microagi (@microagi)

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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 Oct 9

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

Animesh Garg Verified account @animesh_garg Oct 9

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.

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microagi @microagi Oct 9

It is still day 0 for you to join.

Bercan @CoRL2026 Verified account @bercankilic Oct 9

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

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microagi @microagi Sep 29

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.

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microagi @microagi Sep 25

A glimpse into the future

Bercan @CoRL2026 Verified account @bercankilic Sep 25

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.

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