That RailsWorld keynote didn’t just start a debate, it forced a reset on how we talk about Ruby on Rails in the age of AI. We’re live from the Buzzsprout podcast booth with Joël Quenneville (principal developer at thoughtbot and Bike Shed co-host) and Tom Rossi (Buzzsprout co-founder) to unpack what’s changing when many of us barely write code by hand anymore and instead “coach” an LLM agent.

We dig into the practical side of AI-driven development: why Rails conventions still matter, why generators suddenly feel new again, and how token efficiency and consistency become real engineering constraints. Then we get honest about software design. Do classic abstractions and object-oriented patterns help agents reason, or do they just get in the way when explicit code ships faster? We trade stories about broken integrations, duplicated methods that still pass tests, and why naming and domain language remain a steering mechanism for both humans and models.

From there, we zoom out to the future of software products. If personal AI assistants become the primary interface, the “UI” might shift toward CLIs, MCP-style structured endpoints, and agent-friendly actions instead of screens and forms. That opens big questions about APIs reopening, who controls the agent layer, and whether we’re about to repeat the same platform power cycles we saw in the API boom years.

If you’re building on Rails, experimenting with LLM coding tools, or wondering what your job looks like when the craft shifts from typing to verification, this conversation will help you think clearer. Subscribe, share this with a friend from your dev team, and leave a review with your hottest take on where AI is pushing Rails next.

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Your database is slow, your Sentry is screaming, and the backlog is full of “we’ll fix it later.” What if an AI agent handled the boring but high-impact work while you slept and just opened a clean pull request for review the next morning?

We’re joined by Mike Coutermarsh, a software engineer who helped build GitHub Actions and later left GitHub for PlanetScale. We talk candidly about the trade-offs: walking away from big-company comfort, choosing impact over feeling like a cog, and learning to thrive in a flatter org where the best “process” is ownership. Mike shares how he leads the team responsible for everything users see at PlanetScale, from dashboards to APIs to CLIs, and why speeding up CI, reducing bugs, and protecting reliability can matter more than chasing the flashiest feature.

Then we get practical about AI coding tools. Mike breaks down how Cursor, Claude Code, and MCP servers can connect production query patterns and Sentry errors to scoped “bot army” automations that propose fixes, optimize performance, and even keep error queues from becoming a garbage fire. We also dig into AI code review, responsibility (“if your name is on the commit, you own it”), and the uncomfortable question of whether code quality still matters when models can generate code fast. Along the way we touch token costs, local models, and why conventions like Rails can actually help AI work better.

On the database side, Mike explains why PlanetScale started with MySQL via Vitess, how sharding changes operations like backups and restores, why Postgres demand forced a new product push, and what it could mean to bring Vitess-style scaling to Postgres. We wrap with a small but surprisingly powerful workflow upgrade: fast dictation using Spokenly and local speech-to-text.

Subscribe, share this with a teammate who lives in dashboards and PRs, and leave a review with the one workflow you’d want an AI agent to automate next.

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React made a lot of us feel powerful, then maintenance made a lot of us feel tired. Drew Bragg sits down with Delaney Gillilan, creator of Datastar, to argue for a different kind of “modern web” one that keeps the browser’s strengths front and center and keeps your app logic where it belongs: on the server. If you’ve ever looked at your dependency graph and wondered how you got here, this conversation is a reset.

We get concrete about what Datastar is and how it works: a tiny reactive framework built around HTML, data attributes, and signals, with a plugin system that stays out of the way. Delaney explains why most state should live on the backend, why duplicating validation and business rules in the client is wasted effort, and why hypermedia is still the simplest way to communicate what a user can do next. We also unpack the “send strings to the browser” philosophy and how that changes performance, complexity, and even team collaboration.

Real-time is where it gets spicy. Delaney makes the case for Server-Sent Events (SSE) over WebSockets for many apps, leaning on normal HTTP semantics, built-in reconnects, and streaming compression to ship tiny DOM diffs efficiently. From there we talk CQRS as a mental model for command intent vs view updates, plus what this means for Ruby on Rails developers weighing Hotwire, Stimulus, and upgrades. We close with two bold companion projects: Rocket, which makes Web Components more declarative, and Stellar, a Tailwind alternative that uses parametric CSS variables for a modern design system workflow.

If you enjoy deep technical takes on reactive UI, server-driven rendering, SSE, Rails, and modern CSS, subscribe, share the episode with a friend who’s stuck in SPA fatigue, and leave a review with the part you disagreed with most.

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