Cursor announced that Supermaven, the fast context-aware AI coding assistant, is joining the company, with Supermaven creator Jacob Jackson and his team coming aboard. Cursor said it plans a new Tab model version that is fast, context-aware and especially good at handling long chains of large edits, while the Supermaven plugin will keep being maintained even as Cursor becomes the team's core focus.
Cursor has implemented a shadow workspace that lets its AI apply edits in a hidden Electron window and read back lint results without touching the user's editing session. The post outlines the design goals of LSP availability, independence, privacy, concurrency and speed, and explains why copying a TextModel or running separate language servers failed. It also sketches a future kernel-level folder proxy for runnability, noting that macOS and Windows lack a viable FUSE path and that Rust LSP support is currently unavailable.
Cursor raised a $60M Series A from Andreessen Horowitz, Thrive Capital, OpenAI, Jeff Dean, Noam Brown, and founders of Stripe, GitHub, Ramp, Perplexity and OpenAI. The AI coding tool says it has grown to more than 40,000 users, including startups, research institutions and large enterprises, and describes internal systems for next-edit prediction, retrieval across billions of files and speculative-inference code rewriting.
Why it matters: Cursor's $60M Series A announcement also discloses its user base has grown past 40,000 and names the investors backing its AI coding tool.
Cursor published a follow-up to its original "Problems" post, listing the research questions it considers most critical for the next phase of AI coding. The company has extended Copilot++ to predict the next cursor position, and combining that with next-edit prediction lets the model "play" sequences of low-entropy changes—a demo required 11 Tab presses versus 3 presses of every other key, which Cursor calls Cursor Flow. Other directions include action prediction across the codebase, perfect edits via more inference-time compute, hallucinated pseudocode, multi-file editing, optimal context, multi-hop context, and bug detection and debugging.
Cursor published a list of problems it is working on, including training a custom fast reranker that filters 500k tokens from all sources down to the most relevant 8k per request. The list also covers an edit-oriented copilot, constrained in-flow agents for large codebases, bug finding, and larger-scale edits. As of Oct 12, 2023, Cursor had indexed 1.4B vectors and 150k codebases.