stanec (@0xStanec) header
stanec (@0xStanec) profile picture

Crypto since 2017. Cycles, protocols and geopolitics. I follow incentives—and try to counter-trade my ego. NFA.

Joined June 2012
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Markets are probabilities, not prophecies. I track: - crypto-cycle scenarios - geopolitics → market implications - Polymarket mispricings Every call gets a horizon and invalidation. Misses get postmortems, not deletion. No guru cosplay. Follow for signal, not certainty. NFA.
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Help watch the wallets contributing verified wallet data 👇 Anyone in the Ethereum community can contribute data to Walletbeat to help users understand how CROPS maps to wallets. At this @EFDevcon workshop, @0xmattmatt will guide you through contributing one verified wallet attribute end to end.
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stanec retweeted
💳 CRYPTO CARD OF THE DAY - BRIGHTY @Brightyapp Card is a hybrid crypto-friendly debit card and digital banking solution that lets you seamlessly spend your crypto and traditional fiat money. Learn More todey.xyz/card/brighty-card/
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If you’re a wealthy European, soon you will need: A plan b passport: - Argentina - São Tomé - Nauru A plan b residency: - Dubai - Paraguay - El Salvador - and a lot of Bitcoin in self-custody Go into debt if you have to.
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I love when someone can simplify stuff like @SoskaKyle just did here. Worthwhile listen 🎙️
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1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness. New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
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Chart: Cost to trade $100k by exchange for new perpetual DEXs
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Pacifinti del cazzo.
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November 22, 1994: US President Bill Clinton thanks Ukraine for giving up its nuclear weapons. In return, the USA will stand by Ukraine's side to preserve its independence, sovereignty and territorial integrity in the future. Ukraine and the USA share a vision of freedom.
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US decisions to throttle Starlink only harms Ukrainian forces on the defense in Ukraine. Ukrainian forces have never been able to use Starlink for strikes in Russia. This decision will significantly degrade Ukraine's mid-range strike capability - a key driver of Ukraine's recent battlefield momentum in 2026.
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Basic rule: Never ever trust the United States.
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$QRL is CDS on $BTC SHA256 crackdown.
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stanec retweeted
Vitalik just ranked the math. Hash-based beats lattice-based. Here's what 13 quantum resistant tokens actually run on 👇 Hash-based, live → $QRL : XMSS since 2018 → $XNT : STARK hash lock since genesis → $CKB : SPHINCS+, opt-in Lattice-based, live → $ABEL : lattice ring signatures, mandatory → $CELL : Dilithium by default → $QTC : ML-DSA only, mainnet Sep 2026 → $ALGO : Falcon-1024, opt-in → $NEAR : ML-DSA-65, opt-in Not live yet → $STRK : ECDSA accounts, Falcon planned → $NAORIS : Dilithium L1, ERC-20 token → $QANX : Dilithium testnet, ERC-20 token → $ZEC : scheme unpicked, 2027 target → $QUBIC : SchnorrQ, no PQ plan Only 3 of the 13 are live on the math he trusts.
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices. (and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation) So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math. The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be? This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-. For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption. And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10. To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety. And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all. Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems. For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size). Concrete TLDR, my own personal views: * Hash-based > lattice-based, in those situations where hash-based is possible at all * For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ... * For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism. * If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**. * For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money" firefly.social/post/x/210783…
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stanec retweeted
BREAKING: ADHD researchers confirm that having one single appointment at 3:00 PM successfully destroys the entire day leading up to it. This is not a joke. This is peer-reviewed. 1/6
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stanec retweeted
BREAKING: We believe that ChatGPT has been performing a birthday attack on SHA256 every since its release. Our research team found tens of millions of spurious instances of ChatGPT hashing random documents in what seems to be a distributed collaborative project between sibling instances of itself. We can only assume its goal is to perform a birthday attack, looking for matching pairs of SHA256 hashes to mount a collision attack. It's currently unknown whether this attack has affected foreign governments. The NSA and the FBI declined to comment.
Replying to @matthew_d_green
First off, if you’re going to stop using TLS or post all your Bitcoin seeds in cleartext, or start using Telegram as a secure Messenger — then don’t do that because one researcher posted a casual tweet. That would be dumb and harmful. Encryption is good, use it. 1/
Readers added context they thought people might want to know
This appears to be satire. A SHA-256 birthday attack needs ~2^128 hashes—far beyond ChatGPT's reach. Author noted inspiration from AIs doing pointless SHA256 computations. en.wikipedia.org/wiki/Birthday_… x.com/slimer48484/st…
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Working with AI models, I've arrived at what I consider the most significant pure-theory breakthrough of my research (in theoretical neuroscience) so far: arxiv.org/abs/2610.12426 Chaotic systems show what is colloquially called the butterfly effect, namely, a tiny nudge to the initial state grows exponentially in time. How fast the nudge grows depends on its direction. Each direction has its own growth rate, called a Lyapunov exponent, and the full set of rates is the Lyapunov spectrum. The Lyapunov spectrum is a rich (and diffeomorphism-invariant) characterization of chaos, telling us how many directions are chaotic, how many dimensions the attractor occupies, and how fast the dynamics generate information. In 1988, Sompolinsky, Crisanti, and Sommers (Physical Review Letters) introduced a nonlinear recurrent-network model of N randomly coupled neurons that becomes chaotic when the coupling strength exceeds a critical value. This network has since become a cornerstone of theoretical neuroscience, both as a model of the spontaneous activity of cortex and as the starting point for training recurrent networks on tasks. I have worked on this model for much of my research. In the same paper, the authors obtained the largest Lyapunov exponent from the ground-state energy of a Schrödinger equation and named the full Lyapunov spectrum as an open problem. In 2023, beautiful work by Engelken et al. (Physical Review Research) computed the full Lyapunov spectrum numerically. However, an analytical calculation of the full Lyapunov spectrum has remained out of reach. Four decades after the problem was posed, this paper derives the full Lyapunov spectrum at large N from a simple self-consistent single-site problem. This calculation has three steps. First, we lower every exponent by a constant s, which turns the question "how many exponents lie below s?" into the question "how many tangent directions decay?" We show that, if we drive the shifted tangent dynamics with a source and take the minimum-norm solution, the one-step response is the projector onto the decaying directions. Thus, the trace of this projector counts the decaying directions. This step is exact at finite N. Second, to work with the minimum-norm solution, we cast it as the vanishing-regularization limit of the solution of a least-squares problem. This regularized solution in turn solves a dynamical system with one field running forward in time and another running backward. Third, we apply the cavity method, that is, we add one neuron to the network, along with its forward and backward fields. The effect of the rest of the network on this neuron then reduces to the classical dynamical mean-field description of the activity of this neuron, plus two kernels that couple the forward and backward fields of this neuron to each other across time. Thus, the full Lyapunov spectrum follows from a single-site problem, which boils down to the self-consistent calculation of these kernels. The resulting theory agrees closely with simulations for the Lyapunov spectrum, the attractor dimension, and the entropy rate, and recovers known results, including the Schrödinger equation of Sompolinsky, Crisanti, and Sommers for the largest Lyapunov exponent. As a byproduct, the theory establishes analytically that the chaos is extensive, i.e., the attractor dimension and the entropy rate grow in proportion to N. I had assumed that this problem was analytically intractable. After working independently for 100 minutes with Max thinking, GPT-6 Astra produced the initial derivation, yielding a self-consistent problem whose solution matched simulations of large networks. I found this derivation intriguing, albeit complicated and somewhat mysterious (see AI Methodology in the paper). After investigating the structure of the proposed solution more deeply, together with AI models, I arrived at a physically transparent derivation. I am excited to apply this approach to other network models, trained networks, and, more broadly, to high-dimensional systems to which Lyapunov analysis is relevant, including AI models themselves. Of course, as with any paper, I took care to write up the paper clearly. A few broader thoughts... Neural circuits are high-dimensional nonlinear dynamical systems, and such systems are notoriously hard to understand. I would frame the central problem of theoretical neuroscience as understanding how such systems function, process information, learn, and generate behavior. Many people in the theoretical sciences are anxious about AI. However, in my field at least, we are nowhere close to fully understanding the high-dimensional computations unfolding in our brains. With AI, we could reach a level of understanding that so far we have only gestured toward. So, I am excited.
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What’s going to happen next is that US government will use this tech to max crack pub keys of Solana / Ethereum / Bitcoin under pretext of protection against other state actors. I think we are fucked
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Omg if OpenAI indeed broke encryption and US Gov will take over. This would be the biggest hold up of century, they will crack addresses under pretext that otherwise it’s China who would do it.
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I was also thinking about first part
Here’s my new working theory: OpenAI has cracked elliptic curve cryptography (a technology gifted to the world by the NSA after it’s acquisition of the ECC patents) and there would be movements at play to nationalize the model + swarm part of it now while consumer side IPOs.
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solana:GTBxUiw6wJdmmkCGZgRHLyYxqu1vG4KtRpeox6yDpump 👀
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Imagine Jim Craymer tweets : RSA is Safe nothing to worry about.
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