tylers-blogger-blog

Most " data-driven culture " advice sounds like: buy a BI tool, build dashboards, announce a KPI, and voilà-decisions become smarter. Contrarian take: dashboards don't create a data-driven culture. They create a dashboard-driven culture. And that's how teams end up confidently steering toward the wrong destination... faster. A real data-driven culture is less about "more data" and more about habits: curiosity, skepticism, clear definitions, and a willingness to change your mind. It's people using evidence to reduce uncertainty-not people using numbers to win arguments. 1) Stop chasing "data-driven." Build "decision-driven." If your company has dashboards but still argues in circles, it's usually because the data isn't connected to decisions. Try this contrarian move: start with a decision inventory. Pick 5 recurring decisions that matter (weekly or monthly), and write them down: What decision is being made? (e.g., "Shou...

We didn't set out to make a "beautiful dashboard." We set out to stop a weekly meeting from turning into a polite argument about whose numbers were "right." You know the kind: 14 charts, 6 tabs, and a room full of smart people leaving with one takeaway-confusion. The turning point came when our VP squinted at a chart and asked, "So... is this good?" That question stung because it was fair. Our visualization wasn't a tool; it was a maze. That day we decided to rebuild it as a love letter to clarity: one message, readable in 10 seconds, defensible in 10 minutes. The moment we stopped "showing everything" The first change was emotional, not technical: we let go of completeness. We had been cramming in every metric to prove we'd done the work. Instead, we started with a single sentence: "Activation is down because mobile sign-up is failing." If the chart didn't help validate that sentence-or disprove it-it didn't belong...

Offline LLMs feel a bit like discovering a secret room in a familiar house. You already know chatbots live on the internet-massive servers, API keys, usage limits, and that tiny pause where you wonder who's watching. Then you run a model locally, unplug your Wi‑Fi, and it still answers. Same magic, different plumbing. This post is a time-travel tour through how we got here, what "offline" really means, and how people actually use offline LLMs day to day-warts, wins, and practical examples included. From Room-Sized Machines to Pocket-Sized Brains: The Timeline That Made Offline LLMs Possible Offline LLMs didn't appear overnight. They're the result of a few long-running storylines colliding at the right moment: cheaper compute, better algorithms, and a culture shift toward open models. 1950s-1980s: The era of scarcity. Early computing was expensive and centralized. If you wanted "intelligence," it lived where the machines lived. That shaped the default...

I used to assume "unified platform" automatically meant "build a data warehouse." But in my case, a warehouse would've added cost, latency, and a second copy of truth we'd constantly reconcile. We needed something different: a single place for product, ops, and reporting to agree on customers, subscriptions, and activity-without waiting for nightly ETL. Here's what I built instead, and how it held up in the real world. The Core Idea: Unify at the Interface, Not the Storage Rather than centralizing all data into a warehouse, I centralized access through a thin "unification layer." Concretely, that meant: A canonical customer model (the IDs and rules everyone agrees on) A set of APIs that serve "golden records" in real time A small operational store for just what we needed to compute quickly (not a full replica) Example: Customer data lived in three places-Stripe (billing), HubSpot (CRM), and Postgres (product). Instead of copying every...

AI agents aren't just "chatbots with better manners." They're more like digital coworkers: they can take a goal, break it into steps, use tools, check results, and keep going until the job is done (or until they hit a boundary you set). If that sounds like science fiction, it's because we're still using science-fiction words for something that is quickly becoming ordinary. In practice, an AI agent is code that can plan and act. It can read a ticket, open a spreadsheet, query a database, draft a reply, create a pull request, or schedule a meeting-often with minimal nudging. And like any colleague, it has strengths, blind spots, and a need for clear expectations. What an AI Agent Really Does All Day If a standard AI chat feels like asking a smart friend for advice, an AI agent feels like handing a capable intern a task plus access to a few systems. Here's a simplified "secret life" of an agent in the wild: 1) Intake: It receives an objective (...

It was 10:47 PM when the Slack message landed: "Revenue dashboard is flat. Is checkout down?" We'd just pushed a big pricing experiment and a home-page refresh earlier that evening. Everyone's nervous system was already running hot. A flatline in revenue could mean anything from a real outage to a tracking issue-but in the moment, it feels exactly the same: panic. The twist? Checkout was fine. Our tracking wasn't. The 30-Minute Mystery: Outage or Analytics? The first clue was weird consistency: pageviews were up, add-to-carts looked normal, but purchases went to zero across every channel at the exact same minute. Real outages don't usually respect clean boundaries like that. We opened the event stream and saw the problem immediately: the "purchase" event schema had changed in the new release. One field renamed, one nested object moved. Our warehouse load job started failing silently, so downstream dashboards had nothing to count. This is where the n...