tylers-blogger-blog

It started like any other "quick deploy" that absolutely shouldn't have been quick. We'd shipped a small feature behind a flag, everyone was feeling smug, and I was already planning my post-merge snack. Then the alerts hit: latency climbing, error rate spiking, and a weird pattern-only one region was melting down, and only for a subset of users who'd never touched the new feature. And that's when our AI agent-internally nicknamed "Patch"-walked into the room like it owned the place. The incident: 47 minutes of chaos (and one calm agent) Our agent wasn't a "magic button." We'd wired it into three things: read-only observability (logs/metrics/traces), our runbook repo, and a tightly scoped toolbelt (query dashboards, open PRs, suggest rollbacks, and draft incident updates). No direct production writes. No cowboy stuff. At 12:18 AM, it posted in Slack: "Spike correlates with deploy `api-7f3c` but only in `eu-west`." "Mos...

We didn't call it a "data apocalypse" at the time. We called it "that weird spike," "the numbers are off again," and-my personal favorite-"let's just refresh and see if it fixes itself." Then came the day every metric disagreed with every other metric. Revenue looked down 22%. Signups looked up 40%. Paid CAC doubled. And the CEO asked the sentence that makes analytics teams age ten years instantly: "Which one is right?" The problem wasn't one bug. It was a chain reaction: a tracking change shipped late Friday, a backfill job that silently timed out, a dashboard built on top of a view built on top of a sheet someone "temporarily" edited. We were one Slack message away from deleting something important just to stop the bleeding. The Apocalypse: When Manual Analytics Collapses Here's what the failure looked like in practice: Multiple sources, one metric name: "Leads" meant "form submits" in on...

Building a community-driven product is messy-in a good way. People post at odd hours, content spikes unexpectedly, and "success" can mean anything from more thoughtful replies to fewer mod escalations. When we started, the common advice was: "Set up a data warehouse early." We didn't. Instead, we asked a simpler question: what decisions do we actually need to make in the next 90 days, and what's the minimum data setup that supports those decisions? Why we skipped the warehouse (on purpose) We weren't anti-analytics. We were anti-premature analytics infrastructure. At our stage, a warehouse would have added (1) extra pipelines to babysit, (2) new failure modes, and (3) a lot of "maybe someday" data modeling. We had two engineers and a growing community-our bottleneck was shipping improvements and responding to users, not running cross-domain BI. So we defined a "decision list," not a "dashboard list." Examples: Are new member...

It happened on a Tuesday night, the kind where your to-do list feels heavier than your eyelids. I was "updating the project plan" in a spreadsheet-color-coding cells like it was progress-when a teammate messaged: "Which version is the real one?" I had three copies, two "finals," and one that only I understood. That's when it hit me: my project management wasn't organized. It was museum-quality. The next morning, I ran a simple audit. Where do we lose time? For us it was status meetings, manual handoffs, and decisions buried in chat threads. We replaced the weekly 60-minute status call with a 10-minute async check-in: each person posted blockers, next steps, and an ETA. I also stopped tracking "percent complete" and started tracking outcomes: a working demo, a shipped feature flag, a resolved incident. If your work touches data pipelines, it's worth revisiting how modern teams handle integrations too-this primer on stuck-in-the-past da...

Everyone loves the idea of "data-driven decisions" until you're staring at a dashboard that looks like a fireworks factory went off. Too many metrics, too many filters, too many chart types, and (somehow) not a single clear answer. We ran into that exact problem on a product analytics project: dozens of event streams, multiple customer segments, and stakeholders who all wanted "one view" but meant wildly different things by it. The hard part wasn't drawing charts-it was deciding what deserved to be shown, at what level of detail, and with which visual encodings so the picture didn't lie. If you've been following how modern systems are becoming more "algorithm-first" in their UX decisions, it's the same pattern you'll see in pieces like inside the algorithm trends : the best experiences aren't just prettier-they're constrained, guided, and explained. Below is the practical, repeatable approach we used to tame visualization ch...

I didn't "discover" AI agents in a lightning-bolt moment. It was more embarrassing than that. It happened on a Tuesday afternoon when I was wrestling with a refactor I'd been postponing for weeks: a tangle of conditional logic, half-duplicated helper functions, and comments like "TODO: make this not terrible." I asked an AI agent to draft a cleaner version expecting something... fine. Instead, it handed me code that was not only cleaner, but also easier to test, easier to read, and annoyingly consistent. The moment it out-coded me The agent's first win wasn't clever algorithms-it was basic engineering hygiene. I fed it a simplified prompt: "Refactor this function to be easier to test; keep behavior identical; add unit tests." The before-code had a long function doing validation + parsing + persistence + logging. The agent responded with: Small pure functions (validate, transform, persist) Dependency injection for the DB and logger Tests tha...