I have been experimenting with Claude Code and Google Antigravity extensively as part of my development workflow. I have used them to build an enterprise-grade tool. I would not credit the tools alone, though. There was plenty of human input involved throughout the process. Even so, working with them made me realize just how capable these tools have become.

That became even more apparent recently when I gave Claude Code and Google Antigravity an entire project. I wanted to build something useful, not just run another coding tool benchmark. The project was a resume builder microsite for a friend who was actively looking for a job. He already had a solid resume, but he needed a way to edit it properly and create versions tailored to different roles. A single resume rarely works across every application because each job requires a slightly different emphasis.

I wanted the microsite to provide templates, editable sections, and role-specific versions without turning the process into a manual copy-and-paste exercise. I ran Claude Code inside Google Antigravity through the terminal while also using Antigravity's agents to plan and structure the project. That combination finished the build almost twice as fast as my usual VS Code workflow.

Using Antigravity for planning

And Claude Code for execution

My plan was to use both Google Antigravity and Claude Code to my advantage. I started by giving Antigravity a prompt to begin project planning. The plan is for Antigravity to break the project into milestones, generate the architecture, and define the database schema. While this particular project did not require APIs, if yours does, you can create API specs and ask it to identify risks and edge cases. Finally, you can ask it to create implementation tasks.

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Quiz
8 Questions · Test Your Knowledge

How much do you know about Claude?
Trivia challenge

Think you know Anthropic's AI assistant? Put your knowledge of Claude to the test.

OriginsCapabilitiesSafetyFeaturesDesign
01 / 8Origins

Which company created Claude?

Correct! Claude was created by Anthropic, an AI safety company founded in 2021. Anthropic was co-founded by Dario Amodei and Daniela Amodei, among others who previously worked at OpenAI.
Not quite. Claude is made by Anthropic, not to be confused with OpenAI, which makes ChatGPT. Anthropic was founded in 2021 with a strong focus on AI safety research.
02 / 8Safety

What is the name of the safety and values framework Anthropic developed to guide Claude's behavior?

Correct! Anthropic developed Constitutional AI (CAI), a technique that trains Claude using a set of principles — a 'constitution' — to guide its responses toward being helpful, harmless, and honest.
Not quite. The framework is called Constitutional AI (CAI). It is a novel training approach pioneered by Anthropic that uses a written set of principles to help the model self-critique and improve its own outputs.
03 / 8Origins

What is the name most commonly associated with inspiring Claude's name?

Correct! Claude Shannon is widely cited as the inspiration behind the name. Shannon founded information theory, which is foundational to all modern computing and digital communication — a fitting namesake for an AI.
Not quite. The name Claude is most commonly associated with Claude Shannon, the mathematician and electrical engineer who founded information theory. His pioneering work laid the groundwork for the digital age.
04 / 8Capabilities

Which of the following best describes Claude's context window capability in its more advanced versions?

Correct! Advanced versions of Claude support context windows of 100,000 tokens or more, allowing it to process entire books, lengthy codebases, or large documents in a single conversation — a standout feature at the time of its release.
Not quite. Claude's advanced versions support context windows of 100,000 tokens or more. This was a significant leap beyond many contemporaries and allows Claude to reason over very large amounts of text in one session.
05 / 8Design

Which of the following principles is NOT part of Anthropic's core goal for Claude?

Correct! Anthropic's guiding principles for Claude are to be Helpful, Harmless, and Honest — often called the 'three H's.' Hierarchical is not part of this framework. The goal is to make AI that is safe and beneficial for everyone.
Not quite. Anthropic's three guiding principles for Claude are Helpful, Harmless, and Honest. 'Hierarchical' is not one of them. These three H's shape how Claude is trained to interact with users responsibly.
06 / 8Features

What was a key distinguishing feature of Claude 2 when it launched compared to many rival models at the time?

Correct! Claude 2 launched with a 100,000-token context window, which was remarkable at the time. This allowed users to feed in entire books or massive codebases for analysis, setting Claude apart from many competing models.
Not quite. The standout feature of Claude 2 was its 100,000-token context window. Claude does not natively generate images, and real-time browsing and built-in voice were not launch features of Claude 2.
07 / 8Safety

Anthropic describes itself primarily as which type of company?

Correct! Anthropic describes itself as an AI safety and research company. Unlike some competitors who lead with products or platforms, Anthropic's founding mission centers on building AI systems that are safe, interpretable, and steerable.
Not quite. Anthropic is primarily an AI safety and research company. Its founding mission is rooted in making AI that is safe and understandable, which is why safety-focused training methods like Constitutional AI are central to its work.
08 / 8Features

Which of the following tasks is Claude specifically designed to handle well?

Correct! Claude excels at long-form writing, summarization, coding assistance, and complex reasoning tasks. Its large context window and nuanced language understanding make it particularly well suited for handling detailed, multi-step text-based work.
Not quite. Claude is designed for text-based tasks like writing, summarization, analysis, and reasoning. It does not render graphics, autonomously execute system commands, or perform live video analysis — it is a large language model at its core.
Challenge Complete

Your Score

/ 8

Thanks for playing!

This helped get things moving in minutes. I then asked Claude Code to start executing the plan. The best way is to give it executable tasks and milestones generated by Antigravity. For my resume builder, that meant tasks such as building the template system, creating editable sections, and implementing PDF exports. You ask Claude to start working on milestone one and complete the milestones generated by Antigravity. Claude Code performs much better when working on a specific task instead of a prompt like "build everything."

If you want real speed, let Claude Code operate on the repository. Claude Code can create files for you. It can edit existing files, run tests, fix lint errors, and search the entire repository. VS Code, on the other hand, asks you to jump between tabs manually. For example, while working on the resume builder, I could ask Claude Code to update every template to support a new field or modify the PDF export flow. It could work through dozens of files without opening them and get the task done.

Making use of both tools

Both tools have their own strengths

Once you have a reasonable output from Claude Code, you can use Antigravity as a second opinion. You can ask Claude Code to do something, then ask Antigravity to review it, and then feed the review back into Claude Code. Antigravity can identify edge cases, usability issues, or architectural problems. Once those issues are highlighted, you can prompt Claude Code to implement the changes.

While my resume builder project did not require both tools to work over time, if you are working on something much larger, you can also split the work into parallel streams. Instead of giving Claude Code a prompt to build the backend, waiting, then asking it to build the frontend, waiting again, and asking it to write documentation, you could run multiple sessions. In my case, Session A could focus on building authentication, while Session B could focus on the template editor. At the same time, Session C could handle PDF generation or documentation. The idea is to use multiple agents at once so that different parts of the project can move forward in parallel.

I have also found it useful to divide implementation and reasoning between the two tools. I have found Antigravity to be better with architecture planning, design decisions, and reviewing work in general. Claude Code is brilliant at writing code, refactoring code, running commands, and creating tests, so you could essentially divide these functions between the tools to get a much better output.

VS Code feels behind for agentic development

It’s lagging far behind at this point

VS Code still has a massive ecosystem, and I understand why developers use it. It gives you extensions, debugging, Git support, and almost every workflow a developer expects. Microsoft has also recently added AI features and Copilot-based agent workflows to VS Code.

But my issue with VS Code comes from how the workflow feels during a full project. You can add extensions, open terminals, install Copilot, configure tools, and build a decent setup. That setup still depends on you stitching the workflow together.

VS Code also carries baggage because the official product differs from the Code OSS repository. Microsoft develops Code OSS under the MIT license, but Visual Studio Code ships as a Microsoft product with Microsoft-specific customizations under a traditional Microsoft product license. It also explains why tools like VSCodium exist for people who want cleaner open-source binaries. Telemetry also adds to my frustration with VS Code.

Other tools now feel more aggressive about the future of coding. Cursor, Antigravity, Zed, Warp, and similar tools focus more directly on agent workflows. VS Code still gives you most features through extensions and integrations, but that does not automatically make it the fastest workflow.

Using multiple agents is the way to go

We have reached a point where using a single agent for everything is probably no longer the best approach. You can use multiple agents on the same project and get much more value out of them. I also found this to be cheaper than running everything through one agent. It lets you use cheaper models for simpler tasks while saving the more capable models for work that actually needs them.