Markets
Markets force clarity around uncertainty, timing, and which information deserves a review surface.
About
I build by turning vague problems into working systems.
I am interested in the parts of AI that become useful after the demo: structure, memory, queues, review loops, tools, and the judgment needed to decide what should be automated at all.
What kinds of problems I care about
I care about environments where important context is scattered across feeds, notes, chats, dashboards, folders, and memory. The interesting problem is not collecting more of it. The interesting problem is making it usable when a decision or task comes back around.
The attraction is not the technology by itself. It is the chance to build systems that change how someone notices, reviews, decides, and executes.
How the interests connect
AI workflows
Models are useful when wrapped in sources, tools, queues, workers, and repeatable loops instead of treated as standalone answers.
Knowledge organization
Structured notes, source pages, topic bibles, and proposal flows turn research into context that compounds.
Operations and tools
Worker harnesses, mission-control views, and smaller tools pressure-test the same system instincts in other domains.
Build philosophy
I am comfortable rebuilding when the first version teaches the real boundary. A narrow system is useful if it proves the loop. A broader system is only worth building after the repeated work is visible enough to deserve infrastructure.
That pattern shows up across most of the work here: start with a concrete problem, make the material durable, keep the operating loop visible, and let expansion happen after the system earns it.
Adjacent work
Book
The Wealth Waterfall
Separate from the AI systems work on this site, I also wrote a book on building durable financial foundations. It sits next to the same broader interest: structure, compounding, and better decisions over time.