There's something odd feeling about many of the latest AI image models (NB and NB2 included), they feel almost too polished, calculated, and rigid. It doesn't take much effort to get some art with a sense of originality or flow from Stable Diffusion, but I can't seem to prompt NB to get similar results. SD definitely struggles with details especially when trying photo realism, but it'd be nice if newer models could get back some of the roughness that made SD good.
This has been my experience too. The new models don't spit out nightmare fuel as often, but they aren't nearly as creative either. They seem to be very good at creating stock photos and not much else
I've always been more interested in the computer science and software engineering aspects. I did enjoy writing code occasionally, but overall, I was wishing I had some kind of neural implant to convert my thoughts into code. Coding agents are now good enough that I consider that dream as realized, and with that benefit that I do not actually need any implant in my brain. :)
Interesting, I've found AI to be further from the goal of converting my thoughts to code than writing code myself.
English is so ambiguous that by the time I provide sufficient context to the prompt it's taken more typing than writing the code would have been. That's not even accounting for the output often needing further prompting to manipulate into what was intended, especially if I try to be light on initial context.
I like it for quick proof of concept stuff though, the details don't matter so much then.
I really approach is the same way as helping my coworkers to be productive. Most of the context is spent on the initial familiarizing with the code and I just double-check that it has the right understanding, there is minimal prompting on my side for this. The next step is to explain the problem I'm trying to solve and and for the simpler ones, it gets what needs to happen 8/10 times. I don't need to be detailed, because it already knows the context. For the complex problems, I split them into small tasks myself and only ask it to do the small steps, small enough to fit into the first category. I feel like the worst outcomes happen when you specify the problem first and let it do it's own research with that in "mind", then it just overthinks and comes up garbage.
Give it the problem first. Then have it generate the context. Make edits and iterate on the context. Then hit go. Finally, have it write down whatever it needs to for next time.
As far as I'm aware oxlint only supports plugins for non type aware rules, and type aware rules themselves aren't fully stable because it relies on a fork of tsgo.
That is correct, every rule with a custom parser (e.g. vue/svelte/astro tempaltes) and also type-aware rules can't be used as JS plugin.
Type-aware rule are indeed not marked as stable but work like a charm. tsgolint is indeed tsgo + shims + some works, but that won't change soon as tsgo won't have a JS API for a while.
The act of programming will look very similar. The community of programmers will be smaller, as more and more former programmers decide they don't like programming.
> Async splits the ecosystem. I see it as the biggest threat to Rust staying a useful tool.
Someone somewhere convinced you there is a async coloring problem. That person was wrong, async is an inherent property of some operations. Adding it as a type level construct gives visibility to those inherent behaviors, and with that more freedom in how you compose them.
I used Tesseract v3 back in the day in combination with some custom layout parsing code. It ended up working quite well. When looking at many of the models coming out today the lack of accuracy scares me.
