alt Hacker NewsGrace Hopper's Revenge
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1) AutoCodeBranch does not present similar problems, so you can't compare the Upper Bound metric across languages. Actually the Elixir tests are transmuted from other languages.
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
> The amount of training data doesn’t matter as much as we thought.
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
It seems that this is really saying that Functional Programming is The Way.
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.
I don’t think it’s a surprise that statically typed, immutable functional languages are the easiest (most suitable?) translation target for LLMs. I am surprised Haskell wasn’t represented in the table. Ocaml and Standard ML would be interesting as well.
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!
If the tool chain needs to be structured to facilitate people writing programs and documentation and comments, why isn't Literate Programming more popular?
http://literateprogramming.com/
Is LLM what will finally push LP into mainstream acceptance?
Brilliant take! We have spent so much time getting stupid people to program, that we have actually made the machines dumber :D
> I’m still seeing a decent number of people on Twitter complain occasionally that they’ve tried AI-driven coding workflows and the output is crap and they can move faster by themselves. There’s less of these people in the world of Opus 4.5 and Gemini 3 now, but they’re still there.
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 found it interesting that Elixir scores so high, but I'm not sure whether I can agree with the cause.
Against Flintstone Engineering.[1] That’s great.
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.
This article takes a very tiny, questionable bit of data and extrapolates a lot of iffy assertions.
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.
The TL;DR: code should be easy to audit, not easy to write for humans.
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.
Surprised to see Go scoring so low
This is so annoying. First it’s obviously AI generated so it’s hard to even read. But if we get past that it’s making all kinds of uncited claims. Did Grace Hopper envision the translation layer moving directly from English to machine code? I don’t know because I can’t trust the LLM to say, and the article does not include a citation — in a piece whose central claim is that AI shifts the burden from coding to verification.
> We built objects with identity and state because that’s how we experience reality
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.
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The data doesn't well support the claim that FP is best. Elixir tops the table at 97.5%, but C# (88.4%) is OOP and scores almost identically to Racket (88.9%), and Ruby (81.0%) and Java (80.9%) both outscore Scala (78.4%), which is explicitly functional. If FP were the driver, Scala should beat those languages, but it doesn't.
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.