Historical Nanochat
Time-locked language models trained on pre-cutoff historical texts using Karpathy's nanochat pipeline. Exploring whether small models trained exclusively on period texts can reproduce the linguistic patterns of their era.
- 65GB historical text corpus across multiple eras
- Time-locked training methodology (no future-leaked text)
- RTX 3090 local training pipeline
- Parquet-based shard management
Activity Timeline
- Storage Phase 1a: 55.16 GiB reclaimed from 94%-full partition.
npm, HuggingFace strike-list, Docker, pip, pnpm, and Trash caches verified and purged. maya1 voice model held due to live config and service code dependencies.
- Storage audit: root at 89% full; 715G dataset marked NO-GO.
Disk audit identified largest consumers: 715G dataset (execution blocked), 362G workspace, 171G cache. Post-incident CUDA toolchain drift (13.1→13.3, driver 590→595) means original training run is no longer reproducible bit-identical.
- STT degradation root-caused to mSBC codec; VRM avatar stack research initiated.
mSBC Bluetooth codec confirmed as primary STT quality degradation source (7 kHz vs 8 kHz effective bandwidth). LibriSpeech harness built for WER measurement against 354 voice clips. Three VRM/avatar repos identified for image-to-3D pipeline feasibility.
- API key + staging IP scrubbed; HIGH vulns 42→0.
Plaintext API key and live staging IP removed from 6 published files (23 substitutions). Key rotation required before containment. Dependabot sequence complete, total vulnerabilities 94→11.
- Closed all 42 HIGH Dependabot alerts; vulnerability count dropped from 94 to 11.
Torch bumped 2.9.1 → 2.13.0 with smoke tests. fastapi 0.140 / starlette 1.3 compatibility confirmed across 73 tests. Chat web server hardened to bind localhost-only by default.
- All 8 P0 remediation issues verified closed; gated for tier-2a smoke testing.
Empirical verification via code inspection and pytest confirmed every fix. Checkpoint timing defect (P0-1) resolved by consumed_loader_state tracking across base_train.py. Smoke test parameters scoped: SAVE_EVERY=250, MAX_STEPS=300.
- All 8 P0 defects verified closed; system cleared for capped-smoke testing.
Checkpoint-ahead-of-consumption fixed via separate consumed_loader_state tracking (base_train.py:517-523). Sol proxy stalled; pivoted to direct CPU-side verification with test suite tripwires confirming each defect empirically. Moves to tier-2a: GPU canary assertions and CUDA behavior verification remain.
- All 8 P0 defects verified closed; ns-r7 remediation and Hub M2 reconciliation complete.
Fleet monitoring operational with continuous heartbeat. ns-r7: 3 locked items closed, 17 test failures resolved to passes. Hub M2: 41-pass baseline established, F1/F4-F9/F11/F13 defects closed in plan.
- All 8 P0 defects independently verified closed; status advanced to READY-FOR-CAPPED-SMOKE.
Final P0 (checkpoint prefetch tracking via consumed_loader_state) validated by passing test suite. CPU-side work complete. GPU-side canary run pending with capped params (SAVE_EVERY=250, MAX_STEPS=300).
- All P0s empirically closed; READY-FOR-CAPPED-SMOKE verdict issued.
Independent sol-reverify-d26 session confirmed all SOL-PLAN-REVIEW P0 findings closed via direct code inspection. P0-1 checkpoint prefetch race covered by new test parametrizations in base_train.py. Tier-2a smoke test phase cleared for launch at SAVE_EVERY=250, MAX_STEPS=300.
- P0 remediation complete: 8/8 defects verified GREEN, transitioned to capped-smoke testing.
Nine commits across checkpoint consumed-cursor fix, launcher hardening (5 defects), and training guards. RED/GREEN verification confirmed per defect. Remediation phase officially closed.
- d26 training run fully staged; blocked on external GPU provider account setup.
Cache validation passed, owner actions documented in NEEDS-OWNER file, systemd monitoring timer installed. Launch gated on Hyperbolic account email verification and payment method.
- Architecture investigation opened for Design-C shard-ordering; conditional GO.
Bake script and CPU-only traversal simulator gating specified. GPT-Pro brainstorming on cloud run efficiency optimization from contemporary literature queued.
- Security scrub complete: 80+ files cleaned, serve.py hardened, all P0/P1/P2 findings resolved across two audits.
Blind Fable follow-up review found trust_remote_code RCE vector and Windows username leak missed by initial pass — both fixed. SECURITY.md created documenting sandbox design boundary. Git history rewrite still pending.
- Phase 1 complete; two prior claims retracted, core affective finding validated.
8 commits correcting talkie-conversion and post-1930 fracture claims. Affective divergence (providence/duty vs. therapeutic) and era-based Family F clustering confirmed robust. Phase 2 direction crystallized: pre-1914 vs. modern characterology.
- Training outcomes reviewed via 5-model multi-agent analysis; GPT Max decision framework documented.
Multi-agent review (Opus, GPT Max, GPT Council, GPT Pro, Opus 4.7) of nanochat training results. Key output: cost-tiered skill selection framework distinguishing GPT Max (13×, high-stakes disagreement) from codex-council (5×, initial lookups).
- ChatGPT Pro MCP: better-playwright selected; 2 critical issues found in code review.
Orphaned tab memory leak and missing transport retry logic identified. Stepped timeout architecture designed (30–120 min). Fixes specified, pending implementation.
- ChatGPT Pro MCP server built for browser-based GPT-5.4 Pro access; two critical bugs block production use.
Three-layer completion detection with timeout polling implemented. Architecture validated clean by code review. Blocking issues: page leak from orphaned Chromium tabs, no retry on transport failure.
- ChatGPT Pro Browser MCP built; critical resource leaks found; 499GB data migration completed.
MCP server enables GPT-5.4 Pro via browser automation. Code review identified page leak (Chromium tabs never closed) and missing retry logic for dropped responses. Training data migrated from Windows NTFS to native Linux ext4.