Our problems · Cursor

What this post added

This post details several new problems and future directions for Cursor's AI-powered coding capabilities. It introduces the concept of a custom and fast reranker model to improve context retrieval for LLMs, aiming to filter down 500k tokens to the most relevant 8k. It also discusses the need for a 'copilot for edits' to handle small code changes, the development of constrained, in-flow agents for engineering tasks (starting with folder-level agents), and both passive and active bug-finding mechanisms. Furthermore, it addresses the challenge of enabling larger edits to files and directories, requiring smart model capabilities and real-time diff presentation. The post also highlights the significant scaling challenges in indexing, mentioning 1.4 billion vectors and 150 thousand codebases indexed, and the need for a custom indexing system. Future ideas include 'time warp', deep codebase understanding, 'reader mode', 'pseudo-code mode', and automated stack trace debugging.