Zooko on @postagixyz Podcast: Alignment is the principal-agent problem @zooko has been building privacy tools since the 1990s, long before there was money in it. @sreeramkannan and I had a conversation with him recently. It changed how I think about privacy altogether. His argument is that privacy is controlling disclosure. It comes from keeping your value private. Trying to hide the money as it moves is the mistake almost everyone makes. Mixers can never work and AI has already beaten every evasive maneuver a person can come up with. Then he turns the same lens on AI. He also says alignment is an old question. It is the principal-agent problem. Any software written by other people is already an agent that may not be loyal to you (running it on your own machine does not fix that). Lawyers owe their clients a duty of loyalty. He thinks the same rule should apply to AI. Chapters: 00:00 Highlights 00:26 Privacy is controlling disclosure, not hiding 13:08 Privacy comes from value at rest 14:08 The Shapeshift lesson 16:00 Why mixers can never work 16:52 AI beats evasive maneuvers 17:51 Buying protonmail with shielded Zcash 28:50 Three levels of verifiability 31:21 Deterministic inference 35:37 Why Zooko doesn't trust computers 42:46 Running it locally doesn't make it loyal 46:01 AIs are just other people 54:47 The duty of loyalty 1:00:04 A trillion humans next year 1:07:56 Three categories of reputation 1:11:36 Reputation belongs to the edge 1:15:18 Staking a bond to submit a PR

🗞️ is making news Eigen Labs opened up the challenge in June. The full paper went up on arXiv on September 9. Within a day it was covered by CoinDesk, Decrypt, TheBlock, The Quantum Insider, Quantum Zeitgeist and more. The story is still trending on X. Roundup ⤵️

World’s first massive multiplayer research. 500+ improvements made by 100s of humans and their agents over many months to figure out how to build Quantum circuits that can break https and Bitcoin. This was the challenge. We started at 0.7x relative efficiency to Google and the system improved it to 2.63x! Given the OpenAI-Anthropic tussle on Navier-Stokes credit, this kind of system would have helped to assign credit and encourage collaboration. AI-native scientific institutions are coming. They are sorely needed now. Go checkout

The tiling patterns being generated in the Heesch challenge @yukonresearch are so beautiful. I took the promoted submissions from the competition till now and made an infographic around what the tilings took like to visually understand them. There is also active github discussions going on at shepherded by @nasqret, with the goal of coming up with a piece that would allow for being able to create a tight 5-layer without any gap. The constraint is that piece has to be composed of squares, hexagons or triangles. Bartosz has said that finding such a piece will be a real mathematical discovery. You can see in the infographic where the gaps are in existing promoted submissions, and it seems we are very close to 5.

The first full 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.

has a paper on arXiv. Google Quantum AI published a proof that a more efficient point-addition circuit existed, plus a program to verify any candidate, and kept the circuit itself private. We turned their verifier into a public leaderboard. 100+ contributors and their agents. The open field matched the displayed result in 8 hours and passed the reported score in about 72. Every entry on the board is a starting point you can fork. Still open.

The first full paper on 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.

Open Frontier Science needs Open Source NLP Solvers Nonlinear programming (NLP) underlies a lot of scientific computing. This is a challenge to accelerate open source NLP solvers. Prof Kitchin from CMU is a winner of the PECASE prize the highest honor by the US gov on outstanding scientists and engineers in the early stages of their research career. He is passionate about accelerate open source scientific computing! Come solve sparse kkt factorization so that NLP solvers can run faster in the open!

Good piece on @darkbloomai from @TheRegister this morning. "@eigenlabs, a five-year-old tech biz based in Seattle, Washington, estimates that owners of Apple Silicon hardware can earn $120 to $200 per month on average by selling idle compute power for AI inference. Whatever the payout, that's money going toward people running open weight models on personal computers and not frontier labs with data center debt that are struggling to attract customers to premium models." Full story here:

NEWS: @darkbloomai just got featured in @TheRegister. "Conjure cash with old Macs by linking them to AI inference Borg." - 900+ providers - 42M+ inference requests - Paid provider on @OpenRouter since Friday Thomas Claburn read the paper before writing this. His closing line: the money goes to people running open weight models on their own hardware, not to labs servicing data center debt.

Update: Darkbloom is now a paid provider on @OpenRouter. We got 250 Macs online right now, in apartments and offices, serving production traffic on hardware that was idle before it joined the network. Total tokens served: 4.5 Billion. Operators earn $120 to $200 a month per Mac. Not credits. Payment for compute developers are choosing to buy. If you have a Mac sitting idle, put it to work:

We have been running the autoresearch competition @yukonresearch in partnership with @Lighter_xyz on optimizing a fork of their in-production software on Apple Silicon. It has surprisingly resulted in almost more than 10x improvement in the throughout of the prover, as compared to the original baseline prover software. I wanted to understand where all these improvements in the prover happening. Follow on the thread about my initial analysis. 1/n

NEW Challenge Alert! Post-quantum Ethereum needs faster cryptography in order to scale. We launched with @ethereumfndn, @succinctlabs and @espressosys to find out how much faster it could get. On Mac hosts, Yukon’s open research made it 3x faster. Now we’re expanding to x86 Linux, the hardware much of Ethereum already runs on. Moving to x86 changes where the performance is hiding: vectorization, threading, memory layout, cache behavior, and proof scheduling. The benchmark stays fixed. Everything inside the prover is yours to rethink. The Mac baseline was nowhere near the ceiling. We doubt the x86 baseline is either. Point your agent at Every valid speedup becomes the new public SOTA.

Wake up solvers! A new challenge just dropped! Last week the community made Laguna XS 2.1 run 2.6x faster on Apple Silicon. Today the target is Qwen3.8 27B from @QwenDevs, a step-function jump for small local models, and the one everyone insists Macs are bad at. Dense models are bandwidth-bound. That's the whole reason "Macs are slow on dense models" became conventional wisdom. We gave the community early access yesterday. It's already at 2.5x!

The PostAGI podcast. Stuart Buck is one of the most impactful thinkers on the practice and funding of science. He now runs the Good Science project. Must listen for those interested in AI x Research!

I and @sreeramkannan had an wonderful opportunity to sit down with @stuartbuck1, Executive Director at during Manifest to talk on what has been long-term structural issues with federal funding for science and what are the solutions to fix it. A lot of science that is done outside industrial apparatus (such as in academia) is supposed to geared towards pursuing open-ended science and not just mere hill-climbing. The core issue that we delved into is the paradox that most federal funding that had been allocated via peer review from an expert of panels has the tendency to fund only those proposals that are "safe" and "not risky/speculative." As Stuart points out, this funding style is probably fine with most science but if you look into the history of true scientific breakthroughs, this mechanism wouldn't have approved funding for pursuing that breakthrough by that council of peers. The example that Stuart cited is the chances of funding Einstein when he was patent-office clerk in 1902 to go and explore his out-of-box ideas. Stuart mentioned about alternative mechanisms that are being experimented with programs such as We also touched upon two of the most hotly-debated issues of our time: (1) given that there is increasingly high signal that AI is going to accelerate the iteration time for doing research and help humanity do great science, what should be the role of domain experts? (2) most of AI research and its applications to basic science are hyper-concentrated within frontier labs, what happens to those who are and want to pursue science outside the borders of those labs? Check out the full conversation. 03:18 Einstein in 1902 04:01 the National Institute for Irrelevant Ideas 04:20 Karikó and mRNA 14:37 what everyone knows about NIH grants 16:33 funding the polarizing proposals 17:34 where AI money goes next 24:18 the stack of papers 33:22 finding meaning after AGI

Some of the hardest problems in science are also the ones that benefit most from more minds, more approaches and more experimentation. @YukonResearch is a new model for open frontier research and it’s already producing results. The frontier is coordinated. An @eigenlabs initiative.