SAIDMUSTAFASAID

AI coding assistants and developers both need exact answers about a codebase's structure, not the approximate matches an embedding search returns. Conducks is a CLI and MCP server I built that parses a codebase with Tree-sitter, builds a deterministic graph of its symbols and relationships, and stores it in a local DuckDB vault any tool can query. It replaces a guess about where code lives with a graph-verified answer: a symbol's file, line, callers, and risk score, computed the same way every time, with zero LLMs or embeddings anywhere in the analysis path.

GCE is the engine behind my own portfolio site. It reads my project, research, and education logs plus a skills registry, and computes a career rank and a 12-field capability map from pure math, no manual grading and no language model call anywhere in the scoring path. I built it to replace a gut-feel seniority claim with a number traceable back to evidence, sensor by sensor. It runs as a build script that recalculates my technical identity from source every time I add a project.

mycvpath is a CV tailoring platform I designed, built, and run myself. A user brings a CV and a job posting, and a six-agent LLM pipeline parses both, analyzes the gap between them, rewrites the CV against that specific job, and scores the result, then a separate rendering service turns the output into an ATS-safe PDF or Word document. The same pipeline is exposed as an MCP server, so an external agent such as Claude Desktop, Cursor, or a custom tool-calling agent can drive the whole flow with its own model, or ask mycvpath to run it. It replaces hours of manual CV editing per job application with a few minutes of grounded, automated rewriting, and it proves the same pipeline works both as a consumer product and as agent infrastructure.