AI/ML Engineering & Writing
Writing about how AI systems remember, fail, and scale.
Posts, talks, tools, and the work behind Agent Memory—now in Early Release from O’Reilly.
Agent Memory
Building Stateful AI Agents That Remember, Adapt, and Work Across Time
A practical engineering book about deciding what becomes memory, writing it safely, retrieving it well, and operating the system over time.
Current Focus
Building
Enterprise GenAI
Founding engineer at Workhelix, building the Nucleus platform that helps enterprises measure and grow AI ROI. Async LLM APIs, embedding pipelines, and agent deployment for Fortune 50 customers like Autodesk and Nasdaq.
Founding Engineer
Writing
Agent Memory for O’Reilly
Writing a practical guide to stateful AI agents that remember, adapt, and work across time. Chapters 1 and 2 are available now, with new chapters arriving throughout Early Release.
Early Release
Speaking
Agent Memory on Stage
Turning the book into practical talks about persistent state, context, and memory systems. Speaking throughout 2026 at conferences and meetups.
2026 engagements open
Selected work
View archiveAI Agents
Tools and Strategies for Agentic Development (Into the Hopper Podcast)
A conversation with Tim Hopper on spec-driven development, Beads for task tracking, and how AI agents have changed my planning habits.
Read the post →AI
Building DSA Dojo: A CLI-Driven Approach to Learning Data Structures and Algorithms
I'm building my own data structures and algorithms course—a CLI-driven, ladder-based system where mastery comes through doing, not wat…
Read the post →year in review
2025: My Year In Review
Reflections on a year of milestones—getting engaged in Florence, publishing with O'Reilly and the AEA, raising a Series A at Workhelix…
Read the post →Early Release book
Agent Memory
A practical guide to building stateful AI agents that remember, adapt, and work across time.
Explore the book →