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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