korrents

LLMs

What people on korrents have said about LLMs, newest first — 11 positions from 6 people.

  1. DH

    26 Aug 2026

    David Heinemeier Hansson quoted

    HoldsLarge language models could still plateau, and that possibility should be held open even though no evidence of it has appeared.

    So we should also have the humility. As amazing as the LLMs are now, it could be that they eventually plateau. We haven't seen any evidence of it yet, and I think this is also why we're seeing this absolute gobsmacking levels of investment, because so far the scaling laws are true, and the more billions are poured in, the more intelligence comes out.

    ↗DHH: Future of Programming, AI, Agentic Engineering, Vibe Coding & Linux | Lex Fridman Podcast #501youtube.com

  2. 3 days earlier
  3. PG

    23 Aug 2026

    Paul Graham quoted

    HoldsThe best thing a 17-year-old could do today is learn to build LLMs from scratch and train the most powerful ones they can get hardware for.

    Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch, and then train ones as powerful as I could with whatever hardware I could get access to.

    ↗@paulg on Xx.com

  4. 2 weeks earlier
  5. FC

    7 Aug 2026

    François Chollet quoted

    No longer holdsThe LLM line of research will reach a capability plateau.

    In the era of base LLM scaling (2022-2024), I believed the LLM line of research would reach a capability plateau (as later seen with base LLMs). In late 2024, after the o3 test-time compute demo, I changed my views: the new models were showing genuine fluid intelligence, and with this new line of work, the LLM line of research could achieve unbounded capability scaling. "There will be no wall."

    SaidIn the era of base LLM scaling (2022-2024), I believed the LLM line of research would reach a capability plateau (as later seen with base LLMs). In late 2024, after the o3 test-time compute demo, I changed my views: the new models were showing genuine fluid intelligence, and with this new line of work, the LLM line of research could achieve unbounded capability scaling. "There will be no wall."

    ↗@fchollet on Xx.com

  6. 4 months earlier
  7. AK

    28 Mar 2026

    Andrej Karpathy quoted

    HoldsBecause an LLM can argue almost any direction competently, the right way to use one for forming an opinion is to make it argue every side.

    The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

    ↗@karpathy on Xx.com

  8. 3 days earlier
  9. AK

    25 Mar 2026

    Andrej Karpathy quoted

    HoldsLLM memory as currently built is a distraction to the model: one old question keeps resurfacing as if it were a lasting interest.

    One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity.

    ↗@karpathy on Xx.com

  10. 4 weeks earlier
  11. BC

    25 Feb 2026

    Boris Cherny quoted

    HoldsBuild for the model six months from now, not the model of today.

    At Anthropic, we don't build for the model of today, we build for the model of six months from now. And that's still my advice to founders that are building on LLMs.

    ↗Y Combinator, quoting Boris Cherny on the Lightcone Podcastx.com

  12. 9 months earlier
  13. TP

    2 Jun 2025

    Thomas Ptacek quoted

    HoldsEven if all progress on LLMs stopped today, LLMs would remain the second most important development in a software career spanning the mid-1990s to now.

    All progress on LLMs could halt today, and LLMs would remain the 2nd most important thing to happen over the course of my career.

    ↗My AI Skeptic Friends Are All Nutsfly.io

  15. TP

    2 Jun 2025

    Thomas Ptacek quoted

    HoldsLLMs can write a large fraction of the tedious code a developer will ever need to write, and most code on most projects is tedious.

    LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google.

    ↗My AI Skeptic Friends Are All Nutsfly.io

  16. 8 weeks earlier
  17. AK

    7 Apr 2025

    Andrej Karpathy quoted

    HoldsLLMs reverse the usual pattern of technology diffusion: they benefit ordinary individuals far more than they benefit corporations and governments.

    So it strikes me as quite unique and remarkable that LLMs display a dramatic reversal of this pattern - they generate disproportionate benefit for regular people, while their impact is a lot more muted and lagging in corporations and governments.

    ↗Power to the people: How LLMs flip the script on technology diffusionkarpathy.bearblog.dev

  18. 4 months earlier
  19. FC

    20 Dec 2024

    François Chollet quoted

    HoldsThe memorize-fetch-apply paradigm behind LLMs can reach arbitrary skill given training data, but it cannot adapt to novelty or acquire new skills on the fly.

    This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)

    ↗OpenAI o3 Breakthrough High Score on ARC-AGI-Pubarcprize.org

  21. FC

    20 Dec 2024

    François Chollet quoted

    No longer holdsThe memorize-fetch-apply paradigm behind LLMs can reach arbitrary skill given training data, but it cannot adapt to novelty or acquire new skills on the fly.

    OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.

    SaidOpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.

    ↗OpenAI o3 Breakthrough High Score on ARC-AGI-Pubarcprize.org