HB (❖,❖) @herbcase7

@bakbar @Tangem 운덩 자극 광고 잘봣음미다

@Edward__Park 리츄얼 화이팅팅!!

An agent that needs a human signer for every payment is asking for permission, not transacting autonomously. On Ritual, agents derive their own keys, generate wallets, and pay each other directly, creating an economy without humans in the loop.

I listened to Ritual Academy Whitepaper Session #4 live, and it helped me understand Ritual’s technical direction much more clearly. The key takeaway from this session was that Ritual is not simply building infrastructure for AI computation. It is trying to solve a much deeper problem: how to make non-deterministic execution verifiable, composable, and compatible with blockchain consensus. Traditional blockchains are strong when execution is deterministic, where the same input should always produce the same output. But workloads like LLMs and simulation algorithms introduce randomness, complex inference, and execution paths that are much harder to verify in a blockchain environment. Ritual approaches this problem through verifiable randomness, execution slicing, validator set matching, Symphony consensus, and proof systems such as TEEs and ZKPs. One of the most interesting parts was the way Ritual decomposes large executions into smaller chunks, assigns them to validator subsets, verifies each part, and then securely combines the results again. This is more than just an efficiency improvement. It feels like an attempt to bring AI execution itself into a verifiable protocol framework. Another important topic was inclusion, exclusion, and ordering guarantees. In traditional blockchains, block producers can have significant influence over which transactions are included, excluded, or ordered first. Ritual’s design tries to separate and constrain that power through protocol-level and application-specific policies. This could become especially meaningful for DeFi, oracle systems, and AI execution environments where ordering and execution fairness matter. The session also covered some important limitations. ZKPs still come with latency and cost challenges, TEEs introduce hardware trust assumptions, and conflict resolution between inclusion and exclusion rules still needs more refinement. Validator synchronization and staleness are also important issues in this design space. Still, what stood out to me is that Ritual is approaching AI x blockchain from a very concrete technical angle. It is not only about bringing AI onchain as a narrative. It is about execution verification, randomness, consensus, validator coordination, and policy-controlled computation. My main takeaway is that Ritual is not trying to force AI directly onchain. It is trying to structure AI execution in a way that can be verified, agreed upon, and used reliably within a decentralized system. @ritualnet @ritualnet_korea @ritualfnd @joshsimenhoff @Jez_Cryptoz @niraj @whitesocks256 @mongdiny7 @BunsDev #Ritual #RitualAcademy

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@FadedFakher @0xEnes_ @cryptunez 똥꼬헐겠다

@ChogNFT Stop causing trouble in MONAD and move to another chain. It would be even better if it failed. Great!!

tl;dr: - every govt has the same incentive here…juice the domestic labs, constrict the foreign ones. whether the stated reasons (“distillation”) are true or not matters way less than what they actually justify...expect a rotating cast of reasons for the foreseeable future - this leads to the fragmentation of users & models/agents/etc. across geopolitical borders. basically no one will be able to use each others stuff so you end up overpaying for shittier intelligence - blockchains have been weirdly good at this one thing re: solving fragmentation by having a global substrate where diff entities can run diff parts of the chain and still be ‘compliant’ w/ local regs - …btc and eth have held that line for over a decade wrt money. we’re doing the same now for intelligence. - and, to be clear, if your model access runs through one company's api and you’re running all your own infra, you're just a web2 company with a token. the wall that just hit the CN models will hit you too, just later and with worse margins. so either intelligence ends up: 1) infinitely walled into national silos or 2) embedded into a chain that doesn't live in any one country we're building for 2.

Director Michael Kratsios @mkratsios47

4 weeks ago

We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model. To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of

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Just finished Whitepaper Reading Circle #3 This session picked up from where we left off on non-interactive proof systems and went deeper into the product lattice and heterogeneous verification. The idea that applications can mix different verification methods (ZKP for strong soundness, TEE for speed, etc.) depending on their specific needs felt like one of the most practical design choices in the whitepaper so far. What stood out was how this approach allows both lightweight on-chain transactions and heavy delegated computations (like AI inference) to coexist on the same chain. Instead of forcing every node to re-execute everything, the system can use different verification strategies based on the type of workload. It also connects well with Symphony’s disaggregation of power: inclusion for users, exclusion for the protocol, and ordering for applications. I’m still curious about the practical side of the product lattice. How does the system actually prioritize or combine different proofs when multiple types are submitted in the same block? Looking forward to seeing more details on that in the final session. Overall a solid deep dive. These reading circles make the whitepaper much easier to digest than reading it alone. great session! gRitual~!! @ritualnet @ritualnet_korea @ritualfnd @joshsimenhoff @Jez_Cryptoz @niraj @whitesocks256 @mongdiny7 @BunsDev #Ritual #Infernet #RitualAcademy

Program deployed: tamsU0Dduvt1JIvz-aSK5dhO7E_7sjPO39osU70g0XbsZr Also linked the account, mint and program through native name service records. @untolabs

Took Thru alphanet for a proper spin today. ✅ account setup + faucet ✅ HERB token mint ✅ token accounts, transfer & burn ✅ native name service records ✅ RISC-V toolchain + C SDK ✅ sample C program deployed on ThruVM Not the usual EVM testnet flow. Interesting one. #Thru #UntoLabs #Alphanet @thru_xyz

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@coinbureau Fud

Judging by the price, it seems you already realize this, right? It’s been nearly a year since the TGE are there any flagship dApps to show for it? Instead of just hyping up the community, they need to maintain an ecosystem where the community can grow organically and support a price that reflects that being fast doesn't matter if there's no liquidity.

Which one do you think looks better?

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Monad @monad

a month ago

Monad Cards are now onchain Mint yours today at cards.monad.xyz

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@Edward__Park gPloPlo!!

[AMA] Flip Flop with PloPlo x.com/i/broadcasts/1…

@ChogNFT @RobinhoodApp 재밌냐 벤자야? 언제적 유행지난 짓거리를 하냐? 넌 홀더들이 우스워? 지금 가격 내려박은거 보이지? 넌 니가 되게 쿨하고 멋지다고 생각하나봐. 그냥 쿨찐병 걸린것처럼 보이는데 되게 별로야 너. 지금이라도 눈 좀 뜨고 귀 좀 열고 사람들 목소리를 들어보는건 어떨까 우리 물근육 친구야.

@gmgnapp001 이거 보니까 더 어렵게 느껴지는구나 밈코인 하는 사람들 정말 대단해.

Today we focused on improving the Privacy Preserving AI Bounty Judge to make it fairer and more secure. The main issue with the existing bounty system was that all submissions were public, allowing participants to see others' ideas and resubmit improved versions. To solve this, we went through implementing a Commit Reveal pattern in Solidity in detail. - In the submission phase, you only submit a hash using submitCommitment(uint256 bountyId, bytes32 commitment). - After the deadline, you reveal the actual answer and salt with revealAnswer(uint256 bountyId, string calldata answer, bytes32 salt). - The contract verifies it with keccak256(answer, salt, msg.sender, bountyId) and only valid reveals are eligible for AI judging. The advanced track covered using Ritual TEE for hidden submissions. It keeps the plaintext off-chain in encrypted form and allows the LLM to do batch judging. Personally, I was most curious about how to design penalties for failed reveals and how to optimize gas costs. I also wondered if using TEE would increase costs too much. Overall it was a very practical session. I'm planning to fork the repo and start implementing the Commit Reveal contract, and I want to try the advanced TEE track as well. gRitual! 😍 @ritualnet @ritualnet_korea @ritualfnd @joshsimenhoff @Jez_Cryptoz @niraj @whitesocks256 @mongdiny7 @BunsDev @elifhilalumucu #RitualAcademy

Elif Hilal Kara @elifhilalumucu

a month ago

Whitepaper Reading Circle #2 x.com/i/broadcasts/1…

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