These "AI rewrite" projects are beginning to grate on me.

Sure, if you have a complete test suite for a library or CLI tool, it is possible to prompt Claude Opus 4.6 such that it creates a 100% passing, "more performant", drop-in replacement. However, if the original package is in its training data, it's very likely to plagiarize the original source.

Also, who actually wants to use or maintain a large project that no one understands and that doesn't have a documented history of thoughtful architectural decisions and the context behind them? No matter how tightly you structure AI work, probabilistic LLM logorrhea cannot reliably adopt or make high-level decisions/principles, apply them, or update them as new data arrives. If you think otherwise, you're believing an illusion - truly.

A large software project's source code and documentation are the empirical ground-truth encoding of a ton of decisions made by many individuals and teams -- decisions that need to be remembered, understood, and reconsidered in light of new information. AI has no ability to consider these types of decisions and their accompanying context, whether they are past, present, or future -- and is not really able to coherently communicate them in a way that can be trusted to be accurate.

That's why I can't and won't trust fully AI-written software beyond small one-off-type tools until AI gains two fundamentally new capabilities:

(1) logical reasoning that can weigh tradeoffs and make accountable decisions in terms of ground-truth principles accurately applied to present circumstances, and

(2) ability to update those ground-truth principles coherently and accurately based on new, experiential information -- this is real "learning"

> Sure, if you have a complete test suite for a library or CLI tool

And this is a huge "if". Having 100% test coverage does not mean you've accounted for every possible edge or corner case. Additionally, there's no guarantee that every bugfix implemented adequate test coverage to ensure the bug doesn't get reintroduced. Finally, there are plenty of poorly written tests out there that increase the test coverage without actually testing anything.

This is why any sort of big rewrite carries some level of risk. Tests certainly help mitigate this risk, but you can never be 100% sure that your big rewrite didn't introduce new problems. This is why code reviews are important, especially if the code was AI generated.

> Sure, if you have a complete test suite for a library or CLI tool, it is possible to prompt Claude Opus 4.6 such that it creates a 100% passing, "more performant", drop-in replacement.

This was the "validation" used for determining how much progress was made at a given point in time. Re training data concerns, this was done and shipped to be open source (under GPLv2) so there's no abuse of open source work here imo

Re the tradeoffs you highlight - these are absolutely true and fair. I don't expect or want anyone to just use ziggit because it's new. The places where there performance gains (ie internally with `bun install` or as a better WASM binary alternative) are places that I do have interest or use in myself

_However_, if I could interest you in one thing. ziggit when compiled into a release build on my arm-based Mac, showed 4-10x faster performance than git's CLI for the core workflows I use in my git development