tylers-blogger-blog

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...

Local LLMs have crossed a threshold: they're no longer just a curiosity for hobbyists-they're a practical productivity tool for day-to-day software development. If you've ever hesitated to paste proprietary code into a hosted chatbot, or you've wanted an AI helper that works on a plane, in a secure environment, or simply with predictable costs, running an LLM locally is a compelling move. This playbook is tactical on purpose. You'll get concrete setups, repeatable workflows, prompt patterns, and "don't do this" traps. The goal isn't to replace your engineering judgment. It's to reduce the friction in the parts of the job that drain time: searching, spelunking, rewriting, reviewing, documenting, and debugging. Why Local LLMs Are a Developer Productivity Multiplier Cloud AI is convenient. Local AI is controllable. When you run an LLM on your machine (or on a private workstation/server inside your network), you gain three advantages that directly...

Small businesses don't need more dashboards-they need leverage they can trust. Offline LLMs (AI models that run locally on your laptop, mini PC, or in-store server) flip the usual "send everything to the cloud" script. Your customer notes, pricing spreadsheets, contracts, and SOPs can stay on-prem, which means fewer privacy headaches, fewer surprise policy changes, and far less risk of sensitive data becoming training fuel. The result is simple: faster decisions with tighter control. The practical wins are immediate. A clinic can draft patient follow-up templates without exporting PHI. A retail shop can summarize weekly POS exports, suggest reorder quantities, and generate staff shift notes-even if the internet is down. A small agency can keep client briefs local, ask the model to produce proposals in the client's tone, and automatically create a "what changed" diff when scope shifts. The manifesto is this: own your intelligence. Start small-run an offline m...