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
- 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.