Comment by nananana9
4 hours ago
I've been listening to the insane 100x productivity gains you all are getting with AI and "this new crazy model is a real game changer" for a few years now, I think it's about time I asked:
Can you guys point me ton a single useful, majority LLM-written, preferably reliable, program that solves a non-trivial problem that hasn't been solved before a bunch of times in publicly available code?
I'm not sure if you'd call it a productivity gain, but I have to host our infrastructure on a system that runs processes entirely in Linux userland.
To bridge the containers in userland only, without root, I had to build: https://github.com/puzed/wrapguard
I'm sure it's not perfect, and I'm sure there are lots of performance/productivity gains that can be made, but it's allowed us to connect our CDN based containers (which don't have root) across multiple regions, talking to each other on the same Wireguard network.
No product existed that I could find to do this (at least none I could find), and I could never build this (within the timeframe) without the help of AI.
In the 1930s, when electronic calculators were first introduced, there was a widespread belief that accounting as a career was finished. Instead, the opposite became true. Accounting as a profession grew, becoming far more analytical/strategic than it had been previously.
You are correct that these models primarily address problems that have already been solved. However, that has always been the case for the majority of technical challenges. Before LLMs, we would often spend days searching Stack Overflow to find and adapt the right solution.
Another way to look at this is through the lens of problem decomposition as well. If a complex problem is a collection of sub-problems, receiving immediate solutions for those components accelerates the path to the final result.
For example, I was recently struggling with a UI feature where I wanted cards to follow a fan-like arc. I couldn't quite get the implementation right until I gave it to Gemini. It didn't solve the entire problem for me, but it suggested an approach involving polar coordinates and sine/cosine values. I was able to take that foundational logic turn it into a feature I wanted.
Was it a 100x productivity gain? No. But it was easily a 2x gain, because it replaced hours of searching and waiting for a mental breakthrough with immediate direction.
There was also a relevant thread on Hacker News recently regarding "vibe coding":
https://news.ycombinator.com/item?id=45205232
The developer created a unique game using scroll behavior as the primary input. While the technical aspects of scroll events are certainly "solved" problems, the creative application was novel.
The story you're describing doesn't seem much better than one could get from googling around and going on stackoverflow
Isn't that the whole point? We were already burning steam trying to find better ways around search and bettering our search engines.
Now we have something that understands the question better than any search engine on earth (prior to the advent of LLMs), has relevant information about the question, presents the information in a way we can understand it, remembers the whole context so that when we say that it didn't get it quite right, it understands that, and gets us more information until we find what we are looking for.
The process described above (at least for me) replaces googling and SO searches.
In fact, you can try this on your own as well. Here is a challenge: Try to build a simple game. In the game you create a target that's randomly generated on the screen and have a player at the middle of the screen that needs to hit the target. When a key is pressed, the player swings a rope attached to a metal ball in circles above it's head, at a certain rotational velocity. Upon key release, the player has to let go of the rope and the ball travels tangentially from the point of release. Each time you hit the target you score.
Try it without LLMs and only with google and SO first and see if LLMs help with efficiency :)
It doesn’t have to be, really. Even if it could replace 30% of documentation and SO scrounging, that’s pretty valuable. Especially since you can offload that and go take a coffee.
Why even come to this site if you're so anti-innovation?
Today with LLMs you can literally spend 5 minutes defining what you want to get, press send, go grab a coffee and come back to a working POC of something, in literally any programming language.
This is literally stuff of wonders and magic that redefines how we interface with computers and code. And the only thing you can think of is to ask if it can do something completely novel (that it's so hard to even quantity for humans that we don't have software patents mainly for that reason).
And the same model can also answer you if you ask it about maths, making you an itinerary or a recipe for lasagnas. C'mon now.
I don't think that the user you are responding to is anti-innovation, but rather points out that the usefulness of AI is oversold.
I'm using Copilot for Visual Studio at work. It is useful for me to speed some typing up using the auto-complete. On the other hand in agentic mode it fails to follow simple basic orders, and needs hand-holding to run. This might not be the most bleeding-edge setup, but the discrepancy between how it's sold and how much it actually helps for me is very real.
I think copilot is widely considered to be fairly rubbish, your description of agentic coding was also my experience prior to ~Q3 2025, but things have shifted meaningfully since then
There are different kinds of innovation.
I want AI that cures cancer and solves climate change. Instead we got AI that lets you plagiarize GPL code, does your homework for you, and roleplay your antisocial horny waifu fantasies.
> that hasn't been solved before a bunch of times in publicly available code?
And this matters because? Most devs are not working on novel never before seen problems.
Heh, I agree. There is a vast ocean of dev work that is just "upgrade criticalLib to v2.0" or adding support for a new field from the FE through to the BE.
I can name a few times where I worked on something that you could consider groundbreaking (for some values of groundbreaking), and even that was usually more the combination of small pieces of work or existing ideas.
As maybe a more poignant example- I used to do a lot of on-campus recruiting when I worked in HFT, and I think I disappointed a lot of people when I told them my day to day was pretty mundane and consisted of banging out Jiras, usually to support new exchanges, and/or securities we hadn't traded previously. 3% excitement, 97% unit tests and covering corner cases.
Well, it took opus 4.5 five messages to solve a trivial git problem for me. It hallucinated nonexistent flags three times. Hallucinating nonexistent flags is certainly a novel solution to my git ineptness.
Not to be outdone, chatgpt 5.2 thinking high only needed about 8 iterations to get a mostly-working ffmpeg conversion script for bash. It took another 5 messages to translate it to run in windows, on powershell (models escaping newlines on windows properly will be pretty nuch AGI, as far as I’m concerned).
the 100x gains, even 10x, are obviously ridiculous but that doesn't mean AI is useless
Yeah, I would LOVE to see attempts at significant video games that are then open-sourced for communities to work on. E.g. OpenGTA or OpenFIFA/OpenNHL.
baffled that people are still suspicious of ai coding models
Can you point me to a human written program an LLM cannot write? And no, just answering with a massively large codebase does not count because this issue is temporary.
Some people just hate progress.
> Can you point me to a human written program an LLM cannot write?
Sure:
"The resulting compiler has nearly reached the limits of Opus’s abilities. I tried (hard!) to fix several of the above limitations but wasn’t fully successful. New features and bugfixes frequently broke existing functionality.
As one particularly challenging example, Opus was unable to implement a 16-bit x86 code generator needed to boot into 16-bit real mode. While the compiler can output correct 16-bit x86 via the 66/67 opcode prefixes, the resulting compiled output is over 60kb, far exceeding the 32k code limit enforced by Linux. Instead, Claude simply cheats here and calls out to GCC for this phase (This is only the case for x86. For ARM or RISC-V, Claude’s compiler can compile completely by itself.)"[1]
1. https://www.anthropic.com/engineering/building-c-compiler
Pretty much any software that people pay for? If LLMs could clone an app, why would anyone still pay good money for the original?
Even a normal website like landonorris.com. Try copying all those effects with AI.
Another example: Red Dead Redemption 2
Another one: Roller coaster tycoon
Another one: ShaderToy
I wish I could agree with you, but as a game dev, shader author, and occasional asm hacker, I still think AIs have demonstrated being perfectly capable of copying "those effects". It's been trained on them, of course.
You're not gonna one-shot RD2, but neither will a human. You can one-shot particles and shader passes though.
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Why do you believe an LLM can't write these, just because they're 3D? If the assets are given (just as with a human game programmer, who has artists provide them the assets), then an LLM can write the code just the same.
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And some people clearly hate humans.
Great question, here is the link from the future: