// Articles
Guides, tutorials, and comparisons to help you build better backend applications.
Migrations in code, a local Postgres per developer, a fresh database per test and a branch per preview environment, compared for teams where agents write a large share of the schema changes.
Hono ran cheapest in our AI-readiness benchmark and collapsed worst when the test suite started probing for production-readiness.
Fastify was the closest non-Encore result in our AI-readiness benchmark, and reaching that result still cost about twice as many tokens.
Claude Code on NestJS cost about twice as many tokens as the same agent on Encore, and shipped fewer working production-readiness checks.
What Claude Code shipped when we gave it the same backend tasks on Express and on Encore.
What a backend team's week looks like once agents write most of the code: specifying instead of typing, running the backlog in parallel, reviewing by running, and a platform team that sets rules instead of approving changes.
How deterministic provisioning, central guardrails, and a human at the merge let agents ship to your cloud without improvising infrastructure.
Why mocks let backend bugs through, the options for giving agents a working database and queues, and why isolation matters as much as the infrastructure itself.
How the two relate, where CI/CD strains once agents are producing changes, and why a factory uses your pipeline rather than replacing it.
The components of an AI software factory and how a change flows through them, from intake to a deploy on your own cloud, with the control plane kept separate from the loop agents work in.
No single product is a complete AI software factory yet. The layers one is built from, the tools that cover each, and how to decide where to start.
How to put the stages around your coding agent so a task goes from a request to a change deployed on your own cloud, starting from a service you already run.