this post was submitted on 23 Oct 2026
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From what I've seen and heard, AI seems to do well for test-driven development. That doesn't necessarily translate into good code, but it does translate into tested code.
At the very least it can translate the documentation of an API into the appropriate tests to verify the documentation is correct, and I'm being quite humbled by that.
The thing I have to constantly adjust for is: it does things at 10x my speed (on average, some things it does slower, but some things it does 100x faster), and about 80% of my accuracy. So, I have to really aggressively apply the bug catching methodologies that a lot of times I would get away without "because I'm that good..." not really, but there are some things I do that I have justifiably high confidence in, others that definitely need testing which I try to do before handing over to the test team. The reason I don't apply those bug catching methodologies to ALL my code is because that slows me down 3-5x, and it seems to slow down the LLMs about 2-3x, so they're only 3-5x faster than me, on average, when they are writing all the tests and multiply reviewing all the docs and code, compared to me "winging it."
Where I think all this LLM stuff is going to pay off bigger is 10 years in the future, looking back at old code, yeah all that documentation is a bit of overkill, but we tended to underkill the documentation 10 years ago and we're still not doing as much as we probably should when we do things the "just before LLMs came on the scene" ways.