I spent multiple 5-hour sessions spec-ing my climbing app with AI, clarifying interactions, algorithm, workflow etc. It ended up a frankenstein that I didn't recognise or know how each part interact with each other. Command line were a mess, different commands doing the same thing, with similar but redundant arguments. Everything looks kind of doing what I intended but overly convoluted and nothing really works. Real progress was made when I actually dig into the documentation of colmap/OpenMVS (essential tools, which I had never used before, in my workflow).

The AI gave me unprecedented turn around time in experimentation. The same experiments would easily take me over a month in the past. Now it was a few days. But still, real progress is made only when my understanding catch up with reality.

▲

SoftTalker an hour ago | parent | next [-]

It's very difficult to keep AI focused, when it barfs out 3 pages of reply in response to a one-sentence prompt. It's sort of its nature for some reason, it's very impressive if you've never seen it but it's exhausting to use for very long. It's like a very eager assistant who doesn't have enough experience to understand scope.

▲

bigmattystyles an hour ago | parent | next [-]

3 pages of reply or overly verbose code, often without abstractions - I read all the posters here and in other forums say that programming has shifted towards reviewing AI output rather than coding said output manually; I agree, however, I just don't buy that everyone is actually reviewing the code as intensely as one would expect - there is a tendency that arrived rather quickly to assume that the AI is correct and efficient. I guess the ultimate reviewer is another AI agent I guess.

	▲	21 minutes ago \| parent [-]
		[deleted]

▲

a_c an hour ago | parent | prev | next [-]

I find it highly similar between running agents and running human teams.

Clear goal, share context, delegate but verify. Running a team of engineers also inevitably generates pages and pages of material, design spec, code, test, review. Just that we now do that with agents and agents are way less trust worthy

▲

bryanrasmussen 39 minutes ago | parent | prev [-]

>It's sort of its nature for some reason

I've known some people who can never stop talking. Maybe they are overly represented in the training set.

▲

inigyou 2 hours ago | parent | prev [-]

It sounds great for prototyping. Once you do a month's experimentation in a day and generate some shit app that barely works, but looks functional, you have a definite goal to recreate that design but working properly.

▲

ryandrake 2 hours ago | parent | next [-]

It seems like an absolute dream for corporate execs who don't know anything about development, see a taped-together prototype built in a day, and think to themselves "Wow, we're 90% done... we could almost ship that!!"

	▲	vitorfblima 30 minutes ago \| parent [-]
		Well, from my experience, if the AI dev is ill intended, he can just say nothing then the exec will go "we can ship now as it is!"

▲

pizzafeelsright 2 hours ago | parent | prev | next [-]

This was true a while ago. Today we are replacing decades old sloppy production code with 100% verified better code through tests written by AI, code written by AI. This is not looking functional but drop in functional replacement with measurable improvements.

	▲	dfee 15 minutes ago \| parent [-]
		there are two camps: those who have spent the tokens to figure out how to wield AI, and those who haven't. unfortunately, it's not cheap to get to the former category… and i imagine it'd be difficult to lose access to that tooling and fall back to the second category.

▲

a_c 2 hours ago | parent | prev [-]

Indeed it is. I’m very grateful to what LLM enables me.

The revelation to me was that I used to code what I know, now I could code what I don’t know. The common path is that when I face something I don’t know, which is quite often, to move forward I have to level up my understanding.