famouswaffles

Being racist comes easy for you huh?

Today with Opus, Sol, etc there's basically no reason to visit the site, but when chatGPT launched it was powered by GPT 3.5, which would realistically only be able to answer a (minor) chunk of all possible StackOverflow questions.

Software Development is far from the only white collar work frontier agents are very useful for. Most people just have no idea and marketing around them hasn't really struck. If you think it's just code or math that LLMs have gotten rapidly better at then you'd be wrong.

>For Python libraries they would be written in Python, eventually ctypes for syscalls.

So if you can't call/use the 'library' from the 'native' language, it's still a library? Whatever floats your boat. Lol.

I know what bindings are. A front-end to C++ for pytorch was only released from 1.0, literally 2 years later. The relationship is very clear here. There was no native C++ library. It was an afterthought. Even in instances where there was, like Tensorflow, functionally it was still an afterthought.

They are python libraries because people reached for those languages so they could continue work in python. They are there to serve python and nothing else. It's the same with Rust. It's by no means a C++ thing.

EDIT: Not saying this is always the case. Qt>Pyside is an example where it's both technically and functionally bindings, but the vast majority of the time they are serving python. Some don't even have a native library to speak of.

>the problem is that the Python community at large rather prefers their C bindings or nowadays Rust, sprinkle a bit of Python FFI on top, and call them "Python" libraries.

They are Python libraries. Don't make the mistake of screwing up the cause and effect here. C, Rust etc - those are the replaceable parts of the chain. There is an official C++ pytorch library and virtually nobody uses it for ML Research. People want to code in python and build those libraries so they can.

Results come from an internal model that has not been publicly released. And there are lesser problems and conjectures ordinary folks have solved with Astra/Sol.

Because I might look elsewhere if they starting charging several orders of magnitudes more then I'm not sticky. Does that remotely make any sense to you ?

I'm not rich and you don't have to be rich.

Most people buy their apples from the same grocery store every week. They're not shopping around every month to find who has the cheapest apples or 'best' apples. They go where they're accustomed to going, and as long as price and quality are satisfactory, they have little reason to change. Walmart, price-sensitive as they are, have relationships with fruit suppliers they've maintained for well on a decade. Even in commoditized markets, people are sticky.

Google, the titan mega-corp with more money than they know what to do with and a billion+ android devices to ship gemini on and it's made no dent for ChatGPT's consumer growth and adoption. Yet some plucky open source chinese models will?. It takes more than that.

Yes it is. Tiktok's content and content recommendation algorithms are much better than Instagram. Users spend more time on Tiktok than they do Instagram and it's not close. Tiktok's engagement rates are several times better than Instagram. Reels did nothing to dent it (yet another example in the book). Again, nobody will care about Chinese models if they're just more of the same. They can take a piece of the pie, but this idea that they'll spell doom for the frontier labs is nonsense.

Given we are comparing two different substrates, there's no reason to assume this means anything.

Yeah. People have very strange ideas about competition and choice and moat. If all you needed to unseat the entrenched was a competitor then Bing would be going toe to toe with Google, and Edge with Chrome. They have a billion+ devices they can stick those options as defaults and still nothing. There are many examples.

People are naturally sticky. If they're satisfied with a product, they'll stick around unless something comes along that is so much better it gets people talking. Open AI's 1B+ customers won't give a shit about Chinese models. Enterprise customers won't move to Chinese models, especially just to save money. They are a non issue, especially if the M.O is just going to be playing catch-up.

Difussion Gemma performed a lot worse than its equivalent autoregressive counterpart (it was particularly bad because rather than a sort of constant-ish gap you might see between 2 model classes, the gap seemed to worsen the harder the task/benchmark) and the speed benefits got negated at scale. Not really surprising we haven't heard much.

No it's already explicitly stated that translation to lean is a second step that happens after NL proof is done. That's why some of 400 paper dump is without lean proofs.