The recent boom in neural net research will have real applicable results that are genuine progress: signal processing (e.g. noise removal), optical character recognition, transcription, and more.

However the biggest hype areas with what I see as the smallest real return is in the huge model LLM space, which basically try to portray AGI as just around the corner. LLMs will have real applications in summarization, but largely otherwise they just generate asymptotically plausible babble, very good for filling the Internet with slop, not actually useful to replace all the positions OAI, et al, need it to (for their funding to be justified).

https://transform.tools/json-to-java

Writing tests is a good example. It’s not great at writing tests, but it is definitely better than the average developer when you take the probability of them writing tests in the first place into account.

Outside of everything else discussed here, this is something I disagree with on a fundamental level, flawed tests are worse than no tests, IMO.
Not to get too deep in to the very contentious space of testing in development, but when it comes to automated testing, I think we’re better off with more rigorous^[1] testing instead of just chasing test coverage metrics.

I keep getting told that AI is good at boilerplate code, and like, so is eclipse – if you know the kb shortcuts to autogenerate method stubs, classes, etc.