Building in the open for physical AI and robotics. CEO/Founder at @hapticlabsai prev. Eng PhD dropout @usc, @harvard
San Francisco, CA
Joined April 2011
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We're all-in on a new product. @HapticLabsAI
is now AI for construction and development, a land intelligence engine with an agent on every parcel.
Land diligence that used to take 3 months of legal, zoning, permits, earthworks, economics we now compress into 2 days.
Paying customer #1 is live with us. If diligence is still the bottleneck, come talk to us.
Diego Prats | 🤖 retweeted
At @hotchipsorg for the next 2 days. Shoot me a DM if you’re here! Excited to learn more about what’s coming out in the chip world and especially how EDA is evolving to meet the new demands
Diego Prats | 🤖 retweeted
What problems and bottlenecks keep coming up in physical AI research?
We analyzed 64 episode transcripts of the brilliant @RoboPapers podcast to see which bottlenecks robotics researchers mention the most.
hapticlabs.ai/blog/2026/03/0…
Diego Prats | 🤖 retweeted
In March, we mapped the pain across 64 RoboPapers episodes ... this time, we mapped the surprises that most changed our thinking. Here are eight of the surprises after scanning those same episodes.
TL;DR: better systems around a model often beat better models.
hapticlabs.ai/blog/2026/07/1…
What problems and bottlenecks keep coming up in physical AI research?
We analyzed 64 episode transcripts of the brilliant @RoboPapers podcast to see which bottlenecks robotics researchers mention the most.
hapticlabs.ai/blog/2026/03/0…
Diego Prats | 🤖 retweeted
MolmoAct2 (so101 ft) does well in some basic pick and place tasks and runs smoothly on @HapticLabsAI's inf. infra. A bit surprised that it's not been able to distinguish colors or shapes (e.g. doesn't distinguish between a yellow or a blue cube, or between a cube and a cylinder)
Diego Prats | 🤖 retweeted
Robot training pipelines do not always fail loudly
We harvested 267 public datasets tagged 16-DOF OpenArm. Only 45 merged cleanly: 652 episodes and 583k frames
Read about how we mapped what broke and had an agent curate the corpus and fine-tune Pi0.5 ↓
hapticlabs.ai/blog/2026/07/1…
The acceleration is crazy
Three open robot models dropped in three days. @LeRobotHF 0.6.0 on Tuesday, with six benchmarks to evaluate a policy before it touches hardware. LingBot-VLA 2.0 the same day: a 6B open checkpoint trained on 60,000 hours of real robot data. @Nvidia GR00T 1.7 this morning, on a 2B backbone.
Where @HapticLabsAI steps in: we do everything after the download. Shrinking a policy to one robot's compute budget, serving it at control-loop rates, and managing a fleet where some inference runs on the robot and some in the cloud.
Diego Prats | 🤖 retweeted
As we build out these AI Factories at GW scale; procuring space, power and GPUs is just the first step. Maximizing the Return on Investment (ROI) requires continuous optimized software and hardware availability and ensuring we deploy and bring-up the AI Factories in days and not after months of debugging.
NVIDIA CEO JENSEN HUANG: 1GW AI FACTORY ON NVIDIA ARCHITECTURE COULD COST NEARLY $100 BILLION
Diego Prats | 🤖 retweeted
Robot policy deployment should feel more like shipping software.
Today it doesn't. A policy that works in a demo still has to run on the robot, hit latency targets, survive real networks, and be managed across a fleet.
That's what we've built at Haptic: the deployment and inference layer for robot policies.
Diego Prats | 🤖 retweeted
This week @pham_blnh got a robot policy running inside a browser tab. It works because that policy (ACT) is about 80M parameters, small enough to live in a tab.
It's a great on-ramp. It also quietly shows the deployment problem we work on every day. The policies that run in robots are larger: generalist VLAs are around 50 times bigger and still have to hold 20Hz on an 8GB computer bolted to the arm.
you don’t need a robot to see physical ai in action, you just need a browser.
this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser.
the result is the first ever demo of ACT that runs locally on the web.
try it yourself now at lerobot.binhph.am
Diego Prats | 🤖 retweeted
The 10TB to 2TB re-encode is genuinely nice engineering. Credit to LeRobot for the unglamorous part. A lot of the companies we have worked with have cited this as a big pain.
The largest open-source humanoid teleop dataset EVER just dropped on @LeRobotHF @huggingface
huggingface.co/datasets/BitR…
We re-encoded the full dataset into LeRobot format: ~10TB → ~2TB, no loss of fidelity. Same trajectories, a fraction of the footprint, far easier to stream and train on.
Explore the dataset in LeRobot Visualizer: live 3D render of the robot, synced camera feeds, subtask annotations and language instructions, all in your browser.
Most importantly: Unitree G1 is supported in LeRobot, meaning you can load HIW-500 and start training imitation / VLA policies on 20,000 real-home humanoid episodes on your G1.
Huge thanks to @BitRobotNetwork for sharing the data. Go build. 🤖
I couldn’t believe when I saw this at SFO. The obvious take is this is some crazy weak energy.
My half-baked question: is this a sign that tech is forgetting how to market to human consumers? Did that muscle atrophy as B2B dominates, AI slop
permeates, people sell to agents, and old ideas like brand give way to algorithms.
Seems like people who know the old school can dominate in this new world.
Great work by @luckyrobots team.
Props to @devrimyasar !
Diego Prats | 🤖 retweeted
This is true. The bots right now are the worst they’ll ever be.
"Dominate Global Robotics" doesn't seem great
Good read after @corememory @kyliebytes piece on Westmag, Atlas Motion Systems
A great robotics piece by @SemiAnalysis_
This is the best analysis I’ve seen on Unitree.
China's Unitree Will Dominate Global Robotics
The Fastest Iteration Cycle In Next-Gen Robotics Should See Unprecedented Acceleration
newsletter.semianalysis.com/…
Diego Prats | 🤖 retweeted
Everything here is 100% autonomous, 1x video playback speed.
GO ULTRA mode boosts robot speed to 250%
Hot take: as an immigrant, I think visas will become an underrated driver of robotics adoption.
If a company needs work done in a country with difficult visa requirements, it may be easier to send one specialist with three robots than to secure work visas for four people.
Robots don’t need immigration lawyers, sponsorship, or months of paperwork.