I work spec-first — requirements → design → build → test → deploy — so what I ship survives contact with real users. That QA discipline is the part most AI work skips, and it's my edge. Everything below is running, tested, and readable.
🌐 artjeck.com · ✉️ hello@artjeck.com · 💼 LinkedIn
| Project | What it is |
|---|---|
| ifta-agent | Production IFTA fuel-tax filing pipeline for interstate carriers. Deterministic math plus an LLM review agent over 18 grounded tools. 429 tests, backtested to reproduce a real state filing to the penny, ~$0.10/filing. Live, serving a paying carrier. |
| sales-agent-showcase | Clean-room showcase of a multi-marketplace commerce agent running a live resale business. Irreversible actions are human-gated structurally — the reprice call asserts its own execution grant, so there's no code path to the write without an approved approval. 30 tests, CI, runnable on synthetic data. |
| second-brain | Local-first RAG assistant: answers only from your own notes, docs and code, every claim cited, graded by an eval harness. Exposed over MCP. |
| lab-control-mcp | MCP server operating a self-hosted AI lab, with a safety-gated remote shell — allowlist, no shell metacharacters, hard timeouts. |
| email-agent | Self-hosted inbox triage on local Ollama — $0 LLM cost. IMAP → classify → daily Telegram digest, scheduled via launchd, SQLite dedupe so reruns never double-notify. |
| liora-studio | Full-stack e-commerce storefront and admin CMS with TOTP 2FA. Next.js App Router + Prisma. |
Also here: bol-extractor (Bill of Lading PDF → structured data) and dm-express-site (production client site; boundary-value analysis caught a real off-by-one at the 1 MB rollover).
AI — agents · RAG · MCP servers · evals · agent guardrails · prompt engineering · Anthropic SDK · LiteLLM · Ollama · Qdrant Build — Python · TypeScript · FastAPI · Next.js · React · Postgres / SQLite · Alembic Quality — spec-driven development · regression & edge-case testing · equivalence partitioning · boundary value analysis · decision tables
An always-on lab (Mac mini + Alienware over Tailscale) running free local models behind one gateway, with two MCP servers I built and a vector store for retrieval. Free-by-default inference — I only pay for a frontier model when the task is worth it.
I've spent years building and supporting compliance systems for interstate carriers, so the problems I solve in that space — IFTA filing, Bill of Lading parsing, fuel-tax reconciliation — are ones I understand end to end. Most engineers never see this domain; most people in it can't build software for it. That overlap is where my best work comes from.
Open to AI engineering, AI automation, and QA roles — remote, Sacramento, or the Bay Area.