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Jevstiller

How it compares

As of September 2026:

  • stuntd is the closest project. It is also a local Jev-compatible proxy that learns a head per question (on the Laya encoder) and checks 2% of live traffic. The differences:
    • Guarantee: Jevstiller picks its threshold with a finite-sample bound on disagreement over all requests; stuntd uses a point estimate on a holdout.
    • Automation: Jevstiller trains, shadow-tests and promotes by itself; stuntd uses stuntd train / stuntd enable.
    • Training data: Jevstiller learns from Jev’s full probability distributions and gates unfamiliar inputs.
    • Question identity: Jevstiller identifies a question by its exact content; stuntd uses its name.
    • Keys: Jevstiller answers locally only for API keys Jev has accepted.
    • Deployment: Jevstiller serves many tenants from one server, on CPU.
    • Where stuntd goes further: it also speaks the OpenAI API, and can answer with no provider at all (zero-shot Laya).
  • Distil Labs and cloud “distillation” (Amazon Bedrock, OpenAI, Azure) train a small replacement model from your traffic as a separate job, then swap the whole model. There is no per-request fallback to the large model, no bound, and no Jev API.
  • Routers (RouteLLM, Not Diamond, OpenRouter Auto) choose between existing models. Semantic caches (GPTCache, Portkey, jevcache) reuse answers to near-identical inputs. Neither learns to answer new inputs.
  • Open Jev-compatible models (Laya, Kev, jeff) replace Jev outright, at lower zero-shot accuracy.
  • Research:
    • OCaTS (EMNLP 2023), Cache & Distil (ACL 2024) and Online Cascade Learning (ICML 2024) train a student online from an LLM’s answers, without a guarantee.
    • BARGAIN (SIGMOD 2026) and vCache (ICLR 2026) guarantee agreement with the LLM, but without a student that keeps learning.
    • Jevstiller combines the two, with a permanent audit and automatic fallback on top.