Personal Intelligence is now rolling out for Gemini on Android Auto and in the coming months to cars with Google built-in, so you can get even better hands-free help on the road.
With your permission, Personal Intelligence securely connects information from the Google apps you already use — like Gmail, Google Calendar, and Search — so you can make complex requests, get personalized recommendations, retrieve details from past conversations and more.
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Sound on!
Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.”
Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.
We’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development.
Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner.
Welcome @ashokaraj_ and team!
openai.com/index/openai-to-a…
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We just raised a $60M Series B at a $1B valuation to build @daytonaio into the Agent Cloud.
@FirstMarkCap led the round, with all key existing investors and angels doubling down.
We're also thrilled to welcome the founders of some generational companies aboard, including @patrickc (Stripe), @tobi (Shopify), @AravSrinivas (Perplexity), @BrendanFoody (Mercor), @oliveur (Datadog), @adamdangelo (Quora) and @ericsyuan (Zoom)
We're a 35-person team. But that's about to change; we're hiring across the board, and we'd love for you to join us.
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Wabi 2.0 is live!
personal agents are one person, one chat. but the parts of life that matter run on more than one person
Wabi is an agent you use together. you talk to it, it does stuff, builds the apps you need, and anyone can join the thread - your partner, your best friend, the preschool moms groupchat
school changes pickup time → Wabi updates the family calendar, everyone sees it.
a show you’d both love → Wabi finds a night that works, gets the tickets.
the trip everyone keeps talking about → Wabi picks the dates, builds the itinerary, books it
go start a thread with your people 👇
🤖 Made with AI
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We trained our Muse foundational models, including Muse Wonder, at Prometheus, our gigawatt-scale supercluster in New Albany, Ohio.
Across a supercluster with hundreds of thousands of GPUs, even one underperforming host can slow an entire training run. We engineered Prometheus for resilience at scale, achieving 85%+ goodput on pretraining, 90%+ on reinforcement learning and ~47K GPU hours mean time between interruptions during Muse Wonder training.
Take a look at the tech powering Prometheus: go.meta.me/4yXXApZ
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I'd much rather work with someone who flexes much they accomplished with the fewest amount of tokens vs how many tokens they spent, and I sense this is a major trend in general
How we quantify this as an industry is still TBD, probably a major new set of benchmarks
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The happiest people I know aren't chasing big houses or fancy cars.
They're building tiny businesses that let them work 3-4 hours a day and spend the rest of their time actually living life.
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Hark vs Instinct: Dinner analysis
@adcock_brett s Hark took 20 minutes to order me a pizza and got it wrong. Two pizzas, wrong restaurant.
@noahrshinn s Instinct emailed 50 restaurants to organize a private dinner for 20 people and got it right.
That’s backwards.
Finding a restaurant requires research and reasoning. But once you’ve identified the restaurant and what you want, ordering should be deterministic. Select the items, confirm the details, complete checkout. This is a 5 min process at most.
Finding a private dinner for 20 is the opposite. Open-ended research, dozens of venues, inconsistent replies, contracts, and fine print.
Instinct researched 50 venues, emailed each one, followed up on unanswered requests, and organized 41 responses into one comparison sheet.
It caught a contract quoting 20 guests but charging food for 15. It flagged “private” rooms that were actually curtains or mezzanines. It asked for approval before sending each batch.
The simple task failed. The complex one worked.
Agents are becoming remarkably good at open-ended research and communication, but still struggle with routine execution.
Once the reasoning is done, execution shouldn’t require more reasoning. It should follow defined, permissioned workflows.
That’s an infrastructure problem, not just a model intelligence problem.
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Instinct seems to have completely lost mindshare in my network. Current split: @Muse for personal, @bot for work.
Too many AI personal assistants, too little time to assess all of them. Nice work here: assistantbenchmark.com
Muse is the best product on earth @alexandr_wang please when is muse charm out. You really cooked with MUSE




















