Securely indexing large codebases
By securely reusing a teammate's existing index, we cut time-to-first-query from hours to seconds on the largest repos.
Software creation is changing. We are a group of researchers, engineers, and technologists inventing at the edge of what's useful and possible.
We have much to learn, try, and build.




Securely indexing large codebases
By securely reusing a teammate's existing index, we cut time-to-first-query from hours to seconds on the largest repos.
Scaling long-running autonomous coding
We've been experimenting with running coding agents autonomously for weeks at a time.
Dynamic context discovery
As models improve as agents, we've found success by providing fewer details up front, making it easier for the agent to pull relevant context on its own.
Software creation is changing. We are a group of researchers, engineers, and technologists inventing at the edge of what's useful and possible.
We have much to learn, try, and build.




Watching a dozen agent branches merge every day has become normal, and that freed-up velocity shows up everywhere from release cadence to bug-backlog burn-down. Cursor isn't a convenience add-on; it's a scale-multiplier for the whole org.

I will never not be amazed to be merging PRs while riding Peloton. I’m never touching a laptop again.

Leave it to the folks at Cursor to bring out the absolute most from the models. GPT-5.2 is an incredibly hard-working model, but to get it to work for a whole week is crazy. Building a whole browser is even crazier!

Coding agents like Cursor have become the killer app for AI. Not only do coding agents increase the speed at which code is created, they also improve code quality.

Cursor has helped me not only be more productive but also more curious and confident to explore new problem spaces. It's genuinely become an indispensable tool in my daily workflow.

More than 70% of our engineers now use Cursor, and we've seen meaningful gains in day-to-day development, faster execution on large-scale migrations, increased rate of debugging, and even faster onboarding.

Securely indexing large codebases
By securely reusing a teammate's existing index, we cut time-to-first-query from hours to seconds on the largest repos.
Scaling long-running autonomous coding
We've been experimenting with running coding agents autonomously for weeks at a time.
Dynamic context discovery
As models improve as agents, we've found success by providing fewer details up front, making it easier for the agent to pull relevant context on its own.
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