- LLMs can't learn, therefore, LLMs are only good for things on which they are trained.
- Captchas are not friendly with trial and error, so agentic solutions also don't help.
- It's impractical to train LLMs on everything.
- We humans are capable of creating infinite ways of captchas.
While each of these sentences is true, captchas will always win against LLMs.
There are a missing the context: The vibecoded application was written in python while the main code was written manually in C by Torvalds in this side project. He never ever said that AI produces better code than him in the language where he is proficientI.
> The python visualizer tool has been basically written by vibe-coding. I know more about analog filters -- and that's not saying much -- than I do about python. It started out as my typical "google and do the monkey-see-monkey-do" kind of programming, but then I cut out the middle-man -- me -- and just used Google Antigravity to do the audio sample visualizer.
The LLM usage are disclosed only for the projects where this information is relevant.
By the way there are a lot of farmers that doesn't need the power of tractors to make farming their livehood. Makes sense when you realize that not everything needs to be super fast and efficient, sometimes cheap, slow and constant is enough.
It couldn't run "hello, world" on systems where the include files were not located in the directory that it expected -- producing instead diagnostics saying, quite clearly, that the header files were not found. On systems where they were, it built versions of postgresql, redis, and several other things which passed their test suites completely.
If you've heard this problem described as a fundamental limitation of the compiler, and not the kind of packaging glitch that's routine to find in pre-alpha software of all descriptions, whoever described it to you that way is not serving their readers well.
I'm not saying CCC was production-ready, or close -- the total lack of an optimizer would be a killer in any real use, and I assume that there were problems with the diagnostics at least as bad as problems with performance and the include files, for similar reasons -- the LLMs hadn't been asked to optimize for that stuff yet, just test suite correctness. But it did achieve that, and the amount of cope I've seen on social media claiming otherwise is more than a bit disturbing.
I have a colleague who multiple times committed code that doesn't work, like at all. Why? His code is only used in tests but not in the actual application. And apparently he never even bothered to click through things even once, let alone reviewing the code.
If it doesn't work, it doesn't. You can find all these excuses. But at the end of the day, there is a difference between an end user being able to get something out of your code or not.
Anthropic is doing changes on their help support pages on what looks like it will be the next pricing change regarding how users will use Opus models on Pro Plan.
reply