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.
Are people starting to write and talk in this manner, I see so many YouTube videos where you can see a person reading an AI written text, its one thing if the AI wrote it, but another if the human wrote it in the style of an AI.
As someone pointed out to me the way an AI writes text can be changed, so it is less obvious, its just that people don't tend to realise that.
"X isn't A, it's (something opposite A)" I twitch involuntarily.
The video was a biography about some Olympian, and I could tell the prompt included some facts about her wanting to be a tap dancer as a kid, because the video kept going back to that fact constantly. Every few sentences it would reference “that kid who wanted to be a tap dancer”. By the 6th time it brought up she wanted to be a tap dancer I was ready to scream.
https://www.pimlicojournal.co.uk/p/mps-are-almost-certainly-...
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.
Cue in "non-determinism" retort.
Will people start .gitignore-ing their src directories and only save prompts?
You can build a system with non-deterministic properties but you need some sort of deterministic foundation to build working, usable systems. Non determinism from top to bottom is building on quicksand in a swamp.
[1] https://www.oreilly.com/radar/can-language-models-replace-co...
Yeah compilers are deterministic and LLMs are not. The response to that?
The answer could very well be something like what’s in TFA namely formal verification. But an answer here is needed.
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.
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.
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
Weren't we taught that recycling is good?
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.
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.
http://literateprogramming.com/
Is LLM what will finally push LP into mainstream acceptance?
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
ashirviskas•2h ago
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Bolwin•1h ago