Descriptions:
Fahd Mirza demonstrates Symbol, a code search library designed specifically for AI coding agents, integrated with the OpenCode terminal agent and a locally running Ollama model. The problem Symbol addresses is context-window waste: when agents use grep and read entire files to answer questions about a codebase, they dump thousands of irrelevant lines into the model’s context. Symbol replaces this with a hybrid retrieval system that indexes an entire repository in 250 milliseconds and returns precise code chunks in 1.5 milliseconds, all running on CPU with no API keys or GPU required.
Under the hood, Symbol combines a 16-million-parameter static embedding model—distilled from a 137-million-parameter transformer and available on Hugging Face—with BM25 keyword matching fused via reciprocal rank fusion (RRF). The result delivers 99% of the retrieval quality of full transformer models while using 98% fewer tokens than the grep-and-read approach, according to the project’s benchmarks.
The walkthrough uses LooseBox, a 2,000-line C++ CUDA codebase with custom speculative decoding kernels, as the test repository. Mirza configures Symbol as an MCP server in OpenCode’s config file, launches the agent from the repo root, and asks natural language questions about the codebase. The Ollama-powered Qwen 1.5 35B model responds with precise file locations, line numbers, and technical explanations—including details about DD tree verification, attention masks, and SSM rollback—without reading any complete files.
📺 Source: Fahd Mirza · Published May 01, 2026
🏷️ Format: Tutorial Demo
