ExaFunction

The Eyes Are The (Context) Window to The Soul: How Windsurf Gets to Know You — Sam Fertig, Windsurf

Jun 27, 2025 · 13:37

Sam Fertig, Deployed Engineer at Windsurf, explains how the AI coding IDE achieves its uncanny ability to predict developer intent by focusing on relevant context rather than merely expanding context windows. He breaks context into two pillars: heuristics (user behavior like cursor position, open files, clipboard) and hard evidence (codebase, rules, memories). The core challenge is generating code that fits existing codebases, adheres to personal and organizational standards, and remains future-proof. Windsurf optimizes retrieval quality over quantity, using techniques like embedding search, reranking, and AST parsing to balance latency and scale. Fertig also addresses privacy, stating that while code is sent to servers, data is not stored or used for training.

How Codeium Breaks Through the Ceiling for Retrieval: Kevin Hou

Jul 31, 2024 · 18:42

Kevin Hou, AI engineer at Codeium, argues that embedding-based retrieval is hitting a ceiling for AI code generation and that Codeium breaks through by throwing large language models directly at the problem via a system called MQuery. He outlines the limitations of long context, fine-tuning, and embeddings, noting that embeddings are cheap but fail to reason over multiple documents needed for real-world coding tasks. Codeium developed a product-driven benchmark using recall 50 and commit-message datasets to measure retrieval quality. Because Codeium is vertically integrated—training custom models and building its own infrastructure—it can afford to run thousands of LLM calls in parallel per query, giving users 100x more compute than competitors. MQuery retrieves context from thousands of files in seconds, resulting in more accepted completions and better chat satisfaction. Hou compares this shift to autonomous driving: early heuristics like embeddings are giving way to large models that handle more data directly.