Dev Builds Local Agent That Trains Itself Overnight

A developer built a local agent that trains itself overnight: it extracts task and reward data from daytime messages, uses a value model to compute advantages, updates weights, and converts them to GGUF before the author wakes up, with improved window selection to avoid data truncation.

2026-08-18 ~ 2026-08-18 · 4 related posts

• Local agent upgrade: nightly data extraction from daily logs — cephaloform · 2026-08-18
• Value model predicts task duration and tool calls for advantage calculation — cephaloform · 2026-08-18
• Automated agent loop: advantage gradients to GGUF weights overnight — cephaloform · 2026-08-18
• Agent reward upgrade: dynamic window size prevents data clipping — cephaloform · 2026-08-18