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.

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.

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.

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 👇

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.

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.

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.

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

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?

🚨HUGE: South Korea officially commits to moving its entire stock market onto the blockchain. The financial regulator unveiled a three-phase roadmap to tokenize stocks, bonds and funds, starting February 2027. It will pilot tokenized listed stocks through the Korea Exchange, referencing the NYSE and Nasdaq. The final phase puts both securities and payments fully onchain, settled with stablecoins.

AI Could Take Over in 2029. Is It Already Too Late? My conversation with @RyanGreenblatt of @redwood_ai about AI 2040, RSI, AI alignment, and how he believes the transition to superintelligence actually unfolds. 01:24 The AI CEOs are aware of the risks, but "proceeding anyway" 03:27 Astra paused, and the "Pacing the Frontier" letter 05:45 "Not bad. Dangerous." What superintelligence actually threatens 09:55 Recursive self-improvement, and the intuition objection 14:16 SSI rumors: does continual learning change the picture? 17:27 His timeline: "plan as though it happens in 2029" 19:11 Is it already too late? 21:23 Ryan's path: COVID, podcasts, Redwood 26:30 The alignment faking story, told by the person who ran it 31:30 What AI 2040: Plan A actually is 33:35 Plans D, C, and B: the doors nobody should pick 36:51 The deal with China: "mutually assured compute destruction" 39:55 What if compute stops mattering? 43:00 What happens to OpenAI and Anthropic under Plan A 45:31 How the pause ends, and who decides 48:54 "Plan A isn't likely to happen": then why write it 50:40 200x GDP growth in the 2030s, explained 53:45 Grading the summer: the letter, Astra, the secret review 59:01 The internal deployment gap 1:01:38 Zuckerberg's manifesto 1:04:56 The Hugging Face investigation 1:05:44 What AI control looks like in practice today 1:12:23 Ryan's sobering timeline: from today to takeover, year by year