Heroku's git-push-to-deploy was magic in 2012. Sad to see it go out like this.
I've been building Frost https://github.com/elitan/frost, open source and self-hosted. Same idea, git push to deploy, automatic SSL, custom domains, but on your own VPS. Docker-native, no vendor lock-in, no pricing surprises.
The angle that's a bit different: it's designed for AI coding agents. Simple config they write correctly, clear errors, no K8s complexity to hallucinate. You give your agent the install URL and it sets up the whole server.
Building Frost, a simple self-hosted deployment platform. Docker-only, single-user, no Kubernetes. You point it at a repo or image, it handles the build → health check → deploy → stop old container flow.
Stack: Bun, Next.js, SQLite, Kysely. Been building it almost entirely with Claude Code which has been a surprisingly effective workflow for this kind of infra tooling.
Been using Claude Code to build a small deployment tool (Frost) for exactly this use case. The meta experience is interesting - using an AI agent to build tooling that makes self-hosting easier.
What I've found: Claude Code is great at the "figure out this docker/nginx/systemd incantation" part but the orchestration layer (health checks, rollbacks, zero-downtime deploys) still benefits from purpose-built tooling. The AI handles the tedious config generation while you focus on the actual workflow.
Main difference from PG18's approach: you get complete server isolation (useful for testing migrations, different PG configs, etc.) rather than databases sharing one instance.
Hey HN! I built Velo to solve a problem I kept running into: testing database migrations and debugging production issues without copying around massive database dumps or waiting for slow clones.
The core idea: use ZFS copy-on-write snapshots to branch PostgreSQL databases the way you branch Git repos. A 100GB database branches in ~2 seconds and takes ~100KB of space initially. Each branch is a complete, isolated PostgreSQL instance running in Docker.
How it works:
- Runs CHECKPOINT to flush PostgreSQL buffers (ensures consistency)
- Creates a ZFS snapshot (filesystem-level, instant)
- Clones the snapshot to a new dataset (CoW means no data copying)
- Spins up a new PostgreSQL container pointing at the clone
The mental model is: Project = Git repo, Branch = Git branch. Commands like `velo branch create api/test-migration` or `velo branch reset api/dev`.
I also added point-in-time recovery using WAL archiving, so you can create branches from specific timestamps (e.g., "5 minutes before the incident").
> A hybrid of Strong (the lifting app) and ChatGPT where the model has access to my workouts, can suggest improvements, and coach me. I mainly just want to be able to chat with the model knowing it has detailed context for each of my workouts (down to the time in between each set).
I've been building Frost https://github.com/elitan/frost, open source and self-hosted. Same idea, git push to deploy, automatic SSL, custom domains, but on your own VPS. Docker-native, no vendor lock-in, no pricing surprises.
The angle that's a bit different: it's designed for AI coding agents. Simple config they write correctly, clear errors, no K8s complexity to hallucinate. You give your agent the install URL and it sets up the whole server.