Abhishek Anand

As much as I like ai for life sciences, I also like to stay updated on whats happening at the physical AI space. I have been seeing a lot of startups popping up in this space recently. Most of the startups are working as a data provider / vendor. Essentially a problem of ops and scale. While thats all cool, I really like this open source project from @fpv_labs. I was going through their data, and their visualizer is so cool. I was able to see so many types of modalities in real time, and everything on web. Super cool project. I saw them recently open sourcing around 1 TB+ data at huggingface. Something worth checking out!! Great work.

@anindyadeeps Thanks! Small step towards long term goal of enabling an open ecosystem around human data.

@AnjneyMidha The beautiful startup mazes :)

@HappyyPablo Amazing work! Congrats!

open sourcing Marlin-2B 🐟 a tiny VLM to extract structured information from videos Marlin is finetuned for two questions devs want to ask in their videos: what is happening, and when? Best open model in its weight class, competitive with Gemini-2.5-flash at only 2B params 🧵

@fpv_labs So much fun working on this - can't wait to push the next one!

🚨 New Paper (ICRA Workshop, 2026) MobileEgo Anywhere: Open infrastructure for long-horizon egocentric data on commodity hardware. Existing robotics egocentric datasets are limited by short episode durations and rely on gated hardware. We release a framework designed to facilitate the collection of robust, hour-plus egocentric trajectories on a commodity iPhone.

Dedicating the first one to @ParasChopra and @lossfunk for cultivating a research-first mindset during my residency days - knowingly or unknowingly, we are carrying over that culture at @fpv_labs

Really proud of our team for this one - we'll publish a lot more over this year, but the first one is always special. Excited to push the boundaries of robotics from India and treat human substrate as a first-class citizen in robotics learning.

@pablovelagomez1 Thanks - yea we are working on ultrawide behind the scenes - do you have time to chat sometime this week - we've intgerated rerun into our web viewer but need some support to show larger files on web due to limited native mcap streaming support

@levelheaded_94 The fact that yall opened up the sdk is awesome. For the camera, did yall manage to get the ultrawide camera to work with this? I haven’t seen anyone get an iOS app that does this except Apple with arkitscense. I feel like it should be totally doable

I don’t think people are realising how differentiated our recent release is for physical ai - 1. Collected on commodity hardware 2 High fidelity multimodal data with rgb, dense depth maps, imu, room mesh, hand tracking, 6dof poses and dense action labels 3. Fully reproducible by anyone through Stera SDK. The data engine for egocentric data is now open source. huggingface.co/datasets/fpvla…

Been digging through STERA-10M all morning. some notes: 10M FPV samples for training world models, just released by @fpv_labs on Hugging Face. a few reasons I think this matters for embodied AI / spatial intelligence 🧵 huggingface.co/datasets/fpvla…

@JieWang_ZJUI @Penn @GRASPlab @dineshjayaraman @KostasPenn Congrats Jie!

Honored to graduate from @Penn @GRASPlab MSROBO! Huge thanks to my advisors @dineshjayaraman and @KostasPenn for their incredible mentorship and research guidance. Excited to keep pursuing first-principles, interesting, and solid work in Robot Learning ahead!

@ryassho you need an Iphone 12 pro and beyond

@levelheaded_94 impressive but are there any hardware limitations?

Democratised infra for high-quality multimodal data -> more data labs -> more data diversity without trade off in downstream quality -> accelerated progress towards embodied foundation models -> accelerated future towards the future of abundance.

Introducing Project Stera by FPV Labs, an open data infra for embodied AI research. Project Stera includes Stera-10M, with 10M+ frames of long-horizon data with persistent state tracking, and an open-source pipeline that converts raw data into training-ready formats.

LiteFold just got featured in @YourStoryCo It’s been 8 months since Cory and I officially started @try_litefold. We began by building better interfaces and workflows for computational biology, followed by Rosalind, our AI Co-Scientist for therapeutic design. Now we’re gradually moving toward developing our own in-house scientific and AI research focused on next-generation therapeutic models and research environments and partnerships. Still very early. Really cant wait to share whats coming. Story link in the comments

@manishdiesel Great work - is it reproducible ?

AI industry grades its own homework using metrics built for English phonology. In the Global South, language isn't a textbook exercise—it’s a mix of intent, context, and dialect. When I talk to someone, they don’t count my "Word Error Rate." They understand my meaning. The Paradox: Enterprises are making million-dollar decisions based on benchmarks that don't reflect how 5.5 billion people actually speak. We are scaling the human intelligence layer to fix this. The world deserves AI that understands the human, not just the dictionary. At @humynlabs , we built BRIDGE to be the independent auditor the industry lacks. We ran 15 models across 22 languages—no marketing slides, just raw data. Explore the report: 👉 humynlabs.ai/bridge

Gave a talk at Microsoft Research on how @lossfunk operates. We're still iterating but so far our style of operating has resulted in four papers accepted in ICML, RLC, ACL and ICLR main conferences (+ many workshops). All undergrads, btw! 🚀 Slides below 👇

@nikhil_suresh2 Very well articulated!

Excited to launch On Target!, my new blogpost series in collaboration with AWS exploring the frontier of AI and infrastructure. 🎉 Our first piece explores the multi-modal AI stack powering the frontier of physical AI and the researchers building at the frontier like at @ElorianAI . We cover the evolution of world model approaches, the advances in multi-modal reasoning, and the core challenges across data, scale, and infrastructure.

@xiaolonw @QinYuzhe @Meta Congrats Great win for Meta!

Excited to share that Assured Robot Intelligence (ARI) has joined @Meta to help build the future of humanoid intelligence! When we started ARI one year ago, our mission was clear: achieve physical AGI. Through deep customer engagements and real-world deployments, it became clear to us that serving the massive opportunity ahead requires training a truly general-purpose physical agent. We believe this agent will be humanoid — and that scaling will come from learning directly from human experience, not teleoperation alone. Meta’s ecosystem brings together the key components needed to make this vision possible. We will be joining Meta Superintelligence Labs (MSL) to help bring personal superintelligence into the physical world. We are incredibly grateful to the brilliant minds, robotics researchers, engineers, partners, and supporters who have worked with us on this journey. Thank you to our investors and angels, led by @aixventureshq , for believing in our mission. This is just the beginning.

We don't even design binders!! But we @BioMandrake just won @adaptyvbio × @gembioworkshop's RBX1 design competition - 1 Strong binder out of 322 tested, selected from 12,000+ submissions. Couldn't make it to @iclr_conf in Rio. Here's how we did it 👇 open.substack.com/pub/mandrakebi…