[raddan]

Even if you use AI, there's a certain point where it's not clear that an AI would make you faster. F# is my favorite language, and I've been programming in it so long (since 2012) that I feel like I think in F#. Asking an AI for something can be faster if I can state my requirements informally; but if I need to specify many things precisely to an AI... why not just write the code in F#? Part of the beauty of good functional designs is that they are declarative, not imperative, so in some sense you're really just stating what you want, at finer and finer granularities, until what you want is trivial.

Even when I want code written in a different language (e.g., C/C++), I often still start by making a prototype in F#. This helps me nail down the logic without having to worry about things like allocation or layouts. Perhaps I could ask an AI to do this second step for me, and then use the F# implementation as an oracle. Anyway.

[default-kramer]

> I probably spent over 20 hours debugging, scanning the emu-dev Discord, creating tests, and even throwing the issue at earlier AI models. Nothing worked. But then after a few weeks away from the emulator I tried Claude Opus, and it found the issue in just a few minutes.

Even if you want to write all the code yourself (which is a fine decision), the only reason in 2026 to bang your head against a problem like this for 20 hours is if you really enjoy doing so.

(I'm surprised that "earlier AI models" didn't work for the author. For me, free-tier Gemini gets stuff like this correct all the time.)

[wing-_-nuts]

Curious, do you work in F#? I looked for jobs using functional langs in my last job change and I found positions extremely rare.

[raddan]

I am a computer scientist, not a programmer. So the short answer is that the language I choose mostly does not matter.

[rafaelmn]

> Asking an AI for something can be faster if I can state my requirements informally; but if I need to specify many things precisely to an AI... why not just write the code in F#?

One reason I realized recently - when you work it through with an LLM you get full process history linearly serialized, the back and forth, thinking traces, web lookups.

When I need to get back into the task it's much easier to get back in to "the flow".

I think it'll be common practice to start commiting agent logs with the code pretty soon.

[hectdev]

I'm of the mindset that you can use AI however you want to get the speed improvements you're looking for. Personally, I use Agile methods to incrementally implement manually testable features, refine and debug, then commit. Then I use another chat/agent to keep tabs of the overall progress (giving it a summary from the agent that did the work), and then move to the next task by asking the coordinator to draft a prompt for the next bit of work I describe.