SAIDMUSTAFASAID

Engineering Deterministic
Technical Intelligence.

Selected // Production

Engineering
Systems

The materialization of engineering theory into high-availability digital assets.

November 2025open-sourcePrincipal Architect & Solo Engineer

Conducks Structural Intelligence Platform

Architected and solo-engineered a Git-native, deterministic structural intelligence ecosystem that transforms massive, fragmented source codebases into interactive topological resonance graphs — the Synapse. Built from first principles with zero LLMs in the analysis pipeline, the platform delivers provable, mathematically rigorous architectural intelligence through six formal algorithms (Kahn, Tarjan SCC, PageRank, Weighted Dijkstra, Shannon Entropy, DAAC Clustering), a multi-core Map-Reduce ingestion engine, a 9-tool Model Context Protocol (MCP) server for autonomous AI-agent governance, a real-time Visual Command Center (Kinetic Mirror at port 3333), and a Universal Structural DNA schema supporting 14 language grammars. The system ingests 9,230 nodes and 61,352 edges in under 9 seconds, serves cross-repository federated analysis, enforces deterministic CI/CD regression guards, and provides full behavioral execution tracing from entry point to leaf symbol. Simultaneously, a professional Next.js marketing website was engineered as the public face of the platform, complete with a glassmorphic design system, i18n content architecture, and a custom component library. The platform represents a novel approach to AI-pair-programming infrastructure: structural intelligence as a continuous, navigable graph rather than static text.

TypeScript / Node.js ESMTree-sitter WASMDuckDBModel Context Protocol (MCP)+5 more
Architected the first agent-native structural intelligence platform for AI pair-programming — Zero LLMs in the analysis pipeline, every score mathematically decomposable into its 6 constituent signals with no black boxes.
Achieved 93.5% Behavioral Health restoration in fragmented Python/TypeScript codebases (from 9.6% baseline) through Scoped Identity Resolution, restoring 6,814 behavioral edges and enabling full execution tracing across the entire graph.
April 2026enterpriseLead Architect & Systems Engineer

Geometric Calibration Engine (GCE) — Deep Field Orchestrator

Architected and implemented a high-fidelity career calibration engine (v11.0) that transforms multi-modal evidence into a deterministic 12-axis capability radar. Built to replace arbitrary scoring with geometric identity inference.

TypeScriptNext.js 16 (Titan)Geometric Identity InferenceDeep Field Multi-Pass Calibration
Successfully implemented a deterministic career classifier with 1:1 blueprint parity.
Reduced architectural noise by removing legacy multipliers and leadership biases.
December 2025commercialco-founder

MyCVPath — AI-Native CV Intelligence Platform

A production-deployed, polyglot microservices platform that automates the full CV-to-job-application lifecycle using a 6-agent LLM orchestration pipeline, a dedicated ATS-optimized PDF rendering service, a Rust-based fire-and-forget telemetry sink, and a real-time admin control plane. Built across five independently deployable services (Go, Python, Rust, Node.js, Next.js) sharing a single PostgreSQL backend with dual-schema architecture (business logic + analytics). Implements a tiered billing engine with BYOK API key encryption, an atomic guest-to-user migration pipeline, and job-scoped immutable CV snapshots that preserve a reproducible per-application evidence trail. Live at mycvpath.com.

Next.js 16 + React 19 (App Router, TypeScript, Tailwind CSS v4)Go 1.24 (Agentic LLM Orchestration Engine)Python 3.13 + Flask (CV Document Rendering Service)Rust + Axum + Tokio (High-Throughput Telemetry Sink)+3 more
Delivered a production-deployed, 5-service polyglot microservices platform (Go, Python, Rust, Node.js, Next.js) from first commit to live deployment in 10 days (2025-12-10 to 2025-12-19), with the complete 6-agent LLM pipeline, ATS-optimized PDF generation, and real-time telemetry operational on first production deployment.
Six-agent LLM pipeline (CV_PARSER → CV_VALIDATOR → JOB_PARSER → JOB_ANALYZER → CV_TAILOR → COMPARATIVE_SCORER) processes a complete CV-tailoring job in a single automated workflow — reducing manual CV adaptation from 2–4 hours to under 5 minutes of user interaction.