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DeFi has lost over $900M to hacks in the first six months of 2026, +70% YoY. Most of those protocols had audits. A growing share of these losses never touched the smart contracts at all: compromised keys, sophisticated social engineering attacks, operational gaps a smart contract audit can't prevent. That’s why @LotusFi_ takes a holistic approach to security, with independent review from experts across three surfaces: economic design, smart contracts, and operational security. Our team has secured tens of billions of customer deposits, smart contracts, transactions and private keys since 2019. On the contracts, that's meant engagements with top-tier experts early and often: @Iam0x52 for a pre-audit review, @EnigmaDarkLabs on gas and architecture, and early access to Zellic's @v12sec and @cantinasecurity's Apex, two of the newest AI-native scanning tools in the space. All before we even kicked off our smart contract audit with @Cyfrin, supported by their Cygent AI agent for security review and formal verification. AI tools extend what expert reviewers can cover; they don't replace their judgment. As we move closer to deployment, we'll also be pursuing @_SEAL_Org certification for our op-sec practices because as we’ve seen with many recent exploits, security does not stop at the contracts. On the design side, @BlockAnalitica ran an independent economic assessment of Lotus's markets, tranches, and risk parameters. @CredoraNetwork's risk ratings on each tranche give every vault transparent risk you can verify. We want the protocol tested early, repeatedly, and from every angle. That's the standard we think serious onchain infrastructure should meet.

DeFi has lost over $900M to hacks in the first six months of 2026, +70% YoY. Most of those protocols had audits. A growing share of these losses never touched the smart contracts at all: compromised keys, sophisticated social engineering attacks, operational gaps a smart contract audit can't prevent. That’s why @LotusFi_ takes a holistic approach to security, with independent review from experts across three surfaces: economic design, smart contracts, and operational security. Our team has secured tens of billions of customer deposits, smart contracts, transactions and private keys since 2019. On the contracts, that's meant engagements with top-tier experts early and often: @Iam0x52 for a pre-audit review, @EnigmaDarkLabs on gas and architecture, and early access to Zellic's @v12sec and @cantinasecurity's Apex, two of the newest AI-native scanning tools in the space. All before we even kicked off our smart contract audit with @Cyfrin, supported by their Cygent AI agent for security review and formal verification. AI tools extend what expert reviewers can cover; they don't replace their judgment. As we move closer to deployment, we'll also be pursuing @_SEAL_Org certification for our op-sec practices because as we’ve seen with many recent exploits, security does not stop at the contracts. On the design side, @BlockAnalitica ran an independent economic assessment of Lotus's markets, tranches, and risk parameters. @CredoraNetwork's risk ratings on each tranche give every vault transparent risk you can verify. We want the protocol tested early, repeatedly, and from every angle. That's the standard we think serious onchain infrastructure should meet.

Introducing a limited preview of GPT-5.6 Sol, our next generation frontier model, as well as GPT-5.6 Terra, a balanced model for efficient, everyday work, and GPT-5.6 Luna, a fast and affordable model for high-volume work. openai.com/index/previewi…

Building index-tracking assets on top of options instead of debt ethresear.ch/t/building-ind… What if the use options as the base of defi, instead of CDPs and liquidations? So instead of extreme price movements creating a sharp and global "you get liquidated" effect, instead your exposure to the index diverges quadratically from your preferred exposure in a smoother way? A key benefit is getting rid of the need for instant oracles, and instead making everything work on top of "slow oracles" (ie. the type that prediction markets use) This design has a significant downside - the need to do regular rebalancing - and an open question of whether and how this rebalancing can be made slippage-resistant enough. But it's worth considering and trying IMO. I would feel much safer holding algostables inside something like this, than in something that depends on an oracle that has to give real-time answers (and therefore could be tricked into giving wrong real-time answers with no time for human recourse).