Cursor

We're doubling Composer 2 usage through the end of this weekend. We recommend trying it out in our new interface, available in Cursor 3. Enjoy!

We’re introducing Cursor 3. It is simpler, more powerful, and built for a world where all code is written by agents, while keeping the depth of a development environment. The new interface is available as a separate window that complements the IDE. Update Cursor to try it. Learn more here: cursor.com/blog/cursor-3

We're partnering with N-iX to bring coding agents to global enterprises. N-iX will use Cursor to help clients modernize legacy applications, automate repetitive software tasks, and ship high-quality software faster. To learn more, read the full press release: https://lnkd.in/e5XGmi_K

Earlier this week, we published our technical report on Composer 2. We're sharing additional research on how we train new checkpoints. With real-time RL, we can ship improved versions of the model every five hours. Learn more: https://lnkd.in/gpSYbx-B

Cursor cloud agents can now run on your infrastructure. Get the same cloud agent harness and experience, but keep your code and tool execution entirely in your own network. https://lnkd.in/gGNaQp-v

We're releasing a technical report describing how Composer 2 was trained. Composer 2 had three main efforts: continued pretraining, reinforcement learning, and benchmark development. The goal of each was to closely emulate the Cursor environment to produce a highly intelligent coding model. 1. We show how continued pretraining results in consistent improvements in downstream coding performance. 2. The reinforcement learning phase is critical for final performance. We discuss the algorithms we apply for this stage. We find that simple approaches often work best, and improve performance broadly. 3. We describe our internal benchmark CursorBench which represents a more realistic sampling of coding problems. We discuss why we think it is important to include the complex problems software engineers see everyday. 4. We go into detail about the infrastructure behind large scale training including the kernels we developed and open-sourced for the project. We also discuss distributed training and environment scaling for RL. Thank you to the companies and open-source communities behind Kimi K2.5, Ray, ThunderKittens, PyTorch, and more. We'd also like to thank Fireworks and Colfax for their collaboration and partnership. Read the full report: https://lnkd.in/gvKHE5j8

Cursor can now create new components and frontends in Figma using your team's design system.

Cursor can now search millions of files and find results in milliseconds. This dramatically speeds up how fast agents complete tasks. We're sharing how we built Instant Grep, including the algorithms and tradeoffs behind the design. Learn more: https://lnkd.in/gXXzmCVm

Composer 2 is now available in Cursor. It's frontier-level at coding, priced at $0.50/M input and $2.50/M output. There is also a faster variant with the same intelligence at $1.50/M input and $7.50/M output. These quality improvements come from our first continued pretraining run, providing a far stronger base to scale our reinforcement learning. Learn more: cursor.com/blog/composer-2

After seeing its engineers save 15-20 hours a week with Cursor, Money Forward expanded the rollout to product, design, and QA teams. Learn more here: https://lnkd.in/eUgYAHSM