Use cases
Flaky CI is usually an environment you don't control
Run your GitHub Actions jobs on your own boxes: the same machine, the same warm caches, the same resources every time — and a log you can go and read.
version: 1
budget:
max_cost_usd: 5
policy:
require_checks: true
deny_paths:
- infra/prod/**A test that fails once in ten is an environment problem until proven otherwise
Shared hosted runners give you a fresh, differently-loaded machine on every run, which is exactly the condition under which timing-sensitive tests misbehave. developerz.ai turns your VMs into just-in-time GitHub Actions runners: one runs-on: label change, and jobs execute on hardware you sized, with the resource limits you set. Every job still runs in a fresh container that is destroyed on completion, so isolation does not depend on the box being clean.
Predictable hardware
Pick the runner size per job — 2 to 32 vCPU labels — and stop debugging failures that are really contention on someone else's fleet.
Fresh container, every job
Jobs run in a container destroyed on completion. Nothing persists between runs, so a poisoned cache cannot follow you.
The agent watches the PR
A red check is not a dead end: the maintainer agent tracks the PR and escalates when it stops making progress.
Go deeper
One label to move a workflow
The CI page shows the exact diff, the runner sizes, and the security model — token TTL, container lifetime and the fork-PR rule.
Self-hosted CIPut a fleet of AI developers to work
Bring your own keys and your own boxes, drop a .maintainer.yml, and let the loops run — audited end to end. Access is invite-only.