🛠️ Sigmap: Massive Token Reduction for AI Coding Sessions

The direction here is straightforward: solving the problem of excessive token consumption during AI coding sessions. It claims to achieve up to a 97% token reduction, built as a zero-dependency tool that supports 31 programming languages. The interesting part is that it is not just a CLI tool; it also provides an MCP (Model Context Protocol) server. This means it can be plugged into modern AI development environments like Cursor or Claude that support MCP, acting as a context compression layer. As codebases grow, dumping entire files or projects into an LLM is not only expensive but also distracts the model. Sigmap is exploring a lightweight code intelligence extraction approach, feeding only the most critical context to the AI. If you frequently hit token limits due to large code files, this tool's approach is worth looking into.