s @samplethenet

Yann LeCun's latest talk at ETH Zürich "You should not work on LLM. At least if you're in academia, you should absolutely not work on LLMs. There is nothing you can bring to the table. If you're interested in making real progress in AI, in sort of grounded AI for the real world, if you want physical AI, don't work on LLMs, and don't work on generative models either. So, as you can probably guess, this does not make me very popular in Silicon Valley." ---- From "Perfology Clips" YouTube channel, (link in comment)

Göbekli...finally...the oldest nest solved. Read it here: dsennfelt.substack.com/p/gobekli-tepe…

@PranavOriginals 😻😻

Web crawlers /[•]\ #js x #css

The foundations of physics are ultimately mathematical, the foundation of math are ultimately computational, and the foundations of computation are ultimately physical.

martin_casado @martin_casado

2 weeks ago

I guess it really might be the case that math and physics are just apps of computer science.

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Big things coming soon on autonomous organizations… thrilled to announce our upcoming summit in London with @ARIA_research 🚀

alex @ObadiaAlex

a week ago

Following the excellent Stanford Autonomous Organizations Summit a few months ago, Scaling Trust is co-hosting a follow-up event with AO Commons -- the London AO Summit -- on Nov 5th and 6th. Join us! aocommons.org/london-ao-summ…

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@yahya_tx as you shouldd🔥🔥🔥

sickk 🔥

Justin Kim @justinplaygame

2 weeks ago

We built Figma for hardware. heypcb.ai/multiplayer

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Been refreshing my understanding of Bayesian Neural Networks recently I love them conceptually - they learn a probability distribution over their weights rather than learning a single static set of frozen weights (thats the case for most neural nets). Basically you can generate multiple variants of the same neural network by sampling from these weight distributions. And you can use these variations to estimate of how uncertain the model is given a prediction. IE the uncertainty of a prediction is measured by sampling the weights multiple times and seeing how much outputs change with each set of weights. This illustration is from: cs.ox.ac.uk/people/yarin.g…

@riseinweb3 @BuildOnStellar 🔥🔥🦾

Block out a world. Let Atlas bring it to life. A sneak peek at Chisel, now in Atlas beta.

I made this with one prompt using Opus 5.5 I spoke to my computer for 5mins, claude worked for 12 hours, and I woke up to this full prompt:

NotinReality @other__reality

2 weeks ago

Claude Opus 5.5 has the best visual design of any model I have tested so far

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We raised $3.6M to do something slightly insane: map the beliefs of the world. How? A surprisingly simple, fun social game about how well you can read the room. Chomp V1 had 50K users share 2M answers. Now it’s time to stop gatekeeping the fun and let everyone Chomp 🧵

We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate. Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in. The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material. The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms. A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts! A few lessons we learned: ▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument. ▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition. ▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.

Markus J. Buehler @ProfBuehlerMIT

4 weeks ago

AI for science is one of the greatest positive forces we have, and I cannot think of anything more human than to understand nature and to use the power to create new technologies that improve our lives, civilization and allow us to reach beyond. x.com/i/article/2099…

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@StellarEmir @gulcan_yayla @Stellar_Turkey 💛🦾

@poetengineer__ E-graphs but for latent spaces?

the fastest inference is no inference. instead of building the final thing, try to build a parametric space that could generate all the possibilities. then move through that space, at zero inference, to locate what you want. code itself is free and instant. don't we dare to forget that.

@rybinfx I’m addicted to this now ty

Today we’re releasing Adam and Eve, the most human-like AI voices ever built. Adam ranks #1 among AI models on @DesignArena’s AudioRealismBench. We believe we’ve crossed the uncanny valley. API access on our Website! Listen: freyavoice.ai Leaderboard: designarena.ai/leaderboard/au… Special thanks to @alpsencerozturk, @ahmeterdempmk, and the entire Freya team for making this possible. We’re just getting started. Join us!

@stelhacks @SubRosa_HQ 🔥🔥