dkersten

And AI tried telling me that Uber for Dogs (dogs are the drivers) was a terrible idea…

Its on other providers, like Together.ai

> This is not scientific at all, just vibes, YMMV.

This is the problem.

I would love to have a product sheet showing what each models strengths an weaknesses are, so that I can have a clear decision tree of "if this kind of work, use model X", or "model Y should be used in ways Z". But they all look the same from the outside and the only way to figure out which might be marginally better at what is to do extensive, time consuming, and perhaps expensive testing.

Is that true though? From Cherno’s videos it sounded like it was basically the hazel engine, repurposed. So unless he rewrote hazel, purposely for robotics, it’s still not actually the case?

Haven’t Unreal engine and Unity been used for robotics for over a decade?

Hasn’t the Bullet physics engine been used for robotics for over a decade?

I don’t understand this “first game engine for robotics” messaging.

As an aside, this website crashes for me on safari on iOS.

They did, I had something like $50 in FTX and I got it back. I don’t remember when, I think it was about a year ago.

Why am I not surprised that its a YC startup? Lately, being a YC startup seems to have become a negative signal for me, far too many grifters are getting funded by YC, it seems.

> That presumes models are only useful if they are one-shot models

That’s not at all what I’m saying.

I’m saying that in my experience across multiple models, the follow up prompts don’t fix prior underlying issues. They usually patch on top instead, unless you give them significant and time consuming guidance.

I want them to be more useful outside of one-shot uses, but I find that they currently miss the mark.

I haven’t used Fable/Mythos yet, but my experience with recent version of Opus, GPT 5.5 and recent Chinese models is that promoting again isn’t guaranteed to fix the underlying issues, nor is it guaranteed to not introduce more issues. I’ve seen SOTA models make ridiculously stupid architectural decisions that they were then unable to back out of without being prompted very specifically, instead adding a patchwork of “fixes” on top.

I’m not saying that you can’t use AI to do it because I believe that with carefully controlled workflows and context management you can, but it’s not a simple prompt away, it’s requires guidance and understanding, and isn’t the speed demon that raw prompting is.

That’s the one.

The speed is incredible and fun to see, but the model is rather weak to the point where I’m not sure it’s particularly useful for most people.

> You've gone too fast, too much is vague, nothing is clear.

Contrast to when Clojure was released: Rich Hickey had spent years thinking about, researching, and refining the concepts. It was easy to understand what the language is. And it shows in the design quality as even now, almost two decades later, the language has changed surprisingly little and is still really good.

I’ve been playing around with groq and GPT OSS which they run at 1000 TPS (20B) or 800 TPS (120B) and the speed feels quite magical.

I haven’t tried cerebras’ 3000 TPS yet but I did try the demo of that 15,000 TPS model whose name escapes me right now.

I’m not sure if it makes a meaningful difference for my actual work, but it sure is amazing to watch it generate a screen full of text in the blink of an eye.

I do think it’s super useful for rubbing little validation checks like showing it a diff to ensure that the changes are on task, and being able to do those quicker really helps because it means you can do many focused checks without them getting in the way.

I tried to play it about two months ago and it pops up a notice and quits. I found on Reddit that the notice is “we are Rory, but servers for this title have been shut down. Thank you very much for playing” which is roughly like what I remember.

According to Wikipedia: “in December 2023, all servers for mirrors edge catalyst were shut down by EA” but that only says online content was disabled.

It’s possible that the game is only unplayable on PlayStation, but still playable (without online features) on PC. But it does seem to still be listed in stores (steam and PlayStation) so I’m not sure exactly what’s going on. I’d have to redownload it to test it again.

Then they should not be allowed to use words like “buy” in the online stores.

Some games are perfectly fine without online features yet they still got shut off. Mirrors Edge Catalyst only used online for leaderboards and challenges, everything else was entirely single player, yet it’s not possible to play it anymore because they wanted to shut off the servers yet designed the game to not run without them, even though it’s a single player game.

That should not be ok. It wasn’t sold with a disclaimer or expectation that it could be switched off.

Crypto was already a term before cryptocurrencies made it about them. Web 3.0 was already a thing before crypto bros made web 3 about cryptocurrencies.

So what? Terms are reused in different contexts all the time. And most people have moved on from cryptocurrencies anyway, so there’s little chance it’ll confuse anyone.

They still do, but only for enterprise customers.

Together.ai provide many open weights models and as far as I’m are their servers are US based (the company certainly is)

Groq don't train their own models, they serve open ones. They're a few versions behind the frontier open weights models. The top model they serve is GPT OSS 120B, released last summer.

Only briefly for fun (and OCaml too but very long ago). F# does seem like a language I feel I could really like, though.

I’ve tinkered a little in Gleam and Elixir too. Elixir was incredibly enjoyable, and Gleam is a wonderful language that I wish I could work with more, but I don’t really have a use case for it and nobody will pay me to write it…

Back in 2009, I also spent a few months playing with Factor (before switching to Clojure because despite Clojure being niche, Factor was even more so). It was an interesting and somewhat mind bending experience.

Haha thanks. Funny thing is, I’ve been a long time Python fan, learning it in 2001 and using it extensively for a long time. But then I learned Clojure, Typescript, and recently Rust and I’ve found Python to be quite flawed. But it sure does have a cult following.

Deleted from the current head/trunk of the repo, ie the deployed code.

Deleting "from my codebase" doesn't imply deleting it from history or backups. Just that the code isn't present for future edits or deployments.

The way you're talking, it sounds like you never delete code from your codebase. Do you just comment it out when you change a line to something else or replace a function with a new one? Just add new files?

Not the most important thing, but it certainly helps.

Typescript wins in terms of training data IMHO, by which I mean that the training data is large enough that AI does great with TS, and the language is (IMHO) superior to Python in many ways.

I personally now use a mixture of Typescript and Rust for most things, including AI coding. Its been working quite well. (AI doesn't handle Rust as well as TS, in that the code isn't quite idiomatic, but it does ok)

> You deleted them (never delete anything, isn't this what modern VCS is all about?)

The person said: "deleted 100k+ lines this year already moving them to faster languages"

Are you saying that when you move code to another language/rewrite in another language, you leave the original languages code in your repo?

They didn't say they deleted it from their git history. I delete code all the time (doesn't mean its "gone", just that its not in my git head).

But one European countries legal system also doesn’t constitute “most European legal systems”.

It’s GLM 4.7, GPT OSS 120B, or llama 3.1 8B so not exactly the latest or best models.

But GLM is good enough for many small tasks, certainly enough to get a taste for Cerebras’ high speeds!

[edit: actually that’s just their general models, I can’t see what Cerebras code offers. It was Qwen-coder when it launched but I don’t know what it is now. I think GLM 4.7 but I’m not completely sure]

For small enough tasks with tight enough workflows, you can have it right now. Ie if you can constrain the task to work well with GPT OSS 120B/llama 3.3/qwen 3, then you can get upwards of 600 TPS on groq and up to 3k TPS on Cerebras.

Those models aren’t comparable to Opus, or even weaker models like MiniMax, but for certain task (focused context and prompts, strict workflows, single purpose requests) you absolutely can use these models and get insane speeds.

Fair enough. I didn’t mean to push Rust, which is why I removed the last statement from my original comment. I was really just trying to make an observation that devirtualisation isnt something I need to think about in Rust (you could make an argument that dynamic should be the default and C++ got it right, and it would be an interesting, but different, discussion). I thought that was interesting, as a language design enthusiast.

In the OP, there are ten test cases. Some devirtualise. One only on clang, another only on GCC. In Rust, polymorphism is always "devirtualised" unless I say "no, actually, make this dyn", and in many cases, that's actually perfectly fine. It feels like the rational default, you only pay for the dynamic support when you need it. I don't dislike C++, but sometimes it gets exhausting having to remember all the situations where the compiler might do this or that, or I have to write far more complex code via templates or other techniques to get the behavior I want (sure, its been made easier with recent C++ versions, I admit I'm still on C++20 and even then not all of it; originally because compiler support was patchy, now because I just haven't used C++ as much lately). Its that difference that I thought was interesting.

I feel like it’s still missing the point. In a discussion about devitalisation, I thought it was interesting that it’s not something that you have to think about in Rust, while in C++ you do.