- cross-posted to:
- technology@lemmy.zip
- cross-posted to:
- technology@lemmy.zip
You must log in or # to comment.
We need the equivalent investment now. If average code is cheap, then the scarce resource is no longer the ability to produce it. The scarce resource is the ability to read it, to navigate it
You know what would help a lot with understanding the code one is working on? Writing it yourself without turning your brain off via AI.
But that’s an insight the article somehow seems to be missing.
I always ask myself how many of these anti ai warriors are actually proficient professional coders. And I’m talking like engineer level, not hobby level.
LLMs are a tool. Give a package power tool to a fool and the result is stupid at best, bloody at the worst. Let’s call that vibe tooling and ask if there is a difference to vibe coding.
Imho there is not. LLMs are a tool that can lift up the quality of coding work to a common level if used by proficient people. It helps with searching through and understanding vast outputs as long as you know what to expect. Its a miracle in intuition.
Its not a mind reading tool that will just code your fantasy software for you. Hate it all you like, AI is here to stay, this is like hating cars in the age of horses. Cars are not magic, neither is “AI”.
LLMs are a tool that can lift up the quality of coding work
Imagine telling on yourself like this.
And that is right after implying that you are a “proficient professional coder” that is “like engineer level” unlike those pesky “anti ai warriors”. Jesus fucking Christ.
I’ve been training my own employees for years. And I’m suggesting you get a degree before playing keyboard warrior on the internet. ;)
it makes it easy for bad coders to mask as passable but good coders can still spot that in review.
My entire point was in one single sentence, and yet you managed to shit out three sentences, not even remotely addressing that.
I’m saying that if the output puked out by an LLM is of better quality than your own code, something you literally just confessed to, then you’re nothing but a hack. An impostor.
What does the fact that you’ve been training anyone have to do with that? What does a degree, or lack thereof, have to do with anything? I’ve seen plenty of hacks employed as “seniors”, some with a CompSci degree. The kind of hacks that used to be overly reliant on StackOverflow in the past. The kind of hacks that write poorly performing garbage, yet quote Knuth’s “premature optimization is the root of all evil” (completely missing the context) when you confront them about it.
I’m not saying ai code is better than mine. But ai review sees quite a lot normal humans would overlook. Pair programming works with ai just as good. Generally agentic coding is shit. And I have nothing to prove nor get mad about. Somehow you can’t seem to bring up a sound argument but rage. X)
I’m running a successful business with plenty of Devs trained and working for me doing all kinds of specialized real-time engineering. You shout on Lemmy.
That last paragraph is so vague that anybody reading it knows you’re a complete charlatan.
That is right, it is a tool. But how useful will it be as a tool once it will be sold by token at real costs, where every mistake that tool makes costs money and we are talking here maybe about 10 times higher costs than people currently pay for Claude, at the minimum.
Add to that the question how the use of LLMs affects the career pipeline from junior dev to senior dev.
There not so many tool analogies where the tool is especially good at making things look good, even if they aren’t when you dig deeper.
I also think there still hasn’t been a study showing consistent long term significant(!) productivity gain for coders. (Other than lines of code in total, but that alone is a poor measure.) The amount of new hidden bugs and other issues seem to outweigh most of the perceived gains.
The key question is if total costs along the pipeline, from requirements definition down to the final quality controlled fully debugged product can be reduced, at real LLM costs (not with the currently vastly subsidised costs).
I agree. And from the data I’ve seen so far, it doesn’t look convincing at all.
In part because AI seems to be phenomenally unintelligent.
I would argue that adding lines of code is the worst thing a developer can do.
Well i can’t disagree with that take. Skill still plays a role. You still can’t suggest people keep writinga and reviewing solely by hand. That ship has sailed.
Says who, that we can’t suggest that?
Most of my code is public if you’re curious!
There are definitely real engineers being strongly anti-AI. The problem, in my opinion, is that they just didn’t really try working with them.
They’re incredibly powerful tools, and they don’t only amplify bad developers, they amplify every developer that really tries to work with it.
The mistake people make is delegating the decision making to the AI. Let the tool be a tool, not a brain. You architect, you design, you order, it writes the code. You review the code. There you go, you have a pretty good quality code, better than most devs will produce, following your design and architecture, you controlled the entire decision making, and you did it in 5x less time.
I also think that it has become too useful to disappear in engineering.
I fully agree with your take. Thank you :)
I mean its more like self driving cars than cars themselves; it can work, but also steering wheels were created by the devs for a reason - even if most are too lazy to understand that reason.
Like I’d agree hand coding in assembly is (mostly) useless these days, but honestly I feel like the efficiency problems ai is trying to solve were largely solved 50 years ago with compilers.
(and like isnt digesting large outputs the entire point of being an engineering level dev? like if youre just there to pray to the software gods, you’d do much better as a CRUD script kiddie anyways)
AI tools can generate functional, adequate, perfectly average code at a speed and cost that would have been unimaginable even five years ago. And like the outsourcing wave of the early 2000s, the economics are real and rational. Nobody is wrong for using these tools. The code they produce is often fine. It works. It passes tests. It might ship as-is.
Not the first time I’ve read this kind of statement and I always struggle to reconcile this with my personal experience. I’m seriously doubting that I’m just not a “good enough prompter”. I know how to explain context from domain to tech and vice versa, that’s like, a good 20% of my job. I’d say that AI tools are good at producing code that already exists.
The LLMs are an interface to a corpus of written material. They’ve never had a thought, a chat around the coffee machine, or any experience in the largest sense of the world. This is a hard barrier on any induction they may emulate.
You’re both correct, and also wrong.
A lot of code already exists. Or at least in a close enough form that it can be easily adjusted to address a new situation.
When someone comes up with an idea for a new App at this point, it’s almost never because it’s an entirely new branch of computing. It’s very likely just CRUD with a visual design, and then a small more complex algorithm to mix the data around behind the scenes.
What’s the difference between a dating app and an automatic meal plan builder? The algorithm doesn’t care about whether or not the recipe swiped back when it matches it up to you.
You’re right that they’re not going to be inventing entirely new things most of the time, that’s just not what’s needed of them most of the time.
Fortunately software is much more than App ideas fishing for VC investments. A lot of us are building actual tools for nurses, teachers, technicians, artists, students, etc. We have to analyze these human beings’ role in society, their needs, their situation, which is different from merely preying on their attention span. Programming languages are still the most reliable way to specify how the software must behave. And once the software is done, it is merely born. It then lives through a steady flow of continuous adaptation until one day it dies as all things do. Downplaying the human condition is a mistake.
You missed the point. The point is that almost all software today follows the same general ideas, patterns, etc.
The quality of the output of AI is not tied to what these patterns are used towards. Even if, say, your tool has a completely new network protocol. An LLM will still “understand” that it is a network protocol, that it serializes following rules that you tell it, serializes and deserializes the way you decide, then it will write that down in a memory and be able to work with that.
A new file format? Same. A very specialized new kind of No-SQL database that fits your very specific tool better? It will also write down in a file how it works and be able to use that.
It’s as good as the documentation you give it is. Which, for basic things such as setting up a basic REST API, it has learned in its training data. If it hasn’t, it’s up to you to provide it, and it will be perfectly able to use it.
Even if you build some weird unique assembly language it will be able to use it if you give it the set of instructions and their documentation.
A medicine dispenser application for a nurse is still just CRUD operations for the most part. There’s nothing innovative about how the code would be written in an application like that.
Meh, disagree with a lot of this.
AI tools can generate functional, adequate, perfectly average code
Not in my experience.
The outsourcing era taught us that the expensive part of software was never writing it. It was understanding it well enough to change it safely, to debug it under pressure, to explain to the next person why a particular decision was made at 2 a.m. on a Tuesday.
Since AI is adequate, just have AI change, debug, and explain it. You don’t even need devs running the AI. Have AI generate intent. Just have AI scrape Twitter for people complaining about applications they wish existed, and have the AI make them. Let AI do market research. It’s supposedly perfectly adequate.
Just have AI scrape Twitter for people complaining about applications they wish existed, and have the AI make them.
I mean… in 2026, this is probably a viable business strategy tbh.
As if needing to be able to understand code quickly wasn’t already a problem before LLMs.
