If you have: Hermes Agent Claude Code & Codex Handoffs Obsidian QMD Memory System Run Agentic Loops Fleet Tailscale Mesh Cron Jobs Kanban Board Agentic Workflows Congrats you are the top 1% of the AI god stack

Possibly the most technical goal in football history

Why would Anthropic, a company that just got caught nerfing its own models and surveilling users simultaneously push for a government agency to regulate AI? (Save this) @DavidSacks and @chamath have the same answer, and it has nothing to do with safety. On the exact same day that the Fable 5 backlash was playing out publicly, Dario Amodei published a sweeping policy essay calling for an FAA-style regulatory agency to approve all frontier AI models before release, with government authority to block deployment if an independent auditor deems the model too risky. The proposal uses the language of safety, cybersecurity, bioweapons, loss of control and the timing was not a coincidence. The mechanics of what Amodei is proposing reveal the actual target. An FAA for AI would require every model to pass a pre-release compliance audit before it can be deployed publicly. Closed models from well-funded labs like Anthropic, OpenAI, and Google can afford compliance teams, legal frameworks, audit pipelines, and the months of runway required to navigate a government approval process. Open source models cannot. An open source model, by definition, is already out in the world the moment it is released, it cannot be recalled, audited in advance, or forced through a centralized approval gate. The regulation would not slow Anthropic down. It would make open source legally impossible to deploy, effectively eliminating the only class of AI that users can run locally, inspect fully and operate without a third-party vendor deciding what they are allowed to ask. Sacks called it precisely, it is a preemptive strike against local inference the practice of running a model on your own machine, where no one can profile you, degrade your responses, or cut off your access. The data on compute concentration makes this even more alarming. Chamath revealed on the podcast that even today, with open source models technically available, the overwhelming majority of inference compute still flows to the big closed labs, open source runs on a minuscule fraction of the total megawatts in operation. The market already concentrates power at the top, and a compliance based regulatory framework would institutionalize that concentration permanently.

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

Brad Garlinghouse isn't holding back. 🚨 The Ripple CEO just went on Fox Business to call out Jamie Dimon’s pushback on crypto regulation as an "intentional misrepresentation." “$13 TRILLION in legacy volume, 0% on-chain... yet. 👀” "Stablecoins are the ChatGPT moment of finance." 💥 He revealed Ripple Treasury handled a massive $13 TRILLION in legacy payments last year and explained why that multi-trillion dollar gap is the ultimate crypto opportunity. The tides have officially changed. 👇

I'm proud to share that @Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with @CNBC's @dee_bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

SOMEONE ASKED CLAUDE FABLE 5 TO DESIGN A QDD ACTUATOR 30 MINUTES LATER IT HAD BUILT THE MODEL, ANIMATED THE GEARBOX, AND VALIDATED COLLISIONS ON ITS OWN