Yash @coldifl
Co-founder Intelligence Factory (YC P26). Tech/Robots/NFL San Francisco, CA Joined April 2026-
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Last week Jalaj Shukla and I presented Intelligence Factory at the @ycombinator Demo Day. We started Intelligence Factory with only one goal - deploy robots in the real world. We are more excited than ever to make this a reality.
Jay and his team are cooking. Was great to visit them and feel the good old hardware engineering energy at their office
In Sep 2024, I left a job I loved to start @proceptionAI with one obsession: to give robots hands that actually work. 20 months. 20 people in Mountain View. A lot of Coke Zero. Today we ship.
@drxcliu @ycombinator happy to host you at our office, we can’t show everything in the launch
The standard fix is a data flywheel: deploy, collect data, retrain, redeploy. But this is slow and expensive. We should be able to make this data flywheel much faster by patching models with all edge case data at once rather than waiting for them to happen one-by-one.
The deployment flywheel in robotics is broken. In the real world, robots don't compete with other robots - they compete with humans. And no business pays for a robot that works 80% of the time when their current system works 100% of the time. (1/2)
@Theonash_ @ycombinator @lukas_m_ziegler Bro we are not egocentric data collection😭
@bygregorr @ycombinator It’s less about the hardware but the sensor. Waymo transfers sensors across cars but hardware can go from Jaguar to Kia
6 months of work. Happy to finally lauch Intelligence Factory! Just getting started.
Intelligence Factory is building human intelligence for robots. They train general-purpose manipulation models on human demonstration data (vision, action, and touch), then deploy them in warehouses, grocery stores, and data centers. Congrats on the launch, @coldifl and Jalaj!
Intelligence Factory is building human intelligence for robots. They train general-purpose manipulation models on human demonstration data (vision, action, and touch), then deploy them in warehouses, grocery stores, and data centers. Congrats on the launch, @coldifl and Jalaj! ycombinator.com/launches/QbX-i…
Welcome to the cap @sama You’re welcome
A mic drop moment @ycombinator tonight @sama just offered $2M in OpenAI tokens to EVERY YC startup in the current batch in exchange for equity Just like Yuri Milner offering to invest in every startup back when Sam was a YC partner I can't wait to see what's unlocked when you
We live in a bubble in SF. We need to understand that this is not what the world is like. Starting to believe strongly that while being in the bubble has its perks, a successful company (especially robotics) needs to be grounded in reality.
The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen. Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope
I don’t understand why people treat egoscale as the bible. It has 55% success rate.
🐝 @saturdayrobotic Robotics & World Models Reading Club 08 Recap: Embodied Human Data as the “Internet of Motion and Behavior” keynote @ryan_punamiya, hosts @junfanzhu98, @aurorafeng_01. Great Parallel: Egocentric Human Data = Internet for Robot Foundation Models 🤖 Jim Fan’s
This is a great watch! Scaling data (what type of data?) and building the infrastructure required for it, is one of the biggest bottlenecks in robotics. Extra cool that egoscale was one of the first papers that validated our thesis. youtu.be/3Y8aq_ofEVs?si…
@thejesonlee would love to join!! Building general purpose robotics (current YC batch)
Awesome thesis! Super excited to launch Intelligence Factory soon
🚨 BREAKING: Genesis AI has just launched GENE-26.5, what the company is calling the first robotic brain to give robots human-level physical manipulation capabilities. The demo video alone is extraordinary, robots cooking 20-step meals, solving Rubik's Cubes mid-air, playing
@chris_j_paxton @gs_ai_ This makes sense for pre-trained models, but post-training is still unsolved. I imagine when they begin deploying, they’d see multiple edge-cases and the data flywheel approach takes months to hit metrics that a human would do
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