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I recently made a few changes to a small personal web app using an LLM. Everything was 100% within my capabilities to pull off. Easily a few levels below the limits of my knowledge. And I’d already written the start of the code by hand. So when I went to AI I could give it small tasks. Create a React context component, store this in there, and use it in this file. Most of that code is boilerplate.

Poll this API endpoint in this file and populate the context with the result. Only a few lines of code.

Update all API calls to that endpoint with a view into the context.

I can give the AI those steps as a list and go adjust styles on the page to my liking while it works. This isn’t the kind of parallelism I’ve found to be common with LLMs. Often you are stuck on figuring out a solution. In that case AI isn’t much help. But some code is mostly boilerplate. Some is really simple. Just always read through everything it gives you and fix up the issues.

After that sequence of edits I don’t feel any less knowledgeable of the code. I completely comprehend every line and still have the whole app mapped in my head.

Probably the biggest benefit I’ve found is getting over the activation energy of starting something. Sometimes I’d rather polish up AI code than start from a blank file.



If you’re reviewing the code, it’s not vibe coding. You’re relying on your assessment of the code, not on the “vibes” of the running program.




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