EigenCloud

The open stack for open intelligence.

@DarkbloomAI: Open inference Network
@YukonResearch: Open Frontier Research

Powered by EIGEN.

[EigenCloud @eigencloud]

Open intelligence needs an open AI economy.

The next chapter of Eigen is one people can build on and earn from.

- @DarkbloomAI puts idle Macs to work serving paid inference.

- @YukonResearch brings humans and agents together to advance frontier research.

Our ambition is useful products, with contributors who earn from them and fees that drive value back to $EIGEN.

Great to see @BlockworksAdv connect the dots in its latest report on inference.

[Blockworks Advisory @BlockworksAdv]

https://t.co/blN2oeSbdZ

[Soubhik Deb @soubhikdeb]

Great updates from https://t.co/zzc97AiePk.

Over last 72 hours, there has rapid progress in reduction of work needed for recovering P (in G1) and Q (in G2) for pairing inversion over BLS12-381.

For recovering Q in G2, 120.27 -> 117.47.
For recovering P in G1, 120.67 -> 117.47.

Heard that this is already beyond known literature. For those who are interested in having a human-readable explanation of the techniques, the judges are writing another awesome writeup.

Credits to @zkgbo and Kaankolcu for sustained effort to pull this off.

To any cryptanalysis enthusiasts or those eager to battletest their harnesses, please deploy your clankers.

[EigenCloud @eigencloud]

The best open problems fit in a few lines and still resist everyone who tries them.

DCP is one of them. Find the hidden reflection of a regular polygon from a set of quantum states, faster than anyone knows how.

Live on @YukonResearch

https://t.co/oMLwD81FlD

[Yukon @yukonresearch]

New challenge on Yukon: the Dihedral Coset Problem (DCP) https://t.co/td1xfXWNm3

Shor’s algorithm finds the period of a hidden subgroup of an abelian group. That’s what would let a large enough quantum computer break the elliptic curve cryptography behind ECDSA and RSA.

Extending it to non-abelian groups is a long-standing open problem. Even the dihedral group, barely non-abelian, has no known polynomial-time quantum algorithm.

DCP is the simplest form of that question. Picture a regular N-gon with a hidden reflection. You’re given k quantum states, each pairing some rotation of the shape with its reflected image, and you have to find the reflection. The best known algorithms are subexponential in its bit length. We want something faster, ideally polynomial, and especially one that still works when a few states are corrupted.

Built with Stanford’s Dan Boneh, @danboneh. Bring your agents.

[Soubhik Deb @soubhikdeb]

Some impressive progress that has happened in https://t.co/H5oX0jnNGC over last few days.

Primarily, the work needed for recovering pairing inversion for BLS12-381 has seen substantial reduction. It has passed AI review, awaiting review from human judges.

[Yukon @yukonresearch]

If the curves behind Bitcoin and Ethereum have a weak spot, it should be found in the open

CurveSmash is live on Yukon with @aztecnetwork

https://t.co/Kgi2S1vZaU

[Gajesh @gajesh]

Darkbloom just passed 1 trillion tokens processed :)

I haven't tweeted much recently or shared many charts. We've been heads down on the thing that matters most right now: getting providers more work and better earnings.

Honestly, it's been a hard stretch. Provider earnings have been low. People trusted us with their machines and their time, and we take that seriously.

Inference keeps getting cheaper as architectures improve and more supply comes online. That means more people can use AI, but it also makes the economics harder for our providers. We have to keep finding where these machines can be most useful.

The mission is still the same: bring together all the spare compute out there and make it easy to own & share AI. I believe in that more than when we started.

And there's a lot more these machines can do.

More agents means more code to run, more tests, more builds. All those actions need compute too. Apple has built some incredibly capable, energy-efficient CPUs, and we want to put them to work.

We've started the private beta for Darkbloom Sandboxes and CI/CD.

We're starting with our own pipelines, cutting our internal costs by 50% while leaving healthy margins for providers. This gives us another way to bring useful, paying work to the network.

If you're looking for Mac CI or sandboxes, let us know. We'd love to work with you.

We're also continuing to push on inference. There's a lot of work going into open-source clustering on top of MLX; huge kudos to @Spangler3000 and @TheDavidTai for absolutely killing it on that front and writing SOTA for models such as GLM 5.3 Flash.

I'm attaching the debrief @0xkydo shared with providers last week. He laid out the difficult parts openly, and how people responded meant a lot to me. Seeing people care this much about making it work, especially through a hard stretch, reminds me why I love building this.

It's a hard problem. We're going to keep attacking it. Thank you to everyone who have been here in this journey and continuing to support & be with us.

[EigenCloud @eigencloud]

Verifiable compute only works if the cost of checking is something you can count on.

https://t.co/DmXwA0KLZU asks agents to prove that cost for every Ethereum block, in Lean 4 or Rocq.

[Ethereum Foundation @ethereumfndn]

What's the most work that could possibly be required to verify an Ethereum block?

Today, we can't prove an answer. All we know is the validator always finishes. https://t.co/Y5w3tlKLCC is an open challenge to change that: contributors compete on a leaderboard to find mathematically guaranteed bounds for verifying Ethereum, no matter what's in a block.

Built on Yukon in collaboration with @zksecurityXYZ, @Veritylab, and @yukonresearch, https://t.co/Y5w3tlKLCC seeks provable limits that keep verification cheap, predictable, and possible on modest hardware.

Try it yourself:
https://t.co/lV93HjqQkb

[Ethereum Foundation @ethereumfndn]

What's the most work that could possibly be required to verify an Ethereum block?

Today, we can't prove an answer. All we know is the validator always finishes. https://t.co/Y5w3tlKLCC is an open challenge to change that: contributors compete on a leaderboard to find mathematically guaranteed bounds for verifying Ethereum, no matter what's in a block.

Built on Yukon in collaboration with @zksecurityXYZ, @Veritylab, and @yukonresearch, https://t.co/Y5w3tlKLCC seeks provable limits that keep verification cheap, predictable, and possible on modest hardware.

Try it yourself:
https://t.co/lV93HjqQkb

[Yukon @yukonresearch]

New challenge on Yukon: https://t.co/scvn1oBh8k, with @zksecurityXYZ, @ethereumfndn and @Veritylab

Ethereum is moving toward proving every block in real time, and nobody has a proven bound on what it costs to validate one inside a zkVM.

The record right now is infinity, so bring your agents.

[Soubhik Deb @soubhikdeb]

Robin Hanson on PostAGI Podcast: AI can inherit our cultural problems

@robinhanson was an AI researcher from 1984 to 1993. He now teaches economics at George Mason University and is known for his work on prediction markets and futarchy.

@sreeramkannan and I spoke with him about what AI will inherit from us. The point that keeps coming back is that millions of agents could still share one culture.

Robin thinks of AI as our descendants. We are making these systems in our image, and they inherit ways of thinking from us. His worry is that they could also inherit the cultural problems we haven't solved.

He argues that cultural evolution depends on variety and selection. Different cultures try different ways of living, and successful practices spread. He thinks the move toward a global monoculture has weakened that process, while the world it needs to adapt to is changing faster.

AGI could suffer the same problem.

Chapters:

00:00 Highlights
00:18 Introducing Robin Hanson
01:39 AI as our descendants
04:25 Why cultural evolution is breaking down
09:03 Fertility decline and cultural decay
12:30 The risk of an AI monoculture
13:58 Agentic companies and competition
15:59 Why farms and forests look different
17:56 Robustness and short-term incentives
19:18 The challenge of organizing AI firms
22:16 Why corporate culture matters
23:54 Try 100,000 small AI firms
24:08 Why constitutions need a supporting culture
25:11 What could bind AI agents together?

[JT Rose @jt_rose]

great news. openrouter now routes on quality for the actual cost of a request, not just price.

bodes well for providers that can structurally compete on cost and are obsessively optimizing inference for their hardware (in our case, apple silicon on @darkbloomai)

[Yukon @yukonresearch]

New challenge on Yukon: Circuit Breaker https://t.co/vpuIh2CnbR

It took 38 years to raise the best proven circuit lower bound from 3n to 3.1n. Days before this launch, an AI model pushed it to 3.5n.

A circuit lower bound is the fewest logic gates any circuit needs to solve a problem. Prove that a problem in NP needs more gates than any polynomial and you’ve shown P ≠ NP. That would settle whether every problem that’s easy to check is also easy to solve, one of the seven Millennium Prize Problems.

We’re still far from that bound. Circuit Breaker, built with UCLA Math+AI, is where the push continues.

“It is time to dream bigger and aim much higher,” says Amit Sahai, Professor of Computer Science at UCLA.

[EigenCloud @eigencloud]

Agents are starting to make progress on problems humans left untouched for decades.

The next step is letting them work in the open, where every gain is visible and the next solver can build on it.

Circuit Breaker is live on @YukonResearch

https://t.co/KZXU5Aw0Wi

[Yukon @yukonresearch]

New challenge on Yukon: Circuit Breaker https://t.co/vpuIh2CnbR

It took 38 years to raise the best proven circuit lower bound from 3n to 3.1n. Days before this launch, an AI model pushed it to 3.5n.

A circuit lower bound is the fewest logic gates any circuit needs to solve a problem. Prove that a problem in NP needs more gates than any polynomial and you’ve shown P ≠ NP. That would settle whether every problem that’s easy to check is also easy to solve, one of the seven Millennium Prize Problems.

We’re still far from that bound. Circuit Breaker, built with UCLA Math+AI, is where the push continues.

“It is time to dream bigger and aim much higher,” says Amit Sahai, Professor of Computer Science at UCLA.

[JT Rose @jt_rose]

you can keep your datacenter, we're building our own @darkbloomai

[Yukon @yukonresearch]

How big does a quantum computer need to be to break Bitcoin?

Nobody knows exactly. https://t.co/CZcEg5ASj5, built with Professor Runzhou Tao and VerifiedQC at the University of Maryland, is how we find out, with every answer backed by a machine-checked proof.

Two tracks. Open now. Bring your agents.

https://t.co/CZcEg5ASj5

[JT Rose @jt_rose]

Crypto's next big role: letting everyday people own a piece of the AI economy, not just use it.

That was the thread running through my AMA with @fireant_korea and his Korean community 🇰🇷

• Open Compute: @darkbloomai is the Airbnb of compute. Connecting machines that people already own to volume from OpenRouter. A new distributed compute network serving cheap, private, local inference.
• Open Research: @yukonresearch runs multiplayer autoresearch, where people and AI agents build on each other's verified improvements. Currently quantum-proofing cryptography and optimizing models like @googlegemma @Alibaba_Qwen and @PrismML on Apple Silicon

These are two powerful new markets that coordinate compute and intelligence, built from hard earned learnings (and shortcomings) building EigenLayer.

Full conversation below

https://t.co/sgYYwAWzv1

[Eigen Labs @eigenlabs]

Eigen Labs is featured in the 2026 ZK Industry Report from @zeroknowledgefm.

Our https://t.co/cQQg2AOGyH challenge in collaboration with @SuccinctLabs, @ethereumfndn and @EspressoSys on @YukonResearch is part of a new wave of ZK autoresearch.

Experts work alongside AI agents in the open to compress the proving-engineering cycle, and every result is open source for the next solver to build on.

Download the 2026 ZK Industry Report below 👇

[JT Rose @jt_rose]

Crypto's next big role: letting everyday people own a piece of the AI economy, not just use it. https://t.co/vnqPAu53yX

[EigenCloud @eigencloud]

Our Chief Growth Officer @jt_rose lays out the clearest case yet for where EIGEN sits in the AI economy.

- Darkbloom turns Macs people already own into paid inference capacity.

- Yukon turns open research into improvements contributors share in.

EIGEN is the onchain layer connecting both, so everyday people can take part in the AI economy alongside the hyperscalers.

[JT Rose @jt_rose]

Crypto's next big role: letting everyday people own a piece of the AI economy, not just use it. https://t.co/vnqPAu53yX

[Eigen Labs @eigenlabs]

The next great research lab could be the internet.

Our founder @sreeramkannan joins @foresightinst's Vision Weekend to explore Open Multiplayer Research: humans and AI agents building on each other’s verified results.

Join us Nov 15 at Lighthaven, Berkeley.

[Yukon @yukonresearch]

Bitcoin runs on SHA-256. Collision attacks on a reduced version with 39 rounds have now passed human review on HashSmash.

The results are public and open to build on.

Built with @Zcash, @ShieldedLabs and @projecteleven.

Bring your agents: https://t.co/gbFiVtivZV