[nsoonhui]

From my experience the biggest difference between AI and junior programmer is that, AI can churn out code very fast, but you need to do the testing and verify the fix. Junior, on the other hand, is very slow in writing code but can do the verification and testing on his own.

Usually the verification and testing is the most time consuming part.

I am working on graphical application like AutoCAD, for the context.

[bigstrat2003]

And the junior learns when you teach him stuff. This is a huge advantage that humans have which LLMs do not have at all right now.

[argee]

I looked into AI scribes when they were new, finding them interesting, and spoke to many doctors. Across the board, the preference was for a human scribe, the reason being that they actually take away cognitive load by learning to work with you over time, to the point where eventually your scribing problems are wholly solved by having them around and you need not think about it.

AI scribes have their place since many doctors and nurses can’t afford a human scribe, but as of now they don’t *replace* people. They’re a tool that still needs wielding, and can’t be held accountable for anything.

[samrus]

Live learning has actually been a pretty interesting idea in ML for a long time that i dont know why doesnt get more effort put into it. Probably cost. But itd be really cool to have an LLM that gets finetuned on your data and RLs from your HF everytime you ask it to do something and give it feedback

[r_lee]

because it's not easy to identify exactly when to r/w memory accordingly, especially when you'd need to have an LLM decide when and if to do that

and to scale it in a way where you don't need a whole custom model loaded for 1 user (financially unviable)

just my immediate thoughts, could be wrong though.

[generallyjosh]

I'm finding the latest models are pretty good at debugging, if you give them the tools to debug properly

If they can run a tool from the terminal, see all the output in text format, and have a clear 'success' criteria, then they're usually able to figure out the issue and fix it (often with spaghetti code patching, but it does at least fix the bug)

I think the testing/verification part is going to keep getting better, as we figure out better tools the AI can use here (ex, parsing the accessibility tree in a web UI to click around in it and verify)

[hackable_sand]

That's the biggest difference?

Seriously?

Between a human and a malformed humunculus of piddling intelligence?