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  • Hacker News
  • This is over a month old, they released the weights a long time ago.
  • I hope the translation for this is actually "Agree" Deep research. Just a dig at "You are absolutely right!" sycophancy.
  • 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?

  • 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. :)

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

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