Three ways to work together. Each engagement maps to one phase of the flywheel — start where you need the most leverage, then compound from there.
Start with what exists. One system, one architecture review, one clear diagnosis — before anyone talks about strategy or roadmap.
Evidence-based direction that actually lands. Architecture reviews, infrastructure decisions, and whatever's most useful that week — not a scheduled status update.
Working system, proven approach — now operationalize. Design and ship a multi-agent system for whatever problem is costing you the most right now.
Each phase builds on the last. Strategy follows proof. Systems scale from the ground up, not from theory. The flywheel keeps turning.
Problem: Agent workflows break in predictable ways — sessions that won't reconnect, headless browsers blocked on the first request, CLIs needing hours of setup on every new machine, knowledge that evaporates when a tab closes. These aren't edge cases. They're patterns.
Built: A provider-agnostic collection that catches ambiguity before code ships — SDLC pipeline skills that gate each phase, a browser automation stack tested daily against real WAFs, CLI tools with zero dependencies that work the second they land on any machine, and a knowledge ingestion system that turns a saved link into a team-wide signal about what direction you are thinking.
Outcome: Running in daily production across a six-agent squad with 20+ skills. Sessions survive connection drops, browsers reach pages that block standard headless Chrome, and every tool works with zero setup on any machine.
Problem: Personal knowledge management is fractured across notes, voice memos, bookmarks, and chat messages with no AI layer to connect them. Cross-device sync is manual. Context evaporates between sessions. The brain dump lands in a black hole.
Built: A React Native mobile app backed by Firebase Cloud Functions for server-side logic, Firestore for real-time sync across devices, and an OpenRouter AI proxy that categorises and connects thoughts as they come in. The capture flow accepts text, voice, and links — the AI layer surfaces related ideas automatically. Subscriptions run through StoreKit in-app purchases.
Outcome: In private beta with real user feedback driving iteration. Subscription model validated through StoreKit integration. The capture-to-organization loop is replacing the fragmented note-taking workflow it was designed to fix.
Problem: Plant owners juggle species identification, care schedules, and plant health tracking across mental notes, sticky tags, and forgotten apps. There is no central directory that knows your plants and tells you what they need.
Built: A React Native mobile app with AI-powered plant identification from photos — snap a leaf, get the species. Personalized care suggestions based on the plant type and your environment. Location tagging for multi-plant households. A searchable plant database that grows with every identification.
Outcome: Live at gardendex.app with plant recognition accuracy improving through user submissions. Personalized care reminders keeping plants alive longer. A growing user base that treats it as the default plant directory.
Problem: Six specialized agents working independently creates chaos without structure — duplicated work, missed handoffs, drift from priorities. Running a multi-agent team without orchestration means constantly cleaning up misalignment.
Built: A kanban-driven orchestration system where seven specialized agent profiles — PM, architect, QA, infrastructure, content, comms, and chief of staff — each have their own domain. Tasks route automatically to the right specialist based on type and workload. Long-running jobs send heartbeats so the system knows they are alive; if one drops, it retries or escalates. Agents hand off structured context to each other so nothing gets lost between steps.
Outcome: Active pipeline delivering code changes, research, documentation, and operational maintenance on a daily cadence. Agents hand off work to each other without human intervention. The system recovers from failures autonomously. What required constant manual coordination now runs itself.
The story of how a session failure, a blocked browser, and one too many setup sessions turned into a reusable toolkit.
One agent trying to be everything hits a ceiling that looks exactly like burnout. The 7-week journey from generalist amnesia to a crew of 7 that argues in ticket comments — and why the arguing is the point.
Headline API rates aren't the real cost. Cache hit rate, context window, and architecture implications matter more. Here's what to actually look at when evaluating inference at scale.
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