I have been a software engineer for 11 plus years, and I currently work as a senior software engineer. I have written code in a lot of different environments, from greenfield projects to large enterprise codebases.
So when I tried vibecoding with Google Antigravity in my free time, I expected it to be interesting, maybe helpful, and sometimes frustrating.
What I did not expect was how quickly it changed my personal definition of “moving fast.”
Google positions Antigravity as an agentic development platform with an agent first workflow, where agents can plan, execute, and verify tasks across your editor, terminal, and browser.
That framing matters, because the experience feels less like autocomplete and more like delegating chunks of work.
What I mean by vibecoding
When I say vibecoding, I mean working primarily through intent, not keystrokes.
I describe what I want, the agent proposes or implements it, and I spend my time reviewing, steering, and tightening the requirements.
In practice, that looks like this.
- I drive changes using prompts.
- I rarely write code directly.
- I review diffs, spot issues, and prompt corrections until the implementation matches what I actually want.
That has been the biggest shift for me. The bottleneck is no longer typing. The bottleneck is clarity.
My experience so far
Here are the biggest lessons from my early time with Antigravity.
1. My changes have been prompt based, not code based
So far, I have not had the need to actually write any code. I have reviewed some of the code and prompted the agent to correct issues when I see them.
This is the part that feels unreal at first.
It is like pair programming, except your pair can do the first draft of everything, including wiring, refactors, and repetitive changes.
Antigravity leans into that model with an agent management style interface and artifacts that capture plans and progress.
2. It has supercharged my efficiency
Vibecoding with a coding agent has supercharged how efficient I can get things done.
For me, the speedup shows up in three places.
- Fast first drafts, even when the change touches multiple files
- Rapid iteration loops, because feedback is cheap
- Less time spent on boilerplate, more time spent on architecture and correctness
It feels like compressing a bunch of small tasks into a single loop: specify, review, correct, repeat.
3. Vagueness is the tax you pay for speed
One caveat I have noticed is that if your feature and bug requirements are too vague, then it will do what it thinks is best.
That is not always what you meant.
My advice is simple. Be detailed. Be annoyingly detailed.
Having years of experience helps because defining requirements is its own skill. You learn how to describe edge cases, failure modes, and acceptance criteria in a way that reduces ambiguity.
If you want a mental model, think of the agent like a very fast junior engineer with unlimited stamina. It can ship quickly, but you still need to be precise about what “correct” means.
A requirements checklist that makes vibecoding work
When I catch myself being vague, I use a checklist like this before I send the prompt.
- Goal: What should be true when we are done
- Scope: What files or areas we should touch, and what to avoid touching
- Inputs and outputs: Data shape, API contract, UI behavior
- Edge cases: Nulls, empty states, retries, timeouts, concurrency
- Observability: Logs, metrics, tracing, error messages
- Tests: What to add, what to update, what should not change
- Rollback and safety: Feature flags, guardrails, safe defaults
This is where senior experience pays off. You already know what can go wrong, so you can ask for it up front.
Prompt patterns I have found useful
Here are a few prompt patterns that have worked well for me. Feel free to copy and adapt them.
Pattern 1, plan first then execute
“Create a step by step plan. Include tasks, file list, and risks. Do not change code yet. Wait for my approval.”
Pattern 2, define done
“Implement X. It is done when A, B, and C are true. Include tests. If anything is ambiguous, ask questions before writing code.”
Pattern 3, review loop
“I reviewed your diff. Fix these issues: 1, 2, 3. Explain what you will change, then apply the patch.”
Pattern 4, protect constraints
“Do not change public APIs. Do not rename classes. Keep behavior identical except for the bug fix described below.”
These patterns keep the agent aligned, and they make it easier to notice when it is drifting.
A quick note on safety
Any tool that can operate across your editor, terminal, and browser raises the bar for good judgment and guardrails. Antigravity is designed to let agents work across those surfaces.
My personal rule is to treat agent initiated terminal actions like a pull request. I want to understand what it is doing and why, especially in repos that contain secrets or production access.
There has also been public discussion about risks in agentic IDE workflows, including prompt injection style issues, so it is worth being intentional about your settings and review habits.
My takeaway so far
Vibecoding with Google Antigravity is real leverage.
It shifts your job from typing code to steering outcomes.
If you are experienced, the upside can be massive because you already know how to define detailed requirements, spot issues quickly, and insist on correctness.
If you are vague, the agent will still ship something. It just might not be the thing you meant.
Call to action
If you have tried Antigravity, I am curious.
What is the best workflow you have found so far, and what is the biggest failure mode you have hit?
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