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A discovery and compression tool for your Python codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project | Code structure visualization | LLM Context Window Efficiency | Static analysis for AI | Large Language Model tooling #LLM #AI #Python #CodeAnalysis #ContextWindow #DeveloperTools
A lightweight tool to optimize your Javascript / Typescript project for LLM context windows by using a knowledge graph | AI code understanding | LLM context enhancement | Code structure visualization | Static analysis for AI | Large Language Model tooling #LLM #AI #JavaScript #TypeScript #CodeAnalysis #ContextWindow #DeveloperTools
A lightweight tool to optimize your C# project for LLM context windows by using a knowledge graph | Code structure visualization | Static analysis for AI | Large Language Model tooling | .NET ecosystem support #LLM #AI #CSharp #DotNet #CodeAnalysis #ContextWindow #DeveloperTools
A discovery and compression tool for your Java codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project #LLM #AI #Java #CodeAnalysis #ContextWindow #DeveloperTools #StaticAnalysis #CodeVisualization
A CLI tool that vectorizes codebases, stores them in a database, tracks changes, and serves them via MCP (Model Context Protocol) for AI agents like Claude, Codex, and others.
Semantic codebase understanding service — maps application structure, authentication patterns, authorization models, and data flows for intelligent security analysis.
LLM autotooling pipeline with Ollama-only models, Bubblewrap sandboxing, and a provenance-aware tool registry specializing in low parameter model inference
Convert any GitHub repo into LLM-friendly text digests and interactive architecture diagrams. Claude-powered codebase analysis with clickable Mermaid diagrams. Live at gitunderstand.com
Codebase analysis using information theory, graph algorithms, and git history. Cross-references structural dependencies, temporal co-change, and per-file signals to find what's actually wrong.