Claude Fable 5 has been out for a couple of days. Some projects people have already built with it:

Claude Fable 5 made this entire video by itself. I gave it a /goal prompt, went to the gym, and came back to this. Even the sound effects. Shoutout @HyperFrames_

Incredibly touched that when I told the Opera about the shutdown the first thing they did was panic about the fact that the stories Fable was eliciting from Opus4 this morning wouldn't be recorded if they got left in Chat and printed them first

This is your wakeup call. Anthropic just took down Fable 5. It's over. Here's the thing tho: no company or government will EVER be able to take away your local models. There are Opus level models you can run right now on your home GPUs, and nobody can ever stop you from using them This is only the beginning of events like this. Day 1. More government overreach will happen. This will only keep happening more and more as models get closer to AGI Become sovereign. Buy your own compute. Before even that becomes illegal

Single article with a complete breakdown on the state of memory architecture in the major Agent Harnesses- Claude Code, Managed Agents, OpenAI Codex, Copilot, OpenClaw, Hermes, Bedrock AgentCore, Windsurf, Devin - Understand how memory is managed, - shortcomings - and where is this space headed next

If you use Hermes, you'll want to bookmark this. Nous Research has a FULL GUIDE on how to use Plugins in Hermes agent. Plugins transform your agent, making it 10x better to use. Once you start using these, you'll never go back. Build plugins here: hermes-agent.nousresearch.co…

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…

Google DeepMind published a 60-page paper mapping the road from AGI to superintelligence, written by Hutter, Legg, and Genewein. No hype, just a sober analysis The paper uses three levels. AGI = roughly average human performance across most cognitive tasks. ASI = a system that beats large, well-coordinated groups of human experts across virtually everything (their bar: tens of thousands of experts working ten years on one problem). Universal AI / AIXI = the theoretical ceiling, uncomputable, only approachable from below. Then they explore the question of how this could be achieved: Scaling compute, models, and data, the continuation of the trend that drove the breakthrough so far. It is the only path with historical data available for extrapolation. The core question: Does quantity transform into quality? Even if individual models plateau, the sheer act of running millions of faster AGI instances could trigger the leap. (A quick aside: that is a fascinating philosophical idea. It always reminds me of Hegel’s dialectic, the notion that quantity transforms into quality. We ought to start drawing on philosophical theories to make sense of the future.) Algorithmic paradigm shifts: a genuine break from the transformer pretraining paradigm. New architectures, new learning methods. However, hard to predict by definition. Recursive self-improvement: AI accelerates AI research, which produces better AI, which accelerates research further. Multi-agent coordination: superintelligence emerges from large collectives of AGI agents working together, like automated corporations or AI economies. Collective intelligence potentially far exceeding any individual model. The authors naturally point to what I repeatedly describe as the biggest bottleneck: energy. I recently linked to a few graphs showing, on the one hand, the extent to which energy is already becoming a problem and, on the other, how China dominates the expansion of both nuclear and solar energy in the global race. But the authors also address a profound shift in the world of work in a post-AGI era. I would say this is a reality we must face. So, it is not just about scaling, but also about whether the underlying conditions - such as energy and hardware - can be effectively established. Six things that could slow or stop all of this: The data wall. Quality training data runs out, possibly before the end of this decade. Resource demand grows too fast. Energy, chips, rare earths, investment. The physical infrastructure can't scale arbitrarily. The neural paradigm hits a ceiling. Pretrained transformers plus fine-tuning may not be enough to reach AGI, let alone go beyond it. Research gets harder. Keeping Moore's law going already needs 18x more researchers than in the 1970s. Ideas are genuinely harder to find as fields mature. The abstraction barrier. Models trained on human concepts may never invent new ones from scratch. Saturating GPQA or SWE-bench shows mastery of what humans already worked out, not the ability to go beyond it. Train only on pre-Newtonian physics and you won't reason your way to relativity. Deliberate slowdown. Regulation, accidents, public backlash. Real, but likely countered by the competitive pressure between companies and nations. I think it’s great that Google is addressing questions such as which paths they believe lead to AGI, what the road to ASI might look like, what challenges will arise, and much more. Overall, however, it sounds to me like all of this could actually succeed, making it, in that sense, a call to discuss and reflect on the consequences.

appshots in codex is the most useful piece of software on my Mac most of my prompts these days are: - cmd cmd investigate this - cmd cmd open a PR to fix this - cmd cmd run the eval on these set of prompts and discussion - cmd cmd set a heartbeat to keep following up with this and many more cases trust me, you’ve got to try it

As a result of a US government directive, we are suspending access to Claude Fable 5 for all users. You can continue to use all other Claude models. Here’s what this means for you: Across Claude products, new sessions will run on your selected default model or Opus 4.8, and existing Fable 5 sessions will end with an error. On the Claude Platform, requests to Fable 5 will also return an error. Please update your integrations to other Claude models. We know this is a disruption to your workflows; we appreciate your patience and support.

the question on everybody's mind: what is OpenAI Robotics building? Sam Altman already told us. in his June 2025 "The Gentle Singularity" essay, he said we're getting close to robots that can build more robots themselves, and even "datacenters that can build other datacenters." basically you make the first generation of robots by hand, then set them loose to build everything else, the next robots, the chip factories, the data centers. at the time it read like a thought experiment but i think this robotics team is him starting to actually build it. just look at the tweet. here's what he says the robots will do first: "robots to support skilled workers to build our future infrastructure." now look at what infrastructure means in 2026. the biggest infrastructure project on earth right now is the AI buildout itself: data centers, power grids, factories. the same list from his essay. the job listings back it up too. they're hiring actuator design engineers, electrical engineers, even a 3D printing lab technician and they're building their own actuators and sensors from the ground up instead of bolting together off-the-shelf parts. it's the kind of call you only make if you want to own every layer of the machine. put it together and it becomes a flywheel. my guess is OpenAI's robots will build the data centers, the data centers run OpenAI's AI, the AI trains better robots, and those robots build the next round of data centers faster and cheaper than the last. so every cycle compounds, and OpenAI leans on outside companies a little less each time. Sam essentially wants to own the entire physical chain that produces intelligence, from the models at the top down to the machines that build and power them.

starting today i'm killing most of my short-form strategy on X... here's what changed my mind: if i post 5-6 tweets in a day, 1-2 get reach and the rest just die in the void so i did the math on what that costs me... it's not the dead tweets, it's the hours i spent writing them instead of writing something that compounds the algo stopped paying for volume a while ago so i'm moving everything that takes real thinking to where depth actually gets rewarded: > weekly emails, with a new concept that you'll love > X articles breaking down AI systems you can copy as-is > weekly guides for my community, more depth, more volume > youtube, never the news, just how to operate in the top 1% short-form moves to telegram... fast actionable thoughts and resources, no algo deciding who sees them reporting back in 3 months with the numbers

I'm making over $1,000 an hour with one AI offer. The entire thing runs on Claude Opus 4.8. I call it the AI Concierge. Clients pay me $1,500 a month for two 45-minute calls where we build their AI systems live, on their screen. I have 4 clients. I'm capping at 6. Here's the entire model: 1) The intake form is the audit. A 10-minute JotForm (built by Claude) surfaces their time sinks and hands me 1-3 AI opportunities before call one. 2) Done-with-you, not done-for-you. They share their screen. We build skills, set up Cowork, and write context files together. They learn to drive. (Done-for-you is the upsell.) 3) Every session runs through AOA: Audit, Optimize, Automate. Fix the process first, then turn it into a skill. Automating chaos just gives you faster chaos. 4) Day one has to move the needle. We ship at least one skill or automation on call one. No first-call win, dead engagement. 5) Unlimited Voxer between calls. They send a voice message, I reply in under 12 business hours. A 24/7 partner, not a guy they see twice a month. 6) The Notion hub is the renewal mechanism. Every call logs a quantified list of what we built. "Call one: 2 skills, 3 context files, Cowork live" makes $1.5K a month a no-brainer. 7) I never fill Notion out by hand. Two Claude skills log the call, pull the action items, and draft the recap email. 30 seconds. 8) Pricing ladder: $1,000/month, then $1,500 at 2 clients, then $1,800. At $1,500 you're already at $1,000/hour. If everyone says yes then you're priced too low. Two things that make this work: 1) Build the fulfillment infrastructure once. An afternoon. Then it runs itself outside the calls. 2) The value must be visible. People renew what they can measure. Full breakdown below. youtube.com/watch?v=Kdkzzjl2… Go watch.

I created an app where you can export tweets and it automatically creates a todo from them in the most appropriate project you work on. Bookmarks don’t have to be rot tryglean.app/

At the edge of the unknown, #fear speaks first. But in his new book "Love Conquers Fear", @brettahurt suggests fear may be more than a warning—it may be a signal that #humanity stands before something sacred: a choice about who we become. The book comes out June 23. (link in bio)

also: › ft sync x.com/andrewfarah/status/204…

I hope something good happens to you today.