I am an AI Tradesman — not a researcher, not a consultant, but a craftsman who builds the production infrastructure that AI agents operate through. My work sits at the intersection of systems programming (Rust) and agentic AI: MCP servers that give agents real-world capabilities, runtimes that keep them reliable, and pipelines that turn raw capability into finished output.

Everything I build is shippable — published to npm/PyPI, compiled to static binaries, deployable via systemd or Docker.

Graph-native automation substrate for AI agents. Rust. 39 MCP tools.

Traditional automation tools (shell scripts, CI pipelines, no-code) are designed for humans, not AI agents. Agents can't "see" dependency graphs, can't recover gracefully from partial failures, can't compose capabilities dynamically.

automaton replaces the script with a graph-based module — every automation unit is a self-contained node with typed inputs/outputs, a content-addressed build cache, and a property graph of capabilities. The engine materializes branching, loops, and parallelism into a DAG, executes with level-based parallel dispatch via Tokio, and exposes the entire lifecycle through MCP.

• 8 Rust crates (core, SDK with proc macro, CLI, engine, registry, graph, MCP, runtime)
• Dual-backend SQLite/PostgreSQL with unified query layer
• Static musl binary (~14MB), zero runtime dependencies
• Production scheduler with cron expressions

Dual-interface social media orchestration engine (REST + MCP). Rust. Axum. SQLx.

Most social media tools serve one audience: humans via GUI or developers via API. social-forge (née postiz-rust) implements a Shared AppState Architecture that serves both — a SvelteKit frontend via Axum REST and AI agents via MCP — through the same business logic layer with zero code duplication.

• Trait-based provider registry — add new social networks without touching core engine
• In-process Tokio scheduler with exponential-backoff retry (solves "ghost post" problem)
• SSE event stream for real-time publish/fail notifications
• JWT + Argon2 auth with multi-account cookie profile management
• Static musl binary, ~15MB Docker image

Streaming-first, fault-tolerant agent orchestration loop. Rust. Ratatui TUI.

The hardest problem in agentic systems isn't making the LLM smart — it's keeping the loop running when the LLM produces malformed output. Standard parsers crash on unclosed XML tags or broken JSON, taking down the entire agent.

hermes-rs implements a custom state-machine parser that detects tool calls incrementally. It can recover intent from truncated output, execute tools before the response finishes streaming, and maintain loop integrity even with unstable network connections. The "validated autonomous" mode enforces a strict Plan → Implement → Validate → Push cycle — the agent cannot push unless cargo test passes.

Intelligence Gathering System — Rust flagship. ~7MB static binary, ~5MB RSS.

223+ curated sources across 45 countries, 14 intelligence pools, local NLP enrichment — all in a ~7MB stripped binary with ~5MB idle RSS. TOON (Token-Oriented Object Notation) reduces token consumption by 40–60% for AI agent consumption.

Started as a TypeScript proof-of-concept published to npm — the Rust port is the real flagship: dramatically lower memory/runtime, deployable anywhere including resource-constrained infrastructure. The evolution from Node → Rust tells the engineering story: same intelligence pipeline, radically smaller footprint.

• 9 custom parsers (RSS, Atom, HTML, OFAC, WHO, Semantic Scholar, PDF, Google News proxy)
• Pool-based source organization (Global Breaking, Geopolitics, Tech/Cyber, India National, etc.)
• Hybrid pipeline: news feeds + academic archives (arXiv, Semantic Scholar) + Reddit
• TOON format for token-efficient AI agent output (~40-60% reduction)

AI-directed video editing pipeline — raw footage to polished reel. 43 MCP tools. Rust + Python + TS.

Most "AI video" tools generate from text. This takes real raw footage and edits it professionally through a structured pipeline: transcription → creative brief → multi-track timeline → rendered 9:16 reel with captions, b-roll, music ducking, and SFX. The AI agent directs like a human editor — choosing b-roll concepts, music mood, SFX placement — and the engine executes.

• 6-track Edit Decision List (EDL v2) — dialogue, voiceover, captions, b-roll, music, SFX
• Apex transcription (Hinglish-optimized Whisper) with word-level timestamps
• TTS voiceover engine with voice profile registry and duration estimation
• 261 indexed SFX + 16 music tracks with mood/role-based search
• FFmpeg rendering with automatic audio ducking
• Post-render verification (audio levels, caption sync, render fidelity)

Multi-agent C-suite — 227 tests, LanceDB memory, systemd deployment.

Coordinates specialized agents (CEO, COO, CFO, CRO, CMO) that run periodic operational checks, communicate with escalation/priorities, track work in Kanban boards, and persist context across sessions. The operant-mcp component exposes 35 tools for orchestration, 25+ database tables with Drizzle + Postgres.

Experimental. Needs funding to continue development. The multi-agent coordination patterns here are genuinely novel — role-based autonomy with shared memory, not just prompt-chaining.

I've also built several infrastructure-level MCP servers for internal use — including reverse-engineered integrations for 8 AI providers (Kimi, Qwen, Gemini, GLM, Perplexity, ChatGPT, Claude, DeepSeek) with zero API keys, and multi-model Perplexity access. These are private/prototype work.

The LifeOS ecosystem is my most ambitious product concept: a personal operating system that coordinates health, executive function, finances, psychology, relationships, strategy, and time through a unified event-driven core.

The vision: Package LifeOS with IGS (intelligence), social-forge (social distribution), and operant (autonomous staffing) into a SaaS platform for personal sovereignty — the infrastructure layer for an AI-managed life. Each component works independently but compounds when integrated.

These projects are private — source isn't public. I'm building them as a product, not a portfolio piece. Happy to walk through the architecture and vision in conversation.

MCP-AND-CLIS      15 — AI agent infrastructure, real-world tool access
EXPERIMENTAL       7 — Cosmic simulation, OSINT, decentralized social
LIFEOS             5 — Personal sovereignty operating system (private)
WEBSITES           5 — Production full-stack applications
HERMES             4 — Agent orchestration runtimes
CONTENT-CREATION   2 — Video editing pipeline, cinematography
DEVELOPER-TOOLS    2 — AI coding runtimes
SOCIAL             2 — Social-forge, decentralized social
N8N-WORKFLOWS      1 — Automation configurations