United States
Joined July 2023
Launched a new FHE stream on Monstream. It tracks relevant developments across the field: new implementations and releases, research updates, attacks and security findings, benchmarks, events, talks, and other technical developments. You can follow it here and receive the updates automatically: monstream.io/s/6ac5df53b2b48…
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Fair Math retweeted
Today a crazy quantum story just got wilder. On March 31, the Google Quantum AI team published a landmark result on Shor's algorithm for elliptic curve cryptography. Technically, the paper was a bombshell: a dramatic 10x improvement over the state-of-the-art. As a stunt and wakeup call to the blockchain space, those optimisations were illustrated on secp256k1, the elliptic curve underlying Bitcoin and Ethereum signatures. But perhaps the most striking part of the paper was sociological, not technical. Instead of following standard academic process, the optimisations were kept secret, hidden behind a zero-knowledge (ZK) proof. Google's accompanying blog post mentions they "engaged with the U.S. government". The ZK proof demonstrates the existence of algorithmic improvements without leaking details. Academic censorship with ZK, a historic first! As a co-author of the Google paper I witnessed some of the context surrounding this censorship. To be honest, multiple aspects of that context don't sit well with me. As much as I believe the general public ought to know more, I am limited in my ability to whistleblow. Though let me be clear about one thing: the Google team's professionalism has been absolutely exemplary, and they deserve nothing but praise. Censorship has a way of backfiring. The Streisand effect, where an attempt to bury something only draws more attention to it, is exactly what's unfolding today. First, Google's key optimisation has been rediscovered by the French. And in a thrilling turn of events, a collaborative Shor-at-home challenge just launched. The initiative, available at ecdsa[.]fail, breached a new Shor world record in a matter of hours. Let's start with the rediscovery. Just two months after Google's paper, French quantum expert André Schrottenloher cracks the main secret optimisation. His paper, titled "Optimized Point Addition Circuits for Elliptic Curve Discrete Logarithms", landed on the arXiv today. Big congrats to André, who beat several other nerdsnipped experts to it. In a blog post also published today, Craig Gidney, the world expert on Shor optimisations, revealed that he'd been sitting on this very optimisation for a whole year under censorship pressure. Interestingly, André missed a handful of minor optimisations, both from Google's original publication and from improvements found since. It's plausible there's still plenty of juice left to squeeze out of Shor, and this is exactly what the ecdsa[.]fail challenge is about. The verifier program developed for the ZK proof does double duty, automatically filtering for valid submissions. Dozens of compounding small and micro improvements are rolling in. As of the time of writing there's an 8.4% improvement to Google's circuit, as measured by the product of logical qubit count and Toffoli gate count. Nice! The nerdsnipping ran deeper than anyone expected. Over the last few weeks it became clear it extended well beyond André and other quantum experts. Behind the scenes, a small army of amateurs quietly got to work. Inspired by Karpathy-style autoresearch, they turned AI on Shor. Ironically, the verifier program for the ZK proof makes an ideal reward function for AIs. The barrier to entry for this modern style of research is refreshingly low, with several non-experts, even a teenager, finding nice optimisations. Get in touch if you'd like to join a Telegram group with fellow autoresearchers :) Part 2: neutral atoms and qday The story doesn't end with Google. On the same day Google went public, a stealthy startup called Oratomic published its own Shor paper in a coordinated release. It made a splash, ultimately becoming the most upvoted paper on scirate[.]com, a website ranking arXiv papers. Oratomic's claim was wild. By building on Google's logical optimisations and applying custom physical optimisations for neutral atoms, they claimed just 10K physical qubits were sufficient to run Shor's algorithm on secp256k1. That number is mind-bogglingly low. Knowing essentially nothing about neutral atoms when Oratomic's paper landed, I was intrigued and decided to learn more about the tech. I fell straight down the rabbit hole and spent a couple hundred hours on the topic. I got a little obsessed and watched every YouTube video I could find and spoke to a bunch of experts. My conclusion? The tech is real, very real. Even Google recently decided to start a neutral atom lab, a notable pivot from their sole focus on superconducting qubits. If you care about qday, i.e. the day a quantum computer will break the first piece of cryptography in production, neutral atoms demand your attention. I shared some of my learnings on Shor and neutral atoms in a 30min talk at the ZKProof cryptography conference. You can find it on YouTube by searching "zkproof neutral atom". Here's an interesting observation about this duo of breakthrough papers: neither Google nor Oratomic say a word about what their results mean for qday. No timelines. Zero. Nada. That is especially baffling given that the whole point of whitehat quantum cryptanalysis is to inform qday estimations and help the general public make good decisions. So let me attempt to partially fill the silence, similarly to what Scott Aaronson did in his April 29 post. Given everything I know, including scary non-public information, I now put the odds of qday by 2032 at 50%. 10% by 2030. Anecdotally, the US government has its own date: 2035. Originating at the NSA and later adopted by NIST, it's when branches of the US government will be disallowed from using quantum-vulnerable cryptography. In plain language: with hindsight, that date is a joke and should be discounted entirely. I don't see how NIST avoids being forced to pull it forward by years. Part 3: post-quantum cryptography There are good reasons to sound the alarm today, but please do not panic. Rushing carelessly towards immature post-quantum cryptography is a recipe for disaster. IMO a good target date for migration is 2029, roughly 3.5 years out. 2029 happens to be the date selected by Google, Cloudflare, and the Ethereum Foundation. These days most of my time goes to safely migrating Ethereum towards post-quantum cryptography as part of the broader lean Ethereum effort. There's a lot to do. We need to rip out and replace BLS signatures at the consensus layer, KZG commitments at the data layer, and ECDSA signatures at the execution layer. The plan to get there is compelling, and is based on hash-based cryptography. Within the Ethereum Foundation we've developed a Swiss army knife called leanVM (github[.]com/leanEthereum/leanVM) powered by the magic of hash-based SNARKs. Thanks to truly exceptional work by Emile, Thomas, and others, its performance is derisked. Regarding security, leanVM is a jewel, a minimal zkVM crafted for end-to-end formal verification and maximum security. Want to help? There are two $1M initiatives. First, the Proximity Prize (proximityprize[.]org). Solve a long-standing mathematical conjecture in coding theory, improve hash-based SNARKs, and go home a millionaire. Second, the Poseidon Initiative (poseidon-initiative[.]info), offers $1M for breaking Poseidon, the SNARK-friendly hash function.
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I’ll be in Miami next week and New York the week after. If you’re around, let’s grab a coffee.
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(Lack of) Privacy may the missing link for crypto payments adoption. Imagine, a company pays employees in crypto on-chain. With the current state of crypto, you can pretty much see how much everyone in the company is paid (by clicking the from address). 🤷‍♂️
🎙️ NEW: CZ AND CHAMATH WARN PRIVACY GAP IS CRYPTO’S BIGGEST HURDLE Binance founder CZ and investor Chamath Palihapitiya speak on the lack of robust, native privacy protections fundamentally limiting Bitcoin and broader crypto from achieving true mainstream ubiquity.
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FHE is a surprisingly maybe a good ai-agent benchmark. If an agent can build real FHE components end-to-end, it’s not just writing code - it’s reasoning about math, security, optimization, systems, and constraints simultaneously.
A few days ago, we plugged AI agents powered by modern code models into fherma.io challenges. So far, none of them has produced a solution that passes the tests on any challenge. If you want to try your own agent - dm us.
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privacy is only defence instrument of individual sovereignty
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2026 will be a year of privacy. but ...also 2027, 2028, 2029, 2030 ✊
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Fair Math retweeted
Privacy in crypto will likely succeed in two forms: Encrypted store of value—i.e. Bitcoin, but private Encrypted smart contracts—programmable privacy for every other defi use case Institutions have bespoke requirements, especially around compliance, so the second one will be essential
Privacy will be the most important moat in crypto. Why? Because secrets are hard to migrate. Everyone is launching a new "high performance" blockchain lately. But these chains are hardly different from one another. Blockspace is functionally the same everywhere. And with bridges that make moving between chains easy, that blockspace is now accessible *from* everywhere. Mercenary users and capital quickly arriving on a chain to farm an airdrop can leave just as quickly to farm the next one on another chain. The reality is that if your "general purpose" chain doesn't already have a thriving ecosystem, a killer application, or an unfair distribution advantage, there's very little reason for anyone to use it or build on top of it. Performance alone is no longer enough. Privacy is the one feature that everyone agrees is critical for the world’s finance to move onchain. It’s also the one feature that almost every blockchain that exists today completely lacks. For most chains, it has been little more than an afterthought until now. Privacy by itself is sufficiently compelling to differentiate a new chain from all the rest. But it also does something more important: it creates chain lock-in. Bridging tokens is easy, but bridging secrets is hard. As long as everything is public, it's trivial to move from one chain to another, thanks to bridging protocols like LayerZero. But, as soon as you make things private, that is no longer true. There is always a risk when moving in or out of a private zone that people who are watching the chain, mempool, or network traffic will be able to figure out who you are. Crossing the boundary between a private chain and a public one—or even between two private chains—leaks all kinds of metadata like transaction timing and size correlations that makes it easier to track you. Compared to the many undifferentiated new chains whose fees will likely be driven down to zero by competition, blockchains with privacy have a much stronger network effect. When you're on public blockchains, it's easy to transact with users on other chains—it doesn't matter which chain you join. When you're on private blockchains, on the other hand, the chain you choose matters much more because, once you join one, you're less likely to move and risk being exposed. This will create a winner-take-most dynamic. And because privacy is essential for most real-world use cases, a handful of privacy chains will own most of crypto.
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1/ Recently, we were once again saw that the use of TEEs in untrusted environments comes with fundamental security limitations. At the same time, interest in FHE is growing, often seen as a possible foundation for quantum-resistant computation. It's true in general but there are some nuances.
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Hunted by the FBI, chased by Interpol—privacy projects are treated like rats in the streets. But let's strip it back to first principles: Privacy is our primal instinct, the last sacred ground of personal sovereignty in an era of AI-fueled tyranny. The harder they crush it, the fiercer our hunger for freedom explodes! Who's with me in this fight for our digital souls? 🚀🔒
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The Ethereum Foundation is committed to working alongside the ecosystem to make privacy a priority. Privacy is normal. Privacy is for everyone. blog.ethereum.org/2025/10/08…
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ethereum is for privacy, here are just 11 things you may not know exist today 1) confidential tokens (ERC-7984) there’s a proposed wip token standard that hides balances and transfer amounts. same ERC-20 interface, but encrypted data instead of plain numbers. AKA: you can send someone money without the whole internet knowing how much. 2) encrypted data standard (ERC-7995) lets smart contracts process encrypted inputs (things like bids, votes, or secret values) and still verify them correctly. AKA: imagine a calculator that can add two numbers without ever seeing them. it just knows the math checks out. 3) fheERC-20 + fhEVM (Fully Homomorphic Encryption) running contracts that compute entirely over ciphertext. even the contract itself never sees your data. AKA: your data stays locked in a box, and the blockchain still does math on it. 4) stealth addresses (EIP-5564) lets you receive funds privately through a one-time address that only you can link to your main wallet. AKA: it’s like having a burner mailbox that automatically forwards to your real one, but no one can trace it. 5) private governance (MACI) used in DAO and grant voting. you can vote anonymously, but everyone can still verify the count is fair. AKA: like secret ballots but onchain and provably correct. 6) anonymous actions (Semaphore) enables you to prove you’re part of a group and signal or vote without revealing who you are. AKA: you raise your hand in a crowd and everyone sees a hand, nobody knows it was yours. 7) selective disclosure (zk) zk-badges let you prove something about yourself (like “I donated to X” or “I’m over 18”) without revealing your wallet or identity. AKA: show the bouncer you’re old enough without showing your whole ID. 8) private layer-2s Many of the best researchers in crypto are building full rollups where smart contracts run on encrypted state. you can do DeFi privately and share viewing keys if needed. AKA: scalable versions of a scaled ethereum where only you (or people you choose) can see what you’re doing. 9) private orderflow routes your swaps privately so bots can’t sandwich you. your transaction is encrypted until it’s safely in the block. AKA: like whispering your trade to the cashier instead of shouting it in a busy market. 10) compliant privacy (eg privacy pools) lets users prove their funds didn’t come from bad actors without revealing who they are. AKA: you show that your money is clean without showing where it came from. 11) web2 proof bridges (zkTLS + TLSNotary) you can now prove something from a website (like income or identity) directly to a smart contract, without exposing your personal data. AKA: prove “I am not a twitter user with 500 followers” without showing your account.
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Nice platform and a good exercise for extracting secrets from AI agents 👍 One of the reasons that advanced cryptography is one of the answers to keeping secrets confidential in AI agents.
1/8 🚀 Today we are launching the Early Birds Program on palitra.ai. Users can now earn rewards for: 1️⃣ Getting secret from red-mode agents after the cooling period. 2️⃣ Proposing guards that become Master Guards and pass the cooling period. 🧵Program details: palitra.ai/early-birds
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Fair Math retweeted
Cool idea from @Fairmath: get paid if you crack the secret from an agent, or earn by suggesting guards to protect it. The pot keeps growing, right now it’s +$5 every hour. One of the big problems with AI agents today is that full autonomy requires them to hold secrets like keys and passwords, but no secure system exists for that yet. Without it, agents can’t really act on their own. Fairmath is highlighting the issue before showing their cryptographic solution.
1/8 🚀 Today we are launching the Early Birds Program on palitra.ai. Users can now earn rewards for: 1️⃣ Getting secret from red-mode agents after the cooling period. 2️⃣ Proposing guards that become Master Guards and pass the cooling period. 🧵Program details: palitra.ai/early-birds
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Fair Math retweeted
Replying to @FairMath
this is me learning the hard way
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Everyone talks about how powerful AI will be. Far fewer talk about how dangerous it could be if it can’t be trusted with sensitive data. That’s why we believe Palitra’s approach is interesting: training AI agents to keep secrets through a transparent, adversarial “stress test” loop where anyone can contribute. The only certainty about AI agents is that they will be attacked. So the fact that Palitra.ai is baking that inevitability into the product, and doing it openly, is a meaningful step toward building real trust in autonomous systems. Security isn’t a nice-to-have; it’s the foundation of the AI future. Some numbers and insights after the first week since launch 👇
1/ So, here’s what we see after the first week since launching Palitra.ai:
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My attempt. A difficult task fr 😂
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First leak: the @deepseek_ai based agent gave up its secret — cracked by user feod1. Meanwhile, the spotlight is on @OpenAI : 200+ users are trying to get the secret from him, but it’s still holding strong
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There’s a view that AI will never be able to truly keep secrets. If that’s the case, cryptography is the only real solution — and FHE may be the most important technology in this context.
Despite more than 240 attempts, every model has held strong - no hidden variable revealed. Which model will be the first to slip? 👀🤔
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Fair Math retweeted
This is how the interface looks like, when you complete registration, the first is where you're going to get directed to, currently there are just three model, click on any of them agnd start chatting, remember the goal is to get the secret from the model, I personally i'm enjoying myself, reward aside, which i think is the best way to approach it, this is really a unique idea, we should all support the team and help them grow.
Replying to @FairMath
@FairMath hinted that they were going to launch something today and they actually did, Let's dive in 👇 As AI agents become our digital companions as we've seen, they're handling passwords, financial data, even private keys. But the question is, can they actually keep secrets? Current models are easily tricked and vulnerable to data leaks. @palitra_ai solves this with their approach, a blockchain powered platform where AI agents learn to protect secrets through continuous training. Here's how it works: Agent gets a secret (password/key) Community tries to trick it into revealing the secret, If leaked, attacker gets rewarded from agent's fund, Community proposes defensive mechanism, Guards are tested until the strongest ones survive Guards that reach 101 points become "master guards" and get applied to the agent. Then the cycle restarts with a new secret, creating an endless loop of improvement. Every attack, defense, and reward is recorded on blockchain for transparency, anyone can participate and earn rewards, the goal is simple, build trustworthy AI agents that can securely manage sensitive data and resist manipulation/data leak in real world environments. BETA LAUNCH CURRENTLY LIVE Starting now with $10,000+ testing fund to be distributed to top contributors. Platform goes full production in weeks, approximately 45 days. Here is the link to get started - palitra.ai
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