Original Summary

Every app I build ends up asking an LLM the same closed questions thousands of times: which queue does this ticket go to, is this urgent, is this command safe to run. Paying a hosted model per call for that felt wrong, and the open local model (Laya) was not accurate enough on its own for longer label lists. So stuntd watches the traffic, trains a small head per decision, serves it in shadow mode first, and takes over only when it agrees with the teacher at the target I set. Below its confidence threshold the request still goes to the provider. If it drifts, it demotes itself. The demo I like most is Snake: the same game and client, three ways. Base model zero-shot dies in 13 moves. A scripted teacher plays 30 games. The head trained from those games in 138 s plays 99.6% of the moves itself and scores 11.4 to the teacher's 21. GIF in the README. Built alone, Python, Apache-2.0, on PyPI as stuntd . Feedback on the README welcome, it is the first thing people see. Repo: https://github.com/bladedevoff/stuntd (Apache-2.0, pip install, no account and no external calls in local mode). Happy to hear what you would need in it.   submitted by   /u/Inevitable-Log5414 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/23 11:59:33