Background Job Patterns for AI in Rails — Retries, Rate Limiting, Dead Letters, and Priorities
AI workloads are different from typical web jobs. They're slow, expensive, rate-limited, and sometimes they just fail for no good reason. Your backgro
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AI workloads are different from typical web jobs. They're slow, expensive, rate-limited, and sometimes they just fail for no good reason. Your backgro
Every Rails app you deploy is a target. The moment it's on a VPS with a public IP, bots will probe it. This post covers the security essentials every
There are Rails applications that run incredibly fast on a developer's laptop, but the moment they are deployed to production, they crawl to a halt. T
Your AI features work. Your tests pass. But every time a user asks a question, your app hits an external API, waits 2-5 seconds, and burns tokens. Mul
Welcome back to the Ruby for AI series. You've built AI features — chat interfaces, RAG pipelines, image generation, voice transcription. Now comes th
Stop Using SQL LIKE: A Step-by-Step Guide to Elasticsearch in Rails When you build a standard Rails app, searching your database usually st
Webhooks are one of those super simple but really powerful tools that almost every developer has needed to use at least once. From Stripe, GitHub to P
We've built text chat, RAG systems, agents, and image generation. Time to add ears and a voice to our Rails app. Today we integrate OpenAI's Whisper f
Welcome back to the Ruby for AI series. We've built chat interfaces, RAG systems, and AI agents. Now let's make our Rails app create images. We'll in
Most developer blogs are invisible to AI. Not because of content quality. Because of missing structure. I ran into this while working w
Publishing to RubyGems & Retrospective From bundle gem to gem push: looking back at 14 articles, 20 components, and the lessons learned bu
When building a SaaS with Rails, I often have more complex logic that spans multiple models and actions. Over time, I’ve settled on two distinct patte
This is post #21 in the Ruby for AI series. We've been building everything from scratch — API calls, RAG pipelines, agents, streaming. That's great fo
This is post #20 in the Ruby for AI series. Last time we built AI agents with tool use. Today we're solving a UX problem: nobody wants to stare at a s
Rabarber, a role-based authorization gem for Rails, releases v6.0.0. The new version finalizes the API cleanup started in v5, deprecated methods were
The answer is simple: use gemtracker the missing TUI and now CLI tool to keep your gems in check. Since v1.0.5, GemTracker has evolved into an even s
In the last post we built a RAG system — the LLM answers questions from your documents. That's useful, but it's still passive. The user asks, the AI a
Welcome back to the Ruby for AI series. Last time we built semantic search with pgvector. Now we're taking it further — building a full Retrieval-Augm
Streaming AI responses looks cool. But here's the problem: the AI doesn't know anything about your business. Ask it about your users, orders, or docum
You've got Rails down. You've got Hotwire, Stimulus, and background jobs wired up. Now let's wire in actual AI. This post builds a working chat inter