Nassim @nasbrd
AI research at https://t.co/1yyXOe81ve Sharing ideas and experiments on machine intelligence Joined July 2025-
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@runwayml_labs Super interesting! Wonder how easier it makes it to catch reasoning errors early. Provided low enough latency it could be a nice interface to jump-in halfway through to reshape the traces visually
I wonder what could be a strong enough counterweight to this cognitive dependence. For now it seems to be mostly lack of awareness. Curious given how fast ChatGPT spread while frontier AI (agents etc.) remains contained (see x.com/karpathy/statu…) Read: arxiv.org/pdf/2609.03344
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come
@GregoryConti19 I like to think that there will still be value in learning and sharing wisdom. What is at threat is productivity-seeking learning but you still need to think to understand and formulate opinions. That's also part of what makes us human
@cheyava_falls Sounds cool curious to see how well this generalizes on different tasks
@DimitrisPapail The analogy with reasoning basins reminds me of this study arxiv.org/abs/2609.04963 . Interesting to see that basins have a more or less stronger pull not on their own but relative to the agent's capacity
@SakanaAILabs Curious that all components of the system improve from better workflows. Would be interesting to get more fine-grained insights on what makes the optimizer struggle (poor feedback leveraging, weak planning, etc.)
@liuyong9975 @gurtej__gill_ Makes sense, it does seem like most of the gain in the FTB Core bench comes from a few categories of questions so I guess training in protein folding space does favor some kind of specific spatial reasoning transfer. Still fascinating that it helps across the board though
@MaximeRivest Very cool! how frequently does it get updated?
@keenanisalive Yeah just thinking it may struggle translating these visual assessments into shader that conveys the improvements. So the vlm knows the face needs better compositing but the code changes don't capture that intent well. Hard to tell without peeking at the model's internals I guess
@keenanisalive Maybe it doesn't have a good representation of what cute means in sdf-space? I wonder if giving it general guidelines on typical shader ops that translate into cute looking shapes would speed up the discovery process
@GestaltU AI introduces agents devoid of emotional nudges that can deviate from the optimal (e.g panic selling in crashes). We'll see if this solidifies the optimization machinery or uncover ways to value non-priced factors. It will regardless depend on our agency over this new actor.
Very insightful read on the scaling opportunities for BCI
The human brain is approaching its ChatGPT moment. I spent 6 months writing Phase Lock: a manifesto on how brain computer interfaces can help align AI and preserve human agency. If you only ever read one thing from me, read this.
@satpugnet Very compelling, leveraging personalized brain-to-embedding adapters sounds interesting but I wonder how accurate these would be at capturing individual "accent" given how part of the context behind one's thoughts is only lossily observable (past experiences or genetics for e.g)
@neural_avb Interesting to see that despite the range of techniques, some of the open questions relate to the foundational definitions and purpose of interpretability being hazily defined
@AccelerateAI_ @PositivFuturist Just wondering how well it works with variety and if it can infer when to go fast vs slow smoothly from context. Most opus-generated motion design feel very (too?) dynamic imo
@ChenMoneyQ It helps to have an internal reference of do's and dont's for how passages should be structured. But oftentimes I think it's just a back and forth between manual tweaks and llm assisted rewrites
@GergelyOrosz We're getting to a point in the intelligence-to-cost profile where efficient models are enough at handling most of the value-creation needs in society, provided they are used and streered thoughtfully.
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