Video9:55
Your Local AI Tools Should Train From Their Logs
Local assistants already record failures, retries, repairs, and corrections. With training events, redaction, quality gates, and evals, those logs can become a disciplined learning loop.
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Overview
Every time a local assistant fails, retries, repairs itself, or gets corrected, it leaves evidence for future training. Most tools throw that evidence away: they keep diagnostic logs and build reports but lose the decision the system should learn.
Fine-tuning does not begin with the model. It begins with the examples the tool records while it works. The safe path is not raw-trace training, but a deliberate pipeline of witnessed events, structured memory, redaction, quality gates, dataset export, and evals before adapters.
Chapters
- 0:00 — Your Local AI Tools Should Train From Their Logs
- 0:42 — Logs Are Not Datasets
- 1:14 — The Wrong Move Is Raw Trace Fine-Tuning
- 1:54 — The Missing Layer Is Training Events
- 2:15 — Local Learning Architecture
- 2:44 — Layer 1: Witness
- 3:11 — Layer 2: Memory
- 3:41 — Layer 3: Dataset
- 4:06 — Example 1: Transcript Repair
- 4:33 — Example 2: CLI Next Action
- 5:09 — Example 3: Visual Feedback
- 5:47 — Example 4: Build Repair
- 6:23 — Redaction and Quality Gates
- 6:49 — Dataset Export Contract
- 7:14 — Evals Come Before Adapters
- 7:39 — Local Specialist Models
- 8:16 — Practical Training Sequence
- 8:49 — What to Build First
- 9:17 — Local Learning Loops