Engineering deep-dives, benchmarks and customer stories on persistent code context, knowledge graphs, and the AI coding tools we work with daily.
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An ETH Zurich evaluation found machine generated context files lowered success rates and raised costs by roughly 20%, human written files bought about 4% on the harder benchmark, and Claude Code was the only agent tested where even developer written files failed to beat having no file at all. The most revealing result came from a control: strip the existing READMEs out and context files finally start helping.
A $125M Series B into formal verification for defence is the strongest market proof anyone has produced that enterprises will pay real money to know whether code is correct. It also published its own ceiling, in public, and that ceiling is the reason we took a different route: equivalence is a relation between two artifacts, and it is silent about purpose.
CodeGraph went from launch on 18 January 2026 to roughly 47,000 stars in five months by mapping symbols and call edges locally, with no server and no cloud embedding call. It deserved every one of those stars. It also answers one class of question completely and another class not at all, and the second class is where production breaks.
Spec Kit spent a year and gathered well over 100,000 stars teaching several million developers that the specification, not the code, should be the source of truth. That is category creation at a scale no startup could have afforded, and it lands directly on our thesis. It also assumes a blank page, which is true exactly once in the life of a system.
A pull request changes 11 lines, the tests pass, every automated reviewer approves, and three weeks later a reporting service is quietly returning wrong revenue. Nothing about the diff was incorrect. What was wrong lived in another repository nobody in the pipeline had read. This is where diff scoped review has nothing to say, and it is now the majority of what breaks in a large organisation.
Local deterministic parsing over 36 tree sitter grammars, typed nodes, provenance tagged edges, Apache 2.0, no vector store and no telemetry. That is a clean design and the privacy position is better than most of the category. The problem starts the moment your organisation has more than one repository, and it is not a problem more parsing solves.
The deepest MCP integration anybody has shipped, with 16 tools, 7 resources, skills and Claude Code hooks, running with zero server on a local graph. The developer experience is the best in the category and that is why it grew. Two things are worth thinking through before you build anything organisational on top, and only one of them is technical.
Of every commercial tool in automated code review, Greptile made the closest architectural call to ours: index the whole repository first, then review the change against it. That decision is why it catches 82% of bugs against CodeRabbit's 44%. It also arrives with about 11 false positives per run, and the shape of those false positives tells you exactly what an index of symbols can and cannot know about intent.
Repomix is the leader in context packing, with 26,000 stars and 255,000 npm downloads a month, and its tree sitter compression genuinely cuts token count by about 70%. Apply that to 10 million files and you go from three orders of magnitude short of the context window to slightly less than three orders of magnitude short. Every tool in this category publishes a token reduction number. None of them publishes a correctness number.
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.
