AI Models from Google, OpenAI, Anthropic Solve 0% of ‘Hard’ Coding Problems
AI Models from Google, OpenAI, Anthropic Solve 0% of ‘Hard’ Coding Problems | AIM
For instance, if an AI model could complete a one-hour task with 50% success, it only had a 25% chance of successfully completing a two-hour task. This indicates that for 99% reliability, task duration must be reduced by a factor of 70.
This is interesting. I have noticed this myself. Generally, when an LLM boosts productivity, it shoots back a solution very quickly, and after a quick sanity check, I can accept it and move on. When it has trouble, that's something of a red flag. You might get there eventually by probing it more and more, but there is good reason for pessimism if it's taking too long.
In the worst case scenario where you ask it a coding problem for which there is no solution—it's just not possible to do what you're asking—it may nevertheless engage you indefinitely until you eventually realize it's running you around in circles. I've wasted a whole afternoon with that nonsense.
Anyway, I worry that companies are no longer hiring junior devs. Today's juniors are tomorrow's elites and there is going to be a talent gap in a decade that LLMs—in their current state at least—seem unlikely to fill.
Sadly, the lack of junior devs means my job is probably safe until I am ready to retire. I have mixed feelings about that. On the one hand, yeah for me. On the other sad for the new grads. And sad for software as a whole. But software truely sucks, and has only been enshitifying worse and worse. Could a shake up like this somehow help that? I don't see how, but who knows.
I've noticed this too and it's even weirder when you compare it to a physics question. It very consistently tells me when my recent brain fart of an idea is just plain stupid. But it will try eternally to help me find a coding solution even it it just keeps going in circles.
I think part of this comes down to the format. Physics can often be analogized and can be very conversational when it comes to demonstrating ideas.
Most code also looks pretty similar if you don’t know how to read it and unlike language, the syntax is absolute with no room for interpretation or translation.
I’ve found it’s consistently good if you treat it like a project specification list, including all of your requirements in a list format in the very first message and have it psuedocode the draft along with list what libraries it wants to use and make sure they work how you expect.
There’s some screening that goes into utilizing it well and that only comes with already knowing roughly how to code what you’re trying to make.
Sucks for today's juniors, but that gap will bring them back into the fold with higher salaries eventually.
In the worst case scenario where you ask it a coding problem for which there is no solution—it's just not possible to do what you're asking—it may nevertheless engage you indefinitely until you eventually realize it's running you around in circles.
Exactly this, and it's frustrating as a Jr dev to be fed this bs when you're learning. I've had multiple scenarios where it blatantly told me wrong things. Like using string interpolation in a terraform file to try and set a dynamic source - what it was giving me looked totally viable. It wasn't until I dug around some more that I found out that terraform init can't use variables in the source field.
On the positive side it helps give me some direction when I don't know where to start. I use it with a highly pessimistic and cautious approach. I understand that today is the worst it's going to be, and that I will be required to use it as a tool in my job going forward, so I'm making an effort to get to grips when working with it.
ai is basically just the worst answer on stackexchange
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:
cmakeBut then again, I haven't worked in FinTech or Big Data companies, neither have I made an SQL server.
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.
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.
People are actually underestimating the effects that
AIautocomplete will have in the industry
Tripled is an understatement for me. Cursor and Claude Code are a godsend for OE for me.
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.
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, ld instead of load8, load16, load32, load64. Or j instead of jump. 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.
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 😊
Ok, but there's no "AI" involved in this process.
About all they are good for is generating boilerplate code. Just far less efficiently than a snippet library.
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.
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.
The hard problems are the only reason I like programming. If 90% of my job was repetitive boilerplate, I'd probably be looking elsewhere.
I really dislike how LLMs are flooding the internet with a seemingly infinite amount of half-broken TODO-app style programs with no care at all for improving things or doing something actually unique.
A lot of people don't realize how many times the problem they are solving has already been solved. But after being in the industry for 3 decades, very few things people are working on haven't been done before. They just get put together in different combinations.
As for AI, I have found it decent at wruting one time scripts to gather information I need to make design decisions. And it's a little quicker when I need to look up a syntax for a language or like a resource name for terraform. But even one off scripts I sometimes have to ask it if a while loop wouldn't be better and such.
Funny how I never see articles on Lemmy about improvements in LLM capabilities.
i would guess a lot of the pro ai stuff is from corpos given the fact good press is money to them.
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.
This fits my observation of such models. o4-mini-high is able to help me with 80-90% of the problems at work. For the remaining problems, it would come up with a nonsensical solution and no matter how much I prompt it, it would tunnel-vision on that specific approach. It could never second guess itself and realise that its initial solution is completely off the mark, and try an entirely differently approach. That's where I usually step in and do the work myself.
It still saves me time with the trivial stuff though.
I can't say the same for the rest of the LLMs. They are simply no good at coding and just waste my time.
I didn’t see Claude 4 Sonnet in the tests and this is the one I use. And it looks like about the same category as o4 mini from my experience.
It is a nice tool to have in my belt. But these LLM based agents are still very far from being able to do advanced and hard tasks. But to me it is probably more important to communicate and learn about the limitations about these tools to not lose tile instead of gaining it.
In fact, I am not even sure they are good enough to be used to really generate production-ready code. But they are nice for pre-reviewing, building simple scripts that don’t need to be highly reliable, analyse a project, ask specific questions etc… The game changer for me was to use Clojure-MCP. Having a REPL at disposal really enhance the quality of most answers.
For me, it’s the Claude Code where everything finally clicked. For advanced stuff, sure they’re shit when they left alone. But as long as I approach it as a Junior Developer (breaking down the tasks to easy bites, having a clear plan all the time, steering away from pitfalls), I find myself enjoying other stuff while it’s doing the monkey work. Just be sure you provide it with tools, mcp, rag and some patience.
Search engines are able to help me with 100% of work.
I remember those times, too (well, some 99.9%, there are still the few issues I never found solution to).
But these times are long past, search engines suck nowadays.
Not anymore. They've all made deals with each other, and search engines SUCK these days