Show HN: Ctxfw – In-memory AST pruner and token firewall for Cursor and Claude (github.com)

A new tool called Ctxfw has been announced, designed to enhance the functionality of large language models (LLMs) such as Claude and Cursor. Ctxfw operates as an in-memory AST pruner and context firewall, addressing issues like token exhaustion and security degradation caused by bloated context windows and peripheral dependencies. It achieves a remarkable 72.4% reduction in token consumption by replacing complex module implementations with clean interface stubs, effectively compacting code while preserving essential signatures and definitions. Additionally, it enforces Axiomatic Integrity to ensure sound architecture documentation and reduces telemetry egress to zero, safeguarding data privacy. The introduction of Ctxfw is significant for the AI and ML community as it directly tackles critical challenges associated with the performance and security of autonomous coding agents. Its innovative design not only facilitates efficient context management but also promises substantial cost savings, with estimated reductions of up to $87.70 per 1,000 prompts. By providing a structured protocol for code verification and execution isolation, Ctxfw enhances the reliability of LLM applications and sets a new standard for integrating security measures within AI tools, making it an essential addition for teams working with complex LLM implementations.