Show HN: SigMap – shrink AI coding context 97% with auto-scaling token budget (github.com)

SigMap has introduced a new version, v4.1.0, that significantly enhances coding context for AI systems by automatically scaling its token budget, achieving a remarkable 97% reduction in token usage while ensuring that AI frequently accesses the correct code context. By extracting only the signatures of functions and classes from a source code repository—which eliminates unnecessary components like body, imports, and comments—SigMap creates a concise context file that AI models such as GitHub Copilot and Claude rely on. This update allows for a smoother coding experience, reducing the average tokens processed per session from approximately 80,000 to just 2,000, while also majorly improving task success rates (from 10% to 59%) and minimizing the risk of AI inaccuracies. This development is crucial for the AI/ML community as it streamlines the process by which AI tools interpret codebases, making them more efficient and less prone to generating incorrect outputs. The benchmarks provided demonstrate significant performance improvements across various programming languages and real-world repositories, thereby underscoring SigMap's capability to enhance AI programming assistants with a structured and efficient understanding of code. Furthermore, with broad support across 25 programming languages and easy integration options via a standalone binary or popular IDE plugins, SigMap becomes an essential tool for developers aiming to optimize AI interactions in software development.