If your assistant is causing you to work more hours and only sleeping 4 hours per night, is it really a good assistant? I think I'd be firing an assistant that did that to my life.

I'm a big believer is not just doing something because I can. Could AI build me a personal suite of apps to manage my life in the exact way I want... maybe? Should I spend my time doing that, even if AI is writing 100% of the code? Probably not. Will it be better enough to justify the investment? No. When it breaks or has bugs, who has to deal with me? Me. What about the infrastructure? Another thing to do.

You can say AI is writing all the code, but if someone has to be there to babysit and guide it the whole way, it's still work. Less engaging and rewarding work. I mostly find vibe coding to be boring and frustrating, unless it can one-shot it, which it can only do for small stuff.

I use AI, but I use it in the same way I would use a search engine or a hammer. It's a tool to help do what I was already doing. Sure, it grows my capacity to some degree, but pushing that too far ends up being problematic, as I lose my ability to properly oversee it.

Yeh the guy is going nuts. His whole job analysis thing is ridiculous. To me, it tells the guy has no critical sense, because this kind of paper would ruin the career of any economist.

I have learned to temper it, but it is very challenging. The part that I actually struggle with is creating too much code that I can’t keep up with the high level “what does this do”, user and data flow. I went through the plan, refined it and spent all of my cognitive tokens on that part. That by the time I revisit that feature, I personally lost the context of the “why”.

This has forced me to not do anything extra or while I’m in there. Just focus on 1-3 critical features at a time.

I must remember to go back and clear out dead code, tighten up the repo, and make sure all the new services are following the standards of the rest of the codebase

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Not really. Basic stuff, sure, ai nails. However much stuff thats interesting or useful AI sucks at. Try getting it to replicate the performance of microsoft research's image composite editor. The research/knowledge is in public domain (brown et al paper on panoramic stitching via ransac/sift, gain correction etc) and yet ai suck at it. A task that takes ICE less than a minute can take AI version 30 minutes+ and produces worse results. After loads of hand holding you can kind of get it to be close to ICE performance, but never really. Every new model that comes out, that's one of my personal tests among a battery of others. Ironically llms seem to be getting worse at many of these tasks, not better. Need some webapp with a database and a sleek looking ui? AI has your back (kind of, still sloppy or dangerously unsafe half the time) need some simple get data plot data thing? Ai can do that.

however, actually interesting useful things it tends to fail at, and these can be small reasonably sized projects.

worse still, AI models seem to be optimizing for how many tokens they can make you burn before you give up, rather than minimizing turns required to have a finished product, I say that because each new model that comes out, it needs more turns of coaxing and prodding to get to a functional state.

I too get stressed out when I'm in over my head.

Yes a bit, The hard part now is not coding, it is deciding what is actually worth building

Here's the thing. You're kind of dopamine hacking yourself. Using current LLM's is something akin to using a slot machine, and one specifically tuned to work on/predate on knowledge workers.

The fact is, while it can talk a good game, and has been RLHF'd to high heaven to validate you all the bloody time to keep you engaged and burning tokens, your brain is simply tuned to reward any semblance of progress, and you getting a little bit more out of the LLM is in the same damn family of hit you get off coding. The dangerous bit though, is the inherently probabilistic nature of it though. This crank on a prompt may be different from same inputs, but different crank on the machine.

Just remember to get out from in front of the screen, and try to experience the worldly implementations of the systems you think you're building. Without that real world experience, no one's going to trust a bloody thing you do. You are a world model. It's a language model. It may know how to shoot the lingo, you know or can reckon how to do the thing.

Try running yourself a local model on a sufficiently beefy laptop. The lack of instant feedback tends to help soften the feedback loop, and gives you a less "ecstasy" coded position from which to actually objectively evaluate the efficacy of the thing at converting raw electricity -> thing. You'll find the added friction from the additional constraints (no outsourcing to a datacenter funded by someone else's money), suddenly changes the character of the thing.