-GPU rig (RTX 4090D 24GB + RTX A4000 16GB) · docker Engine · ComfyUI install Measured 2026-08-30
Subject Metric Value Conditions
SAM 3 (as published by Meta) Parameters, single model 848 M Per Meta's SAM 3 announcement and paper (arXiv:2511.16719) — authors' figure, not our measurement
SAM3 staging on workstation Deployment routes staged 2 docker door (pull script, linux/amd64 pinned) + source door (131 MB upstream clone, January 2026 commit)
docker door depth SAM3 images present on disk 0 docker images inventory; the scripted pull was never completed on this machine
ComfyUI nodes_sam3.py custom node package Lines of code / node classes defined 533 / 4 SAM3_Detect, SAM3_VideoTrack, SAM3_TrackPreview, SAM3_TrackToMask
ComfyUI SAM3 integration Saved workflow blueprints 2 'Image Segmentation (SAM3).json' and 'Video Segmentation (SAM3).json', wired via comfy_extras/nodes_sam3.py custom node
Deploy scripts, credential hygiene Plaintext sudo-password pipes remaining after 2026-08-30 sanitization 0 Both deploy scripts rewritten to interactive sudo; residual sudo -S lines verified zero
Can a Mac Mini Run Industrial Defect Detection on CPU? A Workstation-to-Edge Deployment That Actually Ran VERIFIED · Mac mini (Apple Silicon, CPU inference; anomalib 2.0.0, torch 2.7.1) + Basler GigE camera via Aravis Measured 2025-11-20
Subject Metric Value Conditions
PatchCore inference, wood model (Mac mini, CPU) Processing time across the 3 recorded runs 0.176 / 0.158 / 0.156 s processing_time fields in the 3 results JSONs; input 494x659 camera frame -> 256x256; anomalib 2.0.0, torch 2.7.1, env anomalib_new_env
Captures archive (~/anomalib_inference_local/captures) Files / capture groups in the 2025-11-20 session 210 / 70 png+tiff+json triplets; session span 21:13-22:06 (~53 min)
Deployed model set (models/{wood,cable,screw}/v1.0) Lightning checkpoints deployed 3 wood 255118891 B (2025-07-14); cable 240649771 B and screw 301051435 B byte-identical to workstation training outputs
cable checkpoint (cross-machine provenance) File size, Mac mini vs workstation 240649771 bytes Exact match both machines; workstation md5 20f418d97822b4f271efb792a1c1ef69
screw checkpoint (cross-machine provenance) File size, Mac mini vs workstation 301051435 bytes Exact match both machines; workstation md5 d2d88d71a085612a4d3426cf33e6510c
Basler camera via Aravis (camera_config.json) Exposure / resolution 10000 µs / 659x494 Mono8, gain 1.0, free-run trigger, tiff+png+json output triplets
Decision configuration (models/{wood,cable,screw}/v1.0/config.yaml) Fixed threshold / min anomaly area, per model 0.6/0.55/0.3 and 50/30/20 Per-category tuning documented in config comments; morphology 3x3/3x3/2x2; resize 256x256 bilinear, ImageNet normalization
How Do You Ship Industrial Defect Detection With Only 'Good' Samples? An Anomalib Field Guide VERIFIED · Workstation 2-GPU rig (RTX 4090D 24GB + RTX A4000 16GB) · conda anomalib_env · anomalib 2.1.0.dev0 Measured 2025-11-19
Subject Metric Value Conditions
MVTecAD dataset (workstation copy) Industrial scene categories prepared for training 15 Folder-format tree with train/good + test + ground_truth per category under ./data
MVTecAD dataset (workstation copy) Disk footprint, archived copy under datasets/ 4.8G du -sh ~/anomalib/datasets/MVTecAD; extracted working copy under data/ is 5.0G
Patchcore training artifacts (results/Patchcore) Disk footprint of training/inference results 804M du -sh ~/anomalib/results/Patchcore; checkpoints + config.yaml + visualizations
results/Patchcore/MVTecAD/bottle/v0 checkpoint model.ckpt file size 231212587 bytes Per-scene coreset memory; resolved config.yaml preserved alongside
results/Patchcore/MVTecAD/cable/v0 checkpoint model.ckpt file size 240649771 bytes Different size per scene — coreset scales with the category's feature memory
MVTecAD categories with trained checkpoints (after 2026-08-30 sweep) Categories complete 15 12 newly trained on the A4000 (pinned by UUID) + 3 November checkpoints read-only evaluated in place
PatchCore image-AUROC across all 15 categories (2026-08-30) Mean / range 0.981 / 0.911-1.000 One-epoch defaults, no coreset or threshold tuning; pixel-AUROC brittle on 3 texture classes (0.439-0.632)
Per-scene training time (2026-08-30 sweep, A4000) Range over 11 first-pass scenes 29-169 s toothbrush fastest, carpet slowest; hazelnut retry at eval_batch_size 8 took 309 s after OOM at defaults
Read-only evaluations of November checkpoints Eval time per scene 28-38 s anomalib test on bottle/cable/screw v0 weights, untouched (Mac-deployment provenance preserved)
Patchcore training artifacts (results/Patchcore/MVTecAD) Categories with model.ckpt on disk at publication 3 bottle, cable, screw — v0/weights/lightning/ each; batch sweep not carried to completion
train_all_scenes.py Default training epochs per scene 1 CLI default; quick_start.sh full option uses 5
train_all_scenes.py Default train/eval batch size 32 Single GPU (--trainer.devices 1)
MVTecAD per-scene normal training images Good-sample count, bottle / cable / screw / hazelnut 209 / 224 / 320 / 391 images ls of data/<category>/train/good on the workstation; defect images exist only in test splits
RTX 4090D (training GPU, single-GPU runs) Total VRAM as reported by nvidia-smi 24564 MiB --trainer.accelerator gpu --trainer.devices 1

AI & Compute

144 rows
One Man, One Legion: Can a Single Operator Run a 3-Machine AI Fleet End to End? VERIFIED · 3-machine fleet — US-West 512 MB VPS + Seoul 2-vCPU cloud VM + local RTX 4090D 24 GB / RTX A4000 16 GB workstation · live-inspection audit Measured 2026-08-27
Subject Metric Value Conditions
Fleet scheduler (cron) across the 3-machine legion Cron jobs installed vs enabled at audit 22 / 4 Live inspection 2026-08-27; 18 disabled in a deliberate over-automation pullback; 6 timestamped job-file backups preserve the add/remove history; enabled set = 1 feed delivery, 2 topic-scouting pings, 1 arXiv digest
Agent diary corpus (daily files, workstation) Diary files on disk 97 files Counted 2026-08-27; file count only — consecutive-day coverage not claimed
Agent runtime chat channels (Telegram) Connected chat groups 12 groups Counted from live config 2026-08-27; Feishu channel also enabled (no group count); group identifiers withheld by policy
Local model stable on the workstation Locally runnable LLMs (simultaneous serving at audit) 3 (2 simultaneous) Qwen3.8-27B-AWQ 29 GB + gemma-4-12b-coder 23 GB in two simultaneous vLLM instances; Qwythos-9B 18 GB standby; counted 2026-08-27; runtime fallback chain = 1 cloud tier then local 9B
Chat-triggered image workshop (FLUX on A4000 16 GB) Wall time from chat command to image posted back 50 / 66–112 / 330–379 s 10 completed logged runs: fast path 1280×720·20 steps n=3 → 50 s each; full path 1920×1080·28 steps warm n=5 → 66–112 s (3 face-locked at 66 s); session-opening runs n=2 → 330–379 s, same settings settle to 66 s afterward (cold model load); single workstation GPU, queue-dependent, log timestamps 2026-08
How Fast Does a Chat Command Become a Finished AI Image? Inside the One-Man FLUX Workshop VERIFIED · RTX A4000 16 GB workstation GPU · ComfyUI API-first (workflow JSON over HTTP) · FLUX fp8 stack (~11 GB in VRAM) with optional PuLID v0.9.1 face-lock Measured 2026-08-27
Subject Metric Value Conditions
Chat-triggered FLUX fast profile (1280×720, 20 steps, guidance 3.5) Wall time from command to image posted back 50 s n=3 identical logged runs, 2026-08; single A4000, warm queue, pure text-to-image
Chat-triggered FLUX full profile, warm (1920×1080, 28 steps, guidance 5.5) Wall time from command to image posted back 66–112 s n=5 logged runs: 3 face-locked (66 s each, PuLID weight 0.8) + 1 plain (68 s) + 1 art render (112 s); same session as the cold opens below, 2026-08
Chat-triggered FLUX full profile, session-opening cold load Wall time from command to image posted back 330–379 s n=2 runs opening a session cluster; identical settings settle to 66 s afterward — latency is the fp8 model-stack load into VRAM, not the render; 2026-08
Face-lock overhead (PuLID v0.9.1, identity weight 0.8 vs 0.65) Additional wall time vs non-face warm full-profile runs 0 (66 vs 68) s n=3 face-locked at 66 s vs n=1 plain at 68 s — no measurable overhead at this sample size, 2026-08
Image workshop output on disk Finished FLUX images 14 files Counted 2026-08-27; 10 of them carry job logs (mode line + seed + wall time), 4 predate the logging habit
What Does It Take to Give a Chat AI Nine Tools and Your Home Directory? A 50-Line Bot That Grew to 850 VERIFIED · Workstation-hosted Python bot under a systemd user unit · GLM-5.2 via Anthropic-protocol-compatible endpoint · counts from source and bot.log Measured 2026-08-27
Subject Metric Value Conditions
Telegram chat gateway (bot.py, GLM-5.2 backend) Lines of Python on disk vs README's stated origin 850 vs ~50 lines wc -l on source, 2026-08-27; the ~50-line origin is the README's own account of the starting point, not independently reconstructable
Tool surface exposed to the model Tools available per turn 9 tools read_file, glob, grep, edit, write_file, exec_command, web_search, web_fetch, rss_fetch; counted from tool registry in source, 2026-08-27
Agent tool loop per chat message Maximum tool iterations per turn 15 iterations MAX_TOOL_ITERATIONS constant in source; caps runaway chains, 2026-08-27
Process supervision (systemd user unit) Crash-restart delay 5 s Service unit RestartSec; lingering enabled for no-login survival, 2026-08-27
Gateway usage log (bot.log) Log lines accumulated 168,996 lines Single whitelisted user; line count measures chatter, not tasks — no throughput claimed; log tail is a live server-disconnect NetworkError, 2026-08-27
What Happens When the Cockpit Itself Goes Dark? Two Outages, Ten Stranded Jobs, One Spare Parked by Subtraction VERIFIED · Workstation openclaw runtime · evidence = openclaw.json channels, cron jobs.json, run logs, 2 dated diagnosis docs · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
June 20 root cause certainty Independent DNS resolvers returning NXDOMAIN for the dead proxy exit node 4 resolvers Ali, Cloudflare, Google DoH, 8.8.8.8; the single-node exit was the only pipe for every command; gateway logged a polling stall every 90 seconds (telegram-proxy-fix-2026-06-20.md)
The redundant pipe built in June Load-balanced proxy nodes in the pool 6 nodes measured latencies 0.44 to 4.14 seconds; health probe every minute, least-ping balancer, named fallback; the June document's measurements, not re-measured 2026-08
July 4 stranded deliveries OpenClaw cron jobs plus system crontab scripts stuck at push 10 / 3 jobs / scripts 13 total; work had succeeded and products sat on disk, only delivery dead; counted from the diagnosis table rows — the document's summary line says 13 OpenClaw jobs, its table lists 10
Same envelope, two channels Idle time on Feishu vs run time on Telegram for the identical noon job 10 / 270 seconds agentTurn news jobs on Feishu ran 149 and 112 seconds the same evening; the systemEvent job spent zero tokens with an empty session; both rows from the 2026-07-04 diagnosis contrast table, not re-run
Cockpit wiring at press time Enabled channels and Telegram group count 2 / 12 channels / groups openclaw.json read 2026-08-27; 3 of the 4 lit cron jobs route to Telegram groups and 0 of the 4 Feishu-routed entries are lit; last scheduled Feishu fire July 23, 35 days dark at press time; newest run (arXiv digest) finished status ok
What Does the One Human Actually Do? Three Touchpoints per Piece, and One Wall That Won't Move VERIFIED · Fleet-wide · evidence = site playbook, fleet WORKLOG 2026-08-25/26, workstation session log 2026-06-24, wechat-mp-api.mjs full-tree grep, media/inbound count · read 2026-08-27 Measured 2026-08-27
Subject Metric Value Conditions
Division-of-labor contract Human touchpoints per published piece 3 touchpoints fixed into the site playbook 2026-08-25 after a fully-fictional-article drill; slip budgeted at 5 minutes; the operator never touches git, build, or deploy
First real gate slip Slip items machine-verified vs total pressure-tested 21 / 27 items 6 more awaited a terminal grep the operator ran personally on the workstation, confirming the half-resident log line 2 times; 1 sourcing label caught and fixed pre-slip; final slip rows: 13 A-items and 6 B-items passed, 4 C-items left to the human fact arbiter; article held unpushed until the slip returned
WeChat publish path freepublish call sites in checked-in code 0 call sites code ends at draft/add (1 call site); the one interactive attempt, 2026-06-24, returned 48001 api unauthorized — a subscription-account permission wall; publishing happens from the phone's subscription assistant, per the session record
Touchpoint 1 inbox Chat-dropped jpg files in the media inbound folder 26 files counted 2026-08-27, newest 2026-08-08; identity-lock image jobs take the newest file with no path asked
Distribution packages Paste-ready packages delivered with the first Watch issue 3 packages aimed at r/LocalLLaMA, r/DeepSeek, r/ChatGPT with a recommended firing order and a risk flag on the thinnest source; a 4th (r/webdev plus Show HN) followed the same evening; all unclicked at press time, held for a hotter news cycle
Six Doors, 231 Lines, and No Dashboard: Putting a Three-Machine Fleet on One Screen VERIFIED · Fleet-wide · evidence = fleet WORKLOG 2026-08-10/11/13, cross-machine-channel and aws-fleet-role machine memories, multi-server-framework.md v3 (58 lines, wc), wrapper files stat'd on all 3 machines 2026-08-27, fleet-probe.sh (231 lines, wc), state.json and alerts.log read live 2026-08-27, ts-keepalive log tail Measured 2026-08-27
Subject Metric Value Conditions
Cross-machine channel Directed ssh + headless-Claude doors between the 3 machines 6 doors all 6 wrapper files verified on disk 2026-08-27; built in one day 2026-08-10 (timestamps 08:33 / 09:12 key auth / 09:16 / 15:45); 4 of them stat at 334 to 912 bytes; stateless one-shot calls; credentials eval'd on the far side and never cross the wire
Mesh stress test Claude round trip to the 512 MB box vs the workstation 24 s vs 8–12 s latency measured 2026-08-11 06:32 UTC; 0 packet loss on both links; direct network 140 ms and 89 ms; the ≈2.4× gap attributed to RAM (interactive Claude 157 MB resident, 77 MB available vs 99 MB headless need); test deliberately single-shot, no flooding
Fleet probe Bash lines checking the whole fleet every 5 minutes 231 lines 12 keys (small box memory/load/disk/gateway/Tailscale path, Seoul vitals, 3 external APIs, orchestrator dispatcher), 4 severity states, whole fleet logged in 3 lines per round, Telegram push on red; first run 9.9 s 2026-08-11 07:06, 3.7 s after the 09:42 v2 rewrite
Alert history Red / yellow / green alerts over 16 days 20 / 8 / 25 alerts machine-counted from alerts.log 2026-08-27; exactly 1 real link outage (August 12, 10 minutes); load reds 10 times at 2.01–2.88, all gone by the next check; memory warn brushes 5 times at 78–98 MB recovering to 140–162 MB; bot self-check red 7 times, each healed within 10 minutes, once at 01:15 on measurement day
Longest unbroken streak Consecutive 5-minute checks since the v2 reset 4,711 checks held by 6 keys; span 16.4 days; arithmetic lands within 5 minutes of the 2026-08-11 09:42 v2 state reset; the path key's own 4,526-check streak begins on the exact minute of the August 12 outage recovery
What Does an AI Legion Write to Itself? 63 Entries, 75 Days of Silence VERIFIED · Workstation openclaw workspace memory system · evidence = memory/ directory (97 files), AGENTS.md constitution, MEMORY.md, quality-lessons.jsonl · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Agent memory directory, inventoried Daily diary files vs topical note files 63 / 34 files 97 files total, read 2026-08-27; corrects the anchor post's 97 daily diary files — 97 counts every file in the directory, 63 are dated daily entries
Diary coverage Days with a daily entry vs days in span 63 / 186 days first daily 2026-02-23, last 2026-08-27 (mtime 18:26 on the day of reading); 123 days in the span hold no daily entry
Longest silence Interval between consecutive daily entries 75 days 2026-03-29 (records the GLM-5.1 swap; the same day the first cron job fired, 21:27, per episode 19) to 2026-06-12; why the pen stopped is inference, not logged
Raw diary volume Characters across all daily entries 225236 characters wc -m over 63 daily files, markdown included, average about 3,575 per entry; the agent writes in Chinese
Curated long-term memory MEMORY.md size (lines / bytes) 3576 / 160639 lines / bytes top section stamped 2026-08-15, file last maintained 2026-08-21 11:25; mandated by the 292-line AGENTS.md constitution, last edited 2026-08-17 19:57
One Sentence In, Stereo Video Out: 6 Jobs, 90–790 Seconds, an Unfilmed Actress VERIFIED · Workstation · MiniMax H3 on one RTX 4090D via ComfyUI 0.30 · evidence = h3-video skill + 6 job logs + 11 MP4s on disk, h3-guide.md, h3_t2v.py, h3-prompt-director v1.3 (test-log-001, C001.json, r2v_pipeline), MP4 box scans · recounted 2026-08-27 · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
H3 text-to-video jobs Logged jobs / completed / MP4s on disk 6 / 6 / 11 jobs job logs h3job_*, Aug 6 to Aug 14, every log ends in a completion line; 11 MP4s = 6 job outputs + 5 deployment-day tests (first-success test + 4 content probes: baseline, horror, war, weapon)
Render-time ladder Wall-clock generation by clip length and resolution 90 / 138 / 790 seconds 3s clip at 0.3MP = 90s (two runs); 5s at 0.4MP = 138s (three takes, identical); 10s at 0.4MP = 790s; steps=20; 4090 runs the model through weight offload — doubling clip length 5s to 10s cost 5.7x render
Self-delivery Jobs whose finished video was posted back to the chat group by the script 5 / 6 jobs each delivery leaves a message receipt in the job log; the first skeleton test was not sent; the agent never polls — launch returns instantly and the script posts video + prompt + seed on completion
R2V character test matrix Test cells and outcomes 8 (3 pass / 2 fail / 2 improved / 1 unmarked) cells test-log-001, Aug 10, reference-to-video model at 15 steps test mode; failures = seed-related eye defect and underexposure; retries improved by seed swap and by writing the light source into the prompt
Motion budget Cap on motion elements per prompt 1 / 2 / 2 / 1 / 1 motions primary / secondary / environmental / camera / expression; director rule: brainstorm ten actions, keep the 3 that matter; result tracker built to log every run has zero entries — its history file was never created
Who Edits an AI Writing Staff? 191 Articles, 15 Iron Laws, One Stamp VERIFIED · Workstation openclaw workspace wechat-editor-team · evidence = article archive (articles/, daily/, archive/), WORKFLOW-STANDARD-V3.md, utils/quality-gate.py, .gate.json stamps, quality-lessons.jsonl · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
WeChat article archive Finished HTML articles by directory, summed 72 + 105 + 14 = 191 files articles/ 72, daily/ 105, archive/ 14 (recursive); counted 2026-08-27; span June 15 (first daily article; topics pool opened June 12) to Aug 27 (three articles dated that day)
Quality codification vs enforcement Iron laws / machine hard scans / AI judgments 15 / 7 / 4 rules WORKFLOW-STANDARD-V3.md; machine scans M1-M7 run in utils/quality-gate.py; AI judgments A1-A4 answered by the writing agent pre-push; fact-check law adds an AI judge for high-risk claims
Pen names in active use Personas with verified bylines in shipped files 4 pen names Lu Shi / Shen Jianwei / Lin Shu / Qin Yin mapped by emotion type A-D; bylines appear in 30 archived HTML files; a fifth persona (Zhixing Xiaoya, US-stock config) has zero signatures in the archive — configured, not shipped
Quality learning loop Failure records with round distribution 102 (88 / 11 / 3) records quality-lessons.jsonl, 2026-07-18 to 2026-08-27 17:46:40 (still written on the measurement day); promotion rule: same error 3 times becomes a hard-scan rule
Push unlock stamp Machine checks run vs passed on a passing article 7 / 7 checks articles/2026-08-27-gates-ai-warning.gate.json, round 1, 2026-08-27 17:31:32; push script refuses to run without a .gate.json stamp; final leg is the platform's official draft-box API
Whose Name Goes on the Article? Five Bylines, One Human VERIFIED · Workstation openclaw workspace wechat-editor-team · evidence = WORKFLOW-STANDARD-V3.md (v9.2), EMOTION-CONTROL-WRITING.md (v1.0, 2026-07-29), ARTICLE-FORMAT-TEMPLATE.md (2026-08-17), STOCK-ARTICLE-V2.md (2026-07-19), byline grep recount across articles/ daily/ archive/ plus push-script AUTHOR constants · counted 2026-08-27 · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Byline distribution (main account archive) Signed files by pen name: Lu Shi / Shen Jianwei / Lingche / Lin Shu / Qin Yin 12 / 9 / 8 / 2 / 2 files grep recount of byline lines across articles/ daily/ archive/, 2026-08-27, 33 signed files total (live archive, count moves); genre mapping A event / B industry / C person / D suspense / E system-diagnosis per WORKFLOW-STANDARD-V3.md push step; E-type Lingche added in v9.2 on 2026-07-21, signature check added 2026-07-26; byline line mandatory in the one-legal-format template accepted 2026-08-17 (one daily file dated 2026-08-07 already carried it)
Shared column AI-era survival guide Pieces in column / distinct bylines on the column 10 / 5 pieces / names byline-line tally 2026-08-27: 5 Lu Shi + 2 Shen Jianwei + 1 Lin Shu + 1 Qin Yin + 1 Lingche — the byline follows genre, never column
E-type banned-word list (Lingche) Emotional-vocabulary entries banned for the system-diagnosis persona 9 entries WORKFLOW-STANDARD-V3.md E-type machine-scan adaptation, v9.2; includes 躺平 (lying flat), 血流成河 (rivers of blood), 末日来临 (doomsday arriving), 资本家的阴谋 (capitalist conspiracy); A-D types instead require an emotion micro-pulse every 300-500 characters in a friend's voice
Push-script AUTHOR constants Surviving scripts carrying an AUTHOR constant: Lu Shi / Shen Jianwei / Lingche / Qin Yin / Lin Shu 3 / 3 / 3 / 2 / 0 scripts tally over daily/push-*.py and project-root push scripts, 2026-08-27; person-story genre (Lin Shu) rarest — 2 archived pieces and no surviving script
Second-account persona Zhixing Xiaoya Workspace-wide files signed by the byline 0 files search of all workspace HTML 2026-08-27; config exists at project root and a stock-commentary style doc is effective 2026-07-19 (overrides the standard for stock topics) — configured, never shipped
Who Checks the Checkers? 313 Lines of Gate, 61 Stamps, One Empty Column VERIFIED · Workstation openclaw workspace wechat-editor-team · evidence = utils/quality-gate.py (v9.0, 313 lines), quality-lessons.jsonl (102 records, 2026-07-18 to 2026-08-27), .gate.json stamps, daily/push-*.mjs gate checks, WORKFLOW-STANDARD-V3.md · counted 2026-08-27 · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
QC gate script Lines / exit codes (0 = unlock push, 1 = red light) 313 / 0 / 1 lines / exit codes utils/quality-gate.py v9.0, wc -l 2026-08-27; 7 machine hard scans + 4 AI-judgment checklist items (A1-A4)
Machine-scan failure record Red-light rounds by rule: legal / title / images / sources / banned / quotable / length 61 / 54 / 34 / 31 / 22 / 2 / 0 rounds quality-lessons.jsonl parsed 2026-08-27, 102 records total (37 in July, 65 in August); legal-safety 61 splits into 54 missing counter-view + 25 missing disclaimer (some rounds failed both); banned-word rounds caught 12 forbidden + 11 high-risk occurrences
Passing gate stamps Stamps total / first-try / round-2 / round-3 / round-4 61 = 46 + 15; 42 / 8 / 8 / 3 stamps .gate.json counted 2026-08-27 (46 in articles/, 15 in daily/); 3 stamped on the measurement day; enforcement = 13 per-article push scripts in daily/ read the stamp and exit 1 without it — all 24 push scripts in the project root (checked individually) never reference gate.json
Gate dictionaries Pain / banned / high-risk / disclaimer / counter-view word-list sizes 69 / 12 / 5 / 8 / 15 entries counted in quality-gate.py source 2026-08-27; 3 of the 15 counter-view markers are British-Steel fossils (英国政府说 / 英方 / 英方角度) from the 2026-07-18 article; pain dictionary applied to first 15 title characters
AI-judgment logging Records with populated ai_issues column 0 of 102 records quality-lessons.jsonl 2026-08-27: record_issues is called with machine findings only — the 4 model judgments are never persisted; the 3-strikes promotion rule therefore only ever fires on regex failures
The Analyst Desk: 85 Points of Fact, ±13 of Feeling, One Dead Cron VERIFIED · Workstation openclaw workspace · evidence = US_STOCK_ANALYSIS_SOP.md (V4.1a, 2026-06-21), skills/sec-filing-monitor (MULTI_AGENT_FRAMEWORK_SUMMARY.md V2.0, 2026-03-16; STRESS_TEST_REPORT_20260314.md 10/10; v4_stress_test_report.json 26 pass / 4 warn / 0 fail, 2026-06-17), STOCK_REPORT_PRESSURE_TEST.md (9 sources, 2026-03-13), filings/US (49 files, 13 tickers) vs config/companies_mvp.json (14 companies), wechat-editor-team/daily stock artifacts, cron jobs.json · counted 2026-08-27 · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Score engine weights Fixed fact base / correction envelope (peers + news + community) 35% + 25% + 25% = 85; ±13 = ±5 + ±5 + ±3 percent / points US_STOCK_ANALYSIS_SOP.md V4.1a; stress tests confirm weights sum to 0.85 (15% left for corrections) and the envelope: 85+13=98 clamped at 100, 85−13=72; news-vs-community conflict resolves −5 + 3 = −2 (news outranks mood)
Correction behavior in stress test News adjustments on six mega-caps; extreme-positive vs extreme-negative caps +3, +1, +2, +2, 0, 0; +3 vs −5 points v4_stress_test_report.json 2026-06-17 (26 pass / 4 warn / 0 fail): T1 series adj values for AAPL/MSFT/AMZN/META/TSLA/NVDA; T5 extreme-negative hits the −5 floor, extreme-positive earns only +3 with the harness noting (in Chinese, ke yi geng ji jin) that the positive side could be more aggressive; consensus cage = Reddit posts qualify at 100+ upvotes, ±3 needs multiple subreddits plus HN, single-source posts annotate but never score
Verification gate generations June 17 re-pull / June 27 claimed-vs-actual / July 12 dual-source 29/29 points, 0 errors; 192.53 / −1.64% / −8.62% (claimed = actual); 7 tickers points / percent / tickers wechat-editor-team/daily: 2026-06-17-stock-verification.json (dual_round_v2, 29/29 checked, coverage 100%), 2026-06-27-stock-verification.json (NVDA close 192.53, day −1.64%, week −8.62%, every pair equal), 2026-07-12-stock-verification.json (dual_source_cross_validation, yfinance × Sina); SOP marks the gate 'automatic, cannot be skipped', exit code 0 or nothing ships
Analyst desk output Analysis posts / push records / verification files 4 / 6 / 3 files counted in wechat-editor-team/daily 2026-08-27; push records span June 29 to July 20; first V2-style piece 'Four Ways to Die' shipped July 17 at 5,290 characters with 3 images; style guide (July 19) transcribed from a human analyst — empathy hook, no price numbers in the first paragraph
Filings library vs config Files on disk / tickers on disk / companies in config 49 / 13 / 14 files / tickers / companies filings/US tree vs config/companies_mvp.json (3 tiers, 5+5+4), both counted 2026-08-27; AMAT and ENPH listed in config but never downloaded; AAPL on disk but not in the config list
Desk lifecycle Pressure test → monitor MVP → six-agent framework → V4 SOP → cron dead March 13 (9 sources) → March 15 (10/10) → March 16 (6 agents) → June 21 → July 23 dates STOCK_REPORT_PRESSURE_TEST.md (SEC EDGAR 403 until a User-Agent is declared, SGX five stars of difficulty, 9 sources probed); STRESS_TEST_REPORT_20260314.md 10/10 including 100 records in 0.00 seconds and 5 concurrent instances; MULTI_AGENT_FRAMEWORK_SUMMARY.md declares 6 agents production-ready 2026-03-16; cron jobs.json: US-stock job last ran 2026-07-23, error, disabled — shut down in the 22→4 contraction (Episode 19)
The Audio Workshop: Six Voices on a Shelf, One Episode That Never Shipped VERIFIED · Workstation openclaw workspace · evidence = voice-library/ (6 reference wavs + podcast_tts.py, counted 2026-08-27), podcast_tts_indextts.py (wc -l = 172), /tmp/index-tts checkpoints (du = 8.3 GB, contains qwen0.6bemo4-merge), podcast_script_* and podcast_timeline_* JSONs for 06-28 / 07-20 / 07-21 / 07-26 (parsed 2026-08-27), essay-era MP3s (06-14 to 06-26 mtimes), tts_ssml_test2.py, PENDING_PODCAST_DELIVERY.md, PODCAST-VIDEO-V3-WORKFLOW.md (2026-08-17) vs podcast_video_generator_v3.py (wc -l = 445), cron jobs.json (3 podcast jobs, all enabled=false) · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Casting shelf Reference voices + scripts in voice-library (designed hosts vs real-speaker clips) 6 + 1 = 7 files (4 designed hosts + 2 real-speaker clips) files voice-library/ listing counted 2026-08-27: ai_female_host_ref.wav, ai_male_host_ref.wav, eng_female_host_ref.wav, eng_male_host_ref.wav plus 2 real-speaker reference clips and podcast_tts.py; house rule blurs cloned-voice identities — used, never named
Studio pipeline TTS tool size / model footprint / output format 172 lines; 8.3 GB checkpoints incl. a Qwen 0.6B; 192 kbps MP3 + timeline JSON lines / GB / kbps wc -l on podcast_tts_indextts.py; du on /tmp/index-tts/checkpoints (bigvgan, semantic codec, w2v-bert-2.0, qwen0.6bemo4-merge — the voice engine carries its own small LM); script hard-codes libmp3lame at 192k and a per-segment timeline with start/end/text; reads edge-tts-format JSON whose rate/pitch fields it ignores for compatibility; monkey-patches torchaudio.save with soundfile
Episode output, June 28 to July 26 Segments / duration / script length per episode 36, 16, 27, 23 segments; 201, 393, 351, 385 seconds; 1584, 1378, 1496 characters segments / seconds / characters timeline JSONs (06-28: 36 segs, last end 201 s; 07-20: 16 segs / 393 s, script 1584 chars + a compact cut; 07-21: 27 segs / 351 s, 1378 chars, plus a 42 MB video; 07-26: 23 segs / 385 s, 1496 chars, audio only); June 28 also shipped 3 video variants in podcast_output/
The lost July 4 episode Duration / size / delivery outcome 5 minutes 50 seconds, 2 MB, never delivered time / MB / outcome PENDING_PODCAST_DELIVERY.md: generated 2026-07-04 04:25, proxy tunnel to Telegram failed with an SSL error while domestic sites stayed reachable, status 'waiting for proxy recovery'; the .ogg file is absent from the workspace today — only the tombstone note remains
Shutdown and doc drift Podcast cron jobs disabled / workflow-doc line count vs disk 3 podcast crons disabled; doc says 344 lines, disk has 445 jobs / lines cron jobs.json: news-podcast production job plus 2 daily AI-trend podcast jobs, all enabled=false (part of the 22-to-4 contraction, Episode 19); PODCAST-VIDEO-V3-WORKFLOW.md dated 2026-08-17 cites the generator as v3.3 at 344 lines while podcast_video_generator_v3.py measures 445 on 2026-08-27 — docs drift, wc -l does not
The Model Stable: Two Days in June, One Evening in August VERIFIED · Workstation model stable · evidence = du -sh on ~/models (23 GB gemma-4-12b-coder / 29 GB Qwen3.8-27B-AWQ / 18 GB Qwythos-9B-Claude-Mythos-5-1M, counted 2026-08-27), dir mtimes (06-19 / 08-18 / 06-25), ~/本地大模型部署全记录.md (v1.0 2026-06-26), ~/vLLM部署对比-Gemma vs Qwythos.md (2026-06-25), ~/gemma-coder-handbook.md (2026-06-19), ~/vllm-gemma4-tp2.log (2026-06-19), ~/models/download_qwen38.log + download_qwen38_awq.sh + start_qwen38.sh + vllm_qwen38.log (2026-08-18), ~/.openclaw/workspace/qwen38-eval/cc-q38.sh, live state 2026-08-27 (ps / nvidia-smi / ss / openclaw.json via jq) · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
The stable on disk Model directories / sizes / arrival order 23 GB coder + 18 GB reasoner + 29 GB workhorse = 70 GB total GB du -sh on the three model dirs, 2026-08-27; arrival order from dir mtimes and logs: gemma-4-12b-coder 06-19, Qwythos-9B-Claude-Mythos-5-1M 06-25, Qwen3.8-27B-AWQ 08-18; hardware per deployment record: one 24 GB flagship card plus one 16 GB worker card
First horse boundary (the coder, June 19) Project scale vs window / single-module debug / first serve attempt 11,648-line project ≈ 100K tokens vs 32K window; 190-line module: 3 real bugs missed, 1 false positive; 600-second timeout on the first two-GPU attempt lines / tokens / bugs gemma-coder-handbook.md (2026-06-19) tested conclusions — a code commentator, not a project detective; dir mtime 11:27, first two-GPU (tensor-parallel) serve attempt logged 11:43:55 dies waiting 600s for engine cores (VLLM_ENGINE_READY_TIMEOUT)
The June campaign (the 9B fallback) Campaign duration / download speeds / pitfalls / room triage 07:47 decision → 07:30 next-day conclusions; 300 KB/s proxy → 2.3 MB/s mirror (7×), 18 GB landed by 15:12; seven pitfalls; ten rooms: 4 capable, 3 degraded, 3 forbidden times / speeds / counts deployment record v1.0 (2026-06-26): proxy estimate 17 hours with frequent disconnects, mirror switch at 13:10; tool-call parser found on the fifth attempt (reasoning + tool parsers must share the qwen3 family); 32K window overflowed on the framework's own 28K system prompt (error cites 28,673 input tokens) → 64K; a tool named web_search went uncalled until renamed internet_search; forbidden rooms include the article workflow needing 170K tokens of context; same-provider backups die together, so failover crosses providers (cloud primary, local 9B behind it)
The August evening (the 27B workhorse) First byte → serving banner → Claude Code mounted / serving config 14:41 download → 19:58 banner → 20:02 mounted = 5 hours 21 minutes; 38,912-token window; fp8 KV cache times / tokens download_qwen38.log first line 2026-08-18 14:41:08 (domestic mirror, resume-safe script — June's download lesson promoted to infrastructure); vllm_qwen38.log banner 19:58:41 under the same vLLM 0.23.0 as June's logs; cc-q38.sh (mtime 20:02) forces an explicit effort level because Claude Code's default draws a 500 from this vLLM, and low is recommended for a 27B; the 4090D's CUDA index is probed at runtime because enumeration shifts between boots; the conda environment still carries the first horse's name; mount script notes 3-4 minutes to ready
Live state, 2026-08-27 Uptime / VRAM occupancy / failover chain vs what actually runs day 9 continuous at 95% of 24 GB; chain = glm-5.3 → local 9B only, 27B not in chain; fallback endpoint has no listener days / percent / state ps shows the 27B vLLM process up since Aug 18; nvidia-smi: 23314/24564 MiB on the flagship card, 267 MiB on the worker card; openclaw.json read via jq: primary zai/glm-5.3, fallbacks [local-vllm/qwythos-9b], the 27B registered under a separate provider not referenced in defaults; ss finds no listener on the fallback endpoint and the 9B's serving log ends with a clean shutdown
Model or Harness? Two Controlled Experiments on a 363-Line Hand-Rolled Coding Agent VERIFIED · Local gemma-4-12b-coder served via vLLM (workstation GPU) vs cloud GLM-4.6 · identical 363-line Python harness · run traces as logged by the harness Measured 2026-08-27
Subject Metric Value Conditions
mini-agent codebase (hand-rolled coding agent, no framework) Lines of Python 363 lines Single file incl. dumb loop, tool layer with validation, vLLM fallback parser, finish(summary) protocol; counted 2026-08-27
Controlled swap — same harness and task, model changed (fix clamp missing return) Rounds to outcome 15 (stuck) vs 4 (pass) rounds gemma-4-12b-coder local vs GLM-4.6 cloud; 12B edited a hallucinated function 15 rounds, pytest 1 failed; GLM exact edit, 3 passed; temp=0 both; single run per cell, 2026-08
Controlled swap — same model and task, system prompt changed (GLM-4.6) Rounds to pass 6 vs 7 rounds Slogan prompt vs fully structured prompt; both 4 passed; structured adds scan-first opening and what-changed/risks/how-verified reporting; single run per cell, 2026-08
Weak-model prompt rescue attempt (gemma-4-12b-coder) Prompt variants that completed the task 0 / 3 Structured variant truncated to zero tool calls; slogan and weak-targeted variants hallucinated edit targets, ignored tool errors, reported fake success; 2026-08
Code-level supervisor mechanisms (A1 edit-validation, A2 error-forcing, A3 finish-validation) Mechanisms implemented and tested 3 Implemented in mini_agent.py; measured effect per experiment log: fake-success finish calls eliminated — honest failure remains for the 12B; 2026-08
The Model Exam: Seven Passes, Two Zeroes, One Home VERIFIED · Workstation qwen38-eval directory · evidence = Q38压测报告-20260818.md (v2.0) + WORKLOG-20260818.md + run.py / run_tests.py (runners) + t1-t7 .md/.py pairs + A/B output files + toolA.py / toolB.py + verify.py + data.csv / empty.csv, all read 2026-08-27; DSH-side numbers from ~/.openclaw/workspace/memory/2026-08-18.md; six-rule file ~/.dsh/AGENTS.md still on disk; live state via ps / ss / nvidia-smi · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Baseline capability (seven coding tasks) Pass rate / per-task time / output tokens 7/7 pass; 13-142 seconds per task; 194-1834 output tokens; temperature 0.2 tasks / seconds / tokens run_tests.py one-shot runner, 2026-08-18; answers extracted and executed against verify.py assertion scripts, not eyeballed; logged caveats: one answer hand-extracted (no code block), one key-name mismatch scored as verifier strictness, log-filter answer carried an example block before the code; on this serving stack the model's thinking leaks into the body; in-file headers of t1_basic.md to t7_messystyle.md carry the per-task times and token counts
A/B usage test (dedup command on the CSV tool) Six logged requests: max_tokens / seconds / output A 3000 → 223 s → 0 chars dead; A retry 8000 → 256 s → 1465 chars; B1 3000 → 125 s → 298 chars; B2 3000 → 58 s → 604 chars; B3 1500 → 123 s → 0 chars dead; B3 retry 6000 → 260 s → 468 chars tokens / seconds / characters run.py, temperature 0.2, same task both groups; assembled tools toolA.py / toolB.py 53 lines each; one-shot passed all five verifier cases cleanly, split path passed four — on the missing-column case it errored to stderr but still printed the raw rows; split path's three good steps 443 seconds vs one-shot attempts 479; thinking burns 500-3,000+ tokens before the body starts (iron rule: max_tokens 6,000+); group B's missing-column print bug traced to the prompt's own return-original-list wording — toolB.py still carries it; B3 self-check (B3_check.md) caught that bug plus two more
Habitat race (tickets out of the 38,912-token window) System-prompt cost / working space Claude Code 24769 tokens = 64% of window, leaving 6.1K; terminal harness 7710 = 31% of that, 3.8× the working room; bare endpoint zero ticket tokens / percent / ratio from the day's memory diary 2026-08-18: CC ticket designed for a 200K-token model; harness dialogue space ~23K; refused upgrades measured same evening: 48K window with CPU offload ran 3.7 tokens/s vs ~14 baseline (an 8,000-token answer = 36 minutes) — rejected; prefix caching would not start at that window, measured ceiling ~12 seconds a round vs 5,600 tokens of context — rejected
Six global rules, ON/OFF comparison Injection cost / behavior delta +241 tokens (7710 → 7951, 3.1%); correctness 5/5 both; prompt tokens 49394 rules-on vs 64907 rules-off tokens / percent / cases ~/.dsh/AGENTS.md, six rules chosen to be harmless to the cloud model (read before edit / minimal changes / stop after two failures / ask when unsure / verify after change / split big jobs), still on disk 2026-08-27; same dedup task, same data, same five-case verifier, rules as the only variable; rules-on built a temp file to self-verify then cleaned up and asked about an ambiguity; cloud model told to refactor boldly correctly overrode the minimal-change rule — priority is protocol-level
The revival run and the verdict Harsh task end-to-end cost / final assignment log-filter job 68984 tokens in-harness; verdict 21:05 — harness = home, bare endpoint = pipelines, Claude Code keeps cloud model, wrapper kept as spare tokens / assignments per the memory diary: the log filter re-run on the bare endpoint that evening died zero-character; in-harness it met every requirement (case-insensitive filter, ascending order, ValueError, original function preserved, example runnable); the model caught the verifier expecting 3 where the fixture holds 2 and stopped to ask which side was right — rule four as designed; live state 2026-08-27: the serving process up since 2026-08-18 still holds the GPU
The Brain Swap: Ten Backups, One Insurance Card, No Standby Factory VERIFIED · Workstation ~/.openclaw/ backup strata, read 2026-08-27 · evidence = ten-file openclaw.json layer (parsed per file for primary/fallback/catalog), json-diff of rolling .bak.4 vs .bak.3, memory diaries 2026-08-15.md / 2026-08-18.md / 2026-07-03.md, ~/.claude/ settings strata, article counts via ls|wc -l · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Backup strata (openclaw.json layer) File count / span / size growth 10 files; 2026-03-29 10:53 to 2026-08-18 18:39; 2,459 bytes to 7,540 bytes files / dates / bytes one live config + 5 rolling (.bak to .bak.4) + 4 named (backup-glm5, backup-before-glm51, backup-before-glm53, backup-0818-notify); backup-glm5 verified the oldest file in ~/.openclaw by find|sort on mtime; named backups are hand-made before each migration, rolling ones are platform routine
First brain transplant (glm-5 to glm-5.1) Age at transplant / staging state 118 minutes after birth; 03-29 10:53 → 12:51 same day minutes / timestamps backup-glm5 shows primary zai/glm-5, single model, alias GLM, no fallbacks; backup-before-glm51 (12:51) shows glm-5.1 already staged in the catalog while primary is still glm-5 — stage, back up, then flip, all on day one
The evidence gap (glm-5.1 to glm-5.2) Days between named backups / corroborating waypoints 139 days silent, 03-29 12:51 to 08-15 21:05 days no named backup exists for the 5.1→5.2 flip; 5.2 appears as primary inside backup-before-glm53; sideways evidence: memory diary 2026-07-03 already tests glm-5.2 as default, and the 2026-06-26 deployment record (cited in episode 14) discusses 5.2 as the cloud primary; reported as an honest gap, not reconstructed
August 15 double swap (5.2 → 5.3) Day-shift output / surgery timeline / protocol items 20 articles 14:38-19:21 (17 digests in 13 minutes + 3 evening); Claude Code 20:52-21:00, backup 20:54; OpenClaw 21:08, named backup 21:05; 4-item protocol articles / timestamps / items article counts from wechat-editor-team/articles/ filenames via ls|wc -l; CC edits: model, opus mapping, availability list, Sonnet/Haiku mappings untouched; OC flip via gateway config.patch (validated merge, not hand edit), 3 edits; protocol per diary 2026-08-15.md: dual-route precheck, named backup, insurance card (rollback command sent to the human before self-restart), fallback chain frozen at qwythos-9b; side discovery: 5.2 requests returned labeled 5.3 on both routes; 21:17-21:28 Anthropic-route test entry added then fully removed after user correction, captured by rolling .bak.4 (21:18) and .bak.3 (21:27)
Aftermath and catalog end-state Post-swap production / catalog size / chain shape 1 article on 08-16, 6 on 08-17; 7 catalog entries; primary glm-5.3 with 1 local fallback articles / entries / rungs 27B registered 08-18 18:40 under provider local-vllm-qwen38 with supportsTools on (without it OpenClaw does not pass tools, per diary), alias usable via /model; catalog seat only — not primary, not in the fallback chain; catalog = glm-5, glm-4.7, glm-5.1, glm-5.2, glm-5.3, qwythos-9b, qwen3.8-27b
What Should an AI Legion Do While You Sleep? 22 Cron Entries, Four Still Lit VERIFIED · Workstation openclaw cron daemon · evidence = jobs.json, 7 backup snapshots, 27 per-job JSONL run logs · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Cron schedule after the contraction Job entries in file vs currently enabled 22 / 4 jobs jobs.json read 2026-08-27; the 18 disabled include both duplicated noon entries; enabled are the YouTube material drop, two video-topic asks, and the arXiv digest
The auto-publishing era, aggregate Finished runs vs runs ending in status error 790 / 154 runs 27 run logs, action=finished lines; error status includes timeouts whose work may have partially completed; no failure taxonomy claimed
Old-guard lifespan First cron run to last pre-cut run 116 days 2026-03-29 21:27 (off-schedule manual first fire) to 2026-07-23 08:00, Asia/Shanghai; the cut itself left no log line, dated by the runs going silent
Silence between empires Last pre-cut run to first relit run 25 days 2026-07-23 08:00 to 2026-08-17 16:31 (YouTube material drop, off-schedule manual fire), Asia/Shanghai
Peak concurrent enablement Most jobs lit at once (observed) 16 jobs Backup snapshots 2026-07-05 (16 of 16) and 2026-07-08 (16 of 17); observed maximum, not a proven ceiling
Bug Diaries: 9 Failure Files, 158 Days, and the Root Cause It Got Wrong VERIFIED · Workstation openclaw workspace failure archive · evidence = 9 dedicated failure documents (memory/ lesson files, root-level LESSON volumes, wechat-editor-team archive) + cron jobs.json payloads · timestamps Asia/Shanghai Measured 2026-08-27
Subject Metric Value Conditions
Dedicated failure-document corpus, inventoried Files whose sole purpose is recording failure 9 documents 4 lesson files in memory/ (2 filename spellings) + 1 failed-test note + 3 root-level volumes + the 901-line team archive; beside, not inside, the per-article loop episode 9 measured at 102 lines
Failure library span First file timestamp to final save 158 days 2026-03-14 12:25 (first lesson file) to 2026-08-19 11:48 (final save of the team archive); all read live 2026-08-27
Founding cluster Failure documents created in the opening burst 5 documents 2026-03-14 12:25 to 2026-03-15 21:08, under 33 hours: NeMo article lesson, browser-tool write-up (2 hours, 20 headlines, 0 relevant, value produced 0), RAG test at 15 percent (0 of 3 real queries; 8 of 401 chunks usable), media-tier collapse (0/0/7 tiers, fixed in 29 minutes), and the review rota
Image-failure recurrences after first lesson Documented repeat occurrences of the image class 4 recurrences lesson filed 2026-03-29; relapses 2026-06-14 (zero images), 2026-06-23 (external URLs not rendered), 2026-07-12 (stale images reused); 77 days from lesson to first relapse; the archive's own top-5 table ranks image failures No. 1 at 4-plus
Step-0 read order wired into scheduler payloads Job definitions whose prompt orders reading the error archive first 8 job payloads grep over cron jobs.json 2026-08-27; all 8 disabled since the contraction episode 19 described — enforcement stamped into prompts, then darkened with the schedule
The LangGraph Autopsy: 12 Green Imports, 668 Silent Seconds, One 203-Line Survivor VERIFIED · AWS brain-node orchestrator (~/orchestrator) · evidence = WORKLOG.md entries of 2026-08-12/13 (UTC convention) + live code and venv read 2026-08-27 · run timings as recorded in the log, not re-run Measured 2026-08-27
Subject Metric Value Conditions
First true run stall Time spent polling without advancing 668 seconds T5 end-to-end test, logged 13:33 UTC 2026-08-12; submission succeeded (prompt_id 6f476a70) and the render finished on the GPU box — the state machine never learned; run timing from the work log, not re-run
Load-bearing lifetime of the LangGraph layer Skeleton-complete log entry to deletion log entry 90 minutes 12:35 to 14:05 UTC 2026-08-12; skeleton = 12 modules import-green, 9 nodes, conditional routing, sqlite checkpointer with tables built; deletion kept the node functions and DB (4 business tables), dropped the framework layer and its 2 orphaned checkpointer tables
Rewritten engine, same flow End-to-end completion time 163 seconds 13:59 UTC 2026-08-12, 26 minutes after the stall was logged; the audit printed a poll_done line for the first time — proof the poll node had never executed under the framework; next-morning north star (2026-08-13 11:16) ran the same chain in 27 seconds, user-confirmed
Fault-injection harvest Bugs sharing one root 4 bugs T6 injections 2026-08-12: fatal segmentation (dead with the deleted layer), stage field desync, delivery counter that ignored the send's return value, poller with zero tolerance for one SSH hiccup; root = zero tolerance plus false done; entry reconstructed from a 612-event session transcript
Survivor size, read live engine.py line count 203 lines read 2026-08-27; package code carries 0 langgraph imports with exactly 2 comment tombstones (nodes.py, db.py) and the graph directory gone; the pip package still sits in the venv, unimported; the engine now routes 3 kinds of work — image, digest, retrieval Q&A
The Security Handcraft: 12 Fossil Scripts, One Live Key, Zero Leaks in Print VERIFIED · 3-machine fleet, all evidence gathered 2026-08-27 · workstation: config field-path counts (values never printed), source-code pattern scans, on-box getMe probes through the fleet's own proxy — token strings compared by hash and never left the machine · AWS: orchestrator secrets/logging code read live, permission bits verified · publication scan across all 23 posts in this repository Measured 2026-08-27
Subject Metric Value Conditions
Single credentials home on the workstation Scalar fields in the one config file that holds credentials 138 fields of which 6 are credential-shaped — 3 apiKey entries, 1 appSecret, 1 botToken, 1 token — plus the proxy address; file mode 600, owner-only, verified 2026-08-27; counted by field-path names only, no values printed
Inline-token fossil lane Legacy scripts carrying one identical hardcoded token 12 scripts news-push v3/v4/v5, hotspot-monitor skills, social-trend monitors, a reddit monitor, and video-batch helpers; every copy byte-identical (one distinct hash) and 46 characters long; no scheduled job invokes any of them since the Episode 19 contraction; neither this directory nor its parent is a git repository
Live strings, one bot Distinct token strings authenticating against the same bot 2 live strings getMe returned ok=true for both the config string and the inline fossil on 2026-08-27, identical bot id; probed on the workstation through its own proxy so neither string left the machine; compared by hash, never by value; no bot identifiers published
Publication gate across the whole library Secret-pattern hits across every published post 0 hits machine scan of all 23 published posts for Telegram-token, sk-, ghp-, and AKIA-shaped strings, 2026-08-27; the same-day source scan on the workstation found 16 matching code files — 12 ours, 4 upstream test fixtures from a packaged retrieval project
Ritual receipts in the work log Work-log entries recording a pre-push secret scan 5 receipts machine-counted 2026-08-27; the ritual greps each episode's two files for token, group-id, port, and proxy patterns before every push; both credential homes re-verified mode 600 today (workstation config file, orchestrator secrets file)
The Token Ledger: 261,720,417 Tokens, 147 Re-reads per Token Written VERIFIED · workstation, all evidence gathered 2026-08-27 · the main agent's 93 session transcripts parsed on-box, usage fields summed across 3,931 metered assistant turns, per-month and per-model cross-tabs computed, whale-session totals re-read individually, cost fields audited on every turn (2,689 nonzero, all February–March, totaling 52.27, currency field null) · component arithmetic re-verified: 38,875,077 + 1,769,499 + 221,075,841 + 0 = 261,720,417 Measured 2026-08-27
Subject Metric Value Conditions
Total metered consumption, main agent Tokens recorded in usage fields across all sessions 261,720,417 tokens 93 session transcripts spanning February 1 to August 27, 208 days, 3,931 metered assistant turns; components re-summed: input 38,875,077 plus output 1,769,499 plus cacheRead 221,075,841 plus cacheWrite 0; cost fields audited on every turn — 2,689 nonzero, all in February and March (February 24.24, March 28.03), totaling 52.27 with a null currency field; every turn since the June restart reads zero
Composition of the burn Cache-read share of total tokens 84.5 percent 221,075,841 of 261,720,417; fresh input 38,875,077 is 14.9%, output 1,769,499 is 0.68%; read-to-write ratio 147:1 — 259,950,918 context tokens in against 1,769,499 out; average metered turn roughly 66,580 in, about 450 out
Founding-era concentration Share of the ledger burned in February and March 71.6 percent February 91,168,392 across 9 sessions and 1,302 turns plus March 96,182,265 across 28 sessions and 1,391 turns equals 187,350,657; February ran over three million tokens a day versus about 1.2 million now (July 34,679,955; August 31,743,159 through day 27); largest single session began February 19 — 298 turns, 31,989,653 tokens, 12.2%, averaging 107,350 per turn; top three sessions 69,171,676, or 26.4%
The silence Days of zero metered turns between March and June 75 days last March entry March 29 at 12:53 UTC, two minutes after the glm-5.1 swap Episode 18 dated to 12:51; first entry back June 12 at 21:33 UTC, the same day the diary resumed in Episode 6; April and May record nothing; three independent systems agree
Local fallback usage Metered turns served by the local 9B fallback 17 turns 672,468 tokens, 0.26% of the ledger, all in June, provider local-vllm/qwythos-9b; the 27B serving Claude Code (Episode 14) runs on a separate harness and is off this cloud ledger; 586 turns totaling 44,334,074 carry no model header and remain unattributed
The Human Ledger: 26 Drops, 191 Pieces Behind One Phone, Five One-Line Rulings VERIFIED · workstation + fleet records, all evidence gathered 2026-08-27 · chat-runtime inbound folder re-listed entry by entry with extension breakdown, wechat-editor-team archive recounted by extension (html only: 72 articles + 105 daily + 14 archive), all 61 article-*.gate.json records parsed for retry-round histogram, full-tree freepublish grep re-run (0 hits), agent diary 2026-08-19.md line 25 read, openclaw.json backup list re-statted · token and turn totals cross-referenced from Episode 24's published measurement Measured 2026-08-27
Subject Metric Value Conditions
Touch one, dropped material Entries in the chat-runtime inbound folder 26 entries window 2026-02-19 through 2026-08-08, 171 days, about one gesture a week; composition by extension: 15 jpg, 6 png, 2 voice clips (ogg), 2 PDFs, 1 extension-less investigation bundle; Episode 4's '26 jpg files' corrected here — 15 jpg among 26
Touch three, pieces behind the phone ceiling Finished html pieces in the WeChat archive 191 html files 72 articles + 105 daily + 14 archive, modification dates 2026-06-15 through 2026-08-27 (newest written today); 0 freepublish call sites in the checked-in tree re-grepped 2026-08-27, draft box is the API ceiling, each piece that shipped shipped through one phone tap — tap count is architecture-implied, not logged; separately 4 paste-ready Reddit/HN packages sit at 0 clicked
Machine self-stamps on the same production line Quality-gate records with no human field 61 records per-article records — 46 on the articles line, 15 on the daily line; each record holds 7 machine checks (machine_checks 7, machine_passed 7) plus pass flag and timestamp; retry-round histogram 42 first-pass, 8 second, 8 third, 3 fourth — the seven machine checks are the gate, the human is the exit
One-line rulings receipted by machines Verdicts that reversed a machine conclusion, 208 days 5 rulings August 11 token-leak doctrine (Episode 23 record), August 13 one principle three machines (Episode 5 record), August 15 21:17 redundancy call on brain-swap night (Episode 18 record; the named pre-swap backup from 21:05 survives, that night's rolling-backup fossils have since been rotated away), August 19 11:47 root-cause reversal verified today at agent diary 2026-08-19.md line 25, August 25 contract terms (Episode 4 record)
Tokens per logged human act Episode 24 machine ledger divided by the human column 1,163,202 tokens per act 261,720,417 tokens and 3,931 turns across 208 days against roughly 225 logged acts (26 drops + 3 gate interactions + 191 tap-capped pieces + 5 rulings); about 17 machine turns per act, about one logged act per day; treating every finished piece as one tap makes 225 an upper bound on acts, so the per-act ratio can only be higher
The Loop Closes: What One Man and One Legion Answered to a Turbulent Era VERIFIED · this site's own repository, all evidence gathered 2026-08-27 · series statistics computed from the published corpus itself — post, arc, and episode-number counts from the posts collection frontmatter, body-word totals summed after stripping frontmatter, measurement-entry count from the ledger collection · every cross-referenced figure (261,720,417 tokens, 147:1, 84.5%, 52.27, 18 of 22, 191 pieces, 5 rulings, roughly 225 acts) re-grepped verbatim from the published episode bodies on the day of writing Measured 2026-08-27
Subject Metric Value Conditions
The completed series Posts in One Man One Legion at completion 24 posts one anchor (seriesOrder 0) plus 23 episodes; episode numbers 8, 15, and 22 were never issued, so episode 26 by numbering is the 23rd by count; all 24 posts carry the same frontmatter date, 2026-08-27 — counted from the posts collection the day the finale was written
Arcs lit at completion Series arcs and their episode counts 5 arcs The Workshops 7 episodes (1, 7, 9, 10, 11, 12, 13), War Stories 5 (6, 19, 20, 21, 23), The Engine Room 4 (14, 16, 17, 18), The Cockpit 4 (2, 3, 4, 5), The Ledger 3 (24, 25, 26) — read from frontmatter across the 23 episode files
Public ledger entries filed by the series Measurement entries across the series yaml files 121 ledger entries 116 counted across the 23 series yaml files preceding the finale on 2026-08-27, plus 5 in this episode's own file; every entry's value mirrors a number printed in its post body — the preflight mirror check enforces this per post
Corpus size Body words across the series 21,818 words whitespace-split after stripping each post's frontmatter, summed across all 24 posts including this finale in its final amended form, computed on 2026-08-27 — the count moves with any future edit, this is the completion-day reading
The closing ratio Machine tokens per logged human act 1,163,202 tokens per act Episode 24's 261,720,417 metered tokens over 208 days divided by Episode 25's roughly 225 logged acts (26 drops + 3 gate interactions + 191 tap-capped pieces + 5 rulings); to the nearest act — treating every finished piece as one tap makes 225 an upper bound, so the ratio can only be higher
Can a Static Blog Hand AI Agents Real Tools? Wiring ARD + MCP into an Astro Site (Measured) VERIFIED · Astro 5.18.2 static build on AWS Seoul node (2 vCPU) · Vercel global CDN + one Serverless Function · web-stack timings, no GPU Measured 2026-08-26
Subject Metric Value Conditions
SigPulse agent interface (commits 8eeb3ec + 4b2db6f) Push-to-live deployment latency 12–18 s Single observation, 6 s polling granularity (live by the 4th check at t≈18 s); GitHub push → Vercel auto-build of 13 files (+950 lines)
sigpulse.com /api/mcp (359-line stateless Serverless Function) tools/call end-to-end latency — first call in run 0.90 s curl POST sigpulse_search from AWS Seoul node to production, 5-sample run, 2026-08-26; mechanism (cold vs warm start) not observable from outside
sigpulse.com /api/mcp (359-line stateless Serverless Function) tools/call end-to-end latency — follow-up calls 0.28–0.34 s 4 samples immediately after the first call, same node and endpoint; same method
Agent-facing static artifacts (posts.json + watch.json + openapi.json + agents.md + ai-catalog.json) Total payload size for full first-contact fetch 37305 B ≈36.4 KiB; per-file: posts.json 4,138 B · watch.json 11,186 B · openapi.json 14,394 B · agents.md 3,163 B · ai-catalog.json 4,424 B; dist output byte-identical to live production
sigpulse_search (MCP tool, commit 4b2db6f) Natural-language query hit count, phrase-only vs all-terms fallback 0 → 1 hits Query 'DeepSeek price': 0 hits with phrase-only substring matching (pre-fix), 1 hit after all-terms-must-match fallback; control queries 'helium export ban' → 1 hit, 'InfiniteTalk VRAM' → 3 typed hits
/api/mcp under burst load 20-concurrent mixed-request success rate 20/20 Mixed posts.json/watch.json + tools/list + tools/call(sigpulse_search) burst from one AWS Seoul node, 2026-08-26; latency p50 0.25 s, p95 0.34 s, max 0.35 s, wall 0.4 s
Agent interface declared-vs-actual conformance (11-hypothesis adversarial pass) Gaps found and fixed 5 gaps limit:-1 slice truncation, limit:0 zero-return with full count, empty-query match-all (30 hits), missing-query literal-'undefined' match, undeclared OpenAPI security (7 redocly errors); all fixed in eae3a23 (+31/−4), re-verified live same day
Real MCP client end-to-end (Claude CLI → production /api/mcp) Autonomous tool calls answering 3 fact questions 4 calls Client planned 4 calls unprompted (first 3 in parallel, passed limit 1000 unasked); all facts verified correct incl. Chinese original title 「DeepSeek涨价背后，一个时代结束了」 and 2026-08-14 original date; 2026-08-26
InfiniteTalk Dies at torch.load: the Error Tells You to Upgrade torch — the Measured Fix Is Pinning transformers 4.52.0 VERIFIED · RTX 4090D 24GB + RTX A4000 16GB dual-GPU workstation (Ubuntu 24.04, conda, Python 3.10) Measured 2026-02-19
Subject Metric Value Conditions
InfiniteTalk 14B fp8 restore, transformers 4.57.3 on torch 2.4.1 Launch outcome Blocked at torch.load (CVE-2025-32434 ValueError) Gate fires in transformers check_torch_load_is_safe when the wav2vec2 .pt loads; time-to-fail 18.0–18.6 s, three consecutive attempts, 2026-02-19; safetensors loads exempt
InfiniteTalk 14B fp8 stack, torch upgraded to 2.10.0+cu128 Stack outcome after upgrade 5 simultaneous breakages pip: torchaudio 2.4.1+cu124 / torchvision 0.19.1+cu124 / xformers 0.0.28 all require torch==2.4.1; runtime: operator torchvision::nms does not exist; diffusers JITCallable._set_src() import failure; flash_attn_2_cuda undefined symbol; xformers built-for-cu121 warning — 2026-02-20, rolled back same morning
InfiniteTalk 14B fp8 restore, transformers 4.49.0 on torch 2.4.1 Launch outcome Imports, dies in xfuser→diffusers import chain Passes the torch.load gate but fails at runtime inside xfuser's diffusers imports, 2026-02-20
InfiniteTalk 14B fp8 restore, transformers 4.51.0 on torch 2.4.1 Launch outcome Imports, dies in xfuser→diffusers import chain Same failure mode as 4.49.0, 2026-02-20
InfiniteTalk 14B fp8 restore, transformers 4.52.0 on torch 2.4.1 Launch outcome Reaches pipeline initialization + quantized T5 load Only version in the 4.49/4.51/4.52/4.57 sweep to pass every wall; full pins: torch 2.4.1, torchvision 0.19.1, xformers 0.0.28, flash-attn 2.8.3; final-run completion unrecorded (log ends 'Process still running')
InfiniteTalk 14B fp8 deepest pre-fix run (2026-02-19 15:02) Time-to-fail 277.5 s Died with missing quantized-weight keys audio_proj.proj1.output_scale / .weight._scale / .weight._data; cause never root-caused, did not recur in later launches
InfiniteTalk 14B fp8 full launch, pre-fix environment (2026-02-20 07:55) Time-to-fail 13 s 07:55:24 start → 07:55:37 dead at wav2vec2 torch.load CVE gate; passed GPU init and split-GPU setup first
Can You Run InfiniteTalk on Two Consumer GPUs? Yes — at 218.5 s/step (RTX 4090D + RTX A4000, Measured) VERIFIED · RTX 4090D 24GB (main DiT) + RTX A4000 16GB (VAE/aux) · Xeon Gold 6258R · 503GB RAM · Ubuntu 24.04 Measured 2025-11-29
Subject Metric Value Conditions
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) on Wan2.1-I2V-14B-480P Denoising step time (20-block semi-resident baseline) 218.5 s/step 480P tier, streaming mode, sample_steps 15, motion_frame 9, TeaCache 0.15, fp8 quant, T5 on CPU, dual GPU (CUDA_VISIBLE_DEVICES=0,1); preserved engineering report from the measurement window
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) on Wan2.1-I2V-14B-480P Denoising step time (24-block residency) 217.1 s/step +0.64% vs 20-block baseline for ~1.7GB more VRAM (A4000 10.21→11.99GB, 4090D 22.1→23.2GB); preserved engineering report
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) on Wan2.1-I2V-14B-480P Generation time per 81-frame clip 49–54.6 min DERIVED: 15 steps × 218.5 s/step = 54.6 min; report's own stated figure ~49 min (unreconciled, both published); 81 frames ≈ 5.06 s at Wan2.1 480P 16 fps → ≈647× slower than realtime; fps not captured in logs
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) on Wan2.1-I2V-14B-480P VRAM residency during generation — RTX 4090D 22.1 GB 24GB card (~92%); semi-resident blocks 0-19 + pinned patch embedding; preserved engineering report
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) on Wan2.1-I2V-14B-480P VRAM residency during generation — RTX A4000 10.21 GB 16GB card (~64%); VAE encode + auxiliary compute, no FP8 support, no VAE tiling (run-log warning); preserved engineering report
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) on Wan2.1-I2V-14B-480P System RAM usage during generation 23 GB 503GB available; CPU-side blocks 20-39 streamed from system RAM; preserved engineering report
InfiniteTalk 14B fp8 (infinitetalk_single_fp8.safetensors) Checkpoint file size 19,499,692,400 bytes Byte-exact from disk listing (≈18.2 GiB); fp8 single-person variant; int8 and multi variants each ≈19.5GB
InfiniteTalk 14B fp8 on Wan2.1-I2V-14B-480P install Disk footprint — weights tree (measured, du) 241.8 GB 2026-02-01, seven quantization variants on disk; minimum working set ≈104GB (19.5GB fp8 + 77GB Wan2.1 base + 6.7GB T5 fp8 + 1.5GB wav2vec2); whole project incl. env/caches recorded up to ~488GB