Posts tagged "code intelligence"

The Simple Graph RAG Strategy That Finally Makes Multi-Repository Code Changes Reliable

Vector search finds relevant code but misses the blast radius. Learn how combining lightweight code graphs with RAG creates cross-repository context that makes code changes across 50+ repositories predictable—without heavy graph infrastructure.

We Benchmarked 14 LLMs for Code Analysis. The Cheapest One That Works Costs 56× Less Than Claude Opus.

A real cost-vs-accuracy benchmark of DeepSeek, GLM, Kimi, Qwen, Sonnet, Opus, and GPT-5 for indexing codebases. Why per-file LLM analysis isn't actually expensive if you pick the right model.

Why Vector Search, AST Parsers, and Raw LLMs All Fail at Code Intelligence — And What Actually Works

Vector embeddings treat code like english prose. AST parsers see structure but not meaning. Raw LLMs forget everything every session. Here is why the LLM compiler pattern with a persistent semantic graph is the only approach that actually works for cross-repository code intelligence, and why open source models at $7 per 1000 files make it practical today.

The Categories Anthropic Didn't Try to Eat

Anthropic shipped Claude Code, an MCP standard, and a frontier model, and then they deliberately stopped. This post maps the 5 adjacent categories Anthropic chose not to build, why they made that choice, and what it means for anyone building infrastructure underneath their agents.