Articles on Artificial Intelligence

In a previous post, AI Assistant for Our Blog Writing Process opens a new window , I introduced the assistant we built to help with our blog writing. At the core of that assistant is an MCP server, which serves as the source of truth for both of our blogs. It exposes that knowledge through tools the client can call and documentation the client can read.

Getting an MCP server running is the easy part. Every quickstart, in every language, gives you a server that runs as a subprocess on your own machine and disappears when the client exits. That’s enough to experiment locally, but it’s a long way from something a team can rely on. Once you want to deploy it, questions about where it runs, state management, authentication, and security become your responsibility. The Ruby SDK’s defaults don’t solve most of those problems, and one of them even comes with a published security advisory.

In this article, we’ll cover what changes when a Ruby MCP server stops being a subprocess: the two shapes it can take in a Rails shop, why session state breaks down when running behind multiple Puma workers, the DNS rebinding vulnerability the transport shipped with, and what the specification asks of you once a shared token is no longer enough.

Wiring an LLM into a Rails app takes a handful of lines. Understanding what it actually costs you (feature by feature, user by user) is harder. Most providers and SDKs already report tokens, latency, and even cost, but that data lives in their dashboard. It’s disconnected from your requests, your users, and the feature that made the call. And it sits apart from the APM and logs where you already watch the rest of your app.

In a previous post, we introduced Rails.event.notify(...) opens a new window , the tool-agnostic Event Reporter shipping in Rails 8.1. In this post, we’ll put it to work on a real problem: instrumenting every LLM call in your app so token usage, latency, and cost become structured events you can log, graph, and forward to any APM or data warehouse.

At OmbuLabs.ai, we’ve explored the importance of identifying meaningful AI opportunities opens a new window before selecting a solution. Once a worthwhile opportunity has been identified, however, a new question emerges:

Is this problem worth solving in the first place?

Too often, teams focus on the technology before evaluating the value. AI can automate tasks, generate content, and process information at incredible speed, but if the underlying work doesn’t matter, making it faster won’t create meaningful business outcomes.

Once a worthwhile opportunity has been identified, however, a new question emerges:

What should we build first?