rikatee
That's my experience with co-pilots too:
- Generating tests
- 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?
AI will not replace devs. Devs that use AI will replace devs that do not use AI.
The most effective devs will be those employing a fleet of AI agents, acting as the glue and guiding hand for what the agents should produce.
This helps us get to that future, so I think this has legs.
I use VS code. I will try this out.
Python old style string formatting is sometimes best practice
However, Code Review Doctor is more of a "this MIGHT be a problem. have you considered..." rather than "it wrong"
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.
We fixed f-string typos in popular Python repos
10% of the 666 most popular Python GitHub repos have these f-string bugs
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.
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)
3 percent of Python codebases we checked had silently failing unit tests
Python repos checked. 11% of 5409 open() calls were never closed
https://github.com/keras-team/keras/issues/15854
resulting in
typo in the url (or in HN's markup) btw: it's https://codereview.doctor
the tests are only as good as the code they're written with, and as good as the code review process they were merged under.