81–90 of 230 posts
- Just think about scientific research. Lots of data analysis results are not cheap to get.
- Even vibe coding is difficult: you need to think very hard about what you want.
What is cheaper now are some building blocks. We just have a new definition of building blocks. But putting the blocks is still hard.
Already wrong.
Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. There's some truth in there that judgement is as important as ever, t…
> ... for AI to be used effectively. I'm continually fascinated by the huge differences in individual ability to produce successful results with AI. I always assumed that one of the benefits of AI was "anyone can do this". Then I realized a lot of people I interact with don't really understand the problem they're trying to solve all that well, and have some irrational belief that they can get AI to brute force their…
I'm sure I could hack together some simple SaaS products with goals and features I'm defining myself in a weekend with these tools all on my own (no communication/coordination overhead, too!), though. I mean for an awful lot of potential products I could do that with just Rails and some gems and no LLM any time I liked over the last 15+ years or whatever, but now I could do it in Typescript or Rust or Go et c. with LLMs, for whatever that's worth. At work, with totally different constraints, the results are far less dramatic and I can't even feasibly attempt to apply some of the (reputedly) most-productive patterns of working with these things.
Meanwhile, LLMs are making all the code-adjacent stuff like slide decks, diagrams, and ticket trackers, incredibly spammy.
[EDIT] Actually, I think the question "why didn't Rails' extreme productivity boost in greenfield tiny-team or solo projects translate into vastly-more-productive development across all sectors where it might have been relevant, and how will LLMs do significantly better than that?" is one I'd like to see, say, a panel of learned LLM boosters address. Not in a shitty troll sort of way, I mean their exploration of why it might play out differently would actually be interesting to me.
Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. There's some truth in there that judgement is as important as ever, t…
> ... for AI to be used effectively. I'm continually fascinated by the huge differences in individual ability to produce successful results with AI. I always assumed that one of the benefits of AI was "anyone can do this". Then I realized a lot of people I interact with don't really understand the problem they're trying to solve all that well, and have some irrational belief that they can get AI to brute force their…
Yeah, for those you can just relax and trust the vibes. It's for complex software projects you need those software engineering chops, otherwise you end up with a intractable mess.
> One of the most useful things about AI is also one of the most humbling: it reveals how clear your own judgment actually is. If your critique stays vague, your taste is still underdeveloped. If your critique becomes precise, your judgment is stronger than the model output. You can then use the model well instead of being led by it. Something I find that teams get wrong with agentic coding: they start by reverse eng…
I've worked in too many large codebases where no one can point to any _single file or class_ and label it "correct," ("the right way") yet management is amazed when the lack of a "North Star" means the codebase is full of overlapping, piecemeal patterns that are lucky to work together at all.
Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. There's some truth in there that judgement is as important as ever, t…
I think you're missing the point. Effort is a moat now because centaurs (human+AI) still beat AIs, but that gap gets smaller every year (and will ostensibly be closed). The goal is to replicate human labor, and they're closing that gap. Once they do (maybe decades, but probably will happen), then only that "special something" will remain. Taste, vision... We shall all become Rick Rubins. Until 2045, when they ship Ru…
As long as you build software for humans (and all software we build is for humans, ultimately), you'll need humans at the helm to steer the ship towards a human-friendly solution.
Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. There's some truth in there that judgement is as important as ever, t…
By the time I'm done learning about the structure of the code that AI wrote, and reviewing it for correctness and completeness, it seems to be as much effort as if I had just written it myself. And I fear that will continue to be the reality until AIs can be trusted.
https://x.com/netcapgirl/status/2024140332963705342?s=46 evergreen.
Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. There's some truth in there that judgement is as important as ever, t…
You make a really salient point about having a clear vision and using clear language. Patrick Zgambo says that working with AI is spellcasting; you just need to know the magic words. The more I work with AI tools, the more I agree. Now, figuring out those words? That's the hard part.
To be clear, this is the hard part for comp sci majors who can't parse other disciplines. Language isn't a black box for everyone.