In 2014, I sold my first company Loom to Dropbox.
Today, I’m proud to announce Roma 2.0 (YC F26): the first to-do list for AI agents.
Everyone uses a different agent: Claude, Grok Bot, Muse…
Until now, none of them could work together.
Roma automatically creates tasks and hands them to the right agent.
Allowing you and your agents to work together, with shared context.
Try it here: roma.appView quoted post on X
The Origin Story of Github Copilot:View quoted post on X
In 2020, @GitHub had just acquired my startup, Semmle, and the CEO, @natfriedman, gave me free rein to find our next product.
At the time, I had a bee in my bonnet: GitHub had more data than anyone else about developer behavior, but it wasn’t using that data to build new products.
I started exploring how to change that.
In June 2020, I got early access to GPT-3 and saw its potential to write code, especially if trained on the code on GitHub. It was the obvious project to work on.
It could generate a little code on its own, but almost always got it wrong. So I gave the model a set of 50 programming puzzles, the kind I used to set at Oxford entrance interviews.
I asked the model to solve each puzzle 1,000 times, using automated tests to check it. Given enough attempts, it got almost all of them right. That's when I knew it would be able to code.
I assembled a small initial team of ~10 people to work on what became GitHub Copilot. The first two people who joined were co-founders of the project: @thewunderalbert and @alexgraveley
Fortunately key people at OpenAI shared our vision, and it was the perfect partnership at the perfect time with @sama, @gdb, @ilyasut, @DarioAmodei and Sam McCandlish (months before Anthropic was born!), @npew and several others.
OpenAI trained the model behind it, Codex, and we built the product around it. It ran inside VS Code and suggested lines of code and functions as you typed.
The VS Code team, led by @ErichGamma, moved mountains to give us exactly what we needed. The Azure AI team of Misha Bilenko also helped make it scalable.
When we launched the technical preview in June 2021, it hit 1.2 million users in 12 months. It was an instant hit with developers and helped write billions of lines of code.
It also left me with an uncomfortable thought. The models had learned to code from public source code, and public source code has a lot of vulnerabilities in it.
It was easier than ever to write vulnerable code, and developers were shipping it faster than ever.
But, I knew that if AI could write the code, it could find the holes in it too. It was imperative that defenders got it before attackers did.
In 2023, I left GitHub to build @Xbow to do exactly that. Since then, we've already helped our customers find and fix more than 14,000 zero-days and the work has only just begun.
The same team that started the AI coding revolution is now leading the charge on AI security.
Anthropic: We have the best model.
Google:View quoted post on X
You can now book our humanoid cleaners on a recurring subscription, for up to 3 hours per cleaning at tau-robotics.com.
Video is first take, uncut, shown at 4× speed. See the reply for detailed footage at 1× speed.View quoted post on X
This is what happens when we give traffic engineers too much power.View quoted post on X
