Antigravity + Claude Code IS INCREDIBLE! NEW AI Coding Workflow Can Build and Automate EVERYTHING!

I didn't even realize this was a workflow I could truly harness until I actually tried combining anti-gravity with claude code. What I ended up discovering is a hybrid setup that

massively increases efficiency across my entire coding workflow. And there are honestly a lot of reasons why you should consider setting this up, which is why I'll be breaking this down throughout

today's video. We're talking about

today's video. We're talking about cheaper token usage, avoiding rate limits, and more controlled multifile operations with sub aent collaboration.

And overall, you will be able to get faster development cycles. For anyone

who is unfamiliar, anti-gravity is Google's free agentic AI IDE that's powered for autonomous multi- aent orchestration. It can control the

orchestration. It can control the browser, run terminal actions, and practically work on a fullon autonomous engineering system. Claude Code on the

engineering system. Claude Code on the other hand is Enthropic's agentic coding tool that many of us love to use directly within our terminal. It also

has an ID integration, but it essentially allows Claude models to understand your entire codebase, edit files, execute commands, and perform everything from routine development

tasks to advanced refactoring using natural language and especially having it powered by these agentic models like Opus 4.6 as well as a Sonnet model, you're going to be able to get the best

output. Now, here's where things get

output. Now, here's where things get interesting. By combining the two, you

interesting. By combining the two, you essentially get the best possible code quality without sacrificing precision.

Anti-gravity is something that excels at high level autonomous orchestration as well as navigating large code bases inside an IDE environment. Fod code is something that would shine when it comes

to precise developer control terminal execution with structured multifile edits and complex refactoring workflows.

Together you can create a hybrid system where anti-gravity handles the big picture automation whereas cloud code can provide granular execution control

and this result is faster with its iteration process fewer bottlenecks and reduces token cost and consumption and overall you'll significantly get higher

quality outputs when you're building prototyping or scaling full applications. If you want the best AI

applications. If you want the best AI tools, workflows, and drops before everyone else, join my free newsletter with the link in the description below, which is completely free. To get

started, you're obviously going to need to have anti-gravity installed and make sure you have a Google account so that you can use it completely for free within your operating system. You can

install it for Mac OS, Windows, as well as Linux. You'll also need to make sure

as Linux. You'll also need to make sure that you have Cloud Code installed. You

can install this globally or you can install this within a desired directory.

On the right hand side, you have your anti-gravity agent. But if you are to

anti-gravity agent. But if you are to open up the terminal, you can open up claude code directly within the bottom section. Now, this workflow may seem

section. Now, this workflow may seem kind of counterintuitive cuz why are you using claude code on one side and then anti-gravity on the other? Why not just

use one for all of the tasks? Well, like

I'd mentioned at the start, we can use anti-gravity to handle the bigger picture. It has different sub aents

picture. It has different sub aents already preconfigured. You can have it

already preconfigured. You can have it work on implementation or work on executing the implementation and with all of these state-of-the-art models for free. Actually, obviously, it's rate

free. Actually, obviously, it's rate limited, but that's when you can switch over to cloud code to help you in certain areas. And whenever cloud code

certain areas. And whenever cloud code has reached a limit in terms of its context, this is where you can use anti-gravity for example where you can feed the context from cloud code to have

it help you handle the bigger picture, the workspace and the agent orchestration to get the task done. You

get the best of both worlds when you combine the two cuz it's going to be faster. Plus, you get better results.

faster. Plus, you get better results.

And I'll showcase this as we showcase a couple of different demos. And just an FYI, if you do not know how to use the terminal properly, you can obviously just use the Claude Code extension. And

this is essentially where you can easily just use it as an extension like you do with the anti-gravity's agent panel.

Works exactly the same way as the cloud code terminal, but it works through an extension like the anti-gravity panel extension. And the beauty with cloud

extension. And the beauty with cloud code is that you can have multiple sub agents running at the same time. This is

with the extension obviously but in the terminal you can obviously deploy multiple cloud code instances. So you

can run these different agents to tackle various sorts of tasks. Obviously within

claude they have introduced sub agents itself that can run parallel to whatever you're working upon. First off what we're doing is going to start off by using the anti-gravity agent. And what

we're going to be doing is using the opus 4.6 using the planning mode. This

is where we're going to start off with a highlevel objective by prompting it to create an implementation plan with the Opus 4.6 for a production ready SAS app.

This is where the Opus model will be able to create a clean road map, a sub agent task tree for our agents to follow within cloud code and it avoids the cloud token waste. When I'm using the

implementation plan, you are essentially going to have anti-gravity as the thinking engine, not your coder. And you

can see that the implementation plan for this production ready SAS app has been created. This is the plan only. No

created. This is the plan only. No

implementation is uh processed until approved. And the target is a

approved. And the target is a multi-tenant B2B SAS platform. The

subscription billing team workspace and a couple other things. Starts off by creating the mermaid chart that showcases the highle architecture of this app. It goes further in detail in

this app. It goes further in detail in terms of creating the tree, the structure, every component that is needed for the agents to essentially process the creation of our app. And

that is why you would essentially want to use the Opus 4.6 cuz it is something that has a 1 million context window that can process higher context and provide

all the specs necessary for the agents to tackle. From here, what we can do is

to tackle. From here, what we can do is move on to the next step. Now, obviously

with this implementation plan, it could be really hard for a model to fully process properly. So, this is why what

process properly. So, this is why what we're going to be doing is converting this plan into an executable engineering task list. This is going to be easier

task list. This is going to be easier for our cloud code agents to handle.

Now, I'm just showcasing this one by one, but ideally, you would want to be using both of these sub agents together to help you tackle these tasks faster.

And that's the point of this hybrid workflow, increase efficiency. And

remember, if you are to essentially have the Opus 4.6 within anti-gravity tackle all of these tasks of having it code out the implementation plan as well as creating the implementation plan, it is

going to easily hit into great limit.

And that is why you would want to use something like Claude Code, especially if you have the Cloud Code Pro plan, cuz you can deviate the token usage with these two different agents to execute

tasks. And we're actually going to be

tasks. And we're actually going to be going back into anti-gravity after we have gone in the task list so that we can essentially use it as a debugging and refactoring agent. Now we have the

execution task list which is something that we can use for our agents to properly follow making sure that our agents do not hallucinate and make sure that they follow through on each

executable step like creating the project directory creating the packages as well as all the components needed for the back end and front end. And now what we can do is deploy the sub agents to

tackle each and every individual task list that we had developed. And that's

the beauty of this combination. You can

create different agents that can tackle all of these executable tasks that the anti-gravity implementation agent had created. So you can have one that

created. So you can have one that focuses on one of the phases which is the integration and polishing after it has completed a certain task. You want

to first start off with obviously the setup process. So one agent could be

setup process. So one agent could be handling task one to I don't know all the way to two. Another one can focus on the back end only and then making sure that connects to the front end and then

obviously the other two agents can be the integration agent as well as the deployment and you can specify each and every execution that it needs to tackle.

You can even provide references by dragging it directly into the cloud code agent and describing what this agent should be handling. And once you describe what the function of that agent

is, Claude Code is going to generate an agent from that description that will be able to follow through with the execution steps. Now, you may seem that

execution steps. Now, you may seem that this is a full-on redundant process. Why

is there so many steps? You feel like you're using a lot of tokens, but this is the way for you to maximize the quality of output. Multiple specs,

multiple instructions help these models stay aligned to what you're trying to accomplish. And there we go. It has

accomplish. And there we go. It has

context of our first phase which is phase zero and it is going to be the expert project scaffolder and it's an engineer with deep experience in bootstrapping software projects across

multiple languages. And now what we can

multiple languages. And now what we can do is create the other agents that can help us with this full-on process. So to

give you a rough idea of how this will perform, you can start off by providing the execution task list and deploying both the two agents I just created. the

project scaffolder as well as the back-end task executor. This is where I can just give it a simple textual prompt to follow through with the backend phase as well as the initial phase of setting

up the full on project directory. And

that is when it's going to deploy both of these two different agents to help us tackle the overall task list. And here

is an example of that. The project

scaffolder has been deployed and what cloud is going to do autonomously is deploy as many agents that it needs to execute these tasks. And what you can even do is add in different instances of

cloud code to help you process all this.

Something you can also do is have a front-end agent powered by the Gemini 3.1 Pro. This is an option that you can

3.1 Pro. This is an option that you can obviously do within the anti-gravity agent. The reason why I say this is

agent. The reason why I say this is because Gemini does a great job with front-end development. It is something

front-end development. It is something that will connect to the scaffolding as well as the back end from the cloud models. That is what I'm going to be

models. That is what I'm going to be doing within anti-gravity. I'm

requesting it to create the front end of this project while providing reference to the execution task list of having it follow through with phase three which is front end. But we can see right now that

front end. But we can see right now that we have multiple agents right now executing different phases. So we have the scaffolding being set up which you see over here. You have the packages

being set up. You have a task list for phase zero and phase one which is the scaffolding as well as the back end from cloud code and then the front end being developed by the anti-gravity agent. It

seems like a headache but it's truly a fullon AI engineering team working for you and developing something that would probably take a lot of time for a dev

team to accomplish. So the scaffolding has been set up as well as the backend connected to a database as well. We just

need to provide our env. And you can see that it has accomplished all of the different subexecution tasks that we had generated initially with anti-gravity which is remarkable. Now we can head on

to phase 2 which is the front end and that is what the anti-gravity agent is doing. But what I'm going to be doing is

doing. But what I'm going to be doing is telling the cloud code agent and saying that the phase 2 has been already created by anti-gravity. So then we can focus on the next steps. There we go. We

have a fullon production SAS that was generated. Now, I will say the Gemini

generated. Now, I will say the Gemini model didn't do as great with its front-end quality. This is a basic front

front-end quality. This is a basic front end that it had generated. Maybe I

should have been a bit more detailed in using different uh dynamic attributes for this front end, but still it did do a decent job. And I guess there is a

functional task app that it had focused on generating which is something that has a dashboard to help you track all of your different tasks that you have in the to-do section in progress review as

well as completed. It also showcases the completion rate. But you can go ahead

completion rate. But you can go ahead and try and use all these features like adding a new task. You can provide a task like creating a YouTube video and

then providing a description like on anti-gravity for example. This is a high priority for me and I wanted to set it as a to-do assign it to me but you can

assign it to other team members which is great. And we can create that task and

great. And we can create that task and you can see that it showcases a change within the main dashboard. And within

our task list we have a lot of different tasks within the to-dos. You can see that the YouTube uh priority is high. So

what we can do is we can even have AI suggest different subtasks for this.

This kind of reminds me of another tool that I used before called Dart. And you

can see that it was able to create the same functionality of it. You have a multiple subtasks that I can use to help me create this YouTube video like researching the best practices for

creating this YouTube video, the technical specifications, etc., etc. And every time I uh complete the to-do, it will list this task as complete. And

then it is something that you can obviously move over to done or to review. There's no drag and drop

review. There's no drag and drop feature, which is kind of unfortunate, but regardless, it did do a decent job.

The analytics showcase all of the tasks that you have, the average completion time, etc. And there's different projects as well, which is great to see.

So, overall, it did do a pretty remarkable job in creating this. It did

create the functional components I was looking for, but I would have looked to generate a better UI if possible. If you

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essentially how you can combine cloud code with anti-gravity. And I believe this is the ideal workflow right now that I had just discovered this past week. This is something that will

week. This is something that will maximize your efficiency, your token usage, as well as getting you the most qualitative output from these AI coding

tools. And it's not just generating AI

tools. And it's not just generating AI slop. This is something that will follow

slop. This is something that will follow your guardrails, your specs to get the best quality outputed. So, let me know what you guys think. I'll leave these links in the description below so that you can easily get started. Make sure

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