Go Migration from Python with Claude Code: Lessons Learned

Here's the full post: https://winder.ai/python-to-go-migration-with-claude-code/

I had the opposite experience. To get a feel for current capabilities, I recently did an experiment to migrate a non-trivial codebase from Python to Go. I asked it to make a detailed plan first which took about 10 minutes. Implementation took 15 minutes. At the end it verified everything and fixed some issues that it found. Then everything worked as expected. The generated code was well-structured and clean. All details in the plan were addressed. No bash loops or agent teams, just did it with a single Claude Code instance. I used Claude Opus 4.6. What model did you use?

Great read! I didn't dare to run full migration using claude but I have made solid refactoring tasks across multiple files usually with positive results and low amount of polishing up or tweaking. Just like you stated AI generated content creates context with a lot of assumptions. The hack to resolve this is very simple, planning mode with multiple agents supported with multiple follow up questions. To get through the questions may take a while but it removes any gaps for ambiguity. I always end the prompt with "... ask for more questions before execution...". Leave no stone unturned.

"The gaps in your design become the bugs in your system" should be the header on every AI migration guide, Phil. Dead code accumulation from deprecated references is a pattern we've seen too - the model faithfully rebuilds things that shouldn't exist because it can't know the history. That's why the pre-migration design phase matters more than the generation phase.

This is a great experiment. I wonder if adding Openspec in the mix would improve results. In my experience claude often tries to maintain backwards compatibility and introduces a bunch of "migration code" and then forgets to clean it up, the result often works but with buts. Openspec has reduced this quite a bit.

This is a great real-world case study. Python-to-Go migration is often painful manually, but having Claude Code handle the structural translation while preserving business logic is a game changer. Curious how it handled concurrency patterns - Go's goroutines vs Python's async model is where most migrations get tricky.

This is a great real world example of where AI-assisted development shines, and where it quietly breaks things. The takeaway isn’t that AI fails, it’s that it demands stronger specs, tighter tests, and clearer boundaries.

Out of curiosity, why the full rewrite? Why not for example target high traffic areas only to remove bottlenecks? Full rewrites are always painful and need proper justification.

I use Claude for R&D as the primary driver. I do perform a cross check with other models to catch the errors. I am able to intercept failures using this method.

excellent content!