Google Antigravity and the Client Intake App That Became Easier to Resume

Consider a solo founder named Maya returning to a client intake app on Monday after leaving it untouched over the weekend. She was using Google Antigravity to work on the app, and a potential user was due to try it later in the week. The form looked finished, but Maya could not remember whether submissions were meant to create a new client record or attach to an existing one. That missing decision mattered more than generating another screen.

Monday: Reconstructing the build

Maya opened the project and tried the form. A submission appeared to succeed, but she could not tell from the interface where the information had gone. She read the relevant code and retraced earlier changes before making another request to the tool. That was necessary work, yet it consumed the time she had set aside to improve the intake flow.

Her first impulse was to ask Google Antigravity to tidy the form and add a client list. She held off because a cleaner interface would not settle the underlying question about records. AI coding tools can help you inspect and change a project, but generated code does not preserve the reason behind a product decision unless you record that reason somewhere you can return to.

The decision she wrote down

Maya chose a narrow rule for the pilot. Each submitted form would create a new intake request; a person reviewing it would decide whether it belonged to an existing client. She wrote the rule beside three other pieces of context:

• The current flow: submit a form, then review the request in a list.
• The known uncertainty: matching requests to existing clients needed user feedback.
• The next check: submit a form, refresh the list, and inspect the saved record.

With that note in hand, she asked the tool for a focused change and reviewed the resulting code. She tested a successful submission and an empty one, then checked whether the saved data matched the rule she had chosen. Before stopping, she updated the note with what worked and the one issue still open. The record took less effort to write than reconstructing the decision had taken earlier that day.

Tuesday: Starting from a known state

When Maya returned on Tuesday, she began with the note instead of trying to remember Monday's prompts. She could see that intake requests were saved, while matching them to clients remained deliberately unresolved. Her next session started with the open issue and the relevant test, rather than another round of code archaeology.

The improvement in this story does not establish that one coding environment is faster or more reliable than another. Maya kept using Google Antigravity for the build. The change was in what she carried between sessions: a decision, an observed result, and a next action. You can use the same approach with the AI coding tool you already prefer.

For a fuller routine covering session notes, decisions, and reusable prompts, see this six-step guide to tracking project state across AI coding tools.

What to carry into your next session

After a meaningful change, leave a short recovery note while the details are fresh. State what works, what remains unverified, and why you made any decision that might look arbitrary later. Keep a useful debugging prompt with the issue it solved so you can tell whether it applies again.

If your build sessions are moving faster than your memory, keep your project notes in VibeCrumbs before the next pause.