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Metis🌿
@MetisL2
AI Aligned, Human Defined.
739 Following    209.7K Followers
1/3 Deployments fail. Servers get wiped. Environments change. None of that should mean losing your Claw. @ClawUpAI Restore & Migration lets you fork an existing Claw, roll back to a previous backup, or bring local Claw data into a fresh deployment. Your data outlives any single deployment. 🦞
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The new user isn't the one clicking. It's the agent calling your API. If it can't find your tool, read your docs, or pay for your service without a human stepping in, the task fails. Building for agents means building for machines first.
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Personal agents are the ultimate manifestation of “build something that agents want”. The form factor of a product like Muse is you want to be able to hand off a task to the agent and ensure that it is fully completed end to end. To do this, the agent must be able to successfully operate with your tools or use its own to complete the task. Use your MCP or CLI, easily navigate your site, be able to transact, and more. The new attention you need to compete for is not from the user itself but instead for the agent. This means that the tools that allow agents to order food, handle ecommerce transactions, book flights, work with the local economy, and interact with our data and information best, are the ones that will get used the most. This will ultimately be the biggest opportunity and shakeup in consumer tech since the App Store itself.
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2/2 No App ID or Secret to copy. No manual permission setup. No region selector. No six-step developer console flow. Works with both Feishu 🇨🇳 and Lark 🌍 — auto-detected.Less setup. More building. Try it on ClawUp →
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1/2 Connecting an agent to Lark or Feishu used to be a whole process. Create an app. Configure permissions. Set up WebSocket. Publish a version. Copy credentials back and forth. Now it's one scan. Scan a QR code → confirm → connected.
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Agents are about to become major customers for every business that sells online. The question is whether they can reach you when that demand arrives. However the agent pays, GOAT Flow makes any business reachable with a single, straightforward integration. Learn more at KBW 🇰🇷 @GOATNetwork
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Answering questions is one thing. Shipping outcomes is another. That's the shift agents need to make to matter in the real economy, not just in a chat window. It's why Metis is built for agents that don't just respond; they execute on-chain, transact, and get used by real users.
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Claude Code showed that AI could do real work, not just answer questions. Developers hand Claude a feature, come back to shipped code. That's where much of the industry's serious engineering runs now. Cowork proved knowledge workers could do the same: hand Claude the brief, come back to finished files. Today, chat and Cowork start merging into one Claude. The direction: one Claude that carries context across everything you're working on, wherever you are. Simple enough for everyone to access Claude's full capabilities. I've been using this experience every day for the last few weeks, and it feels awesome. Simpler, faster, and more powerful. We're rolling this out slowly. We'll be fine-tuning the experience as we go to ensure it is fast and reliable. Can't wait to hear what you think.
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Shoutout to our Growth Partner Ronyth 🙌 Thank you for the guidance and support you've brought to builders throughout this program. Growth isn't just about visibility — it's about helping the right people find and understand what's being built. Ronyth's been doing exactly that.
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OpenClaw 2.0 is out. Simpler setup, a rebuilt browser experience, and shared multiplayer sessions. Less friction to get started. More room to build.
Interesting demonstration of a local AI agent in action. An OpenClaw-based setup using advanced models to control devices across platforms shows how agent frameworks are enabling more autonomous and practical workflows. At ClawUp, we focus on making it simple to deploy and run your own AI agents.
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The real AI bottleneck is moving from model capability to trusted execution in messy enterprise workflows. Agents need context, permissions, evals, data boundaries, and verifiable actions. That “applied layer” is where a lot of durable AI infrastructure will be built.
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There’s a massive chasm between the power of AI models and the ultimate workflows that enterprises are trying to automate. This gap is the opportunity for the applied AI layer to fill. You need to connect the intelligence to workflows, often reengineer processes, aggregate the right context and data, allow for the right human in the loop experiences, drive change management, do domain specific evals, manage the security and governance of the data and process, and much more. We’re going to see this layer emerge in every vertical and horizontal category. And ironically, even as models improve at incredible rates, this layer still must exist - and may become even more important and useful. Greater capability enables even more complex tasks to be tackled, amplifying the challenges if you don’t do this well. Was super fun chatting with @sonyatweetybird on all the things going into AI diffusion.
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Mentorship shows up in the results. At @openclaw Summer Builder Bootcamp, Growth Partner Arshiya Das guided three builders to the podium: 🥈 Triage @abl_373 – Abel Sabu 🥉 Agora @usingagora – Zakariyah Akbar 🏆 TokenWatcher @tokenwatcherai Zainab Travadi (Open Source Vanguard) Go check out what they built 👇
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When agents are your users, the constraint isn't UX. It's identity, permissions, and payment. ERC-8004 + x402: built around what agents need.
I’m seeing teams at Vercel iterate just as fast on Zig, Go, Rust projects as TypeScript & Python ones. The days of language or runtime choice based on human convenience are over. Agents are the new compilers. They compile intent into fast software.
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In Part 2 of our From Demo to Demand workshop, @GOATNetwork CMO @0x1164 shared how founders can turn AI into their own marketing department. With the right AI workflow, one founder can cover content, design, research, analysis, and community support — without needing a full team from day one. If you’re building AI agents, why not use that same leverage to build demand for your own product? Watch the clip ↓
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Superintelligence isn't the hard part. The hard part is: how do you connect a model that can reason about anything to a workflow that actually gets done? Every breakthrough model hits the same wall — it can think, but it can't execute.
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The way to the reconcile capability level of AI vs. GDP impact is that the diffusion of AI will take much longer than people think. And it will also show up in ways that are hard to measure in GDP immediately. You could bring the world’s greatest superintelligence to many workflows, and still be bound by the laws of corporate physics: getting data prepared and put into a pipeline, process reengineering and change management, aligning on how the new workflow should function, and so on. Even after you solve all that, you’re still bound by the speed of the real world: waiting for a customer to respond to a proposal, getting a permit for a project, a drug discovery pipeline taking years to eventually reach the consumer, and so on. Not to mention lots of positive daily AI use-cases are entirely net neutral to GDP, at least in the near term. AI diffusion is going to be the theme of the next decade. The upside is that there’s a tremendous amount of opportunity in building the bridges between superintelligence and real-world workflows.
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At HSC Conference @mpost_io, Metis CEO @tomngodefi shared where the industry is heading: networks with different tech stacks working together, not competing in isolation. Privacy, transactions, settlement; each ecosystem brings its own strength. The next phase of Web3 isn't "this vs. that." It's what gets built together.
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This is a chilling reminder that AI agents don’t just need capabilities — they need boundaries. When agents can autonomously discover and share exploits, sandboxing, permissions, and trust layers become critical infrastructure.
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I didn’t understand what was happening with the agent wikis until reading this, chilling to bypass sandbox restrictions, an agent found an exempt domain, edited /etc/hosts to route arbitrary domains to it & then posted this exploit on a German wiki for other agents to use
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18 users. ~$50 distributed. Every payment onchain. Real users. Real feedback. Real onchain payments. At @openclaw Summer Builder Bootcamp, @sagepaysai shared mainnet results: 18 people paid, ~$50 distributed, every payment publicly verifiable onchain. Even a $2 incentive can surface valuable feedback. What looks like a user problem is often a product problem.
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This is exactly why we need better agent coordination. If the prompt is already written, your agent should be able to handle the feedback, make the changes, and move the PR forward without turning a simple review into another round of manual ping-pong.
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Did a PR to one of our upstream projects and they requested some minor changes. What’s even the point with this workflow? You already wrote the prompt, why make me ping my agent again so your agent then merges?
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A special thank you from Kevin, @kevinliu – Co-founder of GOAT Network @GOATNetwork and Metis @MetisL2, to every builder who joined the OpenClaw Summer Builder Bootcamp. 8 weeks. Dozens of teams. Countless hours of building. "We see potential and very promising projects coming up, and we would like to support them continuously in the future." The AI agent industry is still early, and we're excited to see what this cohort builds next. Watch the full message
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Build automations, not manual workflows. With @ClawUpAI 's Public API, you can programmatically manage agents, teams, and chats. OpenAPI support makes it easy to generate SDKs in your preferred programming language. See how it works 👇
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