Asking an agent to block focus time, summarise yesterday’s numbers or find a meeting slot works, but you pay for it every time, and the answer changes. I measured 14 Claude Code runs on three everyday jobs. Writing the script once costs about as much as asking once. With an interactive calculator, a checklist, and my own projects as examples.
Laya shipped eight releases and nothing moved for routing. Its author also shipped VegaML, a decision model that answers by letting a particle settle in a valley. Zero-shot it isn’t ready for my router, but it can learn from 40 labels in a minute, which none of the other models can do.
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.
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).
