Show HN: TS Compiler Knowledge Graph Reducing AI Tokens About 90%

This breakthrough demonstrates how structured domain knowledge can dramatically reduce token consumption in LLM applications. By converting TypeScript code analysis into a semantic knowledge graph, developers can feed LLMs a highly compressed representation instead of raw source code—achieving 90% reduction in token count while maintaining full contextual information.

The implications for local LLM deployment are significant. Fewer tokens required means faster inference, lower memory usage, and the ability to run larger, more capable models on modest hardware. This technique is particularly impactful for code-focused tasks like codebase analysis, documentation generation, and automated code fixes—all computationally expensive operations on commodity devices.

Practitioners should explore how similar knowledge graph approaches could be applied to their domain-specific problems. Whether analyzing configuration files, processing logs, or understanding domain-specific languages, this method shows that structured representations dramatically outperform naive tokenization when working with local LLMs. The open-source implementation provides a reference implementation for adaptation.

Source: Hacker News · Relevance: 9/10