endy

FWIW you can now price and hedge hourly gpu compute costs on the same onchain options rail as US equities, gold, and oil. been looking into injective options (live on testnet rn) and even though i haven't tried it out yet, two angles here make a lot of sense to me: - the compute market and the builder infra underneath. on the market side, putting options on $SICI (the onchain ai compute index) alongside us500, us tech, gold, silver, and oil is a wild mix. with perps, you can only bet on GPU prices going up or down and you risk getting liquidated if the market spikes against you. with these usdc-backed options, you can protect yourself against rising or falling gpu costs while knowing your exact worst-case loss upfront. on the builder side, injective didn't just build a single exchange frontend. they’ve successfully built the options layer as composable infrastructure. this means that devs can use that foundation to launch white-label exchanges, structured products, or custom derivatives protocols without building the options engine from scratch. still early since it's on testnet rn, but giving builders a reusable options primitive like this is huge for the $INJ ecosystem ngl.

where’s the lie?

institutional adoption is one of the most overused phrases in crypto mostly because people skip the boring operational stuff banks actually ask for. banks and trading desks don't care much about peak tps if their transaction gets stuck in a public rpc queue during a margin call. that's the actual friction injective is targeting with institutional rail. when firms are dealing with strict settlement windows or collateral deadlines, 'hopefully the public endpoint picks this up fast' doesn't really work for their risk teams. so instead of just talking about enterprise adoption in the abstract, injective turned direct validator access into an actual product path: - direct routing into an enterprise validator mempool for predictable next-block inclusion - pre-commit feedback (optimistic execution results) so desks can prep their next orders or catch errors before the block even finalizes - hardware attestation so compliance teams can verify the exact software build for their audit trails the part i like most here is what stays the same. firms still keep self-custody of their keys, the full validator set still votes on and finalizes every block, and injective's batch auctions mean everyone still clears at a uniform price. predictable inclusion doesn't mean unfair execution. mind you, access is rolled out case-by-case rn rather than a public api you can just go test yourself, so we'll see how usage scales from here. still, if you look at the rwa and finance-l1 lane injective has been building toward, giving institutions a rail that actually fits their compliance and timing rules feels huge for the $INJ ecosystem.

privacy is easy to forget when you're just trying to get paid. that's what caught my attention with @TryNoxApp's Pay Links. you share a link instead of your wallet address, the payer picks a supported coin and chain, and you receive $INJ or USDC on @injective. the UX is the part i like here. building privacy directly into the payment flow makes way more sense than expecting people to remember extra steps every time. under the hood, it uses NEAR Confidential Intents so there isn't a direct onchain transfer linking sender and recipient. mind you, it's not complete anonymity - their docs still point out that amounts and timing can be compared. haven't tried it out myself yet, but i really like the direction. they also have a points campaign running for swaps rn btw - 1 point per $1 swapped, extra points for swaps into injective, and 2x points until Nov 8 👀 sayonara 👋🏼

AI can recommend 10 restaurants in seconds. but here's a question we don't ask enough: how many of those recommendations can it actually back up with evidence? i mean, think about it. you ask an ai assistant to find somewhere to eat in a city you're visiting. it gives you a beautiful list. confident descriptions. places that supposedly match your taste. that all sounds great, right? i know... until you realise it might have confused two restaurants with similar names, recommended somewhere without relevant supporting data, or simply presented a guess as a fact. and that's the part that bothers me. the problem isn't just that ai can be wrong but rather that it can sound completely certain when the evidence is incomplete. as we're building more autonomous agents that make recommendations and decisions for people, that gap becomes harder to ignore. so, for the @Qloo agentic hackathon, i built something to explore a different approach. introducing Anghkooey × TasteProof an ai concierge with a Qloo-powered cultural intelligence layer that doesn't just discover places but can also check the evidence behind ai-generated recommendations. here's how it works. imagine you're visiting lagos, nigeria but you love the dining experience at nobu restaurant and the music of ryuichi sakamoto. instead of relying on generic popularity lists, anghkooey lets you use those cultural references to discover places through Qloo's real search and insights APIs. but i wanted to take that further. what if ai agents could check their own recommendations before confidently presenting them to users? that's where 'TasteProof Verify' comes in. give it your cultural references, destination, and a shortlist of ai-proposed venues. it checks the shortlist against actual Qloo results and returns three possible verdicts: - OBSERVED : the venue matches a result in the retrieved evidence. - REVIEW : there's an ambiguous match that needs further confirmation. - NOT OBSERVED : the venue wasn't in the bounded result set but that doesn't mean it doesn't exist. and here's my favorite part. i also built a real model context protocol (MCP) integration, so external ai agents can use TasteProof as a verification tool. when a recommendation is uncertain, the agent can perform a bounded follow-up Qloo search to investigate the identity. in one live test, a proposed venue named "NOK" matched "NOK by Alara" only partially. The follow-up search returned additional similarly named entities. TasteProof didn't pretend they'd all been verified; rather, it kept the recommendation marked REVIEW. that distinction matters. because finding more information isn't the same as proving a claim. under the hood, i built explicit user approval, entity confirmation, structured MCP tools, real Qloo integration, evidence receipts, and strict limits on what the system can investigate. it's live, open source, and working in production. ⊚ Try TasteProof: anghkooey.vercel.app/taste/v… ⊙ Explore the code: github.com/Devendurance/angh… i think the next generation of ai agents needs to do more than generate convincing answers. they need to know when the evidence is strong, when it's incomplete, and when a human should take another look. that's the direction i'm exploring with Anghkooey × TasteProof. built for the @Qloo agentic hackathon. would you trust an ai agent more if it could show you exactly what supports its recommendations? i'd love to hear what builders think. 👇 the video below is a 3-minute walkthrough showing the actual product, real Qloo evidence, and an external agent using the MCP tools.

binance has been hacked of $2.7B tether has frozen $1.9B of the stolen money can’t wait to see how this one ends 👀 new week, new hack tis getting scary