This sounds roughly right to me, except for "maintainability". In my experience, agents really don't like deleting code unless you explicitly ask for it. If you're not careful, you end up with new better implementations of things but with the old implementation still around in perpetuity. Humans do this too of course.
The shadow of Chesterton's Fence is relevant. The LLM doesn't know why code exists, if it doesnt start traversing up the scope of a project. Even then, it can't be sure that the code isn't a dependency of something else outside the project (especially if there's a side effect). I'm not sure it's ever going to be easy to address this concern in a straightforward and portable way.
I do sometimes see duplicate functions, which is troubling.
They're being trained by a lot of repetitions of one-shot bugfixes and feature implementations. If they don't have to maintain code over the long term they have no incentive to not take the easy win with lower immediate risk and higher longer-term maintenance burden.
Humans had to get it drilled into them that "+12 -440" is a damn good line stat, and that keeping around dead code is bad, especially in the age of version control.
Not too surprised that LLMs also don't "get it" by default?
If you're reading this and you're put off by the "uses an LLM", don't be. This is not a low-effort video, quite the opposite. This is one of the best YouTube videos I have ever seen.
Mirrors my experience: LLMs are really good at optimizing, better than most humans. But also, they tend to not reach absolute peak performance where people made an effort to optimize something.
Since most problems see fairly little optimization, that's still a big win most of the time.
> I built one, by porting Boehm’s engine.
> It’s 2026, so I didn’t hand-write the port. I directed Opus 4.8 to translate the source line by line into Swift
I wish I could filter out stuff like this. Cool work by Hans Boehm, but what's the value add in this blog post.
You're assuming all women in your cohort start not pregnant. However, given a random sampling of women across the entire human race, if you have approximately 14,000 women, statistics says you'll have a baby in a month. That is to say, the chances of one of those woman being 8 months pregnant reaches close enough to 1, given about 14,000 randomly selected women.
Also, you can get a baby tonight if you steal one from the maternity ward.
The real question is, how do LLMs turn the mythical man month on its head. If we accept AI generated code, can an agentic AI swarm make software faster simply by parallelizing in a way that 9 women can't make a baby in 1 month because they're am AI, not human, and communicate in a different way.
The pitfall of AI coding is that previously every shiny tangent that was a distraction, is now a rabbit hole to be leaped into for an afternoon, if you feel like it. It's like that ancient Chinese curse, may you live in interesting times. Everybody can recreate an MVP of Twitter in a weekend now when previously that was just a claim a certain type of people made.
> You're assuming all women in your cohort start not pregnant. However, given a random sampling of women across the entire human race, if you have approximately 14,000 women, statistics says you'll have a baby in a month. That is to say, the chances of one of those woman being 8 months pregnant reaches close enough to 1, given about 14,000 randomly selected women.
There's a good point in here along the lines of "if you need X in a month, and someone else has something that's 90% of what you want X to be, can you buy it from them before starting any crazy internal death marches instead?"
> The real question is, how do LLMs turn the mythical man month on its head. If we accept AI generated code, can an agentic AI swarm make software faster simply by parallelizing in a way that 9 women can't make a baby in 1 month because they're am AI, not human, and communicate in a different way.
This is quite possibly only a one-time switch from a changed baseline, though. Give it a few years and "the fastest way an LLM tool can do it" will be what gets tossed out a an estimate, and stakeholders will still want you to do it in a tenth the time...
9 pregnant women produce one baby/month on average (assuming no miscarriages or late births,etc).
On paper your CPU can execute at least one instruction per core per cycle but that's on average too, if you actually only have one instruction to run it takes several cycles.
Actually, I like quite a lot of the subtle jokes on HN. It is harder to notice, fewer to find, and I don’t get it many a times. But when I get it (or someone explains it to me, perhaps out of pity), I chuckle, laugh, and laugh again. And I remember those comments.
I think the occasional joke is fine but when you have too many then the comments get diluted. It's exactly that kind of thing that makes me hate Reddit and so many other places: spam.
I like to think everyone came to the conclusion that it would strengthen the piece if most comments on it appear to miss the point and are slightly robotic.
> As a rule of thumb, Macs will not run any version of macOS older than the one they shipped with when they launched. Apple provides security updates for older versions of macOS, but it doesn’t bother backporting drivers and other hardware support from newer versions to older ones.
So the answer is “no”, they probably won’t be able to downgrade on the models that are about to be released.
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