I call these "embarrassingly solved problems". There are plenty of examples of emulators on GitHub, therefore emulators exist in the latent spaces of LLMs. You can have them spit one out whenever you want. It's embarrassingly solved.
Its license washing. The code is great because its already a problem solved by someone else. The AI can spit out the solution with no license and no attribution and somehow its legal. I hope American tech legislation holds that same energy once others start taking American IP and spitting it back out with no license or attribution.
This is why its astonishing to me that AI has passed any legal department. I regularly see AI output large chunks of code that are 100% plagiarised from a project - its often not hard to find the original source by just looking up snippets of it. 100s of lines of code just completely stolen
Ai doesn't actually wash licenses, it literally can't. Companies are just assuming they're above the law
I've seen many discussions stating patent hoarding has gone too far, and also that copyright for companies have gone way too far (even so much that Amazon can remove items from your purchase library if they lose their license to it).
Then AI begins to offer a method around this over litigious system, and this becomes a core anti-AI argument.
I do think it's silly to think public code (as in, code published to the public) won't be re-used by someone in a way your license dictates. I'd you didn't want that to happen, don't publish your code.
Having said that, I do think there's a legitimate concern here.
The other day I had an agent write a parser for a niche query language which I will not name. There are a few open source implementations of this language on github, but none of them are in my target language and none of them are PEGs. The agent wrote a near perfect implementation of this query language in a PEG. I know that it looked at the implementations that were on github, because I told it to, yet the result is nothing like them. It just used them as a reference. Would and should this be a licensing issue (if they weren't MIT)?
To me, it's just further evidence that trying to assert ownership over a specific sequence of 1s and 0s is an entirely futile and meaningless endeavor.
At the end of the day it's up to the publisher of the work to attribute the sources that might end up in some commercial or public software derivative.
I did have the thought that the SCOTUS ruling against Oracle slightly opened the door to code not being copyrightable (they deliberately tap-danced around the issue). Maybe that's the future: all code is plumbing; no art, no creative intent.
In a way it shows how poorly we have done over the years in general as programmers in making solved problems easily accessible instead of constantly reinventing the wheel. I don't know if AI is coming up with anything really novel (yet) but it's certainly a nice database of solved problems.
I just hope we don't all start relying on current[1] AI so much that we lose the ability to solve novel problems ourselves.
[1] (I say "current" AI because some new paradigm may well surpass us completely, but that's a whole different future to contemplate)
> In a way it shows how poorly we have done over the years in general as programmers in making solved problems easily accessible instead of constantly reinventing the wheel.
I just don't think there was a great way to make solved problems accessible before LLMs. I mean, these things were on github already, and still got reimplemented over and over again.
Even high traffic libraries that solve some super common problem often have rough edges, or do something that breaks it for your specific use case. So even when the code is accessible, it doesn't always get used as much as it could.
With LLMs, you can find it, learn it, and tailor it to your needs with one tool.
It’s 2026 and code reuse is still hard. Our code still has terrible modularity. Systems have terrible to nonexistent composability. Attempts to fix this like pure OOP and pure FP have never caught on.
To some extent AI is an entirely different approach. Screw elegance. Programmers won’t adhere to an elegant paradigm anyway. So just automate the process of generating spaghetti. The modularity and reuse is emergent from the latent knowledge in the model.
I view LLMs akin to a dictionary - has a bunch of stuff in there but by itself it doesn't add any value. The value comes from the individual piecing together the stuff. Im observing this in the process of using Grok to put together a marketing video - theres a whole bunch of material that the LLM can call upon to produce an output. But its on you to prompt/provide it the right input content to finesse what comes out (this requires the individual to have a lot of intelligence/taste etc....) . Thats the artistry of it.
Now that Im here Ill say Im actually very impressed with Groks ability to output video content in the context of simulating the real-world. They seemingly have the edge on this dimension vs other model providers. But again - this doesnt mean much unless its in the hands of someone with taste etc. You cant one-shot great content. You actually have to do it frame-by-frame then stitch it together.
I tried writing a plain text wordle loop as a python exercise in loops and lists along with my kid.
I saved the blank file as wordle.py to start the coding while explaining ideas.
That was enough context for github copilot to suggest the entire `for` loop body after I just typed "for"
Not much learning by doing happened in that instance.
Before this `for` loop there were just two lines of code hardcoding some words ..that too were heavily autocompleted by copilot including string constants.
This makes it really hard for juniors to learn, in my experience. When I pair with them I have them turn off that functionality so that we are forced to figure out the problems on our own and get to step through a few solutions that are gradually refined into something palatable.
I hate aggressive autocomplete like that. One thing to try would be using claude code in your directory but telling it that you want it to answer questions about design and direction when you get stuck, but otherwise never to touch the code itself, then in an editor that doesn't do that you can hack at the problem.
When LLMs first appeared this was what I thought they were going to be useful for. We have open source software that's given away freely with no strings attached, but actually discovering and using it is hard. LLMs can help with that and I think that's pretty great. Leftpad wouldn't exist in an LLM world. (Or at least problems more complicated than leftpad, but still simple enough that an LLM could help wouldn't.)
Strange that noone noticed the article saying "Nobody said 'Google did it for me' or 'it was the top result so it must be true.'"
Because they did. They were the quintessential "Can I haz teh codez" Stack Overflow "programmer". Most of them, third world. Because that's where surviving tomorrow trumps everything today.
Now, the "West" has caught up. Like they did with importing third world into everything.
Which makes me optimistic. Only takes keeping composure a few more years until the house of cards disintegrates. Third world and our world is filled to the brim with people who would take any shortcut to avoid work. Shitting where they eat. Littering the streets, rivers, everywhere they live with crap that you throw out today because tomorrow it's another's problem.
Welcome to third world in software engineering!
Only it's not gonna last. Either will turn back to engineering or turn to third world as seemingly everything lately in the Western world.
There's still hope though, not everybody is a woke indoctrinated imbecile.
Stop repeating this trope. It can spit out something you've never built before this is utterly clear and demonstrated and no longer really up for debate.
Claude code has never been built before claude code. Yet all of claude is being built by claude code.
Why are people clinging to these useless trivial examples and using it to degrade AI? Like literally in front of our very eyes it can build things that aren't just "embarrassingly solved"
I'm a SWE. I wish this stuff wasn't real. But it is. I'm not going off hype. I'm going what I do with AI day to day.
I think we are in violent agreement and I hope that after reading this you think so too.
I don't disagree that LLMs can produce novel products, but let's decompose Claude Code into its subproblems.
Since (IIRC) Claude Code's own author admits he built it entirely with Claude, I imagine the initial prompt was something like "I need a terminal based program that takes in user input, posts it to a webserver, and receives text responses from the webserver. On the backend, we're going to feed their input to a chatbot, which will determine what commands to run on that user's machine to get itself more context, and output code, so we need to take in strings (and they'll be pretty long ones), sanitize them, feed them to the chatbot, and send its response back over the wire."
Everything here except the LLM has been done a thousand times before. It composed those building blocks in novel ways, that's what makes it so good. But I would argue that it's not going to generate new building blocks, and I really mean for my term to sit at the level of these subproblems, not at the level of a shipped product.
I didn't mean to denigrate LLMs or minimize their usefulness in my original message, I just think my proposed term is a nice way to say "a problem that is so well represented in the training data that it is trivial for LLMs". And, if every subproblem is an embarrassingly solved problem, as in the case of an emulator, then the superproblem is also an ESP (but, for emulators, only for repeatedly emulated machines, like GameBoy -- A PS5 emulator is certainly not an ESP).
Take this example: I wanted CC to add Flying Edges to my codebase. It knew where to integrate its solution. It adapted it to my codebase beautifully. But it didn't write Flying Edges because it fundamentally doesn't know what Flying Edges is. It wrote an implementation of Marching Cubes that was only shaped like Flying Edges. Novel algorithms aren't ESPs. I had to give it access to a copy of VTK's implementation (BSD license) for it to really get it, then it worked.
Generating isosurfaces specifically with Flying Edges is not an ESP yet. But you could probably get Claude to one shot a toy graphics engine that displays Suzanne right now, so setting up a window, loading some gltf data, and displaying it definitely are ESPs.
AuthAuth|21 days ago
20k|21 days ago
Ai doesn't actually wash licenses, it literally can't. Companies are just assuming they're above the law
ThunderSizzle|21 days ago
Then AI begins to offer a method around this over litigious system, and this becomes a core anti-AI argument.
I do think it's silly to think public code (as in, code published to the public) won't be re-used by someone in a way your license dictates. I'd you didn't want that to happen, don't publish your code.
Having said that, I do think there's a legitimate concern here.
palmotea|21 days ago
Has that been properly adjudicated? That's what the AI companies and their fans wish, but wishing for something doesn't make it true.
anonnon|21 days ago
Note that even MIT requires attribution.
phpnode|21 days ago
unknown|19 days ago
[deleted]
irishcoffee|21 days ago
sharperguy|20 days ago
candiddevmike|21 days ago
userbinator|21 days ago
Everything is a derivative work.
tty456|21 days ago
Andrex|20 days ago
Nition|21 days ago
I just hope we don't all start relying on current[1] AI so much that we lose the ability to solve novel problems ourselves.
[1] (I say "current" AI because some new paradigm may well surpass us completely, but that's a whole different future to contemplate)
BobbyJo|21 days ago
I just don't think there was a great way to make solved problems accessible before LLMs. I mean, these things were on github already, and still got reimplemented over and over again.
Even high traffic libraries that solve some super common problem often have rough edges, or do something that breaks it for your specific use case. So even when the code is accessible, it doesn't always get used as much as it could.
With LLMs, you can find it, learn it, and tailor it to your needs with one tool.
api|21 days ago
To some extent AI is an entirely different approach. Screw elegance. Programmers won’t adhere to an elegant paradigm anyway. So just automate the process of generating spaghetti. The modularity and reuse is emergent from the latent knowledge in the model.
sdf2erf|21 days ago
Now that Im here Ill say Im actually very impressed with Groks ability to output video content in the context of simulating the real-world. They seemingly have the edge on this dimension vs other model providers. But again - this doesnt mean much unless its in the hands of someone with taste etc. You cant one-shot great content. You actually have to do it frame-by-frame then stitch it together.
albert_e|21 days ago
I saved the blank file as wordle.py to start the coding while explaining ideas.
That was enough context for github copilot to suggest the entire `for` loop body after I just typed "for"
Not much learning by doing happened in that instance.
Before this `for` loop there were just two lines of code hardcoding some words ..that too were heavily autocompleted by copilot including string constants.
``` answer="cigar" guess="cigar" ```
cess11|21 days ago
zjp|21 days ago
Aerroon|20 days ago
When LLMs first appeared this was what I thought they were going to be useful for. We have open source software that's given away freely with no strings attached, but actually discovering and using it is hard. LLMs can help with that and I think that's pretty great. Leftpad wouldn't exist in an LLM world. (Or at least problems more complicated than leftpad, but still simple enough that an LLM could help wouldn't.)
MichaelRo|20 days ago
Because they did. They were the quintessential "Can I haz teh codez" Stack Overflow "programmer". Most of them, third world. Because that's where surviving tomorrow trumps everything today.
Now, the "West" has caught up. Like they did with importing third world into everything.
Which makes me optimistic. Only takes keeping composure a few more years until the house of cards disintegrates. Third world and our world is filled to the brim with people who would take any shortcut to avoid work. Shitting where they eat. Littering the streets, rivers, everywhere they live with crap that you throw out today because tomorrow it's another's problem.
Welcome to third world in software engineering!
Only it's not gonna last. Either will turn back to engineering or turn to third world as seemingly everything lately in the Western world.
There's still hope though, not everybody is a woke indoctrinated imbecile.
threethirtytwo|21 days ago
Claude code has never been built before claude code. Yet all of claude is being built by claude code.
Why are people clinging to these useless trivial examples and using it to degrade AI? Like literally in front of our very eyes it can build things that aren't just "embarrassingly solved"
I'm a SWE. I wish this stuff wasn't real. But it is. I'm not going off hype. I'm going what I do with AI day to day.
zjp|21 days ago
I don't disagree that LLMs can produce novel products, but let's decompose Claude Code into its subproblems.
Since (IIRC) Claude Code's own author admits he built it entirely with Claude, I imagine the initial prompt was something like "I need a terminal based program that takes in user input, posts it to a webserver, and receives text responses from the webserver. On the backend, we're going to feed their input to a chatbot, which will determine what commands to run on that user's machine to get itself more context, and output code, so we need to take in strings (and they'll be pretty long ones), sanitize them, feed them to the chatbot, and send its response back over the wire."
Everything here except the LLM has been done a thousand times before. It composed those building blocks in novel ways, that's what makes it so good. But I would argue that it's not going to generate new building blocks, and I really mean for my term to sit at the level of these subproblems, not at the level of a shipped product.
I didn't mean to denigrate LLMs or minimize their usefulness in my original message, I just think my proposed term is a nice way to say "a problem that is so well represented in the training data that it is trivial for LLMs". And, if every subproblem is an embarrassingly solved problem, as in the case of an emulator, then the superproblem is also an ESP (but, for emulators, only for repeatedly emulated machines, like GameBoy -- A PS5 emulator is certainly not an ESP).
Take this example: I wanted CC to add Flying Edges to my codebase. It knew where to integrate its solution. It adapted it to my codebase beautifully. But it didn't write Flying Edges because it fundamentally doesn't know what Flying Edges is. It wrote an implementation of Marching Cubes that was only shaped like Flying Edges. Novel algorithms aren't ESPs. I had to give it access to a copy of VTK's implementation (BSD license) for it to really get it, then it worked.
Generating isosurfaces specifically with Flying Edges is not an ESP yet. But you could probably get Claude to one shot a toy graphics engine that displays Suzanne right now, so setting up a window, loading some gltf data, and displaying it definitely are ESPs.