231–240 of 349 posts
BorgCfg had exactly the same situation.
mpvl (borgcfg original author, author of https://cuelang.org/) and others had tried to refine bcl while bcl itself is underspecified.
Eventually, the team built a drop-in replacement of bcl and specced out the language almost entirely.
The biggest lesson to me was that engineering never has any short cut.
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I find the people who end up with spaghetti code did so because they didn’t translate their normal processes over. Being completely methodical about development really helps. obra/superpowers, for example, gets close but I think it overindexes on testing and doesn’t go far enough with design document templates, planning, code style guides, code reviews, and more. Being methodical about it takes more time, but prevent…
That works until you make a plan/tests/etc, set the thing loose, and then when it has trouble it decides "actually the pragmatic thing would be [diverge from the plan/change the tests/etc]" and goes off the rails. I'm so frustrated by these things right now.
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really? have you ever learned a skill? Like carving, singing, playing guitar, playing a video game, anything? It's easy to get better at it without understanding why you're better at it. As a matter of fact, very very few people master the discipline enough to be able to grasp the reason for why they're actually better Most people just come up with random shit which may or may not be related. Which I just abstained f…
> architecture is what happens when all those local pieces interact, and you can’t get good global behaviour by stitching together locally correct components This is a great article. I’ve been trying to see how layered AI use can bridge this gap but the current models do seem to be lacking in the ambiguous design phase. They are amazing at the local execution phase. Part of me thinks this is a reflection of software…
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> when Claude fucking Code has been produced this way for like 6 months And people can look at the results (illegally) because that whole bunch of code has been leaked. Let's just say it's not looking good. These are the folks who actually made and trained Claude to begin with, they know the model more than anyone else, and the code is still absolute garbage tier by sensible human-written code quality standards.
Yet it works anyway. What does that say about human code quality standards?
(Anthropic, of course, believes that advances in AI capability over the next few years will so radically reshape society that there's no point worrying about the long term.)
> I paid for that with a total rewrite.
With so much waste and not a single example of the "brilliant at giving you the right answer to a specific technical question"
> The takeaway for me is simple: AI is an incredible force multiplier
Seems more like a feel multiplier, rather than force.
> 500 tests, many of which I felt I could reuse
Indeed, feeling is the only saving grace for a mountain of random unreviewed tests
> In theory, you can try to preserve this context by keeping specs and docs up to date. But there’s a reason we didn’t do this before AI: capturing implicit design decisions exhaustively is incredibly expensive and time-consuming to write down. AI can help draft these docs, but because there’s no way to automatically verify that it accurately captured what matters, a human still has to manually audit the result. And that’s still time-consuming.
I agree that it's time consuming and we don't have a good solution yet, but my guess is that a huge part of the next 3 years of iteration in the craft of Software Engineering is going to be creating tools and practices to make this possible. Especially as AIs get better at the actual writing of the code, the key failure mode for agentic coding is going to be the intent gap between what you asked for and what you wanted.
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I feel like recently HN has been seeing more takes like this one and at least slightly less of the extremist clickbaity stuff. Maybe it's a sign of maturity. (Or maybe it's just fatigue with the cycle of hyping the absolute-latest model?)
It takes time for people to go through these experiences (three months, in OP's case), and LLMs have only been reasonably good for a few months (since circa Nov'25). Previously, takes were necessarily shallower or not as insightful ("worked with caveats for me, ymmv") - there just wasn't enough data - although a few have posted fairly balanced takes (@mitsuhiko for example). I don't think we've seen the last of hyper…
Ironically this itself is one of the hyper/doomer takes.
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I'll take the other side of this. Professional software engineers like many of us have a big blind spot when it comes to AI coding, and that's a fixation on code quality. It makes sense to focus on code quality. We're not wrong. After all, we've spent our entire careers in the code. Bad code quality slows us down and makes things slow/insecure/unreliable/etc for end users. However, code quality is becoming less and l…
However, code quality is becoming less and less relevant in the age of AI coding It actually becomes more and more relevant. AI constantly needs to reread its own code and fit it into its limited context, in order to take it as a reference for writing out new stuff. This means that every single code smell, and every instance of needless code bloat, actually becomes a grievous hazard to further progress. Arguably, you…