[zozbot234]

Unfortunately, LLMs generate useless word salad and nonsense even when working on issues text, you absolutely have to reword the writing from scratch otherwise it's just an annoyance and a complete waste of time. Even a good prompt doesn't help this all that much since it's just how the tool works under the hood: it doesn't have a goal of saying anything specific in the clearest possible way and inwardly rewording it until it does, it just writes stuff out that will hopefully end up seeming at least half-coherent. And their code is orders of magnitude worse than even their terrible English prose.

[GorbachevyChase]

I don’t think you’re being serious. Claude and GPT regularly write programs that are way better than what I would’ve written. Maybe you haven’t used a decent harness or a model released in the last year? It’s usually verbose, whereas I would try the simplest thing that could possibly work. However, it can knock out but would have taken me multiple weekends in a few minutes. The value proposition isn’t even close.

It’s fine to write things by hand, in the same way that there’s nothing wrong with making your own clothing with a sewing machine when you could have bought the same thing for a small fraction of the value of your time. Or in the same fashion, spending a whole weekend, modeling and printing apart, you could’ve bought for a few dollars. I think we need to be honest about differentiating between the hobby value of writing programs versus the utility value of programs. Redox is a hobby project, and, while it’s very cool, I’m not sure it has a strong utility proposition. Demanding that code be handwritten makes sense to me for the maintainer because the whole thing is just for fun anyway. There isn’t an urgent need to RIIR Linux. I would not apply this approach to projects where solving the problem is more important than the joy of writing the solution.

[notpachet]

> Claude and GPT regularly write programs that are way better than what I would’ve written

Is that really true? Like, if you took the time to plan it carefully, dot every i, cross every t?

The way I think of LLM's is as "median targeters" -- they reliably produce output at the centre of the bell curve from their training set. So if you're working in a language that you're unfamiliar with -- let's say I wanted to make a todo list in COBOL -- then LLM's can be a great help, because the median COBOL developer is better than I am. But for languages I'm actually versed in, the median is significantly worse than what I could produce.

So when I hear people say things like "the clanker produces better programs than me", what I hear is that you're worse than the median developer at producing programs by hand.

[h3lp]

A lot of computer users are domain experts in something like chemistry or physics or material science. Computing to them is just a tool in their field, e.g. simulating molecular dynamics, or radiation transfer. They dot every i and cross every t _in_their_competency_domain_, but the underlying code may be a horrible FORTRAN mess. LLMs potentially can help them write modern code using modern libraries and tooling.

My go-to analogy is assembly language programming: it used to be an essential skill, but now is essentially delegated to compilers outside of some limited specialized cases. I think LLMs will be seen as the compiler technology of the next wave of computing.

[Terr_]

The difference is that compilers involve rules we can enumerate, adjust, etc.

Consider calculators: Their consistency and adherence to requirements was necessary for adoption. Nobody would be using them if they gave unpredictable wrong answers, or where calculations involving 420 and 69 somehow keep yielding 5318008. (To be read upside-down, of course.)

[foxes]

But thats the point, an llm is a vastly different object to a calculator. Its a new type of tool for better or worse based on probabilities, distributions.

If you can internalise that fact and look at it like having a probable answer rather than an exact answer it makes sense.

Calculators cant have a stab at writing an entire c compiler. A lot of people cant either or takes a lot of iteration anyway, no one one shotted complicated code before llms either.

I feel discussion shouldnt be about how they work as the fundamental objection, rather the costs and impacts they have.

[h3lp]

The compilers used to be unreliable too, e.g. at higher optimizations and such. People worked on them and they got better.

I think LLMs will get better, as well.

[aaronbrethorst]

nice. 3x.

[torginus]

It can certainly be true for several reasons. Even in domains I'm familiar with, often making a change is costly in terms of coding effort.

For example just recently I updated a component in one of our modules. The work was fairly rote (in this project we are not allowed to use LLMs). While it was absolutely necessary to do the update here, it was beneficial to do it everywhere else. I didn't do it in other places because I couldn't justify spending the effort.

There are two sides to this - with LLMs, housekeeping becomes easy and effortless, but you often err on the side of verbosity because it costs nothing to write.

But much less thought goes into every line of code, and I often am kinda amazed that how compact and rudimentary the (hand-written) logic is behind some of our stuff that I thought would be some sort of magnum opus.

When in fact the opposite should be the case - every piece of functionality you don't need right now, will be trivial to generate in the future, so the principle of YAGNI applies even more.

[notpachet]

I can agree with that. So essentially: "Claude and GPT regularly write programs that are way better than what I would’ve written given the amount of time I was willing to spend."

[zozbot234]

How much time and effort are you willing to spend on maintaining that code though? The AI can't do it on its own, and the code quality is terrible enough.

[est31]

Have you tried the latest models at best settings?

I've been writing software for 20 years. Rust since 10 years. I don't consider myself to be a median coder, but quite above average.

Since the last 2 years or so, I've been trying out changes with AI models every couple months or so, and they have been consistently disappointing. Sure, upon edits and many prompts I could get something useful out of it but often I would have spent the same amount of time or more than I would have spent manually coding.

So yes, while I love technology, I'd been an LLM skeptic for a long time, and for good reason, the models just hadn't been good. While many of my colleagues used AI, I didn't see the appeal of it. It would take more time and I would still have to think just as much, while it be making so many mistakes everywhere and I would have to constantly ask it to correct things.

Now 5 months or so ago, this changed as the models actually figured it out. The February releases of the models sealed things for me.

The models are still making mistakes, but their number and severity is lower, and the output would fit the specific coding patterns in that file or area. It wouldn't import a random library but use the one that was already imported. If I asked it to not do something, it would follow (earlier iterations just ignored me, it was frustrating).

At least for the software development areas I'm touching (writing databases in Rust), LLMs turned into a genuinely useful tool where I now am able to use the fundamental advantages that the technology offers, i.e. write 500 lines of code in 10 minutes, reducing something that would have taken me two to three days before to half a day (as of course I still need to review it and fix mistakes/wrong choices the tool made).

Of course this doesn't mean that I am now 6x faster at all coding tasks, because sometimes I need to figure out the best design or such, but

I am talking about Opus 4.6 and Codex 5.3 here, at high+ effort settings, and not about the tab auto completion or the quick edit features of the IDEs, but the agentic feature where the IDE can actually spend some effort into thinking what I, the user, meant with my less specific prompt.

[zozbot234]

> I am talking about Opus 4.6 and Codex 5.3 here, at high+ effort settings

So you have to burn tokens at the highest available settings to even have a chance of ending up with code that's not completely terrible (and then only in very specific domains), but of course you then have to review it all and fix all the mistakes it made. So where's the gain exactly? The proper goal is for those 500 lines to be almost always truly comparable to what a human would've written, and not turn into an unmaintainable mess. And AI's aren't there yet.

[als0]

You really do need to try the latest ones. You can’t extrapolate from your previous experiences.

[BoredomIsFun]

I do not think they are impartial - all I can see is lots of angst.

[notpachet]

I feel like we're talking about different things. You seem to be describing a mode of working that produces output that's good enough to warrant the token cost. That's fine, and I have use cases where I do the same. My gripe was with the parent poster's quote:

> Claude and GPT regularly write programs that are way better than what I would’ve written

What you're describing doesn't sound "way better" than what you would have written by hand, except possibly in terms of the speed that it was written.

[est31]

yeah it writing stuff that's way better than mine is not the case for me, at least for areas I'm familiar with. In areas I'm not familiar with, it's way better than what I could have produced.

[dnautics]

no. I'm a pretty skilled programmer and I definitely have to intervene and fix an architectural problem here and there, or gently chastise the LLM for doing something dumb. But there are also many cases where the LLM has seen something that i completely missed or just hammered away at a problem enough to get a solution that is correct that I would have just given up on earlier.

The clanker can produce better programs than me because it will just try shit that I would never have tried, and it can fail more times than I can in a given period of time. It has specific advantages over me.

[xyzsparetimexyz]

The verboseness is the key issue as to why LLMed PRs are bad.

[zozbot234]

> The value proposition isn’t even close.

That's correct, because most of the cost of code is not the development but rather the subsequent maintenance, where AI can't help. Verbose, unchecked AI slop becomes a huge liability over time, you're vastly better off spending those few weekends rewriting it from scratch.

[skeeter2020]

let me translate this for the GP: "you're doing it wrong".

[fireflash38]

Having reviewed a lot of Ai-written python code, I think it's absolute nonsense.

It never picks a style, it'll alternate between exceptions and then return codes.

It'll massively overcomplicate things. It'll reference things that straight up don't exist.

But boy is it brilliant at a fuzzy find and replace.

[skeeter2020]

if it wasn't so maddening it would be funny when you literally have to tell it to slow down, focus and think. My tinfoil hat suggests this is intentional to make me treat it like a real, live junior dev!

[mech422]

"you literally have to tell it to slow down, focus and think" - This soo much! When I get an unexpected result from claude, I ask it why - what caused it to do such-and-such. After one back and forth session like this putting up tons of guardrails on a prompt, claude literally said "you shouldn't have to teach me to think every session" !!

[heavyset_go]

> When I get an unexpected result from claude, I ask it why - what caused it to do such-and-such.

No LLM can answer this question for you, it has no insight into how or why it outputted what it outputted. The reasons it gives might sound plausible, but they aren't real.

[hananova]

> Claude and GPT regularly write programs that are way better than what I would’ve written.

I’m sorry but this says more about you than about the models. It is certainly not the case for me!

[emperorxanu]

I feel like every person stating things of this nature are literally not able to communicate effectively (though this is not a barrier anymore, you can get a dog to vibe code games with the right workflow, which to me seems like quite an intellectual thing to be able to do.

Despite that, you will make this argument when trying to use copilot to do something, the worst model in the entire industry.

If an AI can replace you at your job, you are not a very good programmer.

[recursive]

Copilot isn't a model. Currently it's giving me a choice of 15 different models. By all evidence, AI is nowhere close to replacing me, but to hear other people tell it, it is weeks or maybe months away.

I'll just wait and see.

[emperorxanu]

Remember when copilot released? It was running some openai thing at the time, now you can choose from many models sure, but if you want a BMW, buy a BMW, don't buy a Nissan with badly strapped on BMW decals.

[recursive]

No, I don't remember when it was released.

I don't want a Nissan or a BMW. This was provided by my employer, and I've been asked to use it. To be honest, I don't even understand how your car analogy applies to any of this.

[skydhash]

It does generate word salad (and usefulness depends on the person reading it). If both the writer and the reader share a common context, there's a lot that can be left out (the extreme version is military signal). An SOS over the radio says the same thing as "I'm in a dangerous situation, please help me if you can" but the former is way more efficient. LLMs tend to prefer the latter.

[calvinmorrison]

> If an AI can replace you at your job, you are not a very good programmer.

Me and millions of other local yokel programmers who work in regional cities at small shops, in house at businesses, etc are absolutely COOKED. No I cant leet code, no I didnt go to MIT, no I dont know how O(n) is calculated when reading a function. I can scrap together a lot of useful business stuff but no I am not a very good programmer.

[ethbr1]

> no I dont know how O(n) is calculated when reading a function

   1. Confidently state "O(n)"
   2. If they give you a look, say "O(1) with some tricks"
   3. If they still give you a look, say "Just joking! O(nlogn)"

[calvinmorrison]

O(no idea)

[integralid]

>no I dont know how O(n) is calculated when reading a function

This is really, honestly not hard. Spend a few minutes reading about this, or even better, ask a LLM to explain it to you and clear your misconceptions if regular blog posts don't do it for you. This is one of the concepts that sounds scarier than it is.

edit: To be clear there are tough academic cases where complexity is harder to compute, with weird functions in O(sqrt(n)) or O(log(log(n)) or worse, but most real world code complexity is really easy to tell at glance.

[calvinmorrison]

its not hard. Accounting isnt that hard either. I just know more business crap than programming

[emperorxanu]

Do you mean you aren't able to use AI to make software?

The thing you fear is the thing that you could just use to improve yourself?

Why fear a shovel?

Also, I never claimed to be a good programmer either. Just don't see the point fearing something that makes it infinitely easier and faster to get work done.

[torginus]

I suspect the value you bring to the table is that you are good enough a programmer to translate the problems of the people you work with into working code.

LLMs can do it somewhat, but it can probably leetcode better than even most of the the people who went to MIT.

[cyanydeez]

So many people and systems have some how merged into just a slathering of spam to everyones senses. It's no longer about truth statements, but just, is this attention-worthy, and most of the internet, it's social media and "people" are going into the no-bin.