Introduction

Google Antigravity is a developer environment that is built around AI from the ground up. It's like a super version of VS Code, but it works very differently inside. Instead of writing every line of code yourself, Antigravity uses an agent-first development model. You explain what you need, and the system creates agents that carry out the work. Because the tool is free, anyone can try it. After using it myself, it feels like a real upgrade for any engineer’s workstation.

What Makes Antigravity Different

Antigravity understands your entire project the moment you open a folder. It scans your files, understands the structure, and learns how the pieces connect. From there, you can ask it to add features, refactor older code, document missing parts, or even build entire modules. It also works very well with embedded and hardware workflows. You can drag in a sensor datasheet and ask it to generate a full driver for Arduino, Espressif ESP32, or STM32. It reads the registers, timing diagrams, and modes, then builds the whole library with examples.

The real strength, however, is the agent system. Antigravity lets you create small agents that work like “mini employees.” Each one can take care of a single task. One agent can test the UI and  fix bugs. Another can refactor the routes, and another one can update the documentation. While they work, you can focus on things that need your attention. It really feels like having a small team inside your IDE.

These agents do not just read your code. They also run and interact with your app. They open the web preview, click buttons, test forms, submit data, and report the results. If you approve their changes, they write the updates for you.

My Experience Using Antigravity

When I first tried Antigravity, I wanted to see how far it could go in a real engineering workflow. I did not start with simple tests. I jumped straight into a bigger task: a full project management dashboard, something similar to ClickUp. I prompted it to generate a React front-end and an SQLite back-end. Within minutes, it created the full structure: routes, pages, components, state flow, the database schema, and the API. It even created dashboards, task lists, a Kanban view, and a calendar. The first version was simple but solid. With about twenty prompts, it became much more polished. It added filters, multiple spaces such as Engineering and Design, and inside each space it created projects, lists, and tasks with states like “To Do,” “In Progress,” “Review,” and “Done.” I was impressed with how well it handled state syncing and routing.
 
Figure 1: The first prompt I gave Antigravity to generate the entire project management app, including spaces, projects, task lists, board views, and a calendar.
 
Figure 2: The initial raw version of the React + SQLite project management app generated by Antigravity before any refinement prompts.


When the main structure worked, I tested the agent system. I made an agent with one job: “Open the app and test everything.” It loaded the preview, clicked all the buttons, opened each page, tested the forms, changed views, and checked the calendar. It found missing handlers, layout bugs, and broken transitions. Not only that, but it reported everything with file names and line numbers. After I approved the plan, it fixed the entire codebase on its own. Watching it repair UI issues while I did nothing felt unreal.
 

Figure 3: Antigravity launching a browser window and testing the full UI automatically using its built-in agent system.
Figure 4: The app after Antigravity added 30 tasks per workspace and automatically tested every view, list, and interaction through its UI testing agent.


Next, I returned to embedded systems. I asked Antigravity to create a full ESP32 file transfer system using ESP-NOW. I wanted a sender, a receiver, a simple protocol, and a web interface to upload files. It built everything on the first try. It wrote the Arduino sketches, created MAC pairing, broke the file into packets, added a progress counter, and even made a drag-and-drop web UI. I flashed the code and it worked immediately. No debugging. No trial and error. Packet flow, acknowledgments, and error handling all worked perfectly.

This was a surprise because when I asked ChatGPT for a similar task in 2023, it did not perform well. It struggled with ESP-NOW and file handling. But we have come very far. Today’s AI models—ChatGPT 5.1, Gemini, and Claude—are finally able to deliver complete working systems. You can now build the app you always wanted, even if you never had the front-end skills or free time before. AI removes that barrier.

Figure 5: The complete ESP32 ESP-NOW file transfer code generated by Antigravity, including sender, receiver, packet handling, and web interface logic.


I pushed Antigravity even further. I uploaded a sensor datasheet and asked it to build a driver for Arduino and ESP32. It read the tables, timing diagrams, and register map, and produced a full library. The library had initialization code, read and write functions, calibration routines, interrupt setup, and a proper example sketch. Normally this takes a full day or more. Antigravity did it in minutes.

I also used Antigravity to fix older projects. I opened an old ESP32 codebase with messy structure, leftover logs, and inconsistent names. Antigravity scanned everything and rewrote it into a clean, modern layout. It reorganized folders, cleaned the naming, removed unused code, and generated new documentation. It even optimized some loops. This was one of its most useful features.

The agent-first workflow kept proving itself. I made agents for documentation, testing, refactoring, and even UI design. One agent updated the UI after I uploaded screenshots of a modern layout. While the agents worked, I focused on system-level decisions. It felt like having a small team of juniors doing the busywork while I handled the design.

How to Use AI the Right Way

Over time, I learned that many people use AI incorrectly. Most expect the AI to think for them or to produce ideas from scratch. Thinking is a human task. We understand the context, the goal, the constraints, and the logic of the system. AI only works well when we give it clear direction.
The right way to use AI is simple: you think, and the AI does.

You design the flow. You set the structure. You define what happens on a click, what each screen should do, and how the features should work. The AI follows your instructions and fills in the technical details. 

AI is not perfect either. It cannot catch every UI issue or every small bug. Human testing is still important. You still need to click around, try different flows, and spot the problems only real users notice. Those observations become new prompts for the AI to fix. Hallucinations also still happen, especially near context limits. Antigravity hides that limit, unlike the Gemini CLI, but it is still there. Clear prompts reduce these issues.

The workflow is simple:
Think. test. Observe. Then instruct the AI.
When used this way, AI becomes a powerful engineering tool.

Moving Ahead

Google Antigravity represents a major shift in development. It is fast, free, and deeply integrated with AI. It builds entire systems with minimal input, analyzes codebases, generates libraries, and automates testing. It enhances engineers rather than replacing them. It reduces repetitive work and increases creative time. For any engineer or maker, Antigravity is absolutely worth trying. It feels like the next step in how software and embedded systems will be built.