Fig. 10 · Work With Me
Let's talk
AI implementation specialist. Los Angeles, California.
I like collaborative, meaningful work. The kind where the AI matters, but the people matter more.
What I like taking on
- AI workflow implementation
- Designing and integrating workflows for corporations and founders. You bring the process that keeps getting lost or redone; I turn it into something AI can actually carry, so the mechanical, boring work stops landing on people.
- Internal AI tools and automation
- The best AI is behind the scenes, silently doing what it is meant to do. My own daily automation sends me six detailed emails a day and maintains my vaults. I can build the same quiet infrastructure for you: research digests, intake flows, report generators, and glue between systems you already use.
- Founder scaffolding
- Working directly with founders, taking an idea and building the architecture and scaffolding for AI and the founder to iterate on. You keep the vision; I build the structure underneath it so the product can actually move.
- Agentic system architecture
- I built multi-agent coordination when it was still in its infancy: a single prompt fanning out into a family of agents in a coordinated dependency graph, each with its own role and tools, communicating through handoff notes instead of expensive cross-talk. I design structure so agent work becomes artifacts instead of fog.
- Evals, monitoring, and quality layers
- Checks that tell you whether the AI is doing the thing before a customer, teammate, or future version of you finds out the hard way.
- Claude Code / AI coding workflow hardening
- I spend a vast majority of my time talking to Claude Code. I can make the workflow calmer, more accountable, and less like a very expensive chaos machine, including the part nobody warns you about: agents behave very differently against years of legacy code than a project built from scratch.
- Memory, context, and knowledge systems
- Context is, in fact, everything, and it should not depend on whoever happens to remember it that week. I build the memory and knowledge layers that let people and agents hold their context: structured artifacts, richer recall, and transparency into exactly what the AI is referencing.
- Dify / low-code AI app implementation
- Sometimes the right answer is not a custom stack. It is a maintained workflow people can actually understand and change, hands on or hands off.
A good fit
- You care about the people using the system
- You want to build something meaningful, useful, or quietly life-improving
- You are okay starting with the messy version
- You want to learn
Not a fit
- Growth hacks
- Pure marketing funnels
- Work where the human consequences do not matter
Let's talk
For projects, collaborations, or implementation work, we can start with a short conversation.
Project review
A one-time async review of the idea, the architecture, and what to build next.
- Architecture risks and bottlenecks
- Agent and workflow design
- Claude Code, cowork setup, and repo structure
- Memory, retrieval, evals, and reliability
- Vibe-coded or AI-generated projects that need a technical sanity check
- What to build next, what to delete, and what to ignore
Elsewhere
I like written context first, then short calls when they help. The best work usually starts with the messy truth: what you are trying to build, what already exists, what keeps breaking, and what you cannot quite name yet.