Google Antigravity IDE just dropped. I tested it immediately and here's what you need to know

So Google released a new IDE today. Antigravity. I saw the announcement this morning and immediately downloaded it.

Here’s the thing. I co-founded Frugal Indians, a platform where users learn how to save money in today’s age of consumerism. We’ve had persistent issues on our 30-day money saving challenge page. The CSS refuses to cooperate. Code blocks overflow their containers. My Git commits tell a story: “fix layout pls,” “why doesn’t this work,” and my personal favorite from last week, “I hate everything.”

I’ve tried Cursor. I’ve wrestled with GitHub Copilot. I’ve even attempted to manually debug the CSS myself, an experience I’d recommend to anyone who feels they have too much hope in their life.

When I saw the Antigravity announcement at antigravity.google, something clicked. Free for developers. Multi-agent AI system. Supports Claude, GPT, and Gemini 3.

I’ve seen too many “revolutionary” tools that turned out to be average features with excellent marketing. But I was desperate. And desperate people download experimental IDEs on a Wednesday morning instead of doing actual work.

Here’s what happened. And what it means for technical writers who spend half their lives fighting with documentation infrastructure.

First, let’s talk about Gemini 3 (because it powers everything)

Before I get into Antigravity itself, we need to discuss what’s running under the hood. Google launched Gemini 3 today, their latest AI model, and the benchmarks are genuinely impressive.

Gemini 3 by the numbers

Google claims Gemini 3 Pro outperforms GPT-5.1 on reasoning benchmarks. I haven’t verified this independently. I’m testing the tool, not running benchmarks. But what I can tell you is that the responses feel different. There’s less of that “AI is trying too hard” quality you sometimes get with other models.

Here’s the official benchmark comparison from Google:

The numbers speak for themselves. Gemini 3 Pro leads in most categories, though Claude Sonnet 4.5 holds its own in certain areas. What matters for us is how this translates to actual documentation work.

What makes Gemini 3 relevant for technical writers?

The 1M+ token context window is the headline feature for us. In practical terms, this means:

• Entire documentation sets can be analyzed at once
• Full codebases can be understood without chunking
• Cross-referencing between docs and code happens in a single pass
• Style consistency can be maintained across massive projects

When I fed Antigravity my entire Frugal Indians repository (Markdown files, CSS, JavaScript, HTML templates) it processed everything without the “context limit reached” errors I’m used to seeing.

That alone changes what’s possible.

What is Antigravity, actually?

Antigravity is what Google calls an “agentic development platform.” This sounds like someone in Mountain View got paid by the syllable, but the concept is genuinely different from what we’ve been using.

The key difference

How Cursor/Copilot work: You ask AI for help. It gives you a suggestion. You accept, reject, or modify. Repeat.

How Antigravity works: You describe a task. Multiple AI agents start working simultaneously. They produce “artifacts” (deliverables you can review). They learn from your feedback. You approve or request changes.

It’s less like pair programming and more like managing a small team. You set direction; they handle execution.

The multi-model architecture

Antigravity runs on multiple AI models working together:

• Gemini 3 Pro: Core reasoning and code generation
• Gemini 2.5 computer use: Browser automation and terminal operations
• Nano Banana (Gemini 2.5 image): Visual analysis and UI generation
• Claude Sonnet 4.5: Available for complex reasoning tasks
• GPT models: Available for specific use cases

Yes, you read that correctly. Google’s IDE can use OpenAI’s models. Competition creates interesting bedfellows.

You can specify which model handles which task, or let Antigravity route automatically based on complexity.

Setting it up (11 minutes, no tricks)

I expected configuration headaches. I got a surprisingly smooth setup.

What I did

1. Downloaded from antigravity.google
   • Available for MacOS, Windows, and Linux
   • 487MB download
2. Signed in with Google account
   • Immediate Google Workspace integration
   • No API keys needed for Gemini 3
3. Selected AI models
   • Gemini 3 Pro: Free tier (50 requests/day)
   • Added my Anthropic API key for Claude access
   • Skipped GPT for now (maybe later)
4. Opened my project
   • Pointed it at my Frugal Indians repo
   • It auto-detected Jekyll, CSS, JavaScript
   • Suggested relevant agent configurations

The interface looks like VS Code. Intentionally. Same keyboard shortcuts, same layout concepts. Google made the sensible choice to build on familiarity rather than forcing users to relearn everything.

The real test: fixing my broken website

Enough setup. Let’s see if this thing works.

The problem

My Frugal Indians platform had issues that had been annoying me for weeks:

• Responsive layout broken on mobile
• Code blocks overflowing containers
• Inconsistent spacing across pages
• Navigation disappearing on tablets
• A color scheme that screamed “2010 WordPress theme”

I’ve tried fixing this with Cursor (fixed mobile, broke desktop), manual debugging (fixed code blocks, broke everything else), and Copilot (suggestions were technically correct but contextually wrong).

What I told Antigravity

“Fix the responsive layout issues on the 30-day money saving challenge page. Prioritize mobile readability, ensure code blocks don’t overflow, and suggest a modern color scheme. Work in the background. I’ll keep writing content.”

Then I went back to editing a blog post about budgeting apps. Because unlike my previous AI tools, Antigravity actually works asynchronously.

What happened

Total time: 23 minutes and 11 seconds.

My active involvement: About 4 minutes reviewing artifacts and typing feedback.

The website I’d been fighting for three weeks was fixed while I edited an article about meal prep savings. I’m not sure how to feel about that.

Testing for technical writing tasks

Website fixed. But that’s web development. I’m a technical writer. My actual job involves API documentation, Jobs to be done (JTBD), content strategy and migration, and conveying accurate information that doesn’t leave people wanting more.

To test it, I just picked some random things from the internet like API docs and tried to give it a test along with personal documentation—things I control.

The good news? Most technical writing tasks are structurally similar whether you’re documenting an enterprise API or your weekend project. If it works on my personal API, it’ll probably work on yours.

Test 1: API documentation generation

Task: Document a REST API with 23 endpoints. Request/response examples, error codes, authentication requirements.

Input: OpenAPI specification (Swagger JSON) + my style guide

What Antigravity did:

Four agents worked in parallel:

• Agent 1: Parsed OpenAPI spec, extracted endpoints
• Agent 2: Generated curl examples with realistic data
• Agent 3: Created code samples in Python, JavaScript, Java, PHP, Go
• Agent 4: Cross-referenced error codes with HTTP standards

Time: 31 minutes

Quality assessment:

What worked well:

• Accurate technical details
• Consistent formatting across all endpoints
• Code samples actually ran (I tested them)
• Error explanations referenced the right HTTP standards

What needed my editing:

• Some endpoint descriptions were too generic
• Missed one edge case in the OAuth flow
• Python example used an outdated library version

Verdict: 8.5/10. Saved me 6 to 8 hours. Needed about 45 minutes of refinement.

View generated API documentation sample ▼

curl -X GET "https://api.example.com/v2/users/u_1a2b3c4d" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Accept: application/json"

{
  "id": "u_1a2b3c4d",
  "email": "user@example.com",
  "name": "Jane Developer",
  "role": "admin",
  "created_at": "2025-01-15T09:23:41Z",
  "last_login": "2025-11-18T14:32:19Z",
  "preferences": {
    "theme": "dark",
    "notifications": true
  }
}

Test 2: Documentation migration

Task: Migrate 47 pages of old Markdown documentation to a modern docs-as-code setup.

Challenge: Inconsistent formatting, 134 broken links, outdated screenshots, no clear information architecture.

Results:

Important caveat: Antigravity’s proposed information architecture was logically organized but didn’t match how users actually navigate the product. I had to restructure based on analytics and user feedback data.

This is a consistent pattern I noticed: AI agents excel at mechanical transformation but still need human insight for user experience decisions.

Test 3: Interactive code tutorial

Task: Build an interactive JavaScript tutorial with live code editors, progressive examples, and instant feedback.

My previous experience: Spent 4 hours with Cursor cobbling together something functional but visually questionable.

What I told Antigravity: “Create an interactive tutorial teaching JavaScript promises with 5 progressive examples, live code editor, and instant output preview.”

Three agents worked together:

1. Content agent: Tutorial structure and example code
2. UI agent: Interface with syntax highlighting
3. Integration agent: Live execution environment

Result: Functional interactive tutorial in 41 minutes.

Features included:

• Syntax highlighting with CodeMirror
• Auto-completion
• Error detection with helpful messages
• Live output preview
• Reset functionality per example
• Mobile-responsive design

Quality: 9/10. Only needed brand color adjustments.

I’ve been manually building interactive examples for years. Antigravity produced something better than my best work in less time than my morning commute. That’s a lot to process.

The complete comparison: Antigravity vs. Cursor vs. VS Code + Copilot

Since I’ve used all three extensively today, let me give you the detailed breakdown.

Head-to-head testing results

I ran the same five tasks through Antigravity and Cursor:

Summary scorecard

The numbers favor Antigravity, but there’s nuance.

Cursor feels more responsive. The inline suggestions are seamless. Like pair programming with someone who’s actually paying attention.

Antigravity feels like managing a team. You set direction, review deliverables, provide feedback. Less immediate, but more powerful for complex multi-part tasks.

The right question isn’t “which is better?” It’s “which workflow matches how you work?”

What Antigravity gets wrong

I’ve spent most of this article praising Antigravity. Let me balance that with what doesn’t work well.

The hallucination problem persists

I asked Antigravity to document an API endpoint that doesn’t exist in my spec.

It generated beautiful documentation. Complete with examples, error codes, authentication requirements.

All fabricated.

AI agents are still AI. They don’t know what they don’t know. And they’re remarkably confident about their inventions.

For technical writers: You still need to verify everything. Speed without accuracy creates more problems than it solves.

Context bleeding between agents

I had three agent teams running:

• Team 1: Fixing CSS on my blog
• Team 2: Generating API docs
• Team 3: Creating tutorial content

Agent 2 started suggesting CSS fixes in the API documentation.

When too many agents run simultaneously, context sometimes leaks between tasks. Google is working on better agent isolation, but for now I limit myself to 2 major concurrent operations.

The prompt skill gap

Marketing promise: “Natural language interface! Just tell it what you want!”

Reality: You need to learn effective communication with AI agents.

Bad prompt: “Make the docs better”

Good prompt: “Analyze pages in the Getting Started section, identify those with >10% bounce rate, and propose specific improvements to introduction paragraphs and code examples. Prioritize the API Authentication page.”

The difference in output quality is substantial.

Privacy considerations

Every task sent to Antigravity gets processed by cloud-based AI models.

If you’re working with:

• Proprietary code
• Confidential documentation
• Unreleased product details
• Security-sensitive information

You need to think carefully about what you’re submitting.

My approach: Antigravity for public-facing work and personal projects. Local-only tools for client work with NDAs.

What this actually means for technical writers

A new IDE dropped today. Gemini 3 has impressive benchmarks. You might be wondering: is this it? Are we done? Are we getting replaced?

I don’t know.

Nobody does. And I’m deeply skeptical of anyone who claims they do. The people shouting “AI will replace all writers” have the same credibility as the people shouting “AI is just hype.” Both camps are guessing with confidence they haven’t earned.

What I can tell you is what’s changing in practical terms.

The skills shift

Notice what stays constant: writing clear documentation.

AI hasn’t figured that out. Understanding user needs, organizing information for discoverability, explaining complex concepts simply: these remain human skills.

What’s changing is how we implement those insights.

The workflow evolution

Traditional workflow:

1. Research the feature
2. Write documentation
3. Create code samples (manually)
4. Build the page (manually)
5. Test and publish

AI-assisted workflow:

1. Research the feature
2. Write documentation
3. Deploy agent to generate code samples in 5 languages
4. Review and refine AI-generated samples
5. Deploy agent to build responsive page
6. Validate and publish

The thinking is still yours. The implementation is increasingly delegated.

What makes you irreplaceable

AI agents can:

• ✓ Generate code samples
• ✓ Fix broken layouts
• ✓ Convert formats
• ✓ Create interactive examples
• ✓ Automate repetitive tasks

But they cannot:

• ✗ Understand why a user is frustrated.
• ✗ Prioritize documentation based on support ticket patterns.
• ✗ Recognize when technical accuracy conflicts with user needs.
• ✗ Define the “voice” of a product.

Your value isn’t in typing speed anymore. It’s in judgment.

It’s evening now. I downloaded Antigravity this morning when the announcement dropped. I’ve spent the whole day testing it, comparing it, and writing this.

• The Frugal Indians website that frustrated me for three weeks? Fixed.

• The random API docs I threw at it? Handled better than I expected.

• My understanding of what these tools mean for technical writing? Still evolving.

Google released an IDE today. It’s good. Whether it’s better than what you’re using depends on how you work, what you’re building, and which ecosystem makes sense for your situation.

But here’s what I keep coming back to:

The documentation I produced today wasn’t just faster. It was better. Because I spent my energy on “what should this communicate to users?” instead of “why won’t this flexbox behave?”

That’s not a revolution in tools. That’s a shift in what our job actually is.

The question isn’t whether AI will change technical writing. It already has. The question is whether you’re positioned to work with these tools.

I don’t have a comfortable answer for that. But I know which side I’m trying to be on.