If there's an edict that no one is allowed to bring up the topic, how can someone change this part of the code of conduct?

Asking non-rhetorically. It seems like one position "ai in any circumstance = bad" is being enforced. The commenter above didn't even understand why he was ignored.

Interesting to hear! We maintain the project Antfly (entirely zig) and have been nervous about bringing issues to the zig folks or asking questions because of our ai usage.

We’re quite knowledgeable and thoughtful folks fwiw

I think I’ve seen you are a core team member or contributor? I remember your tag?

The second part affirms that it is centralized. There is nothing wrong with that except saying that because you can leave the community and go elsewhere, it is ipso facto decentralized.

>Even in projects where I use LLM liberally, I would rather not read or have to engage with your LLM output.

I don't want to read LLM output on a project either. But you may have misread the quoted section, it isn't saying "you can't post LLM output," it is saying you can't ask an LLM to advise/critique your own comment before posting it.

>>If you use a chatbot to give you advice on a comment on the issue tracker, that comment is unwelcome.

Near bottom on auto coder benchmark from last year, so clearly claiming "consistently" is false or misleading. Did you make that up?

I mean what does "put you at a disadvantage" even mean concretely? To use a less popular language, it means you need to load up context related to the semantics of your language, load context on how to invoke tools to make sure the syntax with your language is correct, load up context related to each tool call you make (which will be more numerous in a niche language), and load up context on architectural decisions that might be specific to your language. All of this is simply a token cost. By forcing a model to load an initial amount of context per harness turn you also effectively shorten the max context window beyond which the model becomes stupid (which itself is much shorter than the max context length.)

Obviously it's not like people are specifically trimming each and every prompt they give a model to tokenmax their models to get the best output / input prompt, we instead live in a spectrum of how many tokens of input and context we're willing to provide to a model to make progress. If the cost of those tokens is low enough for the problem domain you're working in, then it's fine. For some the readability of a personal language may outstrip any of the token costs that one needs to pay to use it. Alternatively maybe you want something like an array language (J, K, APL, etc) which allows array programming and optimizations that conventional PLs just can't do. Maybe you want your language to compile to a target that is highly portable. There's actually a lot of stuff out there that previously wasn't feasible but with LLMs-as-force-multiplier absolutely is.

I also suspect the space is a continuum. There may be pareto optimal points, such as DSLs built atop languages, that are both highly readable but also fairly token efficient.

But you’re very clearly wrong. Just go ahead and try it: write your own language or just a DSL, write a brief description of how it works , let a LLM use it. Many of us have done this and everyone knows that it works extremely well, to most people’s surprise.

> Yes, we're building a sad world. I'm glad you noticed

What is the point of sarcastic, passive aggressive comments like this?