alt Hacker News> A lot of people here seem to be living in a different universe than me
I feel this way on this topic too.
> I think detractors believe you should just let AI do the job blindly instead of leveraging it as a tool to accelerate you.
The problem is; how _should_ I use AI? On a previous thread, I had two replies to the same comment, one saying "provide the LLM all the context it needs and let it go ham", and the other saying "Carefully guide and craft it and review everything". Both subthreads had people agreeing.
My experience with LLMs is leaving them unattended gives very poor results within a handful of iterations, and that carefully guiding them can be a value add, but the effort ot do that is very often as much as just writing the damn code myself, particularly if there's a few iterations that go on.
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What can be concluded is that some get immense value out of Ai and some are incredibly critical of that claim.
I do not know what the secret sauce is.
But I have a responsibility to the team I lead. I need to protect our velocity. Which means that my hiring practices now include specific checks that people are able to work with ai.
In the end, it might be a step thing. Some people are just inherently incapable of working with this technology...
I don't generally use AI to write code I feel I can write efficiently. In these cases, I'll just ask AI for a review. But when I need to write code in areas I'm less confident about, I'll let AI take the reins and the do a thorough review myself. These instances are not too common, though.
I also ask direct questions to AI for brainstorming and advanced programming language usage. In these cases, AI isn't touching the code.
The reality of the current situation is that we have dozens of families of different model; each of which is differentiated further into various revision numbers and power levels, and depending on which one you use and what the nature of the tasks is, you very much need a different usage style that can only be discerned after multiple (Sometimes very many) sessions of using that individual model for that category of task. It is not one size fits all, and trying to pretend that it is will lead to failure.
I think the main cause of different views is just the fact that people have very different products and codebases they're working on. Therefore, the requirements and applicability of AI varies a lot, case-by-case. It would be useful if people always explained their exact use cases when making claims one way or another. I understand it's not feasible for many use cases to reveal exact details, but at least some transparency would be great.
I've found AI to be immensely useful in gamedev, mainly UX development, research, and curation. In those areas I let it go quite wild, with mechanical and visual tests for UX, and summaries for research and curation. I've also made skills, custom subagents, hooks, and scripts (all made with AI assistance) to streamline development.
Gamedev is a good example of where AI shines, since the risks of fucking things up are not so big. If I worked on, let's say banking, I'd be much more careful. On my day job, as a data scientist (moving towards product owner) in a large industrial company, I'm much more careful. UX can run on vibes, but anything that touches data processing or business logic needs to be looked at by a human, row by row. Depending on use cases, it may be fine to look at only the tests. The main problem is that there's so much stuff to review, and maybe 98% of it is fine. It's then very boring to try to spot the 2%. I don't have good solutions for that yet, and idk if anyone does.