Three days after Cloudflare’s announcement, Clef-flash went through the same 80-prompt benchmark as Jev, Laya, Von and Kev. 90% topic accuracy, a confidence temperature that brings it level with Jev on calibration, and one problem no temperature can fix on a laptop.
Laya says “fine-tune me”, so I did, on a 16 GB MacBook. Here is the recipe, how long it takes, what it changes on the 80-prompt benchmark, and why my house still runs on Jev.
A chat with the coach inside the dashboard, a GPS map on every session, a calorie model that checks Garmin’s numbers, a full gear locker with photo inspections, running dynamics, a second contributor, and “The Trail”, a video series drawn in JavaScript that you can watch right here.
A dashboard, a pile of new metrics, guardrails that can say no, and a coach that lives on a Linux box at home and answers me from my phone. Here is what changed in ai-running-coach since the ultra, and why the automatic sync runs on cron instead of a Claude routine.
The fix for “Jev has no memory” was never a different model. It was giving Jev a memory. Here is the history script, the feedback loop, and what the live scoreboard already disagrees about.
My automations are good at thresholds and bad at context. Jev answers typed questions with a calibrated probability, for about two thousandths of a cent each. Here is how I wired it into Home Assistant with the old rules kept as a safety net, and what it really costs.
My first model router let an LLM read a prompt file and pick an agent. This one asks a “System One” decision model four typed questions, scores the models with plain arithmetic, and publishes a benchmark to prove it works (and to show where it doesn’t).
E2E encryption means Proton can never index your photos. So I built a two-container search engine that does it locally, on my own hardware, without ever writing back to Proton.
A Zigbee smart plug, nine automations, and one invisible pump that saves the house from flooding - the “invisible device” pattern, monitored with Home Assistant.
