Dispatches from the work in progress
Every automated check answers a question about code that exists and paths that run. The defects that survive a mature pipeline live outside both — and they announce themselves the same way a healthy system does.
How clear engineering documentation prevented costly misinterpretation while taking Everbowl from design files to physical hardware. Introduction A CAD model shows what a part looks like. A manufacturing drawing has to communicate what the part actually needs to be. That difference mattered more and more as we moved Everbowl from
What a cross-account DynamoDB migration taught us about the gap between "validated" and "true." A dry-run validates your read path. Only the cutover validates your write path. Everything expensive lives in the gap between the two. We learned this while moving a few hundred DynamoDB tables
"Why is Max scratching more this week?" Somewhere in Max's data, the makings of an answer exist: his bowl logged a food switch on July 5, the new kibble's ingredient list includes chicken meal, his tracker has flagged more nighttime restlessness since July 8,
How working through a simple checklist changed the way I look at web design, development, and testing. While working on a Design-Testing Checklist, I started noticing something I hadn't really thought about before. A web page doesn't become “ready” just because the design is finished. There
The workload, before the database. Every smart bowl and collar in our fleet is a small, relentless firehose. A single device emits roughly twenty distinct event types: camera1 and camera2 frames, thermal and segmented images, audio clips, ocular temperature readings, plus a continuous drizzle of humidity, barometer, imu, proximity, weight,
Every team that generates content with an LLM writes the same defensive code twice. First a prompt that ends in "respond with JSON in this exact shape." Then, a week later, the parser that copes with what actually comes back: a stray code fence, a preamble sentence, a
How asking "What are we actually testing?" made Everbowl prototypes more focused, faster, and more useful. Introduction A prototype is easy to build. A useful one is a lot harder. Working on Everbowl taught us that a prototype's value has nothing to do with how close
Three different sources have something to say about Max's appetite this month. His smart bowl has logged five smaller-than-usual breakfasts. A vet note mentions "monitor appetite." A dog-care book says goldens often eat less in summer heat. Do we know Max's appetite has dropped?
What six production incidents and one near-miss taught us about proving not only what a notification did, but why it didn't. A failed notification leaves evidence: an error, a retry, a DLQ entry, a provider response. A notification that never entered the system leaves nothing. That was the
Every over-the-air update hides a bootstrapping problem: the software that applies updates is itself software, and can be broken by an update. When a bad image lands on a sensor module inside a pet feeding station — a device with no screen, no buttons, no port anyone will plug into — nobody
Every app with user photos eventually adopts the same two defaults, each picked independently, each obviously correct on its own: sign every media URL fresh per request, so a link can't be shared, guessed, or replayed after it should have expired — and cache fetched images to disk, so
A controlled-impedance PCB is easy to get right once. The difficult part is getting it right again six months later, when the board has a different connector, a new processor, a changed power section, a second PCB supplier, and someone opens the original layout and says, “We can keep the
While working on the SEO setup of a website, I came across a small task related to connecting the website with its social media presence. The website already had social media profiles, but I needed to make sure that the relevant profiles were properly connected as part of the website&
How architecture rules stop being preferences. Someone on the team opens a pull request. One line, added to a service class: @cache def resolve_plan(code: str) -> Plan: ... It gets approved, and of course it does. It's memoization on a lookup that looks pure, it takes
