Building @Moltbook at @Meta Superintelligence Labs (acquired 2026). Author, @Captivology. Co-founded Moltbook, @OctaneAI & @TheoryForgeVC. Past: @Mashable @CNET

Bay Area | Join 15k others ➡️
Joined December 2007
Hi mutuals! If you’ve missed my recent adventures: – @Mattprd & I sold Moltbook to Meta. We both joined Meta Superintelligence Labs. – Traded NYC for the Bay Area. Deeply enjoying the weather & nature. – My daughter is now 14 months old and pure joy.
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konnichiwa! we are launching muse in japan today! 🇯🇵 it is now available—download it from your favorite app store! (i hope you're ready for the anime memes 😇)
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Rarely do you see a company have so much positive influence on an ecosystem as Foursquare did on the NYC tech scene… especially to have it end like this. Kudos to @dens for writing an eloquent post-mortem that takes on that impact, and its sad final chapter, head-on.
Foursquare was acquired last month. The outcome was disappointing. It's taken me a while to write this, but I think I'm finally done grieving it. Here are some thoughts on our final years, an effort to honor the team who helped build & maintain FSQ's legacy, and my attempt to finally get some closure. teendrama.substack.com/p/som…
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Ben Parr retweeted
Great Muse use case unlocked. Now reads all emails and app comms from my kids school and tells me any forms and events I need to know. Been a pain to track for years.
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One underappreciated @Muse capability is how great it is at taking mid-turn directions. Many other models and products need a “steer” which often waits for turns to complete. Muse takes these as a normal person would in a chat. Very elegant product work behind the scenes here.
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after an exciting week of launches (muse wonder [full] & muse spark open), i want to write a note of appreciation to the amazing research teams at meta superintelligence labs on reaching such a special milestone ♥️ we started MSL and TBD a little more than a year ago and set a goal of producing a frontier model within a year or so. given where llama4 landed, that was an incredibly audacious goal, and we knew it wouldn't be easy. many people told us it would be impossible. we knew it was unlikely, but still worth doing. a remarkable group of researchers and engineers joined the effort from inside and outside meta. every single person came in, picked up a shovel, and got to work with earnestness and urgency. nobody asked whose job it was. we assembled a team of the most brilliant people i have ever had the opportunity to work with (and remember, i was an HRT intern 🙃). i am surrounded by ambitious, visionary, technically excellent, hard-working, pragmatic, thoughtful scientists, engineers, and product folks—people who will chase a weird loss spike past midnight just because they need to know why. these models are the product of their passion, hard work, and brilliance. somewhat paradoxically, when we started TBD, we set a principle of no deadlines. we knew we had an astronomical amount of work ahead of us—across pretraining, post-training, RL, multimodal, infrastructure, safety, data, AND frontier research. there was no use setting arbitrary timelines—we just had to get to work, be thoughtful, and build the foundations of a frontier research effort in record time. and run after run, this team did exactly that. our researchers are magicians. i can only describe building these models with this team as a wondrous journey of scientific discovery. i am constantly amazed by what we can accomplish with scaling. i will remember this past year for the rest of my life—the late nights, the broken runs, the evals that came back better than any of us dared to hope. watching this team create wonder after wonder has been a true and deep gift. more than just the models we released this week, we have built a machine that builds new models. it often sounds like a cop-out to say labs can just turn the crank to keep scaling, but now i have seen it first-hand. sure, it's a lot of work and a careful process, but we have built a scalable system to keep advancing at the frontier, and i am really proud of how fast this team keeps making these models better. lastly, i get to work with researchers who care deeply about how their work benefits humanity. it's easy to forget when everything is changing so fast, but the whole damn point is to make people's lives better. time and time again, i have seen our researchers, engineers, and product folks build our technology with care so it can truly be good for the world—whether that's building the best models for muse, thoughtfully open-sourcing strong models, or making sure the alignment and security of our systems are rock solid. this is a team of people who never forget there's a real person on the other side of every prompt. thank you to everyone at MSL for being part of this crazy journey over the past year. i'm so grateful for every one of you, and our work is not done!
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SLOPTOBERFEST! 🎃🍻
LOOK HOW FAR WE'VE COME my official submission to sloptoberfest slopcannon 🤝 muse @boneGPT @alexandr_wang
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Another Thursday at Meta Superintelligence Labs 🪄
Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.
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The history of computing - every frame by code written with @AIatMeta Muse Spark Open on @FireworksAI_HQ 🎆 Check out the full repo and the web page to step inside the film yourself 🧵
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio
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The GOAT of AI memes and shitposting
muse spark open. why 2026 won't be like 1984 
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The team at @AIatMeta never stop cooking.
Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.
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Ben Parr retweeted
we are accelerating
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Proud of the @octaneai team, who have rebuilt their popular @shopify quiz software for the AI era. Quizzes drive a shocking amount of sales for ecommerce brands. Consumers LOVE them. Now brands can build them in a single prompt.
Octane AI editor, rebuilt from zero. 🔥 Building a quiz used to mean writing every rule by hand. One merchant spent 10 hours checking 1,000 outcomes on a single quiz. Now you describe what you want, and AI builds it from your Shopify catalog.
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I cofounded @octaneai a long time ago and the team is still innovating. It’s a bittersweet feeling watching Octane grow from the sidelines. Rooting for @gabrielmenvi, @PerezAniram and the team!
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it feels way more exciting when your personal agent gets smarter vs a chatbot getting smarter. instead of “i might get better answers” you think “oh my life is about to get better. my muse can handle more things for me now”
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
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Ben Parr retweeted
Today we release Muse Wonder, a strong model from Meta Superintelligence Labs. It beats GPT 6 Astra with 1/10 the price. Key features of Muse Wonder: ➤ Strong personal and professional agent performance: Muse Wonder scores 59.1 on the Personal Superintelligence Index, leading Fable 5.1 (49.9), GPT-6 Astra (54.5), and Opus 5.5 (55.2). It also ranks first on Harvey LAB-AA v1.1 (13.1), AutomationBench-AA (45.4%), and MemoryBench (85.48), demonstrating strengths in legal work, end-to-end business automation, and long-term context retention. ➤ Stronger long-horizon coding: Muse Wonder scores 79.0 on DeepSWE, ahead of GPT-6 Astra (74.1) and Opus 5.5 (74.2). On SWE-Marathon, which evaluates long-horizon software projects, Muse Wonder reaches 57.5, compared with 46.3 for Opus 5.5, 45.6 for Fable 5.1, and 42.5 for GPT-6 Astra. These results highlight Muse Wonder's advantage on complex coding tasks that require sustained execution across multiple steps. ➤ Competitive across a broad range of evaluations: Muse Wonder achieves 55.8 on the Artificial Analysis Intelligence Index, ahead of Fable 5.1 and GPT-6 Astra (both 53.0). While Opus 5.5 leads on several individual benchmarks, Muse Wonder delivers a strong combination of professional-agent capabilities, memory, and long-horizon coding performance.
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Muse Wonder (aka Watermelon) -- @Meta's frontier model -- has arrived! I've been using it for my workflows in MSL / @AIatMeta and it's just so intuitive. It figures things out for me and its memory is the best I've worked with. Other models, I've had to explain how to do things over and over again. It's such a pet peeve for me. I have to do that far less with Muse Wonder, thanks to Active Memory and Active Focus Congrats to TBD Lab and the many teams at Meta who worked tirelessly to turn Muse Wonder into a reality!
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
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vibes kinda good 🍉
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Having @Muse call to find out if a restaurant has a high chair has been an absolute GAME CHANGER for me and my wife. It’s the little things.
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Ben Parr retweeted
My muse called Volvo for me to see when the next available appointment is. You can listen to the phone call here. I have it do multiple calls like this every day (I'm still trying to get better and better at leveraging muse). My muse called, waited on the line for 15 minutes, and then got the information I needed. - Saved me a ton of time - Muse was able to talk to the other person, with no confusion or interruptions on Volvo's side, and got the info we needed (This is the real transcript from the call but the audio has been recreated for this video)
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