- Generating functions consistent with prevailing style of similar functionality in the existing codebase. The greater the consistency, the more helpful the AI is at generating.
- Telling me why my code is crap by adding a `# todo: ` above some code and seeing what the AI suggests should be changed :)
What other tasks do you see as good target for 5-10x boosts?
klyrs was right about the reply from me (a dev behdind Code Review Doctor) being dismissive in the issue. I apologise for that.
FWIW my reaction was classic "expectations not meeting reality": weeks of work to do (what I thought) was a mutually beneficial helpful thing. I was naively not expecting non-positive responses and was ill prepared when you raised valid concerns I had not considered.
Again, I am working on that and sorry I was passive aggressive to you.
Bear in mind only 28% of codebases actually use built-in unittest package that this gotcha is affected by, so really it's 20 of 28% of 666 aka 10% ... but that claim would be hard to justify by folks that dig stats.
Agreed in perfect world, but unfortunately any process that involves humans will involve human error.
We do code review because we expect human error when the code was written by a human, but then we also expect not human error when the code is being read (reviewed) by a human? Any process that expects zero human error will always fail.
That's where linters add value: they allow devs to do what humans are good at (the creative complex and interesting stuff) while the bots do what bots are good at (the boring repetitive stuff)
the post covers the built-in unittest package, which 28% of devs still use. But pytest is nicer to work with. I think brownfield codebases and inertia are the reason 28% of devs work (or have to work) with unittest
cool product :) it is just linting or do any of the tools do code transformation to offer the fix for the lint failure? (code review doctor also offers the fix if you add the github PR integration)