Cursor

Forward Deployed Engineering at Cursor — Pauline Brunet

Jul 14, 2026 · 20:47

Pauline Brunet, VP of Forward Deployed Engineering at Cursor, explains how to build an effective FDE team by mapping customer digital maturity and product customization to the right engagement model—self-service, traditional SaaS deployment, advisory, or embedded transformation. She defines the ideal FDE as a highly technical engineer with strong customer-facing skills, capable of scoping impactful projects, co-developing with customer teams, and feeding insights back to product. Cursor’s FDE team uses a project-based approach, hires experienced unicorns (5+ year software engineers), and structures around geographies, industries, and product SMEs. Best practices include solving the right problem, defining success metrics upfront, keeping scope directional, involving customers at every step, measuring ROI (revenue, cost, risk), and leaving documentation behind. Brunet advises learning fast, pivoting, partnering with system integrators, and not being afraid to say no to mismatched use cases.

Benchmarking semantic code retrieval on Claude Code — Kuba Rogut, Turbopuffer

Jun 3, 2026 · 16:08

Kuba Rogut from Turbopuffer benchmarks semantic code retrieval on Claude Code, finding that raw Claude Code wastes one in every three file reads (65% precision), windowed grep drops that to one in five, and adding semantic search cuts it to one in eight (87% precision). Semantic search wins on behavior-adjacent tasks where files share no keywords; grep wins on import tracing. Cursor's production numbers show a 24% relative improvement in answer accuracy from semantic retrieval plus a 2.6% increase in code retention in large codebases. Rogut explains Cursor's model knows when and why to call semantic search, while Claude Code treats it as just another tool, limiting gains.

Building Cursor Composer – Lee Robinson, Cursor

Dec 2, 2025 · 15:36

Lee Robinson explains how Cursor built Composer, its first agent model for real-world software engineering, by focusing on being both fast and smart — achieving 4x more efficient token generation than similarly intelligent models while matching open-source performance initially and approaching frontier models after reinforcement learning. The model's training posed infrastructure challenges: matching training and inference environments across thousands of GPUs, handling complex rollouts with hundreds of tool calls and up to millions of tokens, and ensuring consistency by using the same tool format and responses as production. Cursor solved these with custom kernels that sped up training by 3.5x on NVIDIA Blackwell chips for mixture-of-experts layers, load balancing across threads to avoid idle time, and co-designing RL infrastructure with its Cloud Agents product using virtual machines that mirror the production Cursor environment. This allowed the model to become a power user of tools like semantic search, which improved all models but especially Composer. RL also taught the model to parallelize tool calls (e.g., reading 10 files simultaneously) and to search more before editing,…

State of Startups and AI 2025 - Sarah Guo, Conviction

Aug 2, 2025 · 23:52

Sarah Guo of Conviction argues that AI's value creation is massive and early, with companies like Cursor reaching $100M ARR in 12 months and Harvey exceeding $70M ARR. She predicts that by end of 2026, AI agents will ship code directly to production, voice AI will replace text for most business communication, and inference costs will drop below a cent per million tokens. Reasoning is a new scaling vector unlocking higher-stakes use cases, and agent startups have increased 50% in the last year. Multimodal models from HeyGen and Eleven are already rocketing past $50M ARR. The model market is more competitive than ever, with GPT-4 costs falling from $30 to $2 per million tokens in 18 months and open-source like DeepSeek competing. Guo advises builders to focus on thick wrappers around LLMs, leveraging domain and workflow knowledge, and warns against generic text boxes: 'The prompt is a bug, not a feature.' Execution, not first-mover advantage, is the moat.