tylers-blogger-blog

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...