5 tips for using Antigravity 2.0 on enterprise codebases, planning phase
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Martin Omander shares 5 tips for using Antigravity 2.0 on enterprise codebases
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Antigravity → https://goo.gle/434x6Vx
Building with Google Antigravity → https://goo.gle/4eeTcuG
Build and deploy to Google Cloud with Antigravity → https://goo.gle/4uIhmn3
Hey everyone, welcome back, Martin Omander here. Most AI tutorials show someone building a toy app from scratch in five minutes. That’s neat, but it’s not reality when you're working on a massive, living enterprise codebase where you don't get a blank slate.
This video kicks off a two-part series on how I use Antigravity 2.0 on complex, existing systems. Before letting an agent write a single line of code, you need a solid setup phase. To keep myself in the right managerial mindset, I treat the agent like a digital intern (represented by my physical stand-in, Ducky).
Here are the 5 practical planning tactics I use to brief Ducky and set boundaries before execution begins:
* 1. Break the repo walls: Linking multiple repositories (frontend, backend, shared packages) into a single workspace so the agent can make cross-repo updates simultaneously.
* 2. The hierarchy of rules: Organizing global, project level, and hyper-local directory standards so the agent automatically adheres to your team’s exact coding DNA.
* 3. Contain the blast radius: Setting up OS level sandboxing, command allowlists, and cloud environment isolation so automated agent loops can never run wild on your system.
* 4. Stop typing your architecture: Using native voice input to context dump messy requirements and talk through tough legacy quirks like you're brainstorming with a coworker.
* 5. Let the agent interrogate you: Utilizing the /grill-me command to force the agent to find fuzzy requirements and challenge your architectural assumptions before coding starts.
Setting these guardrails and rules up front keeps the workspace secure and saves a massive amount of debugging time down the line. In Part 2, we will let Ducky loose to actually execute, run self-correction test loops, and ship features. Until then, how are you setti
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