logicprog

Huh, this is an interesting hypothesis! It would make sense, to a degree. Part of "big model smell" to me is the model being able to pull a huge range of interesting algorithms, architectures, mathematics, and everything else that would come with greater world knowledge and having a much more sort of innate, like, full, well-rounded understanding of it instead of surface-level ICL pattern matching on web results.

That's why they talked about ethanol.

There's nothing about prove2me that couldn't have been coded just like any other huge coding project frontier models have proven themselves extremely good at doing. It just happened to have been made by humans.

> A fact that LLM hawks have categorically denied here before, with opposition naturally flagged.

Yeah, because before now there's been literally zero proof of an automated theorem prover scaffold around the LLMs being used, and big counterexamples and such being found, with raw chat logs available, where no such thing was used.

> Now they have it in writing.

Yeah, because now it's actually being done. They talk about it as a novel thing, because it is. You don't get to claim being "right all along" from this

This seems like a really really great debut model for a new lab. I'm happy

No, they don't. They're more interested in pathologizing and talking down to and about me.

"It's not an issue of usual vs unusual, I'm saying the models are way better at writing and maintaining django or react code bases than your own hand rolled architecture that you define in some docs that it has to learn and keep in context. All of the models do an amazing job making local edits and working in small greenfield projects but once you get to full production systems with close to a million lines things start to rot. The code still works and the models are able to make progress but all of a sudden you have 3-4 different versions of your concepts sprinkled in random corners of your code base.

My second point is that the models are way better at things like Rust or Lua than Python or JS, because the average person producing code in those languages has way more programming experience"

Its hard not to feel insulted here. "Projects using AI like this don't exist, and if they do, they're broken and the people who made them have psychosis." However, although I haven't tested my projects cross machine, or cross platform yet (my editor only works on MacOS because it uses kqueue), I would be happy to take an unedited video demonstration of both, I assure you they both run.

The GP was just talking about not using common languages like TypeScript and frameworks like React, which is a bar of unusual that seems arguably far lower than mine, especially since I literally defined special custom DSLs with unusual semantics, and used libraries like SDL_gpu with little to no pre-existing training data. So this seems like shifting the goalposts.

Also, what would be highly unusual to you?

That's fair, I think my point is there's a nuanced theory behind my choice to be rude, which is essentially "you need to experiment more, be more open minded, really work at it, and then you'll see what others are seeing, it isn't impossible." It's like Dark Souls, nobody can beat it for you, or teach you how to use dodge rolls, you've gotta build that skill yourself, and blaming the tools won't help.

In my experience, because as you say, the flexibility of these tools and how you engage with them is so broad, and how much the nuances of thay engagement can often matter, the only way to teach someone how to do what you do with them is to sit down and pair a program with them. Barring that, any instructions you could give would massively under-determined what they actually try as a result of your instructions, and so it ends up being an infinite loop of them just coming back and saying "it doesn't work." So in the end, people really just have to discover how these things work for themselves.

I haven't hit nearly a million lines, that's true, these projects are "only" 20k and 45k lines of code or so, respectively; however, I do have directed acyclic graphs of worker agents, managed asynchronously by the agent I chat directly with, do huge architectural refactors (the most recent, was to update everything in the codebase that used the sychronous Lua standard library `io` functions to use the asynchronous, multithreaded IO system I created for the editor — at least, everything that could use the new library, which I kept deliberately simple in a few ways, and everything that wasn't already temporary, but you know) and that's turned out quite well, and at least for me, 45k sloc is nothing to sneeze at, especially since with a green field project there's less examples to keep them on track. I do regularly check in to make sure it's using my design to the fullest, as well.

I do highly unusual, off the beaten path projects with AI.

For instance, a game engine written entirely in LuaJIT, but allocating almost all data structures using CFFI, a very unusual, custom object-orientation DSL I designed myself, and that uses SDL3's SDL_gpu library to do all rendering, a ton of GLSL 4.6 compute shaders cross compiled to SPIR-V and Metal (which constraints what GLSL can be written), and is a hybrid tile-oriented and 3D engine.

Another example is my Emacs-like live-modifiable image-based editor, again in LuaJIT+CFFI, with a kqueue+atomic lock-free ring buffer+pthread based actor model, each running a separate LuaJIT interpreter, with a custom advicing system, event bus, async/await implementation riding on the event bus, a custom process management and IO library, and testing via Tmux.

Even DeepSeek V4 Flash mostly does fine with this, once it's built a few memories in the memory system. Let alone something like DSv4-Pro or GLM 5.2. All I can say is skill issue.

Do you have any evidence for Valkey "widely replacing" Redis, instead of just rapidly gaining adoption as well? Additionally, it's important to clarify that the replacement was 100% over licensing issues — iirc predating the vibe coding entirely — not the use of AI to code, and that furthermore, no one has to my knowledge pointed to any flaws in the Redis codebase even correlated with AI use. So this is a disingenuous framing.