Dev tools · September 29, 2026
Jevstiller – Distill Jev into a local model, with a disagreement bound
September 2026. Every number here is from the benchmarks, and bash experiments/bench.sh --no-record reruns them without an API key.
Jevstiller – Distill Jev into a local model, with a disagreement bound launched on September 29, 2026 with 66 votes, #487 of 8,043 launches that month and more than 95% of that year's launches.
In plain words
Jevstiller is a developer tool that creates a local machine learning model trained to replicate Jev's responses. It reduces latency from 300ms to approximately 15ms on CPU for text classification tasks by answering locally when confident, while maintaining a 98% agreement rate with the original model. The tool is designed for applications where fast decision-making is critical, such as agent loops, games, or real-time decision systems. All benchmark numbers are reproducible by running included bash experiments.
written from the facts on this page · September 2026
From the sources
September 2026. Every number here is from the benchmarks , and bash experiments/bench.sh --no-record reruns them without an API key. If you classify text with Jev , every answer is a network call to one vendor and comes back in about 300 ms, at any load. For a batch job that is fine. For an agent loop that decides, acts, and decides again, or a game tick, or anything that classifies then acts, 300 ms per step is the whole budget. Jevstiller sits in front of that call, learns a small local model from Jev’s own answers, and lets it answer what it is sure about in about 15 ms on a CPU. The interesting part is not the small model. It is the contract: Set one number, say 98%. Jevstiller returns…from jevstiller.pages.dev
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