tylers-blogger-blog

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...

Small businesses don't lose to bigger competitors because they lack ideas-they lose because they lack time, focus, and margin for waste. Cloud AI can help, but it often comes with trade-offs: monthly fees, data-sharing concerns, and tools that don't understand your business unless you keep re-explaining it. Local LLMs (large language models that run on your own computer or a small on-site server) flip that script. You get an AI assistant that's always available, tuned to your workflows, and doesn't require sending customer data to a third party. 1) Privacy and control (without the "enterprise" overhead) A local LLM is powerful for one simple reason: your data stays in your building. That matters if you handle customer addresses, invoices, contracts, patient notes, HR info, or anything you'd rather not paste into a browser chat. Practical example: a 6-person accounting firm wants AI help drafting client emails, summarizing tax documents, and generating chec...

Most "productivity advice" for developers is really just a shopping list: a new IDE plugin, a new task app, a new AI tool. But the real secret is protecting your focus like it's production infrastructure. Productivity isn't speed-it's fewer context switches. Every time you jump from coding to Slack to Jira to "quickly" checking a log, you pay a hidden tax: it can take 10-20 minutes to fully regain depth. Try this: schedule two 60-90 minute deep-work blocks per day. Before you start, write a one-sentence goal ("Make the failing test deterministic") and a tiny definition of done ("Test passes 20 runs locally"). Then turn off notifications, close browser tabs, and keep a scratchpad for interruptions so they don't hijack your brain. If you want a broader framework, this guide on developer productivity tactics is a solid reference. The surprising part: the best developers don't code nonstop-they design their day to make deep work ...

At 11:47 p.m. on a Tuesday, our analytics automation pinged Slack with a message that sounded like a superstition disguised as math: "Forecast alert: checkout conversion expected to drop 18-24% in the next 6 hours. Likely drivers: iOS Safari + new promo banner. Confidence: high." We weren't even running a late-night campaign. No one was touching production. And yet, the system was effectively saying: something bad is about to happen-and it's not random. It felt like a fortune teller, except it came with receipts. The alert that didn't just say "numbers changed" Most automated reporting is great at one thing: announcing the past. "Traffic down 12% yesterday." Helpful, but it's like hearing thunder after the lightning. What made this alert different was the combination of three checks we'd quietly wired together: 1) Anomaly detection (is this movement unusual for this hour/day?) 2) Short-horizon forecasting (based on the last 14 days +...

For years, "do analytics right" meant: pick a warehouse, build ETL pipelines, model everything, fight schema drift, and hope costs don't explode. That approach still works in some cases-but it's no longer the default. For many teams, the faster path is to embrace SaaS platforms that bring ingestion, modeling, governance, and activation together, without you owning the plumbing. Why the Traditional Warehouse Project Keeps Failing A warehouse initiative usually starts with good intentions: unify data, create a single source of truth, enable BI. Then reality hits. 1) Time-to-value is brutal. The first 60-120 days often go to standing up infrastructure, permissions, and pipelines-not answering business questions. 2) The "pipeline tax" never ends. APIs change, new fields appear, teams rename events, and suddenly your dashboards break. You're not "done" after launch-you've adopted a permanent maintenance job. 3) Cost and complexity creep. As ...

It started as a joke: "Let's see if the bots can survive a Friday night in our office." We'd been testing AI agents in small, polite ways-summarize a doc, draft an email, pull a quick report. Useful, sure, but not exactly game-changing. Then we looked at the calendar: a Monday exec update, a customer renewal call, a backlog of support tickets, and a spreadsheet that always seemed to multiply after 5 p.m. So we did the thing you're not supposed to do: we let a handful of AI agents run while we went home. The Setup: A Few Agents, Clear Boundaries, Real Work We weren't trying to build "Skynet for spreadsheets." We created three agents with narrow jobs, strict permissions, and obvious stop signs. 1) Inbox Triage Agent (read-only + draft-only): It scanned a shared inbox, labeled threads (billing, bug, onboarding), extracted key details (customer name, urgency, due date), and prepared draft replies using our saved tone guidelines. Nothing sent automatical...

A visualization strategy isn't "make prettier charts." It's a shared system for turning data into decisions-consistently, quickly, and with fewer meetings where everyone argues about what a metric means. We learned this the hard way. Our early dashboards were a patchwork: different definitions, different chart styles, and a lot of "wait, why doesn't this match Finance's number?" Eventually, we decided to stop shipping one-off dashboards and build a real visualization strategy from scratch . Below is the exact approach we used, including the artifacts we created and the decisions that saved us the most time. 1) Start with decisions, not charts Our first mistake was treating dashboard requests like design tickets: "Add a funnel chart," "Make a cohort view," "Show performance by region." That's backwards. The only reason to visualize data is to support a decision. So we ran a simple workshop (45-60 minutes) with each m...

Analytics automation used to mean a few predictable things: scheduled ETL jobs, templated dashboards, and alerts that fired when a metric crossed a threshold. Useful-but rigid. Large language models (LLMs) are changing the feel of analytics automation from "predefined workflows" to "interactive systems" that can translate intent into analysis. If you've ever wished you could just say, "Why did conversions drop last week?" and have your stack respond with the right data pulls, the right segmentation, and a reasonable explanation-LLMs are the first technology that can credibly do that at scale. But it's not magic. It's an algorithmic pipeline: context retrieval, structured query generation, evaluation, and guarded execution. In this post, we'll look inside that pipeline, what's now possible, and the practical engineering patterns that make LLM-driven analytics automation reliable. What "Analytics Automation" Means in the LLM E...