Ready for AI coding on steroids?
Today, we're focusing Autocloud
where you can get 10 times the work done on your projects
with the planning and the quality coding and testing that you should demand from your AI coding system.
Now AutoCloud is designed to work for all skill levels from vibe coders without any coding experience to senior developers.
Hi. My name is Andre Michelson,
and I use AI to code production level applications every day. And today, I'm showcasing Autocloud,
a open source and free project
for using your cloud code subscription
to get more work done.
So when you're opening Autocloud
and want to connect it to a existing project or create a new one, you just come to the top left corner here, add project,
open existing folder,
and when you open it, it's gonna prompt you to initialize Autocloud.
That's gonna create a dot Autocloud folder inside your project. You just initialize
and then the project is ready for you to use. And we're gonna start with showcasing the Kanban board here, which is the system for long running agents that just runs in the background. Now how it works is that you start engaging
the system by just creating a task. And we're gonna start with one simple task where I found a little bug here in actual inside of AutoCode where we have a double crosshair.
So we're just gonna take a screenshot of that and just paste it into the description and we see it gets added as a reference image to the task. And we're just gonna say here, and I'll use dictation.
Please see the image in terms of the double cross for closing out the project files
that's overlapping with the model for closing the create task model.
So you can see now when we have some options here for selecting the model and the thinking level if you want more control.
If you don't want any do any manual things, we have tried to keep things at a good level.
We are gonna do a little bit of a work here where the system itself
figures out what model and what thinking level it's gonna use.
But as you can see now, we also have the section for requiring
a human review before coding.
If you want to work on more complex task where you want more control.
But the system will work autonomously,
and also we can drag and drop part project files here into the description.
But right now, it's not working correctly. So we're gonna create a second task for showcasing
how the system actually
identifies
how a task is either simple
or it's a medium sized task or it's a very complex task. So you can see now it's written the task title itself.
We don't have to do that if we don't want. And we can now just start this task and it will go into a planning phase where it starts to identify
all the context it needs. It finds out what it needs to create as subtasks
and also the complexity level of the task itself.
So while that has started, we can start to see some logs. We will also create a new task here for the issue that
we can't drag and drop files.
When using the project files view, we cannot drag and drop the file into the description. There's only the section for reference files further down in the task section,
but I think we should just mention below the description that the user can both paste screenshots
and drag and drop files from the project file
explorer.
So now, in many cases, this is a description that's like we could give a lot more context into the task in order for it to be a much better task.
And the system we're trying to like, Autocloud is gonna try and identify
everything and also have a self review process. So it's gonna create a plan.
We'll go here and see the logs have started.
You can see now it's in planning phase and it has 27 entries.
And if you want to review and understand what the AI is actually doing, you can see the logs here with all the tools,
all the writings, and everything.
And it has identified here, we can see the complexity level for the fixing overlapping close buttons. It has identified as a simple task with a confidence of 90%,
and that dictates how everything is gonna work. It's just gonna go and have a quick spec and create a simple test for fixing or checking that it's actually fixed once it's done. So it's not gonna produce a lot of unnecessary
tests, a lot of different coding, just so it saves upon the context itself here. So we don't have to pay more for a simple task integration.
And if you're wondering, we are working on the same files here and the same systems,
but how AutoCloud is working is that if we have a full overview over all the work trees that are for this project itself.
So that means a work tree is basically a sandbox or in a environment
where the coding is happening in one place and it won't touch any of the other files so you can safely work on the same as for instance, in this case, we're working on the same files on two different tasks.
And then if you're a developer, you're gonna be like, hey, we're gonna have merge conflicts. But inside Autocloud,
we have this merge conflict AI layer,
which is doing a little bit of programmatic things, but also having an AI that does the refactoring
and handling merge conflicts if there's any in these two tasks. So we're gonna see once it's done, we're gonna see that it's actually a merge conflict and we can just quickly with one button solve it and everything gets done correctly.
So as this is working, you can already see that this simple task is already going over to an AI review. So the AI is gonna review its own work, and that's the power of AutoClaw that you can just have this running in the background, and it's only ready for you once the AI has deemed it ready for you to actually test and utilize straight into the project.
So we also have this agents terminal,
and this is a place where you can spawn up to 12 terminals
and easily invoke Claude in all of the instances.
And there's also some smart functionalities we're working on because myself, I like to have this type of terminal overview
that I can rename
terminals to what there's they are about, So I can hold my my thoughts when I navigate around, especially when you start to get more terminals, like when you start to get six and nine and even 12. Depends how much like multitasking you want to do. But for me, when doing
like tasks, I want to rename the terminals. And also there's a smart functionality here in terms of that. You can add tasks
just straight from the terminal view and you can also reference them
basically like this and it will rename the terminal into the task description,
and it will also give the description straight into the Cloud Code terminal. So you quickly can start new tasks if you have planned out things, but want to not utilize AI system with everything happening in the background, you want to be more hands on. This is the system that allows you to do that.
We also have some session restoring so that you can close down the application and spawn it up again, and it will save the session so you can continue if anything happens.
And we'll continue to iterate on this based upon how many people use the agent terminals in their day to day.
We also have insights, which is basically just a chat with history, which is a cloud called instance that have access to your project, and you can spawn chat history here. Myself, I like to use this for more, like, just
investigating things that I'm wondering about, maybe having a sparring partner, something like that is nice to have. And also the roadmap functionality is a very good thing for you actually getting good traction
with your project.
So here you can see we have different views for creating a roadmap and we're also gonna be integrating
third party software like Canny in order to get feedback from users from those softwares
and just incorporating it straight into the Autocloud system.
So here you can see the AI has tried to identify your target audience,
and then it's breaking down features that you should implement into your project. This is just gonna become better and better as the more you use it because
Autocloud has its own memory layer. So we can go here to context, and the first thing you'll see is the project index. So it's basically what we can programmatically
figure out what the project is about. It has identified we are in the auto cloud repo here, and it is in in identify the electron front end and the Python back end with the API routes and the dependencies
just so that things like this, the actual project infrastructure
is quickly given as context to the user.
And something we're working very heavily on is the memory system. It's a graph memory system
also
incorporated with semantic rag. If you don't know what that means, it's basically that we have different methods for letting the AI know about your project.
And as you're doing and you're working with tasks here,
the more you use it, the smarter the system becomes.
And for people who has a little bit more knowledge about context windows
and token usage, the more you use Autocloud, the smarter it becomes at actually retrieving context at a smaller token usage. So it will become cheaper to actually use Autocloud
over cloud code when you use it over time.
So the roadmap functionality definitely is something to check out and see what it suggests that you should do for your project. But also we have the ideation
tab here where we just can get some quick tasks if you're not sure about what you should do. And also if you have something like,
I just want to see, for instance security
and code improvements at a general step, you can generate that and Autocloud will figure out based upon your project, investigate where how do we have some security issues. We also have performance, so we can do performance enhancement.
But also like code improvements is generally
is trying to locate a low hanging fruits, quick wins,
where we can quickly incorporate something that has good value for the users of the project.
So let's go just go back here and see. You can see now that the two tasks that we have done is done with AI review. We can see here it hasn't identified actually that there is any merge conflicts ready for this.
Let's just recheck it.
In these tasks, you can go in and see the sub tasks, what it has done. You can also check the full logs in terms of what it did for validation, what it did for coding, so you have full overview over what the AI system has actually done for each of these.
We're just gonna let this run because it finds five different things with each category.
And while we do that, we'll talk about the change log functionality inside AutoCloud, and it's something I really like. It has the possibility to create change logs and also it's integrated with GitHub,
so you can create a change log based upon either all the tasks you have done inside AutoCloud.
But what we use most is to get history
because I like to work with both the task Kanban board, but also like I showcased more manually. So I'm manually doing commits to GitHub, and I'm manually doing work with AI.
That makes it so that when you go into Git history here, you can for instance say since last version, say, and we are version 2.1 right now, and it would actually load all the commits that's happening since that version,
and you can just continue.
And we will say two point o version,
and this is a GitHub release, and we want some emojis.
And it's done just like that, it's gonna generate a change log,
And it's quite quick. We don't use opens for this functionality right now, and you'll see it identifies the new features, improvements, bug fixes, and also all the changes.
So we can quickly just save this to the change log. And once it does that, we can just click create release, and that goes straight up until GitHub.
So that's a very fast way to get good change logs and do GitHub releases.
If you want something like that for your project,
I do recommend using the change log for actually
keeping track of what's happening inside your project just so you have control over time.
So we talked about the context as well. It's something we really are working hard on and there's integrations with GitHub issues. So GitHub issues will come straight into Autocloud, and you can create tasks, and it can start to identify what's wrong and fix it automatically in the background.
There's a lot of other things that's coming. There's a lot of good things in the settings.
For instance, if you are a very heavy power user
like me, you can utilize
the integrations tab here. We have multiple cloud accounts. Because what AutoCloud actually allows you to do is to create a lot of tasks
and just run them in the background, so you get a lot of features done quickly.
But that also implies that you're getting things done
not only quicker, but for instance, I get a lot of tasks done while I sleep. So basically, all my Cloud Code usage has doubled,
so I need two Cloud Code Max plans. And we have created a system that you can incorporate both of them into the AutoCloud system and easily swap between them, and we will incorporate some type of like auto switching, so it knows about what the rate limits for one account and it swaps when you need to swap, basically. So now we can go ahead and just get the task into our project.
We open the task which is ready now for human review,
and we just press stage changes.
And that makes it so that if there's any merge conflicts, it's gonna solve that by itself.
And we can go here into the IDE now and we see that the changes have come into our repo and we can repute review them if we want And we now also can just go into the you can see here the changes staged successfully.
So if there is any merge conflicts, and this is a very good, which I appreciate, which is the merge conflict AI layer that fixes merge conflicts automatically.
So you can work on a lot of tasks and also work manually in other branches,
and the system will follow along as files evolve.
But we can now go and see if this is successful. This was the fix overlapping buttons
and that was that if we go here and we see it browse the files, you can see now the task was successful. It implemented it and we can go ahead and merge this with our main branch. So what's coming next? We have a great focus on the framework itself that runs behind the hood. We also have the Python CLI that you can utilize if you don't want to use the front end electron application.
Now we're also working on incorporating
the bMAD method for context engineering,
AI coding, validation,
and we're just gonna put it on steroids by putting it inside AutoCloud
where we get, like, the long running agents, QA looping, rag memory system,
and much more. We also welcome everybody to our free Discord community. The link is in the description to talk about features, bugs, and you can also network with like minded AI enthusiasts,
developers, entrepreneurs, marketers, and also a lot of people there, and then generally a nice community to feel at home. So stay tuned for big productivity
update.
So I just wanted to showcase how you can install AutoCloud if you're wondering about how to do that. We have a very descriptive quick install description if you want to follow along that. If not, here's how you do it quickly. You download the zip file here on GitHub and the link is down in the description.
Then you go and
you create either a new folder or you unzip the autocloud folder
which is which you downloaded.
And you just go to your favorite IDE. Here, we're using cursor just because it's simple to use with AI especially if you're a beginner and we just open up the folder that we just unzipped.
Now we have the Autocloud
project open here. And the first thing you're gonna do is that if you
is that if you haven't installed
the NodeJS
Python and Docker desktop,
just follow
the instructions or just what you also can do is basically just copy and paste this into something like
cursor and just say help me
install this and it will help you do that
based upon your system and that's actually a very quick way for you to get started. Once everything is installed,
you're just gonna do the
first step here which is to
do these commands inside the terminal here. So if you don't have UV, you can basically just tell Opus to or or the agent you have inside cursor or any other a AI agent to just help me use
UV, which is basically just for managing the packages here in a environment.
Or you can use this you can try this also
if you're
if the first command here is failing.
That will basically spin up a virtual
environment
and install everything, so now it's ready to be used.
The next step is to actually start the memory layer, and here we need Docker Compose in order to do that.
So you will just go and do this command in its own database.
It will create the database and spin up what's necessary for the memory to work.
And the last thing is to actually just install and launch the application. So we're gonna do p n p m install that installs everything,
And then just command copy this command
once it's done here. So now it will just build everything and it will start the application as soon as it's done. And it's taking quite a short amount of time.
And now we can see we're getting a new application
up and going here.
So
there you have it. It's up and going.
And if you have any questions, I highly suggest you join our Discord community. I will be very interactive there in terms of helping you if you have any issues or bugs.
And if you have liked the video, be sure to subscribe
and like it and
help other people find this amazing system that we are working on so we can get going with our projects at lightning speed while keep keep still keeping that quality that we want from our AI coding.