Unless you're suggesting the language design should also be vibed together?
Sounds like a fun little project, have a bunch of AI pushers fork Zig and see if they can do a better job. I want to see results, not snarky HN comments. After all this progress, ChatGPT should be able to one-shot a better language since AI is so good now... right?
I'm doing it myself: https://zena-lang.dev/
Curious why you wanted a more ML / Rust / Scala inspired syntax. (Personal preference here is totally valid btw, just curious.)
If you're using AI, a language with a large training set is going to win.
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.
Note the LLMs are trained on language semantics far beyond the mainstream ones, so language design can become quite exotic without straying too much from the training. You really do have to measure these things, I don’t see how you can make a confident assertion without data.
When I design my own languages (I have written several, all terrible!) it's typically to learn about language design.
I wrote about some of my thoughts with Zena and AI here: https://zena-lang.dev/blog/2026/09/languages-for-the-ai-era/
What is the point of sarcastic, passive aggressive comments like this?
The best I can do is understand its edges and try to find some advantage that leaves me well off enough to stave off the worst effects.
https://mech-lang.org/iros-r4r-2026/index.html#5805406811462...
I compare one algorithm across several programming languages and backends. There is no training data for Mech in the LLM yet it beats most other implementations in perf, which were optimized by LLM.
Maybe human performance engineers trained in these languages could write better implementations. But to answer the question of whether the LLM could write more performant code in languages it’s trained in versus languages it’s not, this comparison is at least illustartive.
Not necessarily? What if the training set contains an overwhelming amount if bad code written by neophytes? I imagine Python quality by the LLM suffers from this, for example.
What if the language has extremely confusing syntax constructs (like early php) or bad or no conventions (suppose the standard library has somecollection.put(key, value) sometimes and othercollection.put(value, key) other times), and individual code authors just pick what they want adhoc
Large training set ain't gonna save you.
None of these systems can. They need enormous training. They need alignment and reinforcement. They need harnesses. And most importantly they need a human that knows how to write and develop a C compiler.
The ISO specifications are not sufficient. Neither are the System V guidelines. Not even spec tests and compcert.