Selected work,
in the open.

An 18-skill agentic operating system for the modern job search. Not a chatbot — an actual operating system. It manages the full lifecycle: finding roles, qualifying them, applying, prepping for interviews, debriefing after calls, and negotiating offers. Built on Claude Sonnet 4.6 + Next.js 15 + Supabase, with persistent state across sessions and a self-improving playbook that learns from every correction.

Nine quality gates sit between the LLM and the user — preventing the failure modes that make raw GPT-on-resume tools dangerous: resume fabrication, comp-math errors, persona mismatch in prep docs, fabricated experience claims. The architectural rule: LLM downstream of parsers + provenance + coverage receipts, never raw string-to-output.

Co-founded 50/50 with Zach Berenzweig as a lifestyle business — not optimizing for raise or exit. Binary 90-day gate: 10 paid customers at list price by July 31, 2026 determines kill-or-scale. Launched April 21, 2026 with a 9/9 launch checklist. PostHog telemetry live April 23. Voyage AI embeddings on paid tier. Sentry-bridged structured error logs. Full GTM plan, partner alignment memo, pricing strategy, and channel ranking are part of the showcase above (slide 07).

A two-layer, version-controlled brain: every AI surface I use — Claude Code, Claude.ai projects, Cursor, local Ollama models, the Atlas agents — bootstraps from a single git repo. Lessons captured during work graduate to rules. Drift is reviewed every Saturday at 8am CT by an automated coaching script. The brain compounds; nothing slips out the back.

Architecture: contexts (per-project knowledge), workflows (11 named procedures), templates (PR descriptions, decision docs, incident reports), vocabulary (phrases, glossary, anti-patterns), strategy (10-year plan, three planks, weekly commitments), lessons (capture → promote → archive). A rebuild.sh script compiles 8+ adapters and distributes them to downstream tools. Gitleaks pre-commit. iPhone-editable via Working Copy.

Phases 1–4 complete (Foundation, Protocols, Automation, Integration). 21 lessons captured · 14 in the last week. Includes prevention layers built in real time: never-silent-delegation rule, MCP preflight, atlas-pickup-watch — each derived from a specific incident, each preventing recurrence.

An autonomous dev agent that runs on owned M3 Ultra hardware. Renamed from atlas-direct (May 2026); cloud-triggered worker model retired in favor of a local manager + on-demand worker split with hot-swappable inference backends. Primary inference: Rapid-MLX serving Gemma 4 26B-A4B MoE 4-bit at ~70 tok/s. Fallback: Ollama with llama3.3:70b. Cloud-API path opt-in, OFF by default.

Phase 47 replaced single-worker + 10-second inbox polling with four KeepAlive lane workers (pcm · sfdc · atlas-d · research) communicating with the manager over an in-process event bus. Dispatch latency dropped from ~5s avg to ~50ms — 100× improvement. Per-lane crash isolation, real-time mission-control UI on 127.0.0.1:8766, heartbeat-based liveness.

Intake: Linear webhook → lane inbox → watchdog file watcher → worker. Approval gates via Linear comments for external writes (SF DML, HubSpot mutations, mail send); internal-only changes ship autonomously. Operating principles locked 2026-05-16: honest, fast, methodical dispatch, maximum autonomy. Marginal compute cost: ~$0.

A local autonomous coding stack running on the M3 Ultra under my desk. FastAPI service. Aider as the primary coding interface; headless Claude Code as fallback / review pass. Redis queue. Per-task model selection with batched loading to minimize swap thrash on 96 GB unified memory.

Strict compliant model stack: Mistral · Meta · Microsoft only — US/EU origin enforced at the router. No Qwen. No DeepSeek. Native Ollama (never Docker — Docker'd Ollama loses Metal GPU access). Cursor connects via a private tunnel.

16 Signal OS task specs sit in the queue as structured markdown with goals, acceptance criteria, expected output. Atlas3 consumes them and generates the code. 66 files, ~1,491 lines of Python at 0.1.1.

A production-grade multi-agent platform running on local hardware. Three core agents — Alpha (Salesforce / CRM / Apex), Beta (code, scraping, build tasks), Gamma (R&D experiments) — plus a dedicated Delta coding agent. BullMQ on Redis for job orchestration. Postgres + pgvector for semantic memory. AI router with complexity-based dispatch (0–100 score; threshold 70 routes to Claude API; below stays local).

Single REST + WebSocket + mDNS gateway. ComfyUI for local image generation, prompted by Beta on creative tasks. Operator shell commands wrap the gateway for invocation, review, and curation. The whole thing runs while I sleep.

Twin sandbox copy in bot-army. Both have Alpha/Beta/Gamma, gateway, Redis + Postgres, security policy, runbook, skill audit framework. The lane lock is non-negotiable: Alpha owns Salesforce, Beta owns code/scrape, Gamma owns experiments. Cross-assignment is the failure mode the architecture exists to prevent.

A reusable, config-driven audit primitive that produces, in five minutes, the deliverable Big-Four firms charge $150K–$500K and three to six months to assemble: maturity scorecard, severity-ranked findings, remediation playbook, and AI augmentation roadmap. Same code base for every engagement; one YAML config changes per customer.

Built and immediately run against a live B2B SaaS production environment + partial sandbox + HubSpot portal. 104 findings (22 P1, 18 P2, 63 P3) surfaced — including a previously-unknown twin-label collision the lesson-bound detector caught without any vendor-specific code. The detector generalized beyond the original incident; that's the architectural win.

The audit is the lead. The AI augmentation roadmap — six categories (detect / repair / maintain / augment / audit / decide), three horizons — is the retainer pitch. Defaults to local-first inference for data-privacy reasons; cloud allowlisted to providers with signed DPAs.