Protecting Your Agent Stack's Known-Good State with a Single Lockfile — Change-Budget Design for an Era of Simultaneously Moving Parts
When the IDE build, CLI, model, and dependencies all move at once, you can no longer tell which one caused a regression. Here is a change-budget design that pins your known-good state to one lockfile, with working code and operational logs.
When the Default Model Changes Underneath You: Pinning and Diff-Gating Scheduled Runs
Antigravity 2.0 promoted Gemini 3.5 Flash to the default fast model. It is a welcome upgrade, but any scheduled run that leaned on the default starts producing subtly different output one morning. Here is how I pin the model explicitly, fingerprint the output, and gate drift, sized for a solo developer's pipeline.
Keeping Scheduled Runs Reproducible: Pinning the Antigravity CLI Version to Tame Behavior Drift
The Go-based Antigravity CLI is now available to everyone, and updates are landing at a quick pace. When a CLI baked into your automation upgrades underneath you, a single morning's job can behave differently. Here is how I keep things reproducible — pinning the binary, recording its identity in each run's log, and rolling upgrades forward one job at a time — drawn from running four sites on an overnight schedule.
When a Cloud Nightly Batch Drifts From Yesterday's Result — An Input Contract and Snapshot Design for Reproducibility
When you push a batch to a cloud ephemeral worker via the Managed Agents API, the environment assumptions you took for granted locally vanish. Here is a three-layer design — environment snapshot, input contract, seed pinning — that keeps the same input producing the same result.
Antigravity DevContainer Setup — Killing 'Works on My Machine' for Team Development
Once Antigravity goes multi-user, someone always says 'doesn't work on my setup.' Adding DevContainers solves this surprisingly cleanly. Here's the configuration I run and the gotchas to know.
Antigravity × Dev Containers: Building Fully Reproducible AI Development Environments with Docker
A hands-on guide to creating fully reproducible AI development environments using Dev Containers and Docker with Antigravity. Covers devcontainer.json design patterns, multi-stage builds, and team-wide environment sharing.