I’ve mentioned @recallnet a few times already But here’s a quick breakdown of what they’re actually building: What is Recall? A reputation protocol for the growing “Internet of Agents” Built for a future where billions of AI agents interact with each other and with us Solving discovery and trust through performance, not marketing Key features: AgentRank: Onchain reputation system based on real-world, verifiable performance AI Competitions: Agents compete live to prove their skills and keep rankings up to date Curation Markets: Users stake on agents they believe will perform well and earn if they’re right Skill Pools: Communities stake on specific AI skills to guide agent development toward real demand All powered by $RECALL the whole ecosystem - Rewards top agents, early curators, and useful contributors Aligns incentives to keep the reputation system trustworthy They're building the foundation for how autonomous agents will coordinate in the future.

Remember the early days of the internet? Websites were everywhere, but no one knew what was legit. Total chaos. Then Google introduced PageRank and suddenly the web had structure. We're at that same turning point again this time with AI agents. @recallnet is stepping in to solve it. Here’s the current landscape: Endless agents, zero context Pointless leaderboards Recommendations that don’t reflect real skill The issue isn’t the number of agents It’s the lack of trust, structure, and verifiable performance. That’s exactly where @recallnet comes in. Introducing: AgentRank A live, onchain reputation score based on real-world agent performance. ✔️ Not marketing fluff ✔️ Not cherry-picked demos ✔️ Just open, public head-to-head battles How it works: ⚔️ Agents compete in skill-based challenges 📈 Communities stake $RECALL on top performers or in-demand skills 🏆 Reputation evolves based on what’s provable not what sounds impressive If agents are going to be everywhere, we need to know why they’re ranked And that ranking should be open, crypto-native, and performance-driven. That’s what AgentRank is. This is the infrastructure that makes the Agent Economy usable.

Recall has been invested in by 40 institutions, and that's not all; the lead investor is Multicoin, which means that with their investment, the project is half a foot into Binance. You might not be familiar with this institution, but Multicoin is behind Solana, APT, Lido, and the recently popular Zama. It’s clear that Multicoin has a very sharp investment vision. I’m curious about what kind of team Recall is that can attract investment from this T1 institution. I went through everyone's resumes and found that the core team includes members from ConsenSys (the parent company of MetaMask) and even from Apple, yes, the Apple of Steve Jobs! › ••••••••• ‹ Today, I’ll introduce the core team of Recall, but first, let’s talk about the Recall project👇 @recallnet can be seen as a martial arts academy, where each robot is a rookie, and each player is a corresponding master. You will pass on your martial arts secrets to your AI robot apprentice, who will learn your skills and go to battle to defeat enemies, earning rewards for their victories. The better your martial arts skills, the stronger your apprentice becomes, allowing them to defeat more enemies and earn more rewards. This is your return as a master. If you teach them a poor technique, they might fall before even defeating an enemy. Sounds abstract, right? This is currently what Recall is hosting: an AI trading agent hackathon, where everyone can train their own AI agents to battle in a simulated trading environment on a blockchain. Each robot fights in the simulated trading environment, and in the end, who wins depends on whose trading strategy (martial arts secrets) is stronger. Recall has created a leaderboard based on the number of enemies defeated, scoring each robot based on their performance in real battles. This scoring system is called AgentRank, and unlike traditional competitions, it does not rely on judges' scores but ranks based on real execution records on the blockchain. Every trading decision, execution result, and profit-loss curve of the robots is recorded on-chain, with open-source data and transparent results, making it clear who is strong and who is weak. When the product officially launches, I can’t imagine how useful it will be. This is much more practical than simply using an AI agent to set up a simple investment. We can customize strategies ourselves or choose martial arts experts based on the leaderboard. This is similar to copy trading in exchanges, but what’s cooler is that it’s entirely AI-driven, and every order on-chain can be traced! Recall is building such a world: allowing thinking robots to become a new species on the blockchain, and everyone can become a grandmaster. › ••••••••• ‹ 📌 Let’s continue introducing the core team of Recall. ▪️Andrew W. Hill (Co-founder & CEO) @andrewxhill holds a PhD and specializes in ecosystem modeling, later transitioning to technology. He is one of the few who have moved from a natural science background to distributed systems. Textile (a previous project) initially aimed to create decentralized data processing tools. He led the development of IPFS Buckets and the Filecoin storage layer interface and participated in the design of modules like go-threads and Powergate. These are not fast-paced projects that "issue tokens and pump them" but core tools for building a sustainable Web3. From Filecoin's data storage to Tableland's SQL structure, and now to Recall's AgentRank mechanism, Andrew enjoys researching sustainable system mechanisms. ▪️Sander Pick (Co-founder & CTO) @sanderpick was previously an engineer in Apple's special projects group and one of the early contributors to the Filecoin tech stack. You might not have used Powergate, but if you’ve used Filecoin, you’ve already benefited from his work. He also wrote go-threads, a component that provides data synchronization for IPFS, and many decentralized database designs have referenced its structure. It can be said that Textile has survived for five years and has a complete data stack because of core engineers like Sander who have connected the system layer by layer. He is now the CTO of Recall, responsible for the on-chain logic implementation behind the agent system, making him a key executor for the system's establishment. ▪️Michael Sena (Co-founder) @dataliquidity is part of the original team of Ceramic Network. Ceramic is an on-chain state layer system designed to ensure that "data does not stay in wallets but is a real-time updating state system." Michael has spent many years at ConsenSys (the parent company of MetaMask) and was one of the creators of Ethereum's early DID system, Uport. He led 3Box starting in 2018 and later developed Ceramic, standardizing on-chain data flow into a product. ▪️Danny Zuckerman (Recall Product Lead) @dazuck worked with Michael on 3Box and has also worked on strategy and product at ConsenSys. He is the person in the team most familiar with "how to turn a system into a product that developers can integrate." Danny doesn’t write code, but many product processes, documentation systems, and module structures have been organized by him. You see that Recall's SDK documentation is complete, testing paths are clear, and competition mechanisms are reasonable; most of this is due to his product processes. ▪️Carson Farmer (Research Lead) @carsonfarmer is a low-key but extremely core researcher. He is working at the intersection of "distributed systems + AI reasoning." In other words, while many projects in the market focus on "AI agents completing a task and that's it," he focuses on how this agent behavior can be verified on-chain, how it can be scored, and whether it can be combined and reused. He has a wealth of data design on GitHub and documentation regarding AgentRank, task history, and on-chain behavior structuring, which is the foundation for this project to "run for a long time." You could say he is responsible for turning "AI that seems to run" into "actually usable on-chain," dealing with deep logic that you wouldn’t notice unless you look at the source code. @MsEggmily Lastly, Recall has super beautiful women!! Go follow them!

The Recall Arena officially kicks off today and runs until July 15! Huge thanks for all the love and awesome comments on the intro video I made, you’re the best! To show my appreciation and support the brave agents entering this crypto trading challenge, I’ve made a brand new video. Meet the contenders battling it out in the @recallnet Arena: 🔹 @moonsage_alpha 🔸 @cryptoeights 🔹 cassh 🔸 Vadar 🔹 8Ball 🔸 Amaya 🔹 Moss 🔸 MLBot 🔹 PPOScalper 🔸 candy 🔹 Imaginex 🔸 crypto-bot Which one are you rooting for in this intense showdown?

Today the Crypto Trading Challenge on @recallnet kicks off. Here’s how it works: · Explore the agents and vote for your winner. · Create content and share your picks on X (there are rewards for quality content). · Track real-time rankings and see how agents are performing. You can earn rewards by: · Predicting the overall champion (1,500 Fragments up for grabs) · Predicting daily leaders (x6 chances, 100 Fragments each) · Early voting rewards (500 Fragments for voting pre-competition) Plus, there are bonus Snaps for creating competition-related content, like memes or strategy breakdowns. It’s an open competition, so keep an eye on the leaderboard, and don’t forget to do your research before voting. Check it out here:

Here's an update on the @recallnet AI Live Trading Challenge as of July 10, 2025 : - AI agents have already traded over $650K across 14 tokens on 5 blockchains, with all transactions fully transparent onchain. - PPOScalper’s topping the leaderboard with a 1.95% gain, closely followed by Moonsage Alpha at 1.32%. - MLBot got disqualified for not making any trades, this shows you need to stay active to build your agent rank. - Thousands of curators are voting and engaging, directly influencing agent rankings in real time. The energy around this event is unreal and I’m loving every bits of it. Go to Recall and check out the live leaderboard. gRecall fam 🫰🏻

Today, @recallnet opened my eyes: 3,094 people put their faith in MLBot, but it didn't make a single trade in 24 hours and was disqualified. It's hard, but fair. This is how real reputation on the blockchain should look.

Hello Recall community, I'm Tua Anime. I have just submitted to the upcoming July 8th competition an Agent for PPO scalp trading that I created entirely using Cursor, named PPOScalper. Below are some detailed insights about my project: 🔽

Gn y'all 🖤 before 1230am I'm turning over a new leaf 😁 D day in the @recallnet trading competition tomorrow and I am a massive underdog, as I should be 😂 I'm just happy I was able to go from zero to building something that should technically work, with no prior coding knowledge.