well, it also made by the same guy behind the boring go book
which probably means that boring go is also most likely AI generated
nowadays before reading or buying any book, we need to extensively investigate the author, the author name is now more important than ever, i think whenever a book is shared, the author need to me highlighted
> nowadays before reading or buying any book, we need to extensively investigate the author
I mean not really. You read a paragraph or two and notice the quality of the text, start getting suspicious and continue two or three paragraphs more, notice some very basic inconsistencies/incoherence, realize it's AI-written and ctrl+w the tab (or put back the book in the shelf) and move on with your life.
If there is no samples of the book, I'd hesitate to even consider buying it, just so you can actually sample the text. Very easy in bookstores luckily, so not a huge problem in the end.
If someone wanted an AI generated Zig tutorial, why wouldn't they go to their favorite LLM supplier and simply prompt the AI for one? Why do we need someone to create this spam ahead of time? LLMs are improving all the time and Zig is a moving target. Seems like a win-win-win. The end-user gets a better (and potentially customized) tutorial for the latest Zig, and there is less overall spam pollution.
I don't have the answers to those questions, and I don't know why I'm being asked those either. I don't like AI-slop either, I don't think anyone except the ones who produce it themselves like it.
But ultimately it's a fools errand trying to stop/get people to do something, best you can do is adjust your own approach.
i think for consumer protection, AI products need to be flagged as AI products, clearly labelled as AI produced or assisted , of course for free goods the burden is on us, but for anything we pay for, I hope we get this protection
I think this is a major problem with Zig in that they've cornered themselves into not utilizing AI and have attracted anti-AI absolutist crowd that is going to be a major hinderance.
As a Rust user, AI has put Rust easily in the hands of developers who were previously intimidated by it. I've learned a ton about Rust simply by fiddling with LLMs and asking questions to other Rust devs who don't really care whether AI is used.
As soon as you give into the mob rule, your existence is going to depend entirely on the ideology of the crowd and you lose the ability to adapt and remain competitive. ex) "modern audience" games from large studios.
You are free to use AI to write Zig code just not use AI to write code for Zig. In fact, many people already do build software with zig written by AI. Check out rockorager's github.
This is not true in any meaningful way. The most widely-used zig project (Ghostty) is very welcoming of AI contributions, and the lead developer of the project uses it heavily.
- 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.
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