tylers-blogger-blog

We didn't adopt local LLMs because it was trendy. We adopted them because we were tired of the same loop: too many customer questions, too many proposals, too many "small" tasks that quietly ate the day. A year ago, our strategy meetings sounded like this: "We should post more." "We should follow up faster." "We should tighten pricing." And then we'd go right back to being buried in inboxes and admin work. Local LLMs changed that-not by magically doing everything, but by turning our best repeatable work into systems we could actually run. Why we chose local LLMs (and what 'local' really meant for us) A local LLM is a language model that runs on your own hardware (a workstation, mini-PC, or server) instead of sending prompts and data to a cloud provider. For us, "local" meant three practical things: 1) We could use real business data safely : sales notes, customer emails, internal playbooks, and pricing logic-without cons...

We didn't plan for our analytics automation to become the main character. It started as a "nice-to-have" script that pulled numbers together so we could stop copy-pasting CSVs at 8 a.m. Then came the quarterly review-leaders in the room, dashboards on the big screen, and a single question that turned our quiet helper into the star of the show: "Are we sure these numbers are right?" That's the moment automation either earns trust... or gets politely benched. When the spotlight hit: what we changed in one afternoon The dashboard looked great. Too great. Which is exactly why everyone suddenly cared about the plumbing. Our automation was doing three jobs: ingesting events, transforming them into a reporting table, and refreshing charts every hour. But under pressure, we realized we had a few trust gaps: Definitions lived in people's heads. "Active user" meant one thing to product, another to marketing. No obvious freshness cues. Charts updated, b...

  If you've ever used a single AI assistant for a complicated project-launching a feature, writing a sales sequence, doing market research-you've probably felt the pain: it can be brilliant for 30 seconds, then drift, forget constraints, or confidently hand you something half-right. AI agent teams are the antidote. Instead of one generalist, you orchestrate a small squad of specialists with clear roles, handoffs, and checks. You stop "chatting" and start "running plays." This post is a tactical, practical guide to building AI agent teams that consistently ship usable work-without turning your process into a science project. What "AI agent teams" actually means (and what it doesn't) An AI agent team is a set of role-based agents (often multiple prompts, models, and tool permissions) working together under a shared objective. They collaborate via structured outputs and explicit handoffs. What it is: A workflow : tasks are decomposed, assigned, ve...

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...

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...

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 +...

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...