It's tempting to argue that a more constrained language helps, but Rust (62.8%) vs Elixir (97.5%) is an interesting data point here. Both are highly constrained, but in different directions. Elixir's constraints narrow the solution space because you can't mutate, you can't use loops, and you must pattern match, so every constraint eliminates options and funnels you toward fewer valid solutions that the LLM has to search through. Rust adds another constraint that must independently be satisfied on top of solving the actual problem, where the borrow checker doesn't eliminate approaches but adds a second axis of correctness the LLM has to get right simultaneously.

Overall, it seems like languages with strong conventions and ecosystems that narrow the solution space beat languages where there's a thousand ways to do something. Elixir has one build tool, one formatter, one way to do things. C#, Kotlin, and Java have strong ceremony and convention that effectively narrow how you write a program. Meanwhile JS, Python, PHP, and Perl offer endless choices, fragmented ecosystems, and rapidly shifting idioms, and they cluster at the bottom of the table.

2) The Tesla section is interesting. I'm not saying that you are wrong, just that their methods have not produced the promised results yet

3) Wireless humanoid robots is a bad platform because we don't have the hardware to support them. Both battery density and compute efficiency is too low currently to support freestanding robots. Rip Roomba - long live its legacy

Seems a huge assumption to me. From the data one could equally conclude that JavaScript and Python have lower code quality _because_ the quantity of training data, e.g. more code written by less experienced developers

Maybe so, but I find the right tool, for the right job, is important. Trying to use a screwdriver for every task, is going to result in bruises and cuts (and gouges in the work).

I've used a pretty "chimeric" approach, for most of my career, and the LLM that I use, doesn't seem to have an issue with that. Many of its responses, seem to take a similar approach.

Is there a future in which functional languages finally see wide adoption due to this LLM suitability? I can’t say that I would object!

http://literateprogramming.com/

Is LLM what will finally push LP into mainstream acceptance?

https://news.ycombinator.com/item?id=47300747

The article starts from a false premise: that AI assisted coding makes the code more understandable. This isn't the case. You either understand the code without AI or offload that reasoning onto the AI, at which point its not you that understands the code.

A person could argue AI writes original code more understandable at maintenance time than they could on their own. This is equally problematic for the same reason. If a person has a lesser understanding of the code at original authoring they will have a lesser understanding of the edge cases and challenges that went into the reasoning about that original code and its those thought challenges which inform the complexities of maintenance, not the simplicity of the base code.

As an analog its like being given a challenging game puzzle to solve. After realizing the game requires extended effort to reach the desired goal the person searches online for the puzzle solution. At the game's next level they encounter a more challenging puzzle, but they never solved the prior puzzle, and so cannot solve this puzzle. In effect all understanding is destroyed and they have become utterly reliant on spoon-fed solutions they cannot maintain themselves.

I don’t know about the premises here. All of these articles are written to hammer two points.

- AI is the future/AI has been here since X months ago

- There are still people who don’t believe that—to me an unfathomable position as I have personally spent five gazillion tokens on

And the supposed topic of the article is incidental to that.

But if GenAI is the future I’ll take GenAI formal verification and code generation over mindless code generation, thank you very much.

[1] https://news.ycombinator.com/item?id=47358696

The rest is AI-fluff:

> This isn't about optimizing for humans. It's about infrastructure

> But the bottleneck was never creation. It was always verification.

> For software, the load-bearing interface isn't actually code. Code is implementation.

> It's not just the Elixir language design that's remarkable, it's the entire ecosystem.

> The 'hard' languages were never hard. They were just waiting for a mind that didn't need movies.

In general I’m tired of the “humans need never, and should never look at the code” LLM triumphalism articles. Do these folks ever work with real systems, I wonder.

I mean, we called them objects, but coupling related state (and functions) together seem an objectively (object-ively) way to group data, it's literally just dict-based organisation.