Trained my fish to surface for a Free Willy arm raise, mostly to watch the action-perception loop minimize surprise in real time. Now it's learned to look hungry, which makes me raise my arm, which makes it surface, which makes me feed it, which makes it look hungry. One of us has a better model of the other and I've stopped assuming it's me.
@Scobleizer i've been to Abu Dhabi. Did you check out Snow Abu Dhabi at Reem Mall? A real snow mountain in the middle of the desert! With you on not defaulting to cynicism.. it’s easy to focus on what could go wrong. It takes more awareness to recognize how rare it is to live through a shift like this. If we’re paying attention, it really is a blessing to have a front row seat and be part of what’s coming
😂 GPT is basically an epistemic engine.. constrain its breadth and it over-optimizes depth inside that box.. it’s right within the frame it’s using, but the context window keeps tightening that frame... you get never-ending local iterations without global convergence. Bring in a GraphDB + SQL + vector store early
bitnet.cpp is cool.. but let’s be clear about what it is and isn’t. It’s built for native BitNet models with 1-bit or ternary weights. You can’t just point it at a stock LLaMA, Qwen, or Mistral checkpoint and expect it to work
After ~2 years of research, we still don’t have widely usable, instruction-tuned BitNet models that are interchangeable with today’s frontier LLMs in behavior or general capability.
That doesn’t make the work invalid, but it does matter when people frame it as a replacement.
When folks hype “100B params on CPU,” they’re really talking about parameter feasibility under a different architecture, not matching the output quality or real-world usefulness of a 100B GPU model. The efficiency gains are legit, but they come from accepting different tradeoffs, not from making GPUs irrelevant
Where this does make sense is at a different operating point: high-throughput, low-cost cognition. Things like routing, query rewriting, memory compression, classification, or generating candidates that a stronger model then verifies, especially in CPU-heavy setups with lots of RAM. Used this way, it’s a useful supporting layer, but definitely not the star of the show
@NHL@SanJoseSharks@SanJoseSharks turning out an exciting season. Still a gap to the Cup, but going from 19 & 20 wins the last two years to 27 at same point, a much healthier goal diff, that’s real progress. Push harder!! LFG🦈🦾
@Scobleizer Happy birthday @Scobleizer 🥳 thanks for consistently backing builders and encouraging us to think seriously about the bleeding edge of what’s possible.
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