Product Leader · AI Builder
I use AI to do my job better. This site documents how — from personally building 100 public agent skills that automate my daily workflow, to writing about what actually works when you pair a product manager with an AI copilot.
I'm a product leader with 12+ years building software across B2B SaaS, fintech, and payments. I started my career as a lawyer in Singapore, pivoted into marketing at a 30-person startup, and worked my way up through product marketing and product management at companies from early-stage to enterprise.
Today I lead checkout and payments at Square — the flows that run every time a seller takes a payment on a Square terminal. I also build AI tools that make me better at my job. I've personally created 100 public AI agent skills that automate everything from morning triage to writing PRDs to analyzing data, and I built amp-mem, a persistent memory system for AI agents.
A control plane for product work, built around durable policy, resumable workstreams, and clear boundaries between shared behavior, local adapters, and runtime state.
A walkthrough of the operating model: repo-owned policy, CONTEXT.md continuity, lightweight derived state, and the public starter repo.
A sanitized, tool-agnostic version of the system with starter workstreams, repo-local skills, and a simpler state model you can adapt without importing the private machinery.
open sourceWhy the system got sturdier once policy, adapters, skill runtime, and workstream state each had a clear owner.
thoughtsNotes on building with AI — what works, what doesn't, and what I've learned
The shift from a clever stack of prompts to a system with clear ownership for repo policy, local adapters, runtime state, and archive-backed specialization.
control-planeWhy the best agent workflows prepare the exact action, corroborate inside a bounded lane, and pause before the side effect.
workflowThe AI PM OS became much more stable once the workflow covered the whole day, not just the startup moment.
ai-pm-osThe leap comes from orchestration, durable context, and startup routines around the model, not just the model itself.
ai-pm-osA technical walkthrough of the launcher, the Chief of Staff planner, the public state layer, and resumable cmux workstreams.
Why I turned a collection of AI skills into a product-management operating system with durable workstream context.
ai-pm-osHow a team went from markdown-first specs to an agent skill that writes PRDs with auto-referenced internal sources — in two weeks.
product-managementA practical guide to building 35+ AI agent skills that automate morning triage, email, project management, and more — no engineering degree required.
workflowHow the auto-pilot routing layer turned a long menu of skills into one assistant that can pick and chain the right tools automatically.
orchestrationThe single AGENTS.md file that defines identity, boundaries, startup behavior, routing defaults, and how the whole AI environment behaves.
A curated front door to 100 public skills on GitHub, from daily PM loops to archive-ready specialists.
Most AI agents forget everything between sessions. I built a memory system that doesn't. amp-mem is a ~500-line bash CLI backed by SQLite + FTS5 that gives AI agents persistent, searchable memory — no vector database, no cloud service, no embeddings. The LLM itself is the semantic engine.
Auto-records decisions, discoveries, and workflows from tools like Slack, Gmail, and Linear as you work
Compacts raw observations into structured notes — surfaces patterns across hundreds of sessions
Automatically primes new sessions with relevant past context — the agent remembers what matters
Single SQLite file. No vector DB, no API keys, no cloud. Works offline, backs up with git
$ amp-mem search "checkout flow" ── Results (3 of 47 observations) ── 📌 decision 2026-03-22 Checkout skip-screen removed after A/B test showed 12% lift in completion 📌 discovery 2026-03-18 Cash rounding applies AFTER tax calc, not before — confirmed in register-svc 📌 workflow 2026-03-14 New flow: PR → playpen deploy → smoke test via agent-browser → merge $ amp-mem stats Observations: 2,718 Sessions: 342 Compacted: 89 notes DB size: 4.2 MB $ amp-mem compact Distilling 51 new observations... ✓ 7 knowledge notes created
The systems that make it all work
The full product-work operating system: Chief of Staff planning, durable workstreams, resumable CMUX sessions, and a public starter repo you can adapt.
Explore the system →SQLite + FTS5 memory system that persists across sessions. Auto-captures decisions, discoveries, and workflows. No vector DB — the LLM is the semantic engine.
Learn more →The full guide to getting started — installing Amp, adding skills, configuring AGENTS.md, and setting up cross-session memory.
Setup guide →The real configuration file powering this setup — identity, session startup, memory, to-do system, tool preferences, and output style. Sanitized for public sharing.
View the full file →