tylers-blogger-blog

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...