Production agents,
shipped solo & in flight.
Founder-built and founder-shipped. Five pieces of work that show how I think about agentic systems — from Hale, the messaging-first product I run today, down to the building blocks that hold agents up.
Five projects.
One throughline: ship it, then prove it.
Village Hale — a number your family texts
My current focus. A messaging-first family chief of staff — parents text one number and Hale takes over the invisible admin: it watches city registration windows across 15 GTA municipalities (the 7 a.m. openings that fill before breakfast), plans the week, and graduates from suggesting to executing as it earns trust, action by action — an independent reviewer agent gates every act, and everything leaves an immutable receipt. No app required; the dashboard is just the receipts room. Privacy is the product — PIPEDA and Quebec Law 25 compliant by default, teen data redacted by construction. Live on a real number since August 2026. Empty repo to production, solo.
Empty repo → prod
solo
Registration radar
15 cities
Compliance
PIPEDA · Law 25
Agent autonomy
earned, gated
- Agentic systems
- Privacy-first design
- Messaging-first consumer AI
TripFix — autonomous flight-claim co-pilot
An AI co-pilot for flight-delay refund claims. Reads boarding passes and airline emails, drafts the rebuttal letter, escalates only when uncertain. Built as a small team of specialised agents — not one monolithic prompt — so each piece is testable, swappable, and auditable on its own.
LLMs orchestrated
14+
Eval dimensions
5
Citation grounding
deterministic
Headcount in AI
1
- Multi-agent systems
- Eval harnesses
- Vision reasoning
Cursor Cloud Agent v1 — conversation timeline rebuild
A flight recorder for cloud agents. Stitches prompt, thinking, and tool calls into a single replayable timeline — so any agent run is auditable in under a minute. Design call: optimise for the operator first, not the model.
Stream types unified
3
Replay fidelity
100%
PRs merged solo
9
- Agent observability
- Tool-use traces
Agentic preparation checklist
An agent that reads a case and figures out what’s missing. Instead of one giant ‘knows everything’ prompt, it loads short markdown skills on demand for the stage it’s in. Cheaper inference, sharper answers, knowledge anyone on the team can edit in a text file.
Skills authored
12
Tools wired
7
Snapshot evals
passing
- Agent design
- Skills-as-prompts
LLM-as-judge evaluation framework
The quality bar for every AI change we ship. Five automatic judges grade each output on truth, sourcing, tone, completeness, and safety. New prompt scores worse than the live one — the deploy is blocked. The only reason daily prompt iteration is safe at production scale.
Evaluators
5
Daily judged samples
hundreds
Regressions caught pre-deploy
many
- Evals
- Production safety