I don’t think that’s a surprise to anyone that has actually used them for more than a few seconds.
Please babe! Just one more parameter, then it will be AGI!
Just 1 more kiloton of Uranium.
It will be ready by the time that’s depleted.
The claims that AI will be surpassing humans in programming are pretty ridiculous. But let’s be honest - most programming is rather mundane.
Never have I had to implement any kind of ridiculous algorithm to pass tests with huge amounts of data in the least amount of memory, as the competitive websites show.
It has been mostly about:
- Finding the correct library for a job and understanding it well, to prevent footguns and blocking future features
- Design patterns for better build times
- Making sane UI options and deciding resource alloc/dealloc points that would match user interaction expectations
cmake
But then again, I haven’t worked in FinTech or Big Data companies, neither have I made an SQL server.
Because actually writing code is the least important part of programming.
I mean, not the least important, it is an important part. But way less than a common person thinks.
Pretty sure that autocomplete would be terrible at these tasks too.
There are some times when I wish I were better at regexp and scripting.
Times when I am writing a similar kind of thing again and again, which is just different enough (and small enough number of repetitions) that it doesn’t seem viable to make the script.At those times, I tend to think - maybe Cursor would have done this part well - but have no real idea since I have never used it.
On the other hand, if I had a scripting endpoint from clang, [1], I would have used that to make a batch processor for even a repetition as small as 10 and wouldn’t have thought once about AI.
which would have taggified parts of code (in the same tone as “parts of speech”) like functions declaration, return type, function name, type qualifier etc. ↩︎
Well, this kind of AI won’t ever be useful as a programmer. It doesn’t think. It doesn’t reason. It cannot make decisions besides using a ton of computational power and enormous deep neural networks to shit out a series of words that seem like they should follow your prompt. An LLM is just a really, really good next-word guesser.
So when you ask it to solve the Tower of Hanoi problem, great it can do that. Because it saw someone else’s answer. But if you ask it to solve it for a tower than is 20 disks high it will fail because no one ever talks about going that far and it flounders. It’s not actually reasoning to solve the problem - it’s regurgitating answers it has ingested from stolen internet conversations. It’s not even attempting to solve the general case because it’s not trying to solve the problem, it’s responding to your prompt.
That said - an LLM is also great as an interface to allow natural language and code as prompts for other tools. This is where the actually productive advancements will be made. Those tools are garbage today but they’ll certainly improve.
Well, this kind of AI won’t ever be useful as a programmer
It already is.
My productivity has at least tripled since I started using Cursor. People are actually underestimating the effects that AI will have in the industry
It means the AI is very helpful to you. This also means you are as good as 1/3 of an AI in coding skills…
Which is not a great news for you mate.
Ah knock it off. Jesus you sound like people in the '90s mocking “intellisense” in the IDE as somehow making programmers “less real programmers”.
It’s all needless gatekeeping and purity test BS. Use tools that are useful. Don’t worry if it makes you less of a man.
It’s not gate keeping it is true. I know devs that say ai tools are useful but all the ones that say it makes them multiples more productive are actually doing negative work because I have to deal with their terrible code they don’t even understand.
The devs I know use it as a tool and check their work and fully understand the code they’ve produced.
So your experience vs. mine. I suspect you just work with shitty developers who would be producing shitty work whether they were using AI or not.
I literally don’t write code anymore, I write detailed specs, invest a lot of time into my guardrails and integrations, and review changes from my agents. My code quality has not fallen, in fact we’ve been able to be much more strict about our style guidelines.
My job has changed completely, but the results are the same - simply much, much faster. And to be clear, this is in code bases that are hundreds of thousands of lines deep, across multiple massive monorepos, and using context from several different documentation sites - both internal and external.
If anything, people are understating the effects this will have over the next year, let alone further. The entry-level IC dev is dead. If you aren’t producing at least twice as fast as you used to, you’re going to be left behind. I cannot possibly suggest strongly enough that you start learning how to use it.
Sure, Jan
People are actually underestimating the effects that
AIautocomplete will have in the industryTrue, I use some local model by Jetbrains that only completes a single line and that’s my sweet spot, it usually guesses the line well and saves me some time without forcing me to read multiple lines of code I didn’t write.
They have their uses. For instance the other day I needed to read some assembly and decompiled C, you know how fun that can be. LLM proved quite good at translating it to english. And really speed up the process.
Writing it back wasn’t that good though, just good enough to point in a direction but I still ended up writing the patcher mostly by myself.
the other day I needed to read some assembly and decompiled C
As one casually does lol Jokes aside, that’s pretty cool. I wish I had the technical know-how and, most importantly, the patience for it.
If you’re interested in getting into it, download Ghidra and open an older program/game in it that you like. The decompiler is pretty amazing imo, so you rarely have to look at the assembly. But it also cross-references them so you can look at the decompiled C Code and the associated assembly. It’s pretty fun 😊
Assembly is very simple (at least RISC-V assembly is which I mostly work with) but also very tedious to read. It doesn’t help that the people who choose the instruction mnemonics have extremely poor taste - e.g.
lb,lh,lw,ldinstead ofload8,load16,load32,load64. Orjinstead ofjump. Who needs to save characters that much?The over-abbreviation is some kind of weird flaw that hardware guys all have. I wondered if it comes from labelling pins on PCB silkscreens (MISO, CLK etc)… Or maybe they just have bad taste.
I once worked on a chip that had nested acronyms.
The over-abbreviation is some kind of weird flaw that hardware guys all have
My bet is on the teaching methods in uni. From what I’ve seen, older teaching methods use terrible variable names for a production environment. I think it unfortunately sticks because students get used to it and find it easier & faster than typing things out.
Who needs to save characters that much?
Do you realize how old assembly language is?
It predates hard disks by ten years and coincided with the invention of the transistor.
Do you realize how old assembly language is?
Do you? These instructions were created in 2011.
It predates hard disks by ten years and coincided with the invention of the transistor.
I’m not sure what the very first assembly language has to do with RISC-V assembly?
Ok, but there’s no “AI” involved in this process.
Come on, guys, any second now. Aany second…
Fortunately, 90% of coding is not hard problems. We write the same crap over and over. How many different creat an account and signin flows do we really need. Yet there seem to be an infinite amount, and each with it’s own bugs.
I’ve found that AI is only good at solving programming problems that are relatively “small picture” — or if it has to do with the basics of a language — anything else that it provides a solution for you will have to re-write completely once you consult with the language’s standards and best practices.
Well, I recently did kind of an experiment, writing a kid game in Kotlin without ever using it. And it was surprisingly easy to do. I guess it helps that I’m fluent in ~5 other programming languages because I could tell what looked obviously wrong.
My conclusion kinda is that it’s a really great help if you know programming in general.
Funny how I never see articles on Lemmy about improvements in LLM capabilities.
there aren’t that many, if you’re talking specifically LLMs, but ML+AI is more than LLMs.
Not a defence or indictment of either side, just people tend to confuse the terms “LLM” and “AI”
I think there could be worth in AI for identification (what insect in this, find the photo I took of the receipt for my train ticket last month, order these chemicals from lowest to highest pH…) - but LLMs are only part of that stack - the input and output - which isn’t going to make many massive breakthroughs week to week.
The recent boom in neural net research will have real applicable results that are genuine progress: signal processing (e.g. noise removal), optical character recognition, transcription, and more.
However the biggest hype areas with what I see as the smallest real return is in the huge model LLM space, which basically try to portray AGI as just around the corner. LLMs will have real applications in summarization, but largely otherwise they just generate asymptotically plausible babble, very good for filling the Internet with slop, not actually useful to replace all the positions OAI, et al, need it to (for their funding to be justified).
Probably because nobody really wants to read absolute nonsense.
Because Lemmy is more representative of scientists and underprivileged while other media is more representative of celebrities and people who can afford other media, like hedge funds or tech monopolies.
