knowledge graph code

A Code Graph Disintegrates Your Code. A Verifiable IR Preserves It.

A code graph takes a whole, coherent program and shreds it into a bag of symbols and edges. It is genuinely useful, but it is a teardown, and the meaning never survives the pieces. A verifiable intermediate representation is the opposite move. It lifts your code into a higher layer that keeps the intent intact, then checks every change against it. Here is the difference between shredding and preserving, and why it decides everything downstream.

Code Graph RAG, Explained, and Why a Verifiable IR Is the Next Step Past It

Code graph RAG fixed the biggest flaw in vector search. It follows real edges instead of guessing at similarity. But a graph of symbols is still not a graph of meaning, and it cannot tell you whether a change still does what the system is supposed to do. Here is how code graph RAG actually works, where it stops, and why a verifiable intermediate representation is the layer above it.

From Code Graph to Context Graph: Why a Graph of Symbols Isn't a Graph of Meaning

Everyone building code intelligence ends up at a graph, and then they start calling it a context graph because plain code graph stopped feeling like enough. But adding the word context does not add meaning. A graph of symbols and a graph of meaning are different objects. Here is the line between them, and why crossing it requires a verifiable code IR, not more edges.

The Context Graph You Can Verify Code Against

Context graph is a term people understand and reach for. But most things called a context graph are one-way extractions you can read and never check anything against. The version worth wanting is the one you can verify code against, and that single requirement is what separates an index from a verifiable code IR. Here is what verification demands, and why it is the only honest test of a context graph.

GraphRAG for Codebases: What It Solves, Where It Breaks, and the Layer Above It

GraphRAG for codebases is a genuine step up from vector search. It follows real call edges instead of guessing at similarity, and it wins on architectural questions. But it breaks in three predictable places: cross-repo links that aren't call edges, the why behind the code, and anything that needs intent you can check changes against. Here is a clear-eyed map of what graph RAG for codebases solves, where it stops, and the verifiable context layer that sits above it.

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.

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.

Will Anthropic Build a ByteBell? The Startup Kill List and Why the Context Layer Is Different.

Anthropic has erased $1 trillion in SaaS market cap by absorbing entire product categories — Cowork ate project management, Design ate prototyping, Managed Agents ate orchestration. So why won't they build ByteBell? Because a model-agnostic, self-hosted knowledge graph directly conflicts with their business model. Here is the full kill list and the honest probability analysis.