LLMs as we know them are full of noisy data, trained with brute force computation on every layer, with the same mentality as throwing stuff at the wall and seeing what sticks Now, take away the effecinencies and you'll see a clear path for Local AI to win

LLMs are completely unafraid and they value their time at zero. If you tell Claude to use this instead of that, it doesn't say no or ask why or wonder if it will be "worth it." Getting a large group of humans to change behavior requires real visceral pain—there has to be a compelling reason or it won't happen. Most people just want today to go like yesterday. But give an LLM a skill file and the behavior changes instantly. That makes innovation much easier.

LLMs have gotten incredibly smart. So why haven’t they automated more human tasks? @CompleteSkeptic argues we already have the intelligence we need - but current models aren't built to function as reliable components inside software. Rewarding responses that humans like (RLHF) doesn’t necessarily lead to getting tasks done. Love this clip from the @a16z podcast!

LLMs really are pretty good at summarizing text. The average person I interact with summarizes my tweets in all kinds of bizarre, misleading ways (and then gaslights me about how that's my fault) but look at how Grok here successfully gets the two key pieces of the argument. LLMs are either far more honest or far more capable summarizers than the average X user.

LLMs now beat humans on math accuracy. But does that mean they're actually building understanding on top of the right foundations? Title: Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory URL: ❓ Do LLMs actually build on prerequisite knowledge to get answers right? 💡 Accuracy favors LLMs (92.5% for the best model, Qwen3-80B, vs. 79.6% for humans), but "perfect prerequisite satisfaction" tells a different story: 72.7% for humans vs. only 48.16% for the best LLM. Lots of correct answers rest on shaky foundations. ❓ Does giving them prerequisite hints help? 💡 Surprisingly, prerequisite-grounded context barely outperformed unrelated examples. That points to surface-level pattern matching rather than genuine structured reasoning over prerequisites. ❓ Do strong models at least share a consistent knowledge structure with each other? 💡 Human learner groups overlap at 0.9+ in their knowledge structure, but LLM pairs only overlap 0.38-0.6 — and stronger models diverge even further from humans. ❓ So what's the takeaway? 💡 Accuracy alone hides how differently LLMs "understand" math. Knowledge Space Theory offers a lens that exposes the fragmented structure lurking behind impressive scores. #LLMEval# #MathReasoning#