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Regarding the technical challenges (not the use of AI itself):
Boths PRs don't bring much in isolation and the second currently breaks binary compatibility, but they should fix most of the open issues when applied together. AFAIK, there is still a need to change the Mill code generator for build files, as it currently emits absolute paths in the generated files. |
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Speeding up the AI agent development loop - local snapshot approach
Sharing an approach I've been using to speed up the code → compile feedback loop when working with AI coding agents (Claude Code, etc.) on Mill projects.
The
out/folder occasionally gets invalidated for various reasons, and rebuilding from scratch takes a while. I wanted a fast way to restore to a known-good state.Why not remote cache?
out/can be multiple GBsMy approach: local APFS snapshots
On macOS, APFS
cp -ccreates instant copy-on-write clones with near-zero storage overhead.mill-cache snapshot— syncs to upstream/main, compiles, snapshotsout/mill-cache restore— finds nearest ancestor commit, restores instantly (~60 secs)I only snapshot clean main branch commits as known-good baselines. When things go sideways, instant restore and back to work.
Anyone else optimizing their agent workflow? Curious what approaches others are using to keep the iteration loop fast when working with AI agents on Mill projects.
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