I started my career as a developer in the 1990s and cut my teeth in C++, moving on to Python, Perl, …

> So much of programming is just learning a new API or framework. Once you're good at it in general. I recently witnessed what happens when a junior developer just uses AI for everything, and I found it worse than if a non-developer used AI: at least they wouldn't confuse the model with their half-understood ideas and wouldn't think they could "just write some glue code", break things in the process, and then confidently state they solved the problem by adding some jargon they've picked up. It feels more like an excavator: useful in the right hands, dangerous in the wrong hands. (I'd say excavators are super useful and extremely dangerous, I think AI is not as extreme in either direction)

> primordial swamp of data science This deeply resonates with me every time I stare at pandas code seeking to understand it.

I am pretty much in the same boat, although I was never that advanced a dev to begin with. It is truly amazing what a superpower these LLM tools are for me. This particular moment in time feels like a perfect fit for my knowledge level. I am building as many MVP ideas as quickly as I can. Hopefully, one of them sticks with users.

Also agree. I've been playing with Godot for some super simple game dev, and it's been surprisingly fantastic at helping me navigate Godot's systems (Nodes, how to structure a game, the Godot API) so I can get to the stuff that I find enjoyable (programming gameplay systems). No, it's not perfect and I imagine there's some large warts as a result, but it was much, much better than following a bog-standard tutorial on YouTube to get something running, and I'm always able to go refactor my scripts later now that I'm past initial scaffolding and setup.

Yep, I absolutely relate to this. ChatGPT happened to come out right when I needed to learn how to use kubernetes, after having used a different container orchestrator. It made this so much easier. Ever since, this has been my favorite use case, to cut through the accidental complexity when learning a new implementation of a familiar thing. This not only speeds up my learning process and projects using the new tool, it also gives me a lot more confidence in taking on projects with unfamiliar tools. This is extremely valuable.

I've been using it to learn Lean, the proof assistant language, and it's great. The code doesn't always compile, but the general structure and approach is usually correct, and it helps understand different ways of doing things and the pros, cons, and subtleties of each. From this it has me wondering if AI could increase the adoption of provably correct code. Dependent types have a reputation for being hard to work with, but with AI help, it seems like they could be a lot more tractable. Moreover, it'd be beneficial it the other direction too: the more constraints you can build into the type system of your domain model, the harder it will be for an AI to hallucinate something that breaks it. Anything that doesn't satisfy the constraints will fail to compile. I doubt it, but wishful thinking.

Yep, same. I'm an old hand at pandas, and writing a 300 line script in pandas and asking Claude to rewrite it to polars taught me polars faster than any other approach I've used to learn a new framework.