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.
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.
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.
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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
>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.
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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.
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