Latest posts
Measured, reproducible notes on local AI agents — every claim with code you can run. Proof, not vibes.
- Local AI
Simulation First, Then Go Live: I Built a Bench That Proves an AI Harness Before It Touches Real Data
Building a local, privacy-first agent taught me one discipline worth writing down: never let an AI harness meet real data until its declared invariants have been verified — reproducibly, in simulation, where every tool and inbox is a safe scripted stand-in. I built Harness Lab to make that discipline mechanical: prove the harness in simulation first, then go live by rebinding one tool. This post is the method, the five rules the bench enforces, and the proof — my agent's email path run both ways, the two traces the same shape event for event, differing exactly where going live must differ. The bench is open source, and the proof is one command.
- Local AI
The Spine, Drawn: I Made My Agent's Governance a Graph You Can Sweep — and Can't Bypass
Last post I argued the harness is a deterministic spine and the guards, not the model-judges, hold the line. This one earns the next claim: I rebuilt that spine as an explicit graph on a bench, swept every governance knob, and watched the safety line appear as a number. When governance is topology instead of an aspect, the bypass isn't a test you keep passing — it's an edge that doesn't exist.
- Local AI
The Harness Has a Spine: Why Deterministic-First Beats Pure Agentic for Local AI
Two experiment campaigns on a local, privacy-first agent — a tuning-lever sweep and a judges-on red-team. Both times, reliability and safety came from the deterministic scaffolding, not the model or the knobs around it. For local agents, the leverage is a harness with a spine.
- Local AI
Local AI Agents in 2026: The Model Isn't the Bottleneck, the Harness Is
An eagle-eye view from three experiments on local open-weight models, run on an Apple-Silicon laptop with MLX. The model on your machine is already good enough for most real work; what decides whether it works is the engineering around it — and every failure I found was a locatable, fixable harness problem, not a model limit.
- Local AI
The Buffer-Size Cliff: The One Setting That Stops Your Local AI Agent Hallucinating
A sharp quality cliff at buffer_size ≈ 0.5 × prompt_tokens makes local AI agents stop calling their tools and start hallucinating — measured on six open-weight instruct models on Apple Silicon.