Field Notes
I write about architecture, working with AI, and the decisions that keep software useful and practical to maintain. These are explanations, field notes, and lessons from building data systems and applications.
2026-11-02
A dinner party proves you can cook. Running a restaurant adds records, staff, opening hours and someone who answers when the stove breaks. Turning an agent-built prototype into a shared application brings the same change: people begin depending on what it remembers and how it runs.
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2026-10-30
A data warehouse assembles a shared map of the business. Agents can check the source when a decision needs fresher detail. The map, the source check and the continuous feed each have a job; how the answer gets used should set the pace.
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2026-10-29
Build one chair before arranging a factory to make it. A laptop, DuckDB and an agent give data questions a workbench: inspect the material, test a model and let someone try the answer. Establish that the answer is correct and useful before arranging dependable delivery.
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2026-10-20
Building software used to mean driving every mile yourself in a car that needed constant repairs. Agents are a car that drives itself, with a pit crew on call. Each old constraint has turned into a new one: direction, understanding, verification, measured cost and ownership. Get those right, and you can reach far more destinations than before.
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2026-10-16
An agent works more independently when attempts are cheap, the task stays inside the project's existing patterns, and every attempt shows what to fix next. A data integration explored in local DuckDB and an iPhone app whose sharing feature outran its tests show what that setup involves.
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2026-10-16
An agent's "done" and a passing test suite show that the code exists. Three iPhone apps show why I check which path actually ran, on the real device, before I trust a feature, and why the answer is sometimes plain code.
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2026-07-31
Operational software used to need a crew a data person didn't have, so workflows stayed stitched together by hand. A telehealth platform built by two co-founders and their agents shows the job that remains: producing, deciding what the workflow should do, watching it run, and deciding what ships.
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2026-06-07
A dashboard is a wine list: accurate, organized and silent about what any of it means. Agents can now read the tickets, code, logs and conversations behind the numbers and help build the report as a web experience that explains what changed and what to do about it.
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2026-02-01
Git rarely felt worth the effort in solo, UI-driven data work. Agentic coding changed both sides of that calculation: agents make more changes than one person can track from memory, while also making version control almost effortless to operate. Git becomes the team’s memory, review surface, work ledger, way back when a change breaks something, and record of how the system evolved.
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2026-01-11
Buying software used to give people a head start they couldn't afford to build themselves. My WordPress site shows how that trade looks once agents make building practical, and when buying is still the better deal.
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2025-11-30
As organizations grow, they transform from solo musicians into full orchestras — and that's when the real challenge begins. Data doesn't stop being collected; it stops being aligned. Systems drift, meanings diverge, and interpretation fragments. Data engineering exists to keep the orchestra playing the same piece, in the same key, at the same tempo. In this post, I explore why coordination becomes essential at scale, and why a coordinated data layer is now the foundation custom software gets built on — including an application built on top of a request board to get decisions made faster.
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2025-09-23
Boxed toy-brick kits are ready to ship. Bins make counting easier. Give an agent repeated inventory questions, and the choice of workplace determines how much work it creates and whether that work competes with customer orders. Before handing one the clipboard, decide where its questions belong.
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2025-08-31
Lunchboxes, buffets, and pantries explain how operational databases, warehouses, lakes, and lakehouses organize data—and why an arrangement that suits one job can create extra work for another.
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