
Alex
3.5K posts

Alex
@pilouanic
@clarnium_io co-founder, blockchains researcher & project growth adviser, founder of @wobblytimer & https://t.co/mTf42AZqfm







.@arbitrum Security Council took emergency action to freeze 30,766 ETH held at the Arbitrum One address linked to the @KelpDAO exploit. The key technical point is how this was executed: it was not a normal transfer signed by the exploiter's key. Based on the on-chain trace, this appears to have been executed from Ethereum (L1) via governance-level emergency upgrade powers. The Upgrade Executor temporarily upgraded DelayedInbox, invoked a temporary entrypoint to enqueue a delayed L1→L2 message via Bridge.enqueueDelayedMessage(kind=3, ...), and then restored the original implementation. The critical logic change was that the sender input shifted from the standard msg.sender path to a caller-controlled parameter (then transformed via L1→L2 aliasing), allowing the injected message to carry exploiter-linked sender context. Also, kind=3 maps in Nitro to L1MessageType_L2Message, which allows L2MessageKind_UnsignedUserTx execution on L2, i.e., this path does not require a user signature check. So the L2 transaction view (“from exploiter to 0x…0DA0”) reflects a chain-level forced state transition, not a standard user-signed transfer. TX on L1: app.blocksec.com/phalcon/explor… TX on L2: app.blocksec.com/phalcon/explor…


This 30-min workshop by the creator of Claude Code will teach you more about vibe-coding than 100 YouTube video guides. Bookmark it & give it 30 minutes today. This video will change the way you use Claude forever.




🚨BREAKING: Anthropic just open-sourced a powerful new framework for building AI agents and made it publicly available in a GitHub directory. It’s called “Skills” and it redefines how we work with Claude. Instead of repeating prompts, developers can now create reusable “skills” that package instructions, workflows, and logic into a single unit. A Skill = a structured capability an AI can reliably execute. For example: • Analyze datasets and generate reports • Create structured documents • Automate multi-step workflows • Execute internal business processes Each skill is: • Modular, reusable across projects • Versioned, continuously improvable • Dynamically loaded, used only when needed This solves key problems in today’s AI systems: → Repetitive prompting → Inconsistent outputs → Limited scalability The bigger shift: From: “Prompt engineering” To: “Programmable, reusable AI systems” This is a foundational step toward more reliable, production-ready AI agents. If you're building with AI, this is worth your attention.







my new favorite hobby is watching financial agent swarms looking for the best yield (jumping between defi protocols)



