chad1n

So the author is in a clear conflict of interest with the contents of the blog because he's an employee of Anthropic. But regarding this "blog", showing the graph where OpenAI compares "frontier" models and shows gpt-4o vs o3-high is just disingenuous, o1 vs o3 would have been a closer fight between "frontier" models. Also today I learned that there are people paid to benchmark AI models in terms of how close they are to "human" level, apparently even "expert" level whatever that means. I'm not a LLM hater by any means, but I can confidently say that they aren't experts in any fields.

The guys in the other thread who said that OpenAI might have quantized o3 and that's how they reduced the price might be right. This o3-pro might be the actual o3-preview from the beginning and the o3 might be just a quantized version. I wish someone benchmarks all of these models to check for drops in quality.

To be honest, checking if there is a path between two nodes is a better example of NP-Hard, because it's obvious why you can't verify a solution in polynomial time. Sure the problem isn't decidable, but it's hard to give problems are decidable and explain why the proof can't be verified in P time. Only problems that involve playing optimally a game (with more than one player) that can have cycles come to mind. These are the "easiest" to grasp.

Considering how much they trust their LLMs, why don't they just run o1-pro to make a summary of the responses given in the feedback

It will support both, but considering the previous experiences with avx 512 on intel, I wouldn't that excited

I wonder how much LimeWire pays to buy all of these foss projects, must be a decent amount if everyone is selling his solution

The idea is correct, a lot of people (including myself sometimes) just let an "agent" run and do some stuff and then check later if it finished. This is obviously more dangerous than just the LLM hallucinating functions, since at least you can catch the latter, but the first one depends on the tests of the project or your reviewer skills.

The real problem with hallucination is that we started using LLMs as search engines, so when it invents a function, you have to go and actually search the API on a real search engine.

These "OCR" tools who are actually multimodals are interesting because they can do more than just text abstraction, but their biggest flaw is hallucinations and overall the nondeterministic nature. Lately, I've been using Gemini to turn my notebooks into Latex documents, so I can see a pretty nice usecase for this project, but it's not for "important" papers or papers that need 100% accuracy.

It's not really a hot take, considering the price, they probably released it to scam some people when they to `benchmark` it or to buy the `pro` version. You must be completely in denial to think that gpt4.5 had a successful launch, considering that 3 days before, a real and useful model was released by their competitor.

I quit the original l"Firefox" a long time ago, I've been using librewolf since its release and now zen (also a firefox fork) and I keep ungoogled chromium in case a site is broken on firefox.

I don't think that's the case, when a model is reasoning, it sometimes starts gaslighting itself and "solving" other problems completely than the one you've shown. Reasoning can help "in general", but very frequently, reasoning also makes it more "nondetermistic". Without reasoning, usually it ends up just writing some code from its training data, but with reasoning, it can end up hallucinating hard. Yesterday, I asked Claude thinking to solve me a problem in c++ and it showed the result in python.

Same happened to an app that I published on Play Store, I don't even care that much, I only feel bad for the people that bought the premium version of it. Overall the takeway is that your product is never safe and you shouldn't only rely on these big platforms for marketing/distribution.

I think that OpenAI should reduce the prices even further to be competitive with Qwen or Deepseek. There are a lot of vendors offering Deepseek R1 for $2-2.5 per 1 million tokens output.