51–60 of 103 posts
There is a saying you need to write an essay 3 times. The first time its puked out, the second is decent and the third is good. It’s quite similar with code, and with code less is more. for try 1 and 2
Unfortunately, this post was published at the puked out phase (; (author here)
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Yes, and knowing what to write has always been the more important challenge, long before AI. But - one thing I’ve noticed is that in some cases, LLMs can help me try out and iterate on more concepts and design ideas than I was doing before. I can try out the thing I thought was going to work and then see the downsides I didn’t anticipate, and then fix it or tear it down and try something else. That was always possible, but when using LLMs this cycle feels much easier and like it’s happening much faster and going through more rough draft iterations than what I used to do. I’m trying more ideas than I would have otherwise, and it feels like it’s leading in many cases to a stronger foundation on which to take the draft through review to production. It’s far more reviewing and testing than before, but I guess in short, there might be an important component of the speed of writing code that feeds into figuring out what to write; yes we should absolutely focus entirely on priorities, requirements, and quality, but we also shouldn’t underestimate the impact that iteration speed can have on those goals.
rules of thumb for when to take blog posts about AI coding seriously: - must be using the latest state of the art model from the big US labs - must be on a three digit USD per month plan - must be using the latest version of a full major harness like codex, opencode, pi - agent must have access to linting, compilation tools and IDE feedback - user must instruct agent to use test driven development and write tests for…
I would honestly appreciate constructive feedback on LLM usage, because, as I stated, I am constantly having to rework code that LLMs generate for me. The value I get from LLMs is not in code generation.
rules of thumb for when to take blog posts about AI coding seriously: - must be using the latest state of the art model from the big US labs - must be on a three digit USD per month plan - must be using the latest version of a full major harness like codex, opencode, pi - agent must have access to linting, compilation tools and IDE feedback - user must instruct agent to use test driven development and write tests for…
> LLMs entice us with code too quickly. We are easily led.
Arguably _is_ your argument. That people aren't doing the above and it's causing problems. You probably agree that just spinning up Claude code on the regular plan without doing the above can still generate a fuck-ton of code but that shouldn't be used as evidence either for or against AI effectiveness.
Hey, author here. Never thought I'd see my pokey little blog on HN and all that. Happy to discuss further.
Hey, I like your writing. You got an rss feed or anything?
In practical terms, "productivity" is any metric that people with power can manipulate (cheating numbers, changing narratives, etc) to affect behavior of others to their interests. ALL OF IT is meaningless. It's a pointless discussion.
The full PDF is available for download. It's mostly a series of essays, so you can pick and choose and read nonlinearly. It's worth thinking about beyond nihilistic takes.
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> codegen changes the cost structure of writing code, not the cost structure of knowing what to write. Yes, and knowing what to write has always been the more important challenge, long before AI. But - one thing I’ve noticed is that in some cases, LLMs can help me try out and iterate on more concepts and design ideas than I was doing before. I can try out the thing I thought was going to work and then see the downsid…
Speak for yourself, I have never thrown away code at this rate in my entire career. I couldn't keep up this pace without AI codegen.
honestly the thing that trips me up is when codegen makes me feel productive but I haven't actually validated anything. like I'll have claude write a whole data pipeline in 20 minutes and then spend 2 hours debugging edge cases it didn't think about because it doesn't know our data the speed is real but it mostly just moves where I spend my time. less typing, more reading and testing. which is... fine? but it's not t…
Would getting to the same edge-case-free outcome have taken you less than 2h20min if you didn't have AI? I think it would typically have taken you longer.
That's actually highly doubtful to me.
Tons of studies and writing about how reading and debugging code is wildly more time consuming than writing it. That time goes up even more when you're not the one that wrote the code in the first place. It's why we've spent decades on how to write readable/maintainable code.
So either all this shit about reading/maintaining code being difficult was lies and we've spent decades wasting our time or AIs can only improve productivity if you stop verifying/debugging code.
So I find it very unlikely that it would have taken more than a couple hours to just write it the first time.