If the Zig community carves out a territory of principled engineering like this, I may adopt it as my primary language and make a career out of it. Finally an island of sanity in a sea of slop.
It’s beautiful
They hiring?
Zig is really moving on this one.
Can’t wait for the punchline.
No punchline, just found such hardline requirements potentially entertaining.
What’s wrong with it? Honestly it’s kinda refreshing.
What’s wrong with it?
That’s a complete disregard of a useful tool that brings measurable benefits.
It’s a resolution they’ve arrived after considering the benefits and downsides of that tool. Nukes can be used for efficient large-scale excavation, but people were sane enough to realize why nuking themselves was a horrible idea.
Considering only the benefits and not the risks and drawbacks of anything is not just reckless. It’s zealotry.
where are these measurable benefits? so far they haven’t really realized anywhere
There’s also huge downsides that everyone always seems to forget (enormous power/clean water usage, noise pollution, etc.)
Clearly they don’t find it useful.
Up to them. I only answered the question.
Yeah how do they want to enforce this if people use it for brainstorming?
It might not be enforceable, but why would they want to contribute to something that’s very against what they use? Out of spite?
Like all of this ranges from unenforceable to spuriously enforceable (eg for rule 1, you can guess whether something has AI vibes—with vibe code it might be easier if the AI has just hallucinated a function or something). Seems more for the purpose of making a point than anything, or perhaps relying on others respecting your policy, but other projects with much more lenient no-AI policies still have people flagrantly breaking them.
It should be noted that e.g. DeepL which is a very good AI translation service isn’t an LLM but rather falls into the category of “Neural machine translation”. So this would still be fine
Edit: I leaned it’s an LLM now, my knowledge is outdated.
DeepL uses plenty of LLMs internally and recently laid of around 1000 employees to “shift to AI”.
To be fair, LLMs do really good translations, but as with everything you use them for, you need to be familiar with the subject so you catch their mistakes.
I’m thinking beginner level so the LLM can support instead of replacing you while you get better.
LLMs do not do translations, they approximate something similar to the original statement in another language. They are very accurate when given a common piece to translate, but wildly accurate when given a sentence which is highly improbable.
If it makes more mistakes than humans and therefor requires humans to check all of their work, and it’s been shown to not be very cost-effective, then what’s the point? Better to just not use the AI at all.
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LLM stands for “large language model” in other words, it is a big neural machine. Saying they don’t use LLMs is like saying the ocean isn’t blue, it’s azure.
LLMs work by increasing scale, number of separate neural networks, to increase accuracy when improvement from training hits a wall. Which is very problematic because it means power consumption becomes exponential. I think most people won’t have a problem with neural networks, but certainly do have a problem with LLMs.
Machine learning, neutral networks, AI, in general it’s very useful when trained at a specific task. LLMs are most certainly where things went wrong.
Love that, what a cool dude
