Lorena Fabris 🌐

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Lorena Fabris 🌐

Lorena Fabris 🌐

@blockya_

Blockchain and Tech for all! Regulation • Growth • BD Prev: CM at @graphprotocol • @Polkadot Ambassador • @trustlesscore DV (Cohort IV & V) Tg: @blockya

🌏 Katılım Haziran 2020
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Lorena Fabris 🌐
Lorena Fabris 🌐@blockya_·
Vuelvo a decirlo y voy a pinnear este mensaje. Telegram me taggeó como scam pero no lo soy!! Sigo teniendo el mismo handle. Fin.
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Federico Ast
Federico Ast@federicoast·
Some important figures from the Web3 ecosystem who are Argentina supporters! Vamos Argentina! 🇦🇷🇦🇷🇦🇷
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Federico Ast
Federico Ast@federicoast·
Video recap of the Subtech 2026 conference held on June 24-26 in Buenos Aires! 🇦🇷 A biennial conference organized since 1990, bringing together the brightest thinkers and doers in legal technology. For the first time ever in the Southern Hemisphere.
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Lorena Fabris 🌐@blockya_·
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hexens@hexens

1/ we found a bug in the Aptos Move VM that put up to $70B at systemic risk. type confusion at the execution layer. a ~90% success rate across hundreds of simulated runs on a 30+ validator cluster. cost to build the attack infrastructure: $3,000. Conducted by @kemmio , to our knowledge this is the first public research that showcases how to land a sophisticated multi-block attack in real-world environments. It includes mempool feng shui, block production specifics and about a dozen of other primitives and tricks chained to get to near-perfect exploitation results. Nonetheless, Aptos called it "extremely low exploitability." [hexens.io/research/aptos…]

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Lorena Fabris 🌐
Lorena Fabris 🌐@blockya_·
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vitalik.eth@VitalikButerin

Two weeks ago, Ethereum researchers met in Berlin to continue charting the protocol's long-term trajectory, following along discussions with client teams in Svalbard in April. The updated strawmap is at strawmap.org, and I attached a picture of it to this post. My own high-level takeaways: * "Lean Ethereum" is not a single one-shot upgrade, it is a collection of improvements that will come online to the Ethereum network over the course of three or four years. But make no mistake, this IS the third major iteration of Ethereum in the same way that the Merge was the second. Almost every major piece of the protocol will be replaced: - Verification through recursive STARKs, rather than direct re-execution. Recursive STARKs become an enshrined first-class core component of the protocol - Replacing everything quantum-vulnerable with quantum-safe alternatives - Consensus: decoupled available chain and finality, one or two-round finality. Theoretically optimal security properties, simpler than today, and faster than today - Multidimensional gas - State: not just tree structure, but what *types* of state are available - Changes to client architecture ... At the same time, simplification, cleanup and future-proofing. And this will all be done in a way that minimizes disruption to existing application. We've done this before (the Merge), we can do it again. * H-star (aka Hegota) is probably Ethereum's last thematically "pre-Lean" fork. Starting from I-star, most of everything we do will have a very strong "Lean" feel to it in one way or another. * Privacy is no longer an afterthought, it is a first class goal. When designing Frames, the mempool, additions to the state tree, we explicitly ask the question "okay, how do quantum-safe, intermediary-free privacy protocol transactions go through this, and what is the overhead?" * Formal verification of everything for security. * FV also makes us much more comfortable with canonicalization (having pieces of the protocol that are directly defined as a piece of bytecode expressed in some language). evm-asm is being written in part to become a canonical proof system for the EVM. * Quantum safety has shifted up a LOT in priority. This adds a lot of work (eg. finalizing a quantum-safe blobs design has become urgent; this work has already been ongoing for months) * Probably the single most disruptive part of the plan is the changes to state. There is growing consensus around leaving present-day-style "dynamic state" mostly unchanged, but scaling it only a medium amount, and adding new types of state that are more scalability-friendly (eg. no need for builders to sync/store all of it) but more restrictive, and that will scale a large amount. eg. possible Ethereum in 2030: 2 TB of present-day-style (dynamic) state, and 100 TB of new-style (scalable but restrictive) state This "new-style" state would work very well for ERC20s, NFTs, many defi use cases, but not eg. highly "central" objects like Uniswap contracts, or onchain order books, or other complex things (which are crucial for Ethereum but which only take up a small percentage of state) Hence, it will not be *necessary* to rewrite any apps, but it will be *very cost-effective* to eg. rewrite an ERC20 token into a newer design that uses a new type of UTXO storage that is currently being explored, so that it will have >10x lower txfees. Design of these new state types (current ideas: keyed nonces, ring buffers, UTXOs, statically accessible state, temp state) is an area where we will need a lot of feedback from application developers (incl. privacy-friendly application developers) and probably several rounds of rethinking and iteration. * In the context of a much larger total state size, we need to figure out the incentive issues around who stores this state and what motivates them to. Even saying "each node stores 1%" is not good enough - why do they store that 1% and why are they willing to serve it? This is being elevated as a first-class research area. * Ethereum will need to have a "VM" other than EVM in one form or another - at the very least, we need something like leanISA for recursive STARKs - and the gains are large in exposing it to users so that we support programmable privacy and better scalability. Right now, the most likely contenders are leanISA and RISC-V. My own ideal is that in this world, we adjust the protocol so that the EVM becomes a high-level-language compiler-level feature, and the protocol only "sees" RISC-V / leanISA directly. But this is still far away. * Gas limit increases, blob increases and slot time decreases will happen many times over the next ~5 years. We expect a large gas limit increase with Glasterdam. Each step of increased scale or decreased slot time is a matter of getting to the point where it is safe to do it, which comes from a combination of client optimization and protocol changes. Ethereum is CROPS. Ethereum is scaling. Ethereum is reinventing itself. Onward.

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Lefteris Karapetsas
Lefteris Karapetsas@LefterisJP·
In order to understand what this means you need to know that Brantly was the main reason ENS got so popular back in 2021-2022. The same people who are now stealing the $500m canceled him and fired him even after all he did for ENS. Yet he did the unthinkable and kept working on ENS until today even after having been treated the way he was. Imagine the frustration of Labs. You fire the guy, you mount a canceling campaign against him ... and yet he does not stop working on the ecosystem. What this firing and canceling did not do, Nick and Labs managed to finally achieve today. In killing ENS by capturing the DAO and stealing its treasury there is no ecosystem to build on anymore. A naming system that people don't use is worthless. Shame it has come to that. And despite all that, Brantly and the team shows integrity again here by declining the $1.2m grant from the DAO to keep building on the ecosystem. As there is no ecosystem to build on anymore. They could have just taken it and slow rugged, but no they are people of integrity. I applaud Brantly and his team for doing so. The downside that's $1.2m more for Nick and Labs to steal through their "independent" foundation ...
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brantly@BrantlyMillegan

the .eth comes off, the end of a chapter for me I have decided to move on from ENS, given recent events and other reasons. I'm grateful for my time with ENS and I wish everyone well going forward This includes winding down @ethidorg. I and my incredible teammates are open to new opportunities, DMs are welcome. My team has been amazing! Any of them would make a great addition to your team: @0xthrpw @encrypteddegen @sat_eth @jalilwahdat @quantumly 🔥 Re our projects like @grailsmarket, @ensmarketbot, @efp, etc: Thank you to all of our users, we appreciate you! Most of these projects will sunset over the next few weeks (tho code remains open source), more information will be posted later Godspeed everyone 🤝

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Kleros
Kleros@Kleros_io·
🇦🇷 For the first time in its 19 editions, SubTech came to Latin America. Kleros was there: @federicoast joined the AI and Justice panel with Amy Schmitz and Mendoza Supreme Court Justice Minister Mario Adaro, and we presented new research on AI systems acting as arbitrators. Watch the recap:
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Federico Ast
Federico Ast@federicoast·
Mucha gente todavía piensa en Kleros como un mecanismo donde los jurados se eligen exclusivamente por la tenencia de un token. Eso era cierto en la primera versión, que lanzamos en 2018. La versión actual es mucho más sofisticada: también permite seleccionar jurados entre quienes acrediten cierto conocimiento, como ser abogado matriculado o tener experiencia específica en disputas de consumo. Este caso de Junín es un buen ejemplo: 5 jurados abogados, sorteados al azar, resolviendo una disputa por un plan de pago de auto. En Junín también resolvimos casos de seguros, construcción y muchos más. 👇
Kleros Enterprise@kleros_es

⚖️ Kleros Enterprise | Junín | Caso #142 Una consumidora rechaza un saldo de AR$738.814,73 en gastos de inscripción y sellado que le reclaman tras la baja de su plan de ahorro automotor. La administradora del plan sostiene que el contrato la obliga a pagarlos, mientras que la concesionaria afirma que nunca fue parte del contrato. Ahora está en manos de la corte: 5 jurados, cada uno abogado certificado, sorteados al azar, deliberando en este momento. Seguí el caso → v2.kleros.builders/#/cases/142/ov…

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Yasmine Khosrowshahi
Yasmine Khosrowshahi@yasminekho·
Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible... And why AI hasn't actually learned to "see" the way we do. It completely changes how you think about Human Intelligence v/s Artificial Intelligence: 1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly. 2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it. 3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes. 4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired. 5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary. 6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it. 7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object. 8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs. 9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy. 10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments. 11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce. 12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer. 13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong. 14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly. 15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did. 16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built. Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.
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Lefteris Karapetsas
Lefteris Karapetsas@LefterisJP·
Announcing Ethereum Birdwatching. An independent non-profit dedicated to accelerating the adoption of Ethereum, its L2s, applications and overall ecosystem across the worldwide birdwatching community.
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NatyShi 🦇🔊🏴
NatyShi 🦇🔊🏴@NatyShi_·
📣 Dia de factos: Los agentes de IA sin máquinas de estados son chatbots con pretensiones. Me di cuenta de esto construyendo un bot de WhatsApp que procesa reclamos de edificios. El primer intento: un LLM con un buen prompt. Resultado: el bot se trababa, saltaba pasos, inventaba respuestas y no había forma de auditar qué había hecho. La solución no fue "mejorar el prompt". Fue arquitectura.🤓 ➡️ 10 estados. 4 gates de calidad. Regla simple: si falla 3 veces, escala a humano. Sin estados, no hay lógica. Sin lógica, no hay confiabilidad. Y si tu agente no puede fallar y recuperarse, no es un agente, es una demo. 🤡 Estoy armando una solución para que consejos de administración gestionen reclamos de vecinos sin perder horas en WhatsApp grupal. El bot recibe el reclamo, lo clasifica, verifica al vecino, lo registra y notifica al consejo. Todo con un framework de estados que garantiza que ningún paso se saltee. El post completo con el diagrama de 10 estados está en la infografía. 😃 State machine = esqueleto. Loops = músculos. Necesitás ambos.
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Ethereum Institutional
Ethereum Institutional@ethereuminsti·
We have received an overwhelming level of feedback and support over the past 24 hours - thank you to everyone who has reached out! At this stage, we have not published any official wallet address, and we ask that no funds be sent to any addresses currently circulating, as none are affiliated with Ethereum Institutional. We are grateful for the early support from BMNR, SBET, and Joe Lubin, and are currently conducting an organized ecosystem supporter round with a broader group of aligned industry participants. If you are interested in supporting our mission, you can reach out to us directly below: ecosystem@ethereuminstitutional.org
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Ethereum Foundation
Ethereum Foundation@ethereumfndn·
11/ Ethereum Basics for Governments and Institutions is our effort to help these stakeholders understand the basics of Ethereum, and how it differs from other infrastructures, including existing intermediated systems and other blockchains. Read the report: ethereum.org/reports/basics…
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Ethereum Foundation
Ethereum Foundation@ethereumfndn·
10/ Ecosystem: Ethereum's standards have become the foundation the rest of the industry builds on. Building on Ethereum unlocks unparalleled interoperability, offers greater flexibility, and ensures access to a mature, well-tooled ecosystem.
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Ethereum Foundation
Ethereum Foundation@ethereumfndn·
9/ Counterparty risk: Building on Ethereum does not introduce a new counterparty. No party can change the rules, restrict access, or halt the network. Most other layer 1 blockchains concentrate control in a foundation or corporation, whether through directly subsidising validators, controlling a large share of the token supply, or influencing validator selection, creating material counterparty risk.
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Ethereum Foundation
Ethereum Foundation@ethereumfndn·
8/ Client diversity: Ethereum has 5+ independent clients developed by separate teams in different languages. No other layer 1 has comparable client diversity. Most of them operate with one client each, creating a major risk of network failure.
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Ethereum Foundation
Ethereum Foundation@ethereumfndn·
7/ Economic security: At the time of the OpenZeppelin Report, Ethereum was secured by $76B worth of ETH staked on the network. This amount is substantially larger than all other layer 1s reviewed in the report.
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