与「cryptocrash」相关的搜索结果

A top Ukrainian crypto blogger, 32-year-old Kostya Kudo, shot himself in Kiev amid the crypto crash, triggered by Donald Trump. He managed ~$65M, including $10M from Ukraine’s GUR. Local media say he faced pressure from security forces demanding a share of his crypto profits. A well-known crypto blogger shot himself in Kiev amid a crypto market crash: he managed around $65 million, including $10 million belonging to Ukraine’s military intelligence (GUR). 32-year-old Konstantin Hanich, known online as Kostya Kudo, was found in a Lamborghini Urus with a gunshot wound to the head in Kiev’s Obolon district. According to local media, Hanich handled funds from the GUR, Kirv businessmen, and government officials. In the days before his death, he faced pressure from Ukrainian security forces demanding a cut of his crypto earnings. Last night, Hanich reportedly used an award pistol personally given to him by GUR chief Kyrylo Budanov to end his life. Before that, he sent a farewell message to his close friends.

Over the past few months, we have restructured MEGA to move in a new direction. We’ve brought together new capabilities and reorganized our teams, a process that has resulted in a 20% reduction in our staff. These are people who helped build MEGA from its earliest days ( building out the chain / wallet, supporting our ecosystem, and telling our wild stories). To everyone who is leaving: I appreciate you. Your work is foundational in what we’re doing next. If you are hiring, please reach out. These are some of the best people we have worked with, and we will gladly make introductions. Our founders, early employees and core engineering team remain committed to MEGA and to the work we started three years ago with MegaETH. We believe cryptography and distributed systems will change how the world produces and accesses purified intelligence. We have a lot to prove, and a lot of work ahead.

Ever wondered how #XELIS# decodes encrypted balances in milliseconds? Here's the mathematics behind the magic! 👇 🔥 Encrypted on chain: Your balance stays confidential. 🔥 Baby Step, Giant Step (BSGS): A clever mathematical algorithm that avoids checking billions of possibilities individually. 🔥 2⁴⁸ decoding range: Handles an enormous range of possible values. 🔥 Precomputed tables: Around 330 MB of data helps accelerate the decoding process. 🔥 Cuckoo hashing: Enables efficient lookups to find the right values. 🔥 Milliseconds: Complex cryptography becomes a fast, practical wallet experience. 🔥 The result? Strong financial privacy without sacrificing usability. That's how #XELIS# turns advanced mathematics into real world technology. 🎬 Enjoy watching! 👁️ @xelis_project @xelis_fans @web3 @alkazarauniiii @xelisvault #Web3# @Awen__crypto @cryptoprvcy #Crypto# #CryptoMining# #Smartcontracts# #Privacy#

And yeah @fermah_xyz started with a problem that sits deep inside the ZK stack. Zero knowledge applications can prove that a computation was done correctly, but producing those proofs at scale takes serious infrastructure: specialised hardware, several proving systems, workload scheduling, and reliability and cost controls. A team building a ZK rollup, zkVM, bridge or coprocessor cannot simply ask for "a proof" and move on. Fermah's first answer was a universal proof generation layer. Provers with GPUs and other hardware supply capacity, applications that need proofs create the demand, and Fermah matches the two. That marketplace is now called Froben. The problem behind the proof Running this in production exposed something bigger. A proof is one step in a longer sequence. Something has to trigger the work, the work has to run, the result has to be verified, and then the result usually has to start the next action. Fermah's argument is that blockchains are good at recording state and weak at starting what happens next. Take a lending position that crosses its liquidation threshold. The chain records the fact, but nothing forces the liquidation. A keeper, bot or backend still has to notice the condition, build the transaction and submit it. Consensus settles what happened, and a human run service still decides what happens after. Fermah calls the missing piece protocol agency: a protocol's ability to act on a verifiable condition without depending on an outside operator. What Kernel does Kernel is Fermah's Protocol Agency Engine, and its pitch is short: Define. Execute. Attest. A developer defines a workflow, Kernel runs it, and the result is published onchain with a cryptographic proof. Three design choices matter here: -Typed nodes. Workflows are built from a library of verified building blocks such as HTTP calls, onchain reads, price feeds and math functions. The type system validates how they connect before anything runs. -Sandboxed execution. Workflows run in an isolated environment built for untrusted code, and the same inputs always produce the same output. - AI where it helps, code where it counts. Fermah says AI agents can compose workflows, but deterministic code executes them, with no LLM in the execution path. Kernel can work as an oracle too. A smart contract requests a workflow, Kernel runs it, and the contract receives the result with a proof that the workflow ran exactly as written. Froben and Flashcast Fermah did not walk away from ZK. Froben still runs, now on top of Kernel, and its paying customers include ZKsync Era and Abstract. In Froben, a workflow defines the proving pipeline and Kernel schedules the work across the prover fleet. Flashcast Social shows a different use of the same engine. A user posts a question, and within seconds it becomes a live prediction market. Kernel then runs the resolution workflow and settles the market automatically when the event happens, with no committee and no dispute window. Proof generation and market resolution have little in common on the surface. What they share is a condition that has to produce an action, and that is the workload Kernel is built around. Fermah also lists data aggregation, pricing oracles and cross chain coordination as things Kernel can run. Where agents fit Fermah's latest thinking extends this to autonomous software agents. An agent working onchain needs to find available actions, check whether it is allowed to run them, pay, execute, and use the output as the input to its next step. Fermah describes this as action manifests, which make actions machine readable across discovery, eligibility, execution, payment and output. The shift in one line The old question was how to generate ZK proofs efficiently at scale. The new one is how a protocol can turn a verifiable condition into a complete, executable sequence. Proving is still part of the system, but it is now one workload among several that Kernel coordinates.

Every step between your agent and the GPU can read your prompts. The proxy. The host. The operator. Every plan. Every tool call. Every retry. Plaintext. The only thing stopping them is a policy. Not cryptography. We seal prompts on the client. They open only inside an attested GPU. Nobody holds the key. End-to-end encrypted inference. Richard Rizk, Co-Founder & CTO 🫡

Ethereum is both an abstract philosophical concept (the ontological Turing machine) and a practical research and development effort that aims to answer this question that pertains to the nature of Ethereum. How the nature of "Ethereum" as a protocol comes to be determined through the ACD governance mechanism, how cryptographic truth comes to be defined, and how convergent consensus is reached using majority representation of Ethereum's observers is what reduces the ontology of the world computer down to what we today call the Ethereum network. Here's how the world computer comes to be defined:

The cryptographic world computer: My attempt to express in somewhat concise terms the true meaning of basically everything planned to happen to Ethereum starting from the fork after Hegota. It's really not just a blockchain anymore. It's a hybrid architecture that combines together blockchains and modern cryptography, to enable much more powerful properties. FOCIL, EIP-8288, Lean consensus, state management, formal verification, advanced mempool improvements (including privacy), and the longer-term specter of obfuscation all mentioned.

🚨 On September 6, 2026, @Liquid_BTC was affected by a cache key collision vulnerability in rangeproof verification. An attacker minted ~3,998.5 L-BTC with no corresponding peg-in. Within minutes, the unbacked L-BTC was pegged out into real BTC on the Bitcoin mainnet. About 3,400 BTC was later returned to the federation peg wallet, while ~598.5 BTC remains under the attacker’s control. The SlowMist Security Team traced the fund flows on the #Bitcoin# side using @MistTrack_io and fully analyzed the incident. 🧩 Attack flow: 1️⃣ Two setup transactions first landed valid rangeproofs and commitments, while embedding a crafted payload in the locking script to seed node caches. 2️⃣ A follow-up minting output reused a colliding cache key — the same raw concatenation of proof, commitment, asset commitment, and scriptPubKey, but with different field boundaries. 3️⃣ On a cache hit, nodes skipped secp256k1_rangeproof_verify and min-value checks, accepted an unbacked commitment, and minted ~3,998.5 L-BTC. The fake UTXOs were consolidated and pegged out within minutes. ⚙️ Root Cause: The Elements rangeproof cache key concatenated variable-length fields without length prefixes. Distinct argument tuples could hash to the same key, so a positive cache hit meant skipping cryptographic verification. 🛡️ SlowMist Insight: A positive-result cache in a consensus verification path is itself a cryptographic primitive. Every field the verifier reads — and every field boundary — must be unambiguously bound into the key. Treat cache-key integrity as a mandatory item in consensus-layer audits. Full analysis👇