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1/8 Report shows how AI speeds up research. But imagine every failed experiment, domain insight, protocol change and decision compounding across agents and teams. solana:4KxtNWc5XTL3cuyc8PJMchqB6RYggvEFeMub727GBAGS provides that. Its use case agnostic. Let's explore its benefits in this real-world, medical use case 🧵

A 135K-star GitHub repo is built around one painfully obvious idea: Your coding agent should map the codebase once, not grep it forever. Graphify turns an entire project into a persistent, queryable knowledge graph that AI coding agents can use to understand how different parts of the system connect. Functions, classes, files, SQL schemas, infrastructure configurations, documentation, PDFs, images and videos can become connected nodes instead of isolated pieces of context the agent has to rediscover. So instead of: → Search for authentication → Open twelve files → Follow imports and function calls → Lose the trail as the context fills up The agent can ask: > What connects authentication to the database? > Trace the path from UserService to DatabasePool. > Explain RateLimiter. > Which components are central to this architecture? Graphify returns relevant graph relationships and paths, rather than another collection of keyword matches. For source code, the implementation is particularly interesting. It uses tree-sitter to parse approximately 40 programming languages locally, extracting functions, classes, imports, calls, inheritance and cross-file relationships. No embeddings, vector database or LLM calls are needed to construct the code graph. Document and media processing works differently. PDFs, images and other non-code content can go through an optional semantic extraction step using a configured model. Every relationship carries a confidence label: → EXTRACTED: Explicitly present in the source → INFERRED: Derived through analysis or resolution → AMBIGUOUS: Uncertain and flagged for review Graphify also uses community detection to identify related subsystems and highlights highly connected nodes that may be important to the overall architecture. But the part that makes this useful for coding agents is what happens after the graph is built. Graphify integrates with Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot and more than 20 other assistants. Depending on the platform, it installs hooks or persistent instructions that encourage the agent to query the existing graph before repeatedly searching and reading source files. The graph can be committed to Git, updated incrementally as files change, and exposed through MCP. There's also support for examining pull requests against the graph, including identifying PRs that touch related graph communities and may need closer review. The local tool produces three useful artifacts: an interactive HTML graph, a Markdown architecture report and a reusable graph.json. Long context windows help coding agents read more code. A persistent knowledge graph gives them a way to navigate what they've already analyzed. And as coding agents spend more time working inside large repositories, avoiding repeated discovery of the same architecture becomes increasingly useful. Here's the GitHub Repo: github.com/Graphify-Labs/gra…

We've onboarded TWO new @SalvorLabsAI developers. The dev team is now 3 engineers and growing. In discussions with more AI devs who've taken an interest after seeing GitHub. Got calls Monday on the marketing side too. Let's cook!