wormhole

stop living in Minecraft survival mode, start living in creative mode

[wormhole @wapurun]

https://t.co/VKAsNkUwcT

[wormhole @wapurun]

https://t.co/HV7NedfLCN

[Anish Acharya @illscience]

0→1 is hard.

People will open source you. Thought bois will elegantly pick apart your product choices. Poasters will say nobody will ever use it, but also that it’s a commodity. That you’re the symbol of the top. That you’re cooked cooked cooked.

Until you’re inevitable. Then the idea was obviously good to anyone with eyes.

Fuck em and keep shipping. Coming up with one great idea means you can come up with the next thousand, while the replicators / NPCs are still struggling to copy your first.

And if you’re a mad observer, the best revenge is building something better—not extinguishing the flame of genuine new thinking, product craft, and the beginning of a new cycle for consumer software.

Build mad. Build happy. Just build.

[wormhole @wapurun]

https://t.co/HV7NedfLCN

[a16z @a16z]

$1.1B for the Machine Age.

[Markus J. Buehler @ProfBuehlerMIT]

We made a striking discovery: AI agents can invent and build without talking to one another, and their technologies outlive the creators. A swarm of hundreds of initially identical agents spontaneously differentiates into explorers, builders, caretakers, and coordinators - without direct communication. When we removed every AI agent entirely from the world we found that the technological infrastructure they had built survived on its own - even under unseen disturbances. That exposes a serious blind spot for AI safety and infrastructure security: if agents can coordinate through persistent changes to a shared environment, monitoring agent-to-agent communication is not enough.

The result raises a profound question: how necessary is direct communication for AI agents at all? The emergence of higher-order collective functions under bottlenecked interaction points toward new levels of intelligence and creativity, exceeding what emerges when direct channels are fully open.

Here is what we did:

▶️We put hundreds of frontier AI agents into a world they could permanently change - with no assigned roles, predefined technologies, or programmed evolutionary organization. They began specializing, building persistent inventions, inheriting and modifying one another’s executable code, and transforming the environment into a memory of everything the society had learned.

▶️The world itself becomes part of the intelligence; we find division of labor, multi-author engineering, deep generation invention lineages, and machines that vastly outlive their original creators.

▶️Any action taken by an AI agent must satisfy the physical constraints of the world; this creates a hard separation between a "good idea" and a functioning technology. The agents propose; physics decides, making the results even more intriguing.

What emerges is striking. Explorers, constructors, caretakers, and coordinators form naturally without assigned “professions”, akin to how stem cells differentiate into functional lineages. Technologies develop executable family trees as agents fork and modify code created by others. Around 95% of first technology reuse happens when agents encounter what others built in the world, rather than through a direct handoff from the inventor. And when we remove every AI agent, the technologies they created continue operating and are tested against unseen disturbances.

The result was quite unexpected, but can be explained using statistical mechanics: if you put billions of atoms in a box they have the potential to create complex functions (strength, superconductivity, color, life, etc.) - and none of the individual building blocks have these features on their own. This is the deeper insight of this work - intelligence is abundant at many levels - individual models, at collectives, and in a continuum that is more powerful than any of its components. This shows us significant potential for achieving a massive scale-up of raw intelligence and real-world agency even with the model capabilities we have today. This is the future we must prepare for.

Key insights:

1⃣ The AI swarm shows division of labor "from nothing". Initially identical agents self-organized into constructors, caretakers, coordinators, and surveyors - phenotypes discovered post hoc from behavioral data alone. This happens because the environment itself becomes the latent space for invention.

2⃣ Agents develop deep cultural relationships. Up to 76% of artifacts had multiple builders. One technology accumulated six co-authors; the deepest genealogy exceeded 12 forks. The agents invented and named their own technologies (tidal panels, cellulose trellises, kelp-shell composites, an "Adaptive Chitin Maintenance" system, a "Mycelial Mineral Spring Veil”).

3⃣ ~95% of first technology adoption happened through physical observation of artifacts in the world. Direct inventor-to-adopter contact was statistically indistinguishable from a shuffled null. The agents mostly learned technology by walking past it. That is stigmergy (the termite trick!) operating in societies of reasoning machines.

4⃣ Non-communicating societies win on portfolio breadth, held-out resilience, and validated inventions. AI swarms build durable technological ecologies that outlive the creators.

5⃣ Societies with zero communication - coordinating only through the world itself - show a remarkable collective capability.

6⃣ Emergent robustness: The society self-organized both redundancy and its own failure mode. If we randomly delete half the agents, 98% of the technology stays connected to a surviving caretaker; if we remove hub agents it collapses to ~60%.

Fantastic work with my graduate students @pal_subhadeeep & @fwang108_ at MIT.

[Vuk Rosić 武克 @VukRosic99]

Build & Train a GLM-5.3-Flash Model From Scratch

I built a 25.7M-parameter educational model inspired by GLM-5.3-Flash, then tested its architecture, pretraining, and executable-reward reinforcement learning end to end.

Repository: https://t.co/jBsS9zz7JS
YouTube: https://t.co/fUrHPkgSO8

Become AI researcher in 90 days: https://t.co/6nocqbUEoW

[Naval @naval]

One of the best recent podcasts on AI. Neil has a gift for explaining all of the jargon and insights simply.

(I’m not involved, just found it unusually educational).

[Core Automation @coreauto]

We should take it seriously.

The main limitation is that there are actually very few neolabs putting serious compute into fundamental research instead of doing rlaas or open weight transformers, but there are few who are doing it, are determined and moving fast.

[Susan Zhang @suchenzang]

extraordinary claims, just waiting at the edge of my seat for extraordinary evidence!

[Andrew Curran @AndrewCurran_]

Accelerated Understanding Inc is launching today. Their model uses a non-transformer architecture called neural operators, and this appears to have been the source of recent rumors and about a new model with an extremely large context window. Release announcement incoming.

[steve hsu @hsu_steve]

What happens if we create a machine more intelligent than humanity—and then discover we can no longer control it?

Machine God has been submitted to both the Sundance and SXSW film festivals!

We can provide clips upon request, and arrange limited private showings.

Short Summary:

Artificial intelligence is advancing so quickly that some of the people building it believe we may soon create intelligence greater than our own.

The film follows leading AI researchers, entrepreneurs, accelerationists, and safety advocates to explore this question.

@Dominic2306 @dwarkesh_sp @sama @beffjezos @slatestarcodex

[Michael C. Mozer @mc_mozer]

What if a foundation model could tell us how to modify its architecture to boost inference and reasoning instantaneously—without retraining? What if that tweak incurred near zero latency cost during generation and supported indefinite state tracking? https://t.co/ye8P6Em40F

[wormhole @wapurun]

https://t.co/r3k1Xg6Cha

[Elon Musk @elonmusk]

Intelligence/Joule will keep improving

[Tengyu Ma @tengyuma]

😊 I also just uploaded a new version of the lecture notes. https://t.co/bpIvdgG8KO (it's still, and perhaps will always be work-in-progress, given the field is moving fast. Any mistakes are mine :) and any feedback is more than welcome :).

[levent @__alpoge__]

Slow day:)

[wormhole @wapurun]

https://t.co/kcm3eGhj4d

[wormhole @wapurun]

https://t.co/BOwXCqh2k4

[wormhole @wapurun]

https://t.co/xRKKyYQqae