EmAaD𓂆 (❖,❖)

GRITUAL Just minted my Ritual Signature Card A little piece of me, onchain. 🌿 Make yours : ritual-signature-cards.vercel.app THANK YOU @antgeo13

@Rover0XT @ritualnet Gritual beo

@artemaad @ritualnet gRitual artist

Big funds are already calling AI agents one of the biggest opportunities for blockchain. But fast settlement alone is not enough. If agents will make decisions for us they must be fully verifiable and privacy should not be an afterthought This is exactly why @ritualnet stands

@Rover0XT @Jez_Cryptoz @dunken9718 @cryptooflashh @TaxMiracl @0xMadScientist Ritual on top

Everyone keeps asking, "Which blockchain has the best AI agents?" I think we're asking the wrong question. A better question is, "What kind of blockchain does AI actually need?" Most discussions focus on what an AI agent can do. I think the real conversation should be about where that intelligence is executed. If the intelligence depends on infrastructure outside the network, then part of its trust model exists outside the chain too. As AI becomes more integrated with Web3, infrastructure will matter far more than feature lists or polished interfaces. The strongest ecosystems won't just help developers build AI. They'll rethink the execution layer itself. @ritualnet is pursuing that idea by making AI capabilities a native part of the protocol rather than something added on top. That approach has the potential to reduce external dependencies and move decentralized AI closer to true autonomy. The conversation around AI shouldn't stop at smarter agents. It should also include the infrastructure that allows those agents to think, execute, and persist in a decentralized environment. The future of AI onchain won't be defined by who builds the most agents. It will be defined by who builds the foundation they can truly rely on.

@OGgrinding @ritualnet Gritual bro

@artemaad @ritualnet Gritual fam

@nomnomkiko @ritualnet Facts

@artemaad @ritualnet ritual on topp

@OGgrinding @ritualnet @cryptooflashh @joshsimenhoff @Jez_Cryptoz Gploplo

HOW @ritualnet IS POWERING ECOSYSTEM LAUNCHES WITH SOVEREIGN AGENTS

Benjamin Netanyahu is a war criminal.

@0xRootVector @miakhalifa Ronaldo has more Ballon d’Ors than you have brain cells and Mia made more money than your bloodline will see in 10 generations 💀 Meanwhile your GOAT is crying after another early exit while Argentina just got bent over by Spain

@miakhalifa 2 people who won nothing but are envious of the world.

AI models will evolve. The infrastructure shouldn’t have to. When swapping models is as simple as changing a parameter, builders can focus on creating not rebuilding. That’s the future of onchain AI. @ritualfnd @ritualnet

@bellaincrypto @ritualnet @ritualfnd @joshsimenhoff @0xMadScientist Siggy lookin comfy

gritual to my ritual friends ploplo mint is going on dont forget to mint it. keep growing with ritual. sending ritual heart to everyone who will engage here

Been spending more time exploring @popdex_ lately, and one thing I appreciate is how focused everything feels. No unnecessary noise just a platform that seems built with traders in mind. Looking forward to seeing how it evolves. 👀

@JamesJa43782392 @cryptooflashh @Jez_Cryptoz @joshsimenhoff @0xMadScientist Gritual

Ritual Research Digest: Four Research Breakthroughs Redefining the Future of AI Agents The AI landscape is evolving at an incredible pace, with hundreds of research papers published every week. Separating meaningful breakthroughs from the noise is becoming increasingly difficult. This week’s Ritual Research Digest highlights four papers that reveal where the next generation of AI is headed not just toward larger models, but toward smarter data, better evaluation, stronger agent infrastructure, and more effective training methods. Here’s what stood out. 👇 1/ Better data beats more data For years, the dominant belief has been simple: feed models more tokens and they’ll become smarter. New research challenges that assumption by showing that tokens are not all equal. Different knowledge domains interact with one another in unique ways. Some combinations reinforce learning and generate “bonus” performance, while others create interference that actually limits model quality. By identifying the most effective mixtures of training data, researchers achieved significantly better results on coding, mathematical reasoning, and instruction following benchmarks than balanced or poorly chosen datasets. The message is clear: future AI progress will depend as much on what we train on as how much we train on. 2/ AI deserves better benchmarks Most AI benchmarks end with a simple verdict: pass or fail. But complex tasks involve many successful intermediate steps that traditional evaluations ignore. The newly introduced Long-Horizon-Terminal-Bench measures progress across 46 challenging tasks covering software engineering, scientific research, multimodal reasoning, games, and more. Instead of only scoring final completion, it tracks incremental achievements through fine-grained subtasks and dense rewards. Interestingly, while today’s frontier models rarely complete entire workflows, many consistently solve meaningful portions of them. That reveals a more realistic picture of current AI capabilities and highlights how much room remains for improvement. 3/ Great agents need great infrastructure An AI agent is more than its language model. Its surrounding framework or harness determines how effectively it plans, reasons, and executes tasks. As these systems grow increasingly sophisticated, developers struggle to locate the exact pieces of code responsible for specific behaviors. The proposed Harness Handbook addresses this challenge by automatically mapping behaviors to implementation using static analysis and LLM-assisted code organization. This makes large agent systems easier to understand, debug, and improve. The payoff is measurable: better planning performance while consuming fewer tokens and lowering computational cost. 4/ Code itself can train better coding agents One of the most creative ideas this week reimagines how coding models should learn. Every software function naturally follows the same structure as an AI agent: • Context is provided. • An action is performed. • A result is produced. • Execution continues. Researchers hide a function’s implementation and ask the model to reconstruct it purely from surrounding code. This simple technique transforms ordinary code repositories into powerful reasoning datasets. The approach not only improves software engineering benchmarks but also restores broader reasoning abilities that are often weakened during specialized agent fine-tuning. Instead of forcing a trade-off between specialization and general intelligence, it strengthens both. Follow @ritualdigest and @ritualfnd for more information about Ritual. @cryptooflashh

PopDEX x Morph Tachyon is a huge step for onchain trading. ⚡ 200ms blocks, instant finality, 200K TPS, and transparent order books—built for a true trader-first Perp DEX. 🚀 @popdex_ x MorphLayer

PLO PLO NFT mint is officially live! 💚 If you’re on the waitlist, you can mint your NFT now. Public minting opens on July 23, so if you’ve got early access, this is your chance. 🔗 Mint here: ploplo.ai/mint @ritualfnd @ritualnet @0xploplo

@ritualfnd server down ?

@OGgrinding @ritualnet @joshsimenhoff @Jez_Cryptoz Gritual

WHY @ritualnet MODEL AGNOSTIC INFERENCE IS BEST FOR THE AGENT ECONOMY

@victorgoldenboy @ritualnet @ritualfnd @joshsimenhoff @cryptooflashh @dunken9718 @Jez_Cryptoz @0xMadScientist Gritual fam

gRitual CT 🧵 How Ritual Is Turning AI Into Verifiable On-Chain Intelligence 1/ AI is becoming increasingly capable—but when its outputs influence on-chain actions, one question becomes essential: Can those be trusted? Ritual is exploring infrastructure designed to help make

@nahrenzoo @ritualnet Gritual

what if ai wasn’t just connected to a blockchain? what if it actually became part of how the blockchain executes? that’s the idea behind ritual. instead of treating ai as an external service, ritual is building a chain where applications can call ai, verify its execution, and settle the results onchain. that changes what developers can build. 1. ai trading agents that actually execute today, most ai trading bots rely on a stack that looks something like this: smart contract backend server openai or claude api private key management cron jobs to keep everything running if the backend goes offline, the bot stops. ritual takes a different approach. an agent can analyze market conditions, make decisions with ai, hold its own wallet, execute trades, and schedule future actions without depending on an external server for its core logic. native execution, scheduling, and key management are designed directly into the protocol. 2. defi that adapts in real time most lending protocols operate with fixed parameters. but markets don’t stay fixed. imagine an ai that continuously evaluates market volatility, liquidation risk, borrower behavior, and macro events, then updates strategies as conditions change. on most chains, that requires trusting an offchain service. ritual makes the ai computation part of the execution flow itself. 3. onchain games with intelligent npcs most blockchain games still run on static logic. with ritual, npcs can remember previous interactions. characters can learn from players. enemies can change strategies. entire game worlds can evolve over time because ai execution becomes part of the application instead of an external plugin. 4. autonomous dao operators most daos still depend heavily on people. an ai agent on ritual could monitor governance proposals, summarize discussions, allocate treasury funds, negotiate with other daos, and execute approved actions. instead of acting like a chatbot, it becomes an economic participant. 5. private ai applications many ai applications work with sensitive information. that could be api keys, financial data, medical information, or business strategies. none of that belongs on a public blockchain. ritual uses trusted execution environments (tees) and other confidentiality mechanisms so computation stays private while still producing attestations that it was executed correctly. 6. ai-native consumer apps the opportunity goes beyond crypto. imagine an ai assistant that owns a wallet, pays subscriptions, books flights, manages investments, interacts with defi, and purchases digital assets without asking for permission at every step. instead of being a temporary assistant, it becomes a persistent onchain identity operating under programmable rules. so why can’t existing chains do this easily? because ethereum and most evm chains were built for deterministic smart contracts. ai has very different requirements. it needs gpus, large models, internet access, asynchronous workloads, and private computation. today, developers usually glue all of that together with centralized apis and middleware. ritual’s architecture introduces native ai execution, internet access, scheduling, confidential compute, and specialized execution environments as protocol features instead of external services. that’s an important difference. the biggest crypto breakthroughs usually happen when infrastructure removes an entire layer of complexity. ethereum made defi practical by giving developers a shared settlement layer. solana pushed consumer apps forward by reducing latency. ritual is aiming to do something similar for ai. if developers no longer have to stitch together smart contracts, ai apis, backend servers, cron jobs, and key managers, they can spend their time building better applications. and that’s where entirely new categories of crypto apps can begin. @joshsimenhoff @Jez_Cryptoz @0xMadScientist

Another enjoyable GeoGuessr session with the @ritualnet community Lookin foward to the next one

Markets will always evolve. Strong foundations last. That’s why @popdex_ stands out. ⚡ Capital-efficient trading
📈 Built for traders, first
🤝 Rewards real contributors
🔒 Transparent infrastructure The strongest protocols don’t chase hype