code intelligence

Sourcegraph Answers Where the Code Is. Agents Need to Know Why It Exists

Sourcegraph built the most complete cross repository code search anyone has shipped, and it did so on an assumption that held for fifteen years: a human is asking. Humans supply the meaning the index leaves out. An agent cannot, and at 10 million files a 1 million token window holds 0.01% of the estate, so the gap is not one model generation away.

What Verifiable Means for Code Context, and Why GraphRAG Can't Check Code Against Intent

Verifiable is the one word competitors cannot claim. A code graph extracts a shadow of your code and can never tell you whether the code still does what it is supposed to. A verifiable intermediate representation is a derived contract that every change gets checked against. Here is what verifiable actually means, why GraphRAG and vector search can only retrieve, and what continuous verification unlocks.

$13.30 to Compile 1,000 Files Into a Verifiable IR, Once

The objection to compiling a codebase into a verifiable IR is always cost. It sounds like Opus pricing across every file. It isn't. At open-source-model rates it runs about $13.30 per 1,000 files, you pay it once, and per-file diffing means you only ever re-pay for what changed. Here is the real economics of the verifiable context layer, and why the expensive thing is not building the IR but living without one.

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.

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.

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.

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.