HN Reader

It makes me wonder if we'll see an explosion of purpose trained LLMs because we hit diminishing returns on invest with pre training or if it takes a couple of months to fold these advantages back into the frontier models.

Given the size of frontier models I would assume that they can incorporate many specializations and the most lasting thing here is the training environment.

But there is probably already some tradeoff, as GPT 3.5 was awesome at chess and current models don't seem trained extensively on chess anymore.

This whole series of work is quite cool. The use of `word-break: break-word;` makes this really hard to read though.

I recently got a 5090 with 64 GB of RAM (intel cpu). Was just looking for a strong model I can host locally. If I had performance of GPT4-o, I'd be content. Are there any suggestions or cases where people got disappointed?

Has anyone found these deep research tools useful? In my experience, they generate really bland reports don't go much further than summarization of what a search engine would return.

Is China dominating the US in terms of AI? Given that they currently have a model that beats the best models at all formal quantitative benchmarks?

What is the state of AI in China? My personal feeling is that it doesn't dominate the zeitgeist in China as it does in the US and despite this because of the massive amount of intellectual capital they have just a small portion of their software engineering talent working on this is enough to go head to head with us even though it only takes a fraction of their attention.

This is over a month old, they released the weights a long time ago.

Sunday morning, and I find myself wondering how the engineering tinkerer is supposed to best self-host these models? I'd love to load this up on the old 2080ti with 128gb of vram and play, even slowly. I'm curious what the current recommendation on that path looks like.

Constraints are the fun part here. I know this isn't the 8x Blackwell Lamborghini, that's the point. :)

I made a 4B Qwen3 distill of this model (and a synthetic dataset created with it) a while back. Both can be found here: https://huggingface.co/flashresearch

Unfortunately soon China will take lead in AI.

It's a Qwen 3 MoE fine tune...

In my experience using these supposed expert models, they are all more or less the same given they all are trained on the same internet data. The differentiation and value is in the context window management and how relevant info from your session is pulled in. So it’s the interface to the model that makes all the difference. Even there the differences are quite minimal. That is because all these companies want to toe the line between providing functionality to keep the users engaged and pushing them to sign up for the subscription.

All this to ask the question, if I host these open source models locally, how is the user interface layer that remembers and picks the right data from my previous session and the agentic automation and others implemented? Do I have to do it myself or are the free options for that?

It still feels to me like OpenAI has zero moat. There are like 5 paid competitors + open source models.

I switch between gemini and ChatGpt whenever I feel one fails to fully grasp what I want, I do coding in claude.

How are they supposed to become the 1 trillion dollar company they want to be, with strong competition and open source disruptions every few months?

Has anyone tried running this on a 5090 or 6000 pro? What throughput do you see?

Isnt it huge deal, that this 30B model can compare and surpass huge closed models?

I hope the translation for this is actually "Agree" Deep research. Just a dig at "You are absolutely right!" sycophancy.

Juju

Isn't OpenIA "Deep research" (not "DeepResearch") a methodology/tooling thing, and you'll get different responses depending on what specific model you use with it? As far as the UI allows you to, you could use Deep research with GPT-5, GPT-4o, o3 and so on, and that'll have an impact on the responses. Skimming the paper and searching for some simple terms makes it seem like they never expand on what exact models they've used, just that they've used a specific feature from ChatGPT?