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As human developers, I think we're struggling with "letting go" of the code. The code we write (or agents write) is really just an intermediate representation (IR) of the solution. For instance, GCC will inline functions, unroll loops, and myriad other optimizations that we don't care about. But when we review the ASM that GCC generates (we don't) we are not concerned with the "spaghetti" and the "high coupling" and…
> review the ASM that GCC generates (we don't) Of course we do not. Because there is no need. The process of compiling higher order language to assembly is deterministic and well-tested. There is no need to continue reviewing something that always yields the same result. > We care that it works, and is correct for what it is supposed to do. Exactly. Which is something we do not have with an output of an LLM. Because…
I'm a solo dev. In fact I'm hardly a dev; it's just a helpful skill. Code writing speed IS a problem, because it takes valuable time away from other tasks. A bit like doing the dishes. I just set up Claude Code tonight. I still read and understand every line, but I don't need to Google things, move things around and write tests myself. I state my low-level intent and it does the grunt work. I'm not going to 10x my pr…
Earlier quoted context omitted.
AI is really good when: 1. you want something that's literally been done tons of times before, and it can literally just find it inside its compressed dataset 2. you want something and as long as it roughly is what you wanted, it's fine It turns out, this is not the majority of software people are paying engineers to write. And it turns out that actually writing the code is only part of what you're paying for - much…
> It turns out, this is not the majority of software people are paying engineers to write. The above are definitely the majority of software people are paying developers to write. By an order of magnitude. The novel problems for customers who specifically care about code quality is probably under 1% of software written. If you don't recognise this, you simple don't understand the industry you work in.
As it turns out - 4 years before LLMs - at least one of the FAANGs already had auto-complete so good it could do most of what LLMs can practically do in a gigantic context.
But, sure...
Companies genuinely don't want good code. Individual teams just get measured by how many things they push around. An employee warning that something might not work very well is going to get reprimanded as "down in the weeds" or "too detail oriented," etc. I didn't understand this for a while, but internal actors inside of companies really just want to claim success.
> The bottleneck is understanding the problem. No amount of faster typing fixes that. Why not? Why can't faster typing help us understand the problem faster? > When you speed up code output in this environment, you are speeding up the rate at which you build the wrong thing. Why can't we figure out the right thing faster by building the wrong thing faster? Presumably we were gonna build the wrong thing either way in…
> Why can't we figure out the right thing faster by building the wrong thing faster? Because usually the customer can only tolerate so many failed attempts per unit of time. Running your fitness function is often very expensive in terms of other people's time. This is easily the biggest bottleneck in B2B/SaaS stuff for banking. You can iterate maybe once a week if you have a really, really good client.
when you're building a feature and have different ideas how to go about it, it's incredibly valuable to build them all, compare, and then build another, clean implementation based on all the insights
I used to do it before, but pretty rarely, only for the most important stuff. now I do it for basically everything. and while 2-4 agents are working on building these options, I have time to work on something else.
if you ever played factorio this is pretty clear.
Earlier quoted context omitted.
AI is really good when: 1. you want something that's literally been done tons of times before, and it can literally just find it inside its compressed dataset 2. you want something and as long as it roughly is what you wanted, it's fine It turns out, this is not the majority of software people are paying engineers to write. And it turns out that actually writing the code is only part of what you're paying for - much…
> It turns out, this is not the majority of software people are paying engineers to write. The above are definitely the majority of software people are paying developers to write. By an order of magnitude. The novel problems for customers who specifically care about code quality is probably under 1% of software written. If you don't recognise this, you simple don't understand the industry you work in.
These “LLM programming ain’t nothing special” posts are becoming embarrassing for the authors who - due to their anti AI dogmatism - have no idea how truly incredibly fast and powerful it’s become. Please stop making fools of yourselves and go use Claude for a month before writing that “AI coding ain’t nothing special” post. Ignorance of what Claude can actually do means your arguments have no standing at all. “I hat…
As human developers, I think we're struggling with "letting go" of the code. The code we write (or agents write) is really just an intermediate representation (IR) of the solution. For instance, GCC will inline functions, unroll loops, and myriad other optimizations that we don't care about. But when we review the ASM that GCC generates (we don't) we are not concerned with the "spaghetti" and the "high coupling" and…
> review the ASM that GCC generates (we don't) Of course we do not. Because there is no need. The process of compiling higher order language to assembly is deterministic and well-tested. There is no need to continue reviewing something that always yields the same result. > We care that it works, and is correct for what it is supposed to do. Exactly. Which is something we do not have with an output of an LLM. Because…
Here are the reported miscompilation bugs in GCC so far in 2026. The ones labeled "wrong-code".
https://gcc.gnu.org/bugzilla/buglist.cgi?chfield=%5BBug%20cr...
I count 121 of them.
I've posted this 3 times now. Code-generation by compilers written by experts is not deterministic in the way that you think it is.