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 .dz/maintainer/maintainer.yml, and let the loops run. Audited end to end. Access is invite-only.