If you are a software developer like me, you have the same problem. You start using Cloud Code, and then you start using Codecs,
and then you start using Gemini, Antigravity, Cursor AI, and all of those AI tools that we have right now. The problem is each of them decided to implement their own way to make memories files,
to create and manage agents,
to deal with the skills.
So you are in the nightmare of markdown files. Welcome to the nightmare of markdown files.
And I don't want you to live in this nightmare, so I have created the AI coders contacts to fix this stuff that all of these tools should be fixing right now. But as I'm seeing, they don't want to do that. So let's do it together. In this video, you're going to see what I'm trying to do to fix that stuff for me and
for you as well. I have created an open source project to fix that nightmare. English is not my mother language. Disclaimer set. Let's sign me to it. The proposal here is to have only the dot contacts folder.
Inside of that dot contacts folder, you have your agents, your docs, your plans, your skills,
your anything else they want to put a name in a markdown file organization.
After we have it configured here,
it is pretty straightforward.
You would just quick sync
with the tool you want to use. So if you are using CloudCode,
quick sync with CloudCode,
all your agents will have a sync link in your CloudCode folder.
So, basically, if you change the files inside of context folder, then it will automatically be recognized by cloud that this file has changed as well. Same for the other two. So you got the point. One place, one folder to rule them out. I just want a comment that I can write in my terminal or an MCP
that I can connect in my AI agent and say, hello. I just want to initialize my context.
And that's what I did. So if you prefer to use the CLI, there's an interactive CLI.
We have the AI agents and everything else that you need to have there. It will check your code base, analyze it, and then we'll suggest the next steps for you. Pretty straightforward. You just need to hit enter enter enter, accept the terms that you never read, and there we go. Or you can just connect the MCP. With the MCP,
there are some steps. First one, install the MCP. Second one, prompt to the agent. Emit the contacts. That's pretty much it. It will generate the files you just saw in my screen. But the thing is, these files, they are already mapped to understand your code base. Your agents are for your code base. They are not, like, just general agents. They are not general skills. They are not general docs. Next step, this will set up the context and field according with the code base. With the context ready, you need to prompt plan
and then the task you want to implement.
So, for example, plan develop a new authentication
method for me using AI context.
This will create a new plan for you,
but not just a random plan generated by AI. It will line up the agents that need to be used for this. It will figure out what files of your documentation
need to be considered for this plan, and, also, it will create a workflow of execution.
So if you start doing that in Cloud Code, you can continue doing that in Codecs. You can continue doing that in Corsair. No matter which tool you're using, if you want just to switch then, it's straightforward.
We have a memory that's persisted with exactly this step. It stopped it and which agent should go ahead and develop the feature in this step. Once you have planned the workflow, you have reviewed everything,
that's pretty much it. You can just hit start workflow and let AI work for you. And for the workflow system, I'm using something that I call Prevs.
In Portuguese, it's Prevs.
It is a method that I have been using for years already since 2023
to develop software to use AI at all. Because
first step,
b,
you plan.
R, you review your plan. So, basically, you will iterate with AI until your plan looks correct for you, until it looks legit, and then you move forward.
Moving forward, we have three steps, execute,
validate, and confirm.
So in the execution step, it will execute the code. In the validation step, it will validate for you. The idea here, it is not for you to run everything on autopilot.
The idea is for you to understand what's going on, to review step by step, to be assisted by AI. It is you that should be using AI as your copilot and and not you being the copilot of AI. Here's a quick example. Most of AI coding workflows look like this. User, add authentication.
Then AI generates 500
lines of code.
That's not what I wanted. That's you, user, and that's your workflow right now. But with this method, you will have p,
what type of auth,
OAuth,
JWT,
session, what providers?
R, here's the architecture. Dependencies, x, y, z. Risk is z. Do you want to approve that? Do you want to move forward? You understand what's going on. You understand the plan. You will review that with AI. Execution,
implementing the approved design, implemented the approved
feature.
Validation, all 15 tests are passing, and the security of it is passing, is complete. And see, it is deployed, or depending on your workflow,
you can confirm and you can manually do that. The idea is if you start doing something on Cloud Code and you are out of credits, you can just move away to Codecs and continue it exactly from where you were. Same thing. If you want to move to another tool, that's the same idea. We'll have a memory file that will keep the track of the progress
in your workflow. And here's an example on antigravity. Basically, I just said, this
is in Portuguese, in it is context. And then it started working, writing the files for me. Next step, I wrote, write a plan to identify bugs and initiate an workflow in Previz. And then it started it and run every tools that were that were necessary,
and then we have the plans.
As I was validating and checking the plans, I noticed that we had
more than one problem. We had three problems. The first one, we have
type errors. The second one, attack depth. And the third one, a most tenancy risk. Three different problems. Three different plans. So I asked, hey. Let's generate three different plans for it and link it to the current workflow. And that's it.
That happened.
And then I said, okay. Let's jump into the execution process
and execute the plan. And that's what Integraft is doing for me right now. I won't spend more time here. I want this to be as simple as the tool is. So next step here is for you to help me to validate that tool, to find bugs, to write pull requests,
to help us
to fix this problem so our lives become better. Okay? This is my first YouTube video in English. This is my first open source project that is actually becoming bigger than I imagined. As I mentioned in the very beginning, when I started this, it was something it was something more for me and my colleagues, and then it started getting bigger. And now I'm organizing this in a structured way for collaborating with you guys. You're welcome. If you have experience, you're welcome. If you want to just like four kids, you're welcome as well. This is MIT license, and this is AICoders channel. I'm Vinny from Brazil. It's nice to meet you.