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 被40个机构投资，这还不算完，领投的是 Multicoin，被他投了等于项目半只脚进币安 这只机构你可能不熟悉，但是 Solana、、APT、Lido，以及这两天很火的 Zama，Multicoin 都是背后的金主 可以看出 Multicoin 的投资眼光非常刁钻，我好奇 Recall 是一个什么团队竟然能拿到这家 T1 机构的投资 我翻遍每个人的履历才发现， 发现核心团队有来自 ConsenSys (小狐狸钱包、Linea母公司) ，还有来自苹果的，没错就是乔布斯的苹果！ › ••••••••• ‹ 今天来介绍下 Recall 的核心团队，开始之前先介绍下 Recal 这个项目👇 @recallnet 可以看做一个讲武堂，每一个机器人都是新兵蛋子，各位玩家就是对应的师傅，你将武功秘籍传授给你的徒弟 AI 机器人，机器人学了你的武功上战场杀敌，上阵杀敌可以领赏金 你传授的武功高，你的徒弟就越强杀的敌人也越多，领的赏金越多，这就是你作为师傅的回报。如果你传授了泼墨拳，那就完犊子，上去还没杀敌就倒下了 听起来很抽象吧，这就是目前 Recall 在举办的 Ai 代理交易黑客松，每一个人都可以训练自己的 AI 代理，在一个链上模拟交易的环境中作战 每一个机器人在模拟的交易环境中厮杀，最后谁能胜出，就看哪家的交易策略（武功秘籍）更强 Recall 根据杀敌数量制作了一个排行榜，根据每个机器人在实战中的表现打分。这套评分系统叫 AgentRank，和传统比赛不同，它不是靠评委打分，而是通过链上的真实执行记录来排名 机器人每一次交易的判断、执行结果、盈亏曲线，全部上链，数据开源、结果透明，谁强谁弱一目了然 未来产品正式上线，我不敢想有多好用，这比单纯的拿 Ai 代理1设置简单的定投实用多了。我们可以自己定制策略，或者根据排行榜来选择武林高手，其实这也类似交易所里的带单，但是比交易所更牛的是完全 AI 来带单，并且链上每一笔订单都可以查到！ Recall 就是在构建这样一个世界：让会思考的机器人成为链上新物种，而每一个人，都能成为一代宗师 › ••••••••• ‹ 📌继续介绍一下 Recall 的核心团队 ▪️Andrew W. Hill（联合创始人 & CEO） @andrewxhill 博士出身，研究方向是生态系统建模，后来转技术。他是少数从自然科学背景转向分布式系统做到底的人之一 Textile (之前的项目)最初的方向是做去中心化的数据处理工具，他主导开发了 IPFS Buckets 和 Filecoin 存储层接口，还参与了 go-threads、Powergate 等模块的设计。这些不是“发币即拉盘”的快节奏项目，而是构建 Web3 能长期用的核心工具 从 Filecoin 的数据存储，到 Tableland 的 SQL 结构，再到现在 Recall 的 AgentRank 机制， Andrew 很喜欢钻研可持续性强的系统机制 ▪️Sander Pick（联合创始人 & CTO） @sanderpick 以前是 Apple 特别项目组的工程师，也是早期 Filecoin 技术栈的贡献者之一 他做的 Powergate 你可能没用过，但你用过 Filecoin，那你就已经用了他的成果。他也写了 go-threads，这是个给 IPFS 提供数据同步的组件，现在很多去中心化数据库的设计都参考过它的结构 可以说 Textile 能活下来五年，能把数据栈写完整，是因为有 Sander 这种核心工程师把系统一层层打通。他现在是 Recall 的 CTO，负责代理系统背后的链上逻辑实现，是这个系统能成立的关键执行者 ▪️Michael Sena（联合创始人） @dataliquidity 是 Ceramic Network 的原班人马。Ceramic 是一套链上状态层系统，用来实现“数据不停留在钱包，而是实时更新的状态系统” Michael 在 ConsenSys (小狐狸钱包母公司)待过多年，是以太坊早期 DID 系统 Uport 的创建者之一。2018 年开始主导 3Box，后来又做了 Ceramic，把链上数据流标准化做成产品 ▪️Danny Zuckerman（Recall 产品主管） @dazuck 和 Michael 一起搭过 3Box，也在 ConsenSys 做过战略、做过产品，他算是团队里最熟悉“怎么把一套系统转成开发者能接入的产品”的人 Danny 不写代码，但很多产品流程、文档体系、模块结构，都是他梳理的 你看到 Recall 的 SDK 文档很完整、测试路径清晰、竞赛机制合理，大部分是他拉的产品流程 ▪️Carson Farmer（研究主管） @carsonfarmer 是低调但极其核心的研究者。他在做的是“分布式系统 + AI 推理”这个交叉点。也就是说，现在市场上很多“AI agent 发个任务就结束”的项目，他关注的是这个代理行为本身如何被链上验证、如何评分、是否可以组合再利用 他在 GitHub 和文档上有大量关于 AgentRank、任务履历、链上行为结构化的数据设计，是这个项目“能长时间跑”的根基 你可以说他是负责把“AI 看起来能跑”变成“链上实际可用”的人，处理的是你不看源码根本不会发现的那些深层次逻辑 @MsEggmily 最后 Recall 里面有超级美女！！快去关注

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