Video
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