microagi

Accelerating embodied AGI

[microagi @microagi]

As our customers scale to training robot models using millions of hours of our recordings, we needed to build a new kind of data infrastructure.

Terabytes of data delivered in minutes, and the capacity to deliver over a Petabyte per day to each of our customers, to multiple customers at the same time. That’s more than 10 Gigabytes a second, delivered into any cloud, wherever our customers need it.

Using our customer portal, customers select precisely the data they want and it starts arriving in minutes at their destination, including AWS, GCP, Cloudflare, Hugging Face and any cloud provider who can handle the ingest speed.

We’ve written about how we worked with @huggingface and @Cloudflare to build this secure, scalable infrastructure. Link in comments.

[microagi @microagi]

@bercankilic https://t.co/Vmd2z9Am4e

[microagi @microagi]

As our customers scale to training robot models using millions of hours of our recordings, we needed to build a new kind of data infrastructure.

Terabytes of data delivered in minutes, and the capacity to deliver over a Petabyte per day to each of our customers, to multiple customers at the same time. That’s more than 10 Gigabytes a second, delivered into any cloud, wherever our customers need it.

Using our customer portal, customers select precisely the data they want and it starts arriving in minutes at their destination, including AWS, GCP, Cloudflare, Hugging Face and any cloud provider who can handle the ingest speed.

We’ve written about how we worked with @huggingface and @Cloudflare to build this secure, scalable infrastructure. Link in comments.

[microagi @microagi]

https://t.co/Vmd2z9Am4e

[microagi @microagi]

@bercankilic Interstellar by microagi

[Bercan @bercankilic]

Sparks of abundance by 2029 https://t.co/uKjmoLpc0p

[akin @akinaydogaan]

we were eurosummer maxxing

[microagi @microagi]

Join @animesh_garg @YoanIlievX @ZenoInMotion @EastlondonDev and rest of our Atlas team to deploy 1 million robots by 2029

[Animesh Garg @animesh_garg]

0 to $100M ARR in 6 months is an absurd benchmark. But this is still ground zero.
The mission is clear: 1 million robots in the real world.

Winning Physical AI comes down to four primitives: compounding data loops, top-tier talent, compute scale, and execution velocity.
@microagi has all four firing in lockstep.

We are just getting started.
Researchers and engineers looking to build on the frontier of robotics: our DMs are open.

[Animesh Garg @animesh_garg]

0 to $100M ARR in 6 months is an absurd benchmark. But this is still ground zero.
The mission is clear: 1 million robots in the real world.

Winning Physical AI comes down to four primitives: compounding data loops, top-tier talent, compute scale, and execution velocity.
@microagi has all four firing in lockstep.

We are just getting started.
Researchers and engineers looking to build on the frontier of robotics: our DMs are open.

[Gianni Hodel @notgiannei]

microagi just crossed $100M in 6 months. Faster than Lovable, OpenAI, Cursor, Wiz, and every software company in history.

We are living through an unusual moment where physical companies are starting to scale as fast as software companies.

Huge thank you to everyone who made this possible! <3

[microagi @microagi]

It is still day 0 for you to join.

[Bercan @bercankilic]

We went from 0 to $100M ARR in just 6 months after launching shift.

Also we hosted Europe’s first Physical AI summit to accelerate distirbution of robots.

And it still feels like day 0.

Onwards

[Bercan @bercankilic]

We went from 0 to $100M ARR in just 6 months after launching shift.

Also we hosted Europe’s first Physical AI summit to accelerate distirbution of robots.

And it still feels like day 0.

Onwards

[Animesh Garg @animesh_garg]

I've spent most of my career on one problem: Generalizable Autonomy for Robots.

Robotics have started to get out of the lab.
Frontier AI is remarkably good, hardware is available and we believe in data. Yet robotics need a lot of hand holding.

The next unlock will come from advances in Self-improvement, RL-based post-training, and agentic interactions. And as frontier AI has shown this requires real world environments.
@microagi is the one of the few places that has both sides of coin: Data and Deployments under one roof.

@usablerobots is joining @microagi in the shared vision: Physical AI should be a community without gates.
Robots are for everyone.

[microagi @microagi]

@animesh_garg @usablerobots https://t.co/btfSFRAiuq

[microagi @microagi]

We’re excited to welcome @animesh_garg as our new Chief Research Officer at @microagi, with the team from @usablerobots joining him.

He has spent most of his career working on one of the hardest problems in robotics: getting robots out of the lab and into the real world.

Frontier AI now works. Hardware is becoming cheap. And the industry is finally learning how to collect robot data at scale.

What’s still missing is post-training.

Taking a general-purpose model and teaching it one specific job, in one specific environment, until a company can actually depend on it.

In LLMs, post-training is what turned impressive demos into products. In robotics, that layer barely exists yet.

A strong model is a capability. A robot that can perform the same job, on the same line, every shift, without an engineer standing next to it, is a product.

And that process has to happen again for every task, every site, and every customer.

We believe the company that can do this well, fast, and at scale will be the company that puts robots into production at massive scale.

MicroAGI is building both sides of that system.

With @joinshift, we’re building one of the most diverse and largest egocentric datasets in the world.

With Atlas, we post-train and harness models directly on enterprise deployments.

Our goal is simple:

Deploy 1 million robots.

[microagi @microagi]

@bercankilic @animesh_garg https://t.co/quzyo9wyU4

[microagi @microagi]

@animesh_garg @usablerobots https://t.co/quzyo9w14w

[ElevenLabs @ElevenLabs]

Asking out loud is the fastest way to get almost anything done. Even if you're asking a robot.

This early case study from @microagi showcases how they're using ElevenLabs to explore what working alongside a humanoid robot looks like when you can simply talk to it.

[microagi @microagi]

A glimpse into the future

[Bercan @bercankilic]

microagi x ElevenLabs: what happens when you work together with a robot?

Language has always been at the heart of human connection. It's how we share ideas, build communities, and understand one another. We believe this will be just as important in how we connect with robots. At microagi, we envision a world where robots work alongside us as a normal part of everyday life, where asking one for help feels as natural as asking the person beside us.

For that to happen, we need to be able to speak to them directly, without a screen or control panel, and trust that we have been understood.

With ElevenLabs, we are working to make those conversations possible. We are giving our robots a voice, and shaping how they listen and respond to the people around them.

Here’s an early look at how future robots could talk with us.

[Gianni Hodel @notgiannei]

Today we're announcing microSLAM, a monocular SLAM system built by our computer vision team in Zürich with ETH Zurich's Computer Vision and Geometry Lab. It ranks first on the LaMaria benchmark for monocular SLAM, and holds up against systems that carry many more cameras and IMUs. Ours runs on a single RGB stream.

Robot learning is bottlenecked on data, and the largest untapped source is people going about their work. A camera on someone's head for an afternoon in a plant is a record of how that plant actually operates, including all the parts nobody writes down.

That data is only useful if you can recover the geometry, and geometry from one moving camera in a world that will not sit still is the hard version of the problem. Solving it monocular is what makes the data cheap. Rigs do not scale to every worker. Glasses do.

microSLAM turns that footage into interactive 3D environments where behavioral foundation models can be trained. One person walks a factory floor. A robot learns to navigate it.

[microagi @microagi]

https://t.co/SkVFkJCTiA

[Bercan @bercankilic]

A great first step toward making large-scale mobile manipulation deployments possible; well done to our Zurich lab for the breakthrough!

The best non-hardware-bound SLAM on earth to our knowledge.