[joshvm]

An important caveat here is yes, for coding. Apps are fine for coming up with one-liners, or doing other research. I haven't found the quality of IDE based code to be significantly better than what ChatGPT would suggest, but it's very useful to ask questions when the model has access to both the code and can prompt you to run tests which rely on local data (or even attached hardware). I really don't trust YOLO mode so I manually approve terminal calls.

My impression (with Cursor) is that you need to practice some sort of LLM-first design to get the best out of it. Either vibe code your way from the start, or be brutal about limiting what changes the agent can make without your approval. It does force you to be very atomic about your requests, which isn't a bad thing, but writing a robust spec for the prompt is often slower than writing the code by hand and asking for a refactor. As soon as kipple, for lack of a better word, sneaks into the code, it's a reinforcing signal to the agent that it can add more.

It's definitely worth paying the $20 and playing with a few different clients. The rabbit hole is pretty deep and there's still a ton of prompt engineering suggestions from the community. It encourages a lot of creative guardrails, like using pre-commit to provide negative feedback when the model does something silly like try to write a 200 word commit message. I haven't tried JetBrains' agent yet (Junie), but that seems like it would be a good one to explore as well since it presumably integrates directly with the tooling.