271–280 of 303 posts
I have been programming since 1978 and thoroughly enjoy it. Nothing more satisfying to me than thinking about nifty algorithms, how to wring out every last drop of performance from a system, or more recently train AI models or build agentic systems. From object models to back end, graphics to communications protocols, I love it all. But that said, I am getting on a bit now and don't particularly enjoy all the typing.…
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> I'm over here making food use of this tooling, but it's not for everyone. Yup, you're the one using perfect tooling that never produces any issues, or erroneous output, and is perfect in every way. Unlike these idiot opponents of yours who don't have the intelligence and skill to use these amazing magical tools. Or, perhaps, your opponents actually use these tools daily and actually understand what they can and can…
Who says these tools don't make any mistakes? Don't put words in my mouth or project your own fallacies. Nothing in the world is perfect, but there is absolutely useful stuff to be had. Keep your head in the sand though.
You heavily imply it in every comment you write. You've literally started debating my comment where I say that everyone runs into AI bullshit. You literally keep debating me when I point out that despite all the progress AI bullshit remains.
Your literal answer to "It's not different from most people. Everyone runs into AI bullshit." was literally "Or, you know, there are different ways of using these tools."
> Nothing in the world is perfect, but there is absolutely useful stuff to be had.
Says the person claiming I put words in his mouth or projecting my fallacies. Where exactly did I say this stuff wasn't useful? Literally nowehere.
> Keep your head in the sand though
Someone recently told me: "Don't put words in my mouth or project your own fallacies. "
Adieu.
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I'll add another category: people who've coded in many languages but never specialised. I've earned money by coding in c, php, c#, flex, arduino-c, rust and python. And I've hacked at projects written in a few more. Like a lot of people here, my earliest memories of coding are of me and my siblings typing games printed in a BASIC book, on a z80 clone, for 30-60 minutes, and then playing until we had to go to bed, or…
I see myself in that description, and I LOVE this new way of working. It eliminates most yak shaving that comes my way, and that's what I hated most from the before times. I can quickly build mental models on things and focus on the solution building instead of bike shedding the code. It's not perfect, but it's pretty great.
But it is amazing how many people really are playing it like a slot machine and hoping for the best.
That's just people and as always some will better utilise new tech than others. On aggregate I think everyone still wins
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What a weird set of claims to make. I like how you worked backwards from "couldn't be a top not software engineer", whatever that means.
It’s the standard response anytime someone claims they get productivity boosts from ai. Either you are inexperienced or bad at your job. On a board that would deride any other no true Scotsman take, this one happens on every ai discussion. It’s no longer worth engaging with. I’m now just posting basic statements like “I’ve been programming professionally for 25 years and I find ai to be extremely helpful”. That way t…
The bit that triggered my response is this:
> Guess what: a lot of the work programmers do, maybe even most of it, is rote.
Yes, I know a lot of programmers who do that. In fact it seems most programmers I see in the industry do that. Many of them are very good at it, in the sense that a good brick layer will be content to devote his lifetime to laying bricks in a straight line and will be far more reliable and conscientious at it than I could every be. However, that personality type is unlikely to become a top engineer or architect. To be good at those jobs you need to get bored in a couple of years at most. You have to be internally driven to try new things, and experiment with new ideas.
When such a person is confronted with the prospect of a lifetime of churning out code that almost follows a template (and I daresay could easily be produced by today's LLM's) he doesn't sit still and do it. He finds something more interesting to do. Since he's a computer programmer and the task is well suited a computer doing it, he probably take up a skunk works project of churning out the repetitive parts of that code, so he didn't have to do it. It's far more fun, saves time in the long run, and if he is a good software engineer in the long run the systems it produces will be more reliable, and use less code than something programmer code have produced by churning out the same stuff every day for years.
Going by the OP's comment, he hasn't done that, despite apparently being bored shitless with what he does do. There is a solution, but it isn't the LLM's he suggests. All using LLM's change is the language are giving to the "compiler". The end result is still him creating the same CRUD app using the same platforms, libraries and tools his is using now.
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Except LLMs aren’t humans. Why do we continue to say “well, yeah but humans ”? A major point/goal is for these to be better and less error prone than humans. It’s just coping at this point.
That goes too far in the opposite direction. Humans come with a broad range of skills and performance; LLMs are inside this range. The fact LLMs are not human, and the fact that the best humans beat them, is as economically relevant as the fact that a ride-on lawnmower isn't human and (typically) an athlete can outrace them — i.e. it resolves to what you're actually using them for.
They may excel at solving very narrow problems with decent results, like in that programming competition recently. But those are indeed very narrowly defined problems, and while they may solve it decently in limited time, that is roughly their overall limit, while a human, given more time, can excel to a much higher level.
It becomes a question of whether we want mediocre things, that are not very extensible and maintainable, relying on the very thing that produced these mediocre codes to maintain and extend them, or do we want high quality work.
For the latter one would want to hire qualified people. Too bad though, that hiring is broken at many companies and they don't recognize qualifications, when right in front of them.
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Except LLMs aren’t humans. Why do we continue to say “well, yeah but humans ”? A major point/goal is for these to be better and less error prone than humans. It’s just coping at this point.
That isn't needed, as LLMs are way cheaper. Even if they never advance beyond strong new grad, their cheapness is enormous value add on its own. GitHub will presently sell you 1500 AI generated PRs a month for $40. You used to have to pay a human 10K a month, even if it was small stuff. All kinds of software that are worth as little as 10K a year are now worth building, as making and supporting them is so trivial.
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It’s the standard response anytime someone claims they get productivity boosts from ai. Either you are inexperienced or bad at your job. On a board that would deride any other no true Scotsman take, this one happens on every ai discussion. It’s no longer worth engaging with. I’m now just posting basic statements like “I’ve been programming professionally for 25 years and I find ai to be extremely helpful”. That way t…
For what is worth, it had nothing to do with whether he uses AI. I'm north of 60yo, started on punched cards and paper tape and have been programming ever since. My experience suggests if you aren't keeping up by using AI now there is a good chance you won't be a relevant software engineer in a decades time. The bit that triggered my response is this: > Guess what: a lot of the work programmers do, maybe even most of…
But more importantly, by dismissing the comment that way, you’ve made an implicit claim. You are claiming to be able to judge a persons capability or the challenges they are working on off of a couple hn comments. Thats bad engineering and anti-social behavior.
Imagine telling someone that learning to touch type was the sign of a bad software engineer because you don’t type that much because your libraries are all named with single letters. Thats what these arguments sound like to me.
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I'll add another category: people who've coded in many languages but never specialised. I've earned money by coding in c, php, c#, flex, arduino-c, rust and python. And I've hacked at projects written in a few more. Like a lot of people here, my earliest memories of coding are of me and my siblings typing games printed in a BASIC book, on a z80 clone, for 30-60 minutes, and then playing until we had to go to bed, or…
This is me. I want to solve a problem, I know how to solve the problem, but I don't want to spend 30 minutes figuring out how to do the thing in Language X with Framework Y. I know _what_ I need to do, but can't remember _how_ it's done in this specific language. The AI knows and can either tell me straight away by examining the project or I can just have it do it and review the solution.
However, when I am learning a new space (teaching myself SwiftUI, coming from a Java/infra background), Claude lets me preserve my forward momentum, which helps with motivation.
I tend to go back and rewrite a lot of the first iterations, but it has proved super useful unblocking me when I don’t yet understand how to tackle something I want to do in this new space, or sparking some ideas.
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That goes too far in the opposite direction. Humans come with a broad range of skills and performance; LLMs are inside this range. The fact LLMs are not human, and the fact that the best humans beat them, is as economically relevant as the fact that a ride-on lawnmower isn't human and (typically) an athlete can outrace them — i.e. it resolves to what you're actually using them for.
But it is not merely the best humans. Any good developer is able to write better code, because by definition LLMs tend towards the mean, which is mediocre code, mostly from GitHub, they were force-fed as training data. They may excel at solving very narrow problems with decent results, like in that programming competition recently. But those are indeed very narrowly defined problems, and while they may solve it decen…
> because by definition LLMs tend towards the mean
This part is false: the mean human can't write code at all. Also, as per your own point:
> They may excel at solving very narrow problems with decent results, like in that programming competition recently.
LLMs are often in the top decile of coding challenges, which are already limited to better-than-average developers. Now, these same models that get top decile scores in challenges are still not in the top decile overall because the role of software developer is much broader than just leetcode, but this still demonstrates the point: LLMs do not tend towards the mean.
> But those are indeed very narrowly defined problems, and while they may solve it decently in limited time, that is roughly their overall limit, while a human, given more time, can excel to a much higher level.
Except "code" is itself not narrowly-defined even despite what I just said. Even within one programming language, comprehension of the natural language task description is itself much harder and more general than any programming language, and both the programming language and all the libraries are described in a mixture of natural and formal language. Even just the ability to recognise if it's looking at examples of C or JavaScript is something it had to learn rather than being explicitly programmed with knowledge of.
Now sure, I will absolutely say that if the working definition of "intelligence" is about how few examples are needed to learn a new thing, then transformer models are "stupid". But, to a certain degree, they're able to making up for being very very stupid by being very very stupid very very quickly and very very cheaply — cheap enough and fast enough that when you do hit their skill limits, there's many cases where one can afford to boost them a noticeable degree, and it's affordable even though every m-times-n-quality-points you need to boost them by comes with 2^n increase in their cost in both time and money.
Not always, and it's an exponential cost per linear improvement, but often.