Research Director, @Meta Superintelligence Labs Co-founder of ARI Associate Professor @UCSDJacobs Postdoc @berkeley_ai PhD @CMU_Robotics
San Diego, CA
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Xiaolong Wang retweeted
Teaming up with Shopify to make shopping and checkout easier in Muse. Shoppers find more. Shops sell more. More partnerships like this coming soon.
We are excited to announce we are partnering deeply with Muse to enable agentic checkout with Shop Pay on all Shopify stores, offering people an easy and delightful way to shop and check out with Muse.
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Xiaolong Wang retweeted
We are excited to announce we are partnering deeply with Muse to enable agentic checkout with Shop Pay on all Shopify stores, offering people an easy and delightful way to shop and check out with Muse.
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Super exciting!
some belated life updates: earlier this year, i left academia and joined meta to work on personal superintelligence. these days i’ve mostly been trying to make our models really good at browser use. back in 2020, i wrote in my phd sop that i hoped one day my mom could use a product i helped build.
i guess that day is here :)
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Xiaolong Wang retweeted
we're opening muse connectors to developers!
we have seen so much excitement in the developer community, integrating muse into everything from robots to mood lights and more.
plug your API into muse, then people can use your service just by asking for it. every request runs in a secure VM, and muse will ask before anything consequential.
come build with us! muse.ai/platform
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Xiaolong Wang retweeted
Opening access for developers to build Muse connectors. You bring the API -- Muse brings the agent, the browser, and the context of what the person actually wants. People reach your service just by asking for it, and their agent takes it from there.
New connectors are live today. Come build with us. muse.ai/platform
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Xiaolong Wang retweeted
muse for mac is HERE!! your agent can now get stuff done right on your computer — files, messages, calendar, notes, all of it.
you're in control of what it can access, and it always asks before doing anything sensitive.
Add it to the dock - ai.meta.com/muse/download/
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Xiaolong Wang retweeted
Evolving dexterity with GPT-6 Astra 🖐️
Been trying Astra recently. Its zero-shot dexterous manipulation is already quite surprising. More interesting is seeing it learn and improve through simulation training, from pen spinning and Rubik's Cube to hammer use.
The real goal would be to evolve this dexterity in the real world.
github.com/jianglongye/dexte…
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Xiaolong Wang retweeted
Fascinating how multi-billion dollar startups can claim a major AI breakthrough, then demo what smart kids with a few GPUs can do today 🤦
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Xiaolong Wang retweeted
The craziest thing I’ve ever worked on: $1M to $100M ARR in just 3 months. We’ve been heads-down building physical AI infrastructure at @worldengine_ai .
And with everything recently released in the field, physical AGI is clearly closer than people think.
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Xiaolong Wang retweeted
i am so proud of the @Muse team.
if you knew them you wouldn't be surprised muse came out the way it did. they are soulful and thoughtful people, hard workers, brilliant, fighters - the kind who take the hard problem, stay with it through every dead end, and don't let up until it's solved.
i’m lucky to work with them. every detail of the product and the model was turned over in somebody's hands, worried over, chosen on purpose by a thoughtful soul. that’s the whole secret. good people, good work.
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Xiaolong Wang retweeted
I’m sensing deep despair in academics over the past week. Astra, Fable, Muse are zero-shotting benchmarks in robotics & world models.
Uneasy pill to swallow, but this is what step jumps in progress looks like.
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We indeed need large moe.
If the target market is real time robotics, the Jetson Thor system, with 128 GB of memory, but only 273 GB/s of bandwidth, seems over-provisioned with expensive memory. You want the model evaluating at tens of fps, so it can't use more than 10 GB of weights at most.
More memory could be used if the model is a wide mixture of experts, or a large strategic planning model is operating in small time slices over a longer period of time, and more memory always makes development life easier, but for cost optimized systems, you should be able to get by with much less.
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Xiaolong Wang retweeted
We're launching @muse, our new personal AI agent! Always on, fast, and built to be private, safe, and secure. Our biggest focus was the security architecture underneath. Each Muse runs in its own secure VM, a separate Sentinel checks every action, it never sees your passwords, and it asks before anything sensitive. More here: security.muse.ai
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Xiaolong Wang retweeted
1/ today we're rolling out Muse, our new personal ai assistant. Muse is always-on, wicked fast, can use a browser, connect to your apps, and is designed to be secure.
try it now: muse.ai.
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Xiaolong Wang retweeted
1/ we just publicly released Muse Spark 1.3 max!
we see significantly stronger coding and agentic performance on muse spark 1.3 max, so would strongly recommend trying it out even if you've already tried muse spark 1.3 high or muse spark 1.3 xhigh.
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Xiaolong Wang retweeted
Wow!! we’re blown away by @Meta Muse Spark progress: 1.1 → 1.2 → 1.3.
Same building, same task of 3D simulation given only photos. The leap in geometry, structure, and visual fidelity is striking—even as the total cost stays at just $0.60.
Only 55 days from 1.1 to 1.3. Incredible pace, @AIatMeta! Congrats @alexandr_wang @shengjia_zhao @ren_hongyu etc! 🚀🚀
1/ today we’re releasing muse spark 1.3—available in muse code & the meta model api.
this is our most capable model yet—frontier performance almost too cheap to meter. much stronger at agentic and coding with better usability. we think users will really notice the jump.
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Xiaolong Wang retweeted
1/ today we’re releasing muse spark 1.3—available in muse code & the meta model api.
this is our most capable model yet—frontier performance almost too cheap to meter. much stronger at agentic and coding with better usability. we think users will really notice the jump.
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