# 2026-02-25 Session Notes

## Exploring Character-Level GPT Training on Memory Database

Nicholai proposed training the microgpt character-level model (Rust implementation at `references/microgpt/src/main.rs`) on personal memory data from `~/.agents/memory/memories.db` to see what the model "hallucinates" - essentially a funhouse-mirror experiment on his own agent memories.

Investigated the memories database schema and found 5348 memory records. The `memories` table contains rich metadata: content (TEXT), type (fact/preference/decision/etc), tags, importance (0.0-1.0), confidence, category, timestamps, source tracking, and extraction status. The database includes FTS5 full-text search indexes and vector embeddings infrastructure.

The character-level model is tiny (48 embedding dimensions, 2 layers, 4 attention heads) and expects line-delimited text input. The feasibility depends on extracting memories in a format the model can meaningfully learn from—raw content lines or structured summaries.

No implementation work began; this was pure exploration of the concept and data shape.