It’s kinda crazy to me that we melt sand down, clean it to 99.99999% purity, literal wizards have to draw it slowly into an ingot, we cut it, inscribe a hundred billion little on/off switches on it, and when you run electric current through it you can do basic arithmetic, which when you do enough of it can do any math on the planet, and which describes reality well enough that we can teach it the ontological meaning of words and their distance from each other on a high-dimensional space, and if you ask, it can tell you what word comes next and that apparently is powerful enough that if you do it a hundred billion times it’s the smartest thing to ever exist oh yeah and we use this primarily for email

Happy to announce that we are live and accepting merchants 🫡. Connect your e-commerce or online store once and Kifly allows you to sell your products through AI agents in many channels. Nike and now Walmart are building their own in house infrastructure to serve agents. Kifly is built for the small to medium size merchant who can't. 1 - On your existing store, you get a sales agent that understand your products, reviews, policies and can help customer checkout. 2 - Connect your own agent to Instagram and other places customers reach out to you. It monitors your DMs, sends product links, creates a cart and hands the customer a checkout link to pay. Offering a seamless experience to shoppers. 3 - You get your own AI agent facing store through an MCP that Muse, ChatGPT and Claude can access to search, cart and get a checkout/payment link to pay on behalf of buyers. Kifly's native payment rail is powered by @stripe, and compliant with UCP. The future of shopping is here, is your store ready?

Ok, I guess now is a good time to announce my return to X... Burp! Enjoy.

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

1/4 Today, we’re releasing Mercury 3. Mercury 3 is by far our most powerful model and the most intelligent diffusion LLM ever released. It outperforms GPT-6 Luna and Haiku 5.5 on quality while running at 1000+ tok/sec.

Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.

we're dropping something heavy tomorrow @every it's the 8k word definitive history of Codex written from deep interviews with the team that did it including @gdb @thsottiaux @ajambrosino @embirico @mia_glaese @edbayes and more it'll also be agent-native so you and your agent can explore the full text of all the interviews in addition to reading the story :)

after switching our approach to building a "FirmOS", I'm increasingly confident that the future of agent collaboration within organizational settings is going to be a "company operating system" or CompanyOS, designed for agent and human collaboration instead of a "company brain" that holds everything, the OS is for storing rules, processes, decisions made, which tools to use and how. it can mirror some data, but the source of truth is often best held in your existing tools (CRM, etc.). it's continuously updated and used by your agents i think of this as a "flipped playbook" where instead of writing down the company rules first and following them (which is kind of like waterfall development), you're constantly updating the playbook as you decide or learn new things day-to-day running the business

We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper. It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2 weeks.

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…

You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀

Walked into a busy coffee shop in NYC today to buy a coffee and I did something very autistic. Saw a guy working next to me on the free tier of ChatGPT. I got curious. I spent the next 30 minutes walking around checking out 22 laptops. 16 people were working completely by hand (IE i didn't see them open an AI tool) 6 people were using Al. All 6 were running ChatGPT in a browser tab. Nobody was using Claude, Claude code or anything else. 5/6 were on the free plan.

Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.