I built TokenPatch to measure AI coding cost per applied patch
TokenPatch is a new tool designed to measure the cost of AI coding per applied patch. It allows developers to use existing AI coding tools while routing implementation tasks to cheaper executors. The tool focuses on task-level economics rather than just API request costs, aiming to provide insights into the efficiency of coding changes.
- ▪TokenPatch routes bounded implementation work to a cheaper executor while keeping the strong model in charge of planning.
- ▪The tool reports the cost per applied patch, allowing developers to see the savings compared to using only strong models.
- ▪TokenPatch is open source and requires users to bring their own executor API key.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3947301) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } leo Yan Posted on May 23 I built TokenPatch to measure AI coding cost per applied patch #opensource #ai #devtools #programming AI coding tools are getting very useful, but I kept running into one problem: Expensive frontier models are often used for everything, including small file-scoped implementation patches. That feels wasteful. For many coding tasks, I want the strong model to stay in charge of planning and judgment, but I do not necessarily need it to write every narrow diff.
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