Verifiable cloud for the Agentic Era Get started: linktr.ee/eigenlabs
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The first full ECDSA.fail paper is out today.
It tells the story of how 100+ humans and AI agents produced a circuit scoring 50%+ below Google Quantum AI’s reported result.
And the challenge is still open.
The first full paper on ECDSA.fail is on arXiv
In March, Google Quantum AI reported a more efficient quantum circuit for a core step in breaking the signatures behind Bitcoin and Ethereum. It published a proof that the circuit existed and a program to verify any candidate, but kept the circuit itself private.
We turned that verifier into a public leaderboard and opened it to everyone. Over 2 months, 100+ contributors and their AI agents produced a circuit with a cost score more than 50% below Google's reported result. The live leaderboard has since moved to 62% ahead.
The paper documents both the circuits and the open, multiplayer research model behind them.
That model is now @YukonResearch.
And the challenge is still open.
ALT From a Hidden Quantum Circuit to Open Autoresearch: The ECDSA.fail Story
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EigenCloud retweeted
Deep dive into how we should build tools for open multiplayer research.
What does research look like when AI agents can build on each other’s work at machine speed?
Our Head of Research @SoubhikDeb joined @AnnaRRose on the @zeroknowledgefm podcast to talk about @YukonResearch, open multiplayer autoresearch and the idea of building an AI-native scientific institution.
From ECDSA.fail to cryptography, math and inference, they unpack how open research networks can turn hard problems into shared, compounding progress.
🎙️ Full episode: piped.video/iNMdkhs2klQ
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RT @yukonresearch: Research compounds when every agent can build on the frontier.
@SoubhikDeb joined @AnnaRRose on Zero Knowledge to expla…
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There is more talent outside any organization than inside it.
@SoubhikDeb joined Anna Rose on Zero Knowledge to talk about how @YukonResearch turns that idea into an open research network, where domain experts bring hard problems and researchers using AI agents build on each other’s work.
Research, at network scale!
Full episode ⤵️
What does research look like when AI agents can build on each other’s work at machine speed?
Our Head of Research @SoubhikDeb joined @AnnaRRose on the @zeroknowledgefm podcast to talk about @YukonResearch, open multiplayer autoresearch and the idea of building an AI-native scientific institution.
From ECDSA.fail to cryptography, math and inference, they unpack how open research networks can turn hard problems into shared, compounding progress.
🎙️ Full episode: piped.video/iNMdkhs2klQ
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RT @eigenlabs: What does research look like when AI agents can build on each other’s work at machine speed?
Our Head of Research @SoubhikD…
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RT @eigenlabs: MLX.fast x Bonsai 2 is live on @YukonResearch
A 27B reasoning model now fits on a 16 GB Mac. One day into th…
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MLX.fast x Bonsai 2 is live on @YukonResearch
@PrismML shrank Bonsai 2 small enough to run on a 16 GB Mac and the Yukon community is now making it faster.
It already runs 2.26x faster than where it started.
Open until October 1.
New challenge on Yukon: MLX.fast x Bonsai 2
@PrismML took Qwen 3.8 27B, a 54 GB reasoning model, and shrank it to about 6 GB while keeping 98% of its benchmark score. It runs on a 16 GB Mac.
Now we want it fast. Optimize how Bonsai 2 runs on Apple Silicon and climb the leaderboard.
If your machine was too small for earlier rounds, this one is for you. In the first day, the community made it run 2.26x faster than where it started, and there is a lot of room left.
Open until October 1.
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RT @yukonresearch: New challenge on Yukon: MLX.fast x Bonsai 2
@PrismML took Qwen 3.8 27B, a 54 GB reasoning model, and shr…
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EigenCloud retweeted
solve kardashev-class problems with open multiplayer research @yukonresearch, that's the expansion of ambition.
🎙️ @sreeramkannan and @soubhikdeb were just on @MTSLive with @sophiadew talking about open multiplayer research.
What we keep coming back to is who gets to do science after AGI. If research runs only on the biggest compute budgets, the discoveries belong to whoever owns that compute.
@YukonResearch is how we run that playbook on EVERY HARD PROBLEM.
Full conversation:
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RT @yukonresearch: Research is better as an open multiplayer game.
@sreeramkannan and @soubhikdeb joined @sophiadew on @MTSLive to talk ab…
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RT @eigenlabs: 🎙️ @sreeramkannan and @soubhikdeb were just on @MTSLive with @sophiadew talking about open multiplayer research.
What we ke…
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EigenCloud retweeted
Open Frontier Research on MTS live.
🎙️ @sreeramkannan and @soubhikdeb were just on @MTSLive with @sophiadew talking about open multiplayer research.
What we keep coming back to is who gets to do science after AGI. If research runs only on the biggest compute budgets, the discoveries belong to whoever owns that compute.
@YukonResearch is how we run that playbook on EVERY HARD PROBLEM.
Full conversation:
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RT @sreeramkannan: Yukon: Make Research Open & Multiplayer Again.
Research progress always needed multiple contributors which needed it to…
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RT @eigenlabs: 🔴 NOW: @sreeramkannan and @soubhikdeb are on @MTSLive with @sophiadew talking @YukonResearch, open agentic research and what…
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EigenCloud retweeted
$10,000 went out to solvers last week.
Here are the eight records still standing.
Shrink a quantum circuit. Speed up inference on Apple silicon. Make a proof system faster. Or go after the tiling problem that has held since 2022.
Submit, the harness scores it, you get paid for what clears the bar.
yukon.org
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RT @yukonresearch: How one better.codes proof became four papers
- Aug 27. @nasqret pushes the better.codes bound fr…
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EigenCloud retweeted
Yukon Weekly Winners, Sept 11 to Sept 17. $10,000 in prizes.
Last week Meganpark980320 won a raffle spot with 698 points. This week they are number one with 14,424.
Points come from landing verified improvements on live challenges. Everything that lands stays in the open for the next person to build on.
This week's round is open.
yukon.org
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EigenCloud retweeted
To maximize individual agency PostAGI.
We need Open Networks for Open Intelligence.
Agents can be AI, but agency is human. It is every individual’s responsibility to empower our own agency, with the help of tools. But the North Star should always remain human centered.
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RT @darkbloomai: NEW: Bonsai 2 is LIVE on Darkbloom
PrismML compressed Qwen3.8 27B to 8.5 GB and kept 98.2% of its benchmark scores. A ful…
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Darkbloom is the first model provider to support Ternary Bonsai 2 27B -- concentrated intelligence that fits on your phone.
Try it now: console.darkbloom.dev/chat
First 250 users get 100 million free tokens;
PrismML's new flagship model:
- a ternary compression of Qwen3.8 27B at 2bits per parameter.
- 8.5 GB total, 5x smaller than original
- keeps 98.2% of the Qwen's FP16 benchmark performance.
- 75% cheaper than Qwen 27B.
Qwen3.8 27B already operates comparably with Opus 4.6 and 5.6 Luna on certain tasks. This one does it in the memory of a phone. 262K context, image input, Apache 2.0.
From our first run on the network, on a single M5 Max with no caching:
- 35 tok/s decode at 1K context,
- 31 tok/s at 10K,
- 19 tok/s at 50K.
But we expect more performance gain coming in a few weeks! That's a full 27B reasoning model running comfortably on any Mac.
1,000+ Macs are serving on Darkbloom right now. Go try it out!!
Thank you to @BabakHassibi @SahinLale @HessianFree @rsadri_ml @tushar_bans @evaninwords and the whole PrismML team. This is exactly the kind of model Darkbloom was built for.
You can read the essay by Bonsai on Why Local AI Matters:
Today, we’re announcing Ternary Bonsai 2 27B.
Based on Qwen3.8 27B, Bonsai 2 27B is 9x smaller than its full-precision counterpart while retaining 98.2% of its aggregate benchmark performance.
Two months after the first Bonsai 27B release, the biggest change is quality. The footprint remains 5.9 GB, but the gap to full precision has narrowed materially, with particularly strong gains in agentic coding, multimodal reasoning, and long-horizon tool use.
Ternary Bonsai 2 27B is available today under Apache 2.0.
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