Mindhub Cowork Setup Guide — Step-by-Step Guide & Transcript

[00:00] This dashboard was built by an AI agent called Anton. I briefed it once, walked away, and this is what was waiting for me. Now, I use Cloud Code and Cloud Cowork every single day, but it has one fundamental problem,

[00:12] and that's that you're locked to one company's pricing, one company's ideology, and one company's models. Now, the tool I use to build this dashboard is called Mindhub Cowork. Now, it's fully open source, you can run it hosted,

[00:24] or you can self-host it on your own machine, It has a real model router, so you can run Claw, GPT, Gemini, DT, or even your own local models. Now, they sponsored today's video, and I'm going to show you exactly how to set it up and use it start to finish.

[00:38] The key differentiator here is that it's free. You don't need to pay for it. Again, it's completely open source, so you're in control. And as soon as a new model comes out, you can immediately start using it, and you're not locked into one company's presence.

[00:50] Anyways, let's dive in. So here's a quick look at MyHub Colt. You'll notice it's a very familiar user interface, looks like many of the other co-work applications. You have projects, artifacts, connected apps, channels, memories, settings, skill libraries, you get the idea.

[01:04] And then you can click into a task and obviously interact with the model and see what it's produced and the progress that it has. Now again, the main differentiator here is that this is fully open source. That means that there's multiple ways to run this application.

[01:17] You can download the desktop app, you can self-host it yourself, or you can even run it in the cloud directly from Mindhub, which I'm going to talk about in a second. Now, in my case, I'm just using the desktop application because it's the easiest to set up, but the reason you would even use a tool like this is because you have all of the options.

[01:32] Like I said, it's open source, you can play with the code on your own, and you can choose any model you want at any point in the future. And that's super important now because we've seen Kimmy's come out, we have GLM, we have all of these great models which are pretty much as good as something like Fable,

[01:47] but are so much cheaper. So rather than running everything through something like Cloud Cowork, where you're completely locked into Anthropix environment, you can use an open-source harness like we're using right here and switch out the model whenever you want.

[02:00] Now, of course, you can still use cloud models, but you can also run local models. You can choose any provider at any time with no vendor lock-in, which is the reason I'm even sharing this to you. So what I'm going to do is quickly show you how to download and set it up.

[02:13] Then what I'm going to do is walk through the process of adding skills, with connecting data, and how you use this trip like an agent co-worker, except with all the benefits that I discussed. Now, the easiest way to get started with this tool is to download the desktop application.

[02:27] So from this page here, I'll leave a link to it in the description. You can just press the button that says Download for your operating system. So let's start with that. Now, we'll run to the installer in a second, but if you want to self-host this, you can go right to their GitHub. All of the code is open source.

[02:39] You can download it, run it, do whatever you want with it directly from here. Elsewhere you can open it directly in the web, they have web UI if you just want to do it from here and you don't want to download anything. Okay, so let's run through the installer and let me show you how we get this set up.

[02:52] Okay, so the installer is finished and from here you can either continue without an account if you just want to do everything yourself and create your own models and set everything up. However, if you just want to use this super easily, then you can connect to your Minds account.

[03:04] So you can create a free one or sign into it and then from there you'll get a bunch of free credits to use some of the cloud models. or you can, of course, bring in your own providers, which I'll show you in a second. So from here, if you don't have one, create a free account.

[03:16] In my case, I'm going to sign into my account. Okay, so I've signed into my account here, and now I can start using this just like any co-work tool. Now, behind the scenes, what this tool uses is something called Anton. Now, Anton is an agent harness, somewhat similar to something like Hermes, okay, which you've

[03:31] seen like Hermes agent before. And actually within Mindset Co you can change between the harness that you want to use So if I scroll here and I go into the settings and I go agent you can see that I can select the harness of either Anton

[03:44] or Hermes. So I can go between them and then save the settings if I want. Now Anton is the best default option because it's the best for actually providing usable output, whereas Hermes is more for like running things automatically on a schedule. Now here I'm connected to MindSub already because

[03:58] I've signed in with my account. That means that I can use any of the cloud models. You can see they have a bunch of ones here, like MindSub Air. This is a free model they provide that gives you a ton of additional usage. Or you can pick any of the other models, like GLM 5.2, or Grot 4.5, right,

[04:13] or Kini K3, or whatever you want, and you can choose what you want for planning, for routing, and then for coding. Now, if you don't want to use this, you can connect to your own providers, so Anthropic, OpenAI, whatever, or use any OpenAI-compatible APIs to something like LM Studio or Ollama.

[04:30] And if you want to see your usage here with MindSub again, and you'll get a bunch of free credits. And then if you want to keep using their model router, you can, of course, buy a subscription, buy additional credits, etc. The way you would see that is from the website, you can go to your console.

[04:42] From the console, you can go to Cowork. You can top a balance for using the cloud models, or you can get a ton of additional credits, again, for their Mindhub Air model, as it describes here. Okay, so let's go back. Actually, for the coding model, I'm just going to switch this to GPT 5.6 SOL,

[04:57] or SOL for now, and let's save that. Okay, and then let's just give it a task. So I'm just going to ask you something simple. Can you do research on the top LLMs right now and create a dashboard that goes over the performance versus the price?

[05:09] I want you to cover at least 10 different LLMs, go through the model families, and you need a ton of information in an interactive dashboard that I can view. Okay, so let's do that, and then go ahead and press on Enter. And I just want to show you what it generates and how it works, then we'll go into all the other settings.

[05:23] Okay, so just wrapped up now. We can see we've got all of the info here, and we can actually go to the artifact that it generated, which is a full web dashboard, and we can view everything inside of here. And then if I want, what I can do is actually just share this, I can add a password, I can select users,

[05:38] or I can make it fully public and then anyone is able to view this. Now this is kind of the difference between Anton and the Hermes harness that you have here inside of MindSub. With Anton, it's meant to produce these artifacts that you can then reference later and actually give you fully finished work,

[05:54] whereas with Hermes, it's typically better for automations, running things, like on a schedule, you get the idea and then kind of that's skill exploration. So anyways, if we weren't happy with this, of course, we could do a follow-up prompt and we could make it give us something better.

[06:06] But for now, I want to show you a few other features. For example, I want to show you how we can change to use something like a local model. So for example, if we go into the settings and we go to the models here, we can add another provider.

[06:18] So if we go to provider, we can go OpenAI compatible. What I can do is just call this like LM Studio, for example. and then if I pop open LM Studio, let me just load up a model here. So let's load Java 4, for example.

[06:31] Let me just pump up the context window here in a second. And then what I can do is find the API endpoint. So it's just this one right here. And what I'm going to do is just reload this quickly so it gets the updated context.

[06:43] From here, I'm just going to put nothing for the API key. I'm going to put this slash v1. And then we're just going to wait a second. So once LM Studio loads this up and I can save this, and then what I'll be able to do is actually adjust the model here in just one second.

[06:56] Okay, so we can see this provider is added, so for example, if I want my planning model to be LM Studio, I change the provider, and then for the model ID, I just have to copy it here, which is Google Gemma 4, so let's paste that right there, and then I can save the settings.

[07:10] And now I adjusted this so for my planning model if I go and I make a new task I can say hey what model are you using right now And if we hit enter it should use the planning model and we should get the LM Studio response And there we go We get it Jonah 3 4 billion just using a small one And you can see

[07:24] how fast it is again, because I'm running that local model on my own machine. Now you can switch to any provider that you want, but I'm just trying to indicate that, you know, it's very good, easy to switch and to use local ones directly in this harness. Okay. So for now, let's go back and

[07:38] change the model because this one's not going to be quite as good as what I want for these examples. Let's see, what do I want to actually use here? Let's try like DeepSeek v4, for example, reasoning level. We can go with high. We'll change the routing one here to use MindSub Air,

[07:51] just because we don't need super high intelligence for this one. And for the coding model, I want to go with something a little bit faster. So let me see if I can find Grok here, which is pretty fast. Yeah, let's use Grok 4.5. Okay, so we're going to just save those for right now.

[08:04] And then you'll notice that there's a bunch of other stuff we can do here, like there's a backend, the account, channels, all this stuff. I'll show you how we set that up in a minute. Okay, now that we've got that, let's have a look at adding skills, for example,

[08:16] because similar to kind of Hermes, there's a skill library here, and you can manually create a new skill by asking co-work. You can write the instructions, or you can upload one. So let's just do a very simple skill. Let's create a skill together.

[08:28] First, ask me what this skill should do. Let's hit enter. I just want to show you how that works. So this is going to be a super quick skill here. Let's do something like this. I want to create a skill that can generate a YouTube title for me. The idea should be to generate something that piques curiosity and interest.

[08:43] Overall, we want to keep it under 100 characters in length. We want it to be to the point, and we want it to be kind of tutorial-based and full course-based. Go look at my YouTube channel, Tech with Tim. Find some of the titles that have performed well, and then build this skill around that

[08:56] so that we always get titles that kind of fit that narrative. Not sure if that makes 100% sense, but let's tell it to go do that and see the skill that we get. Alright, so we can see it actually went. It looked up a bunch of my YouTube video titles, saw how they were performing, gave me a bunch of, you know, stuff here, output.

[09:11] And then I can just save this skill directly. And if I go to the skills library now, we now have the YouTube title generator that was written. Now, if we want to invoke the skill, of course, as usual, we can just go slash. And then we can just do the skill, so YouTube title generator, and then just ask it what to generate, and it will create it for us.

[09:28] Now, skills are great, but we also can connect to different applications. So if we go to connect here, there is a ton of connectors that are built in. So for example, we've got Gmail, Google Drive, Salesforce, Slack, right? You name it, pretty much all of the integrations are here.

[09:41] Now, one that I'll connect you to this video is Asana, because this keeps track of my video pipeline and stuff, so I can show you a demo. So let's go here. I'll grab my personal access token and then show you the connection. Okay, so I've got the token. I'm now just authorizing it here, so I've given it to Anton.

[09:56] And in a second, what it should do is start connecting this and then allowing me to talk with Asana. Now, it's worth noting that all of the credentials that you put here go into something called a credentials vault. This means that it's actually not viewable by the agent or the LLM itself,

[10:10] but it will be used when it actually needs to access the SERP. So it's automatically handled, and that's kind of part of the software. You can look at it, obviously, in the open source code if you want, but it's called the credential vault, so it keeps everything secure.

[10:22] All right, so it looks like we're connected here. We've got the connector, and if we want, we can go to a new task and say, give me a quick summary of what I have coming up for videos in Asana. And this is because it tracks all my video status, what needs to be edited, kind of like the pipeline there.

[10:36] So let's see what it shows us. Okay, you can see that it's pulled all this information, two videos that are being edited, and then a whole sequence of shorts that I'm doing for a new school community that I have. By the way, I'll leave a link to that in the description if you want to build AI agents yourself.

[10:48] But you can see that it pulls everything up. Nice so I think the last thing to mention here is the memory so like pretty much all of these agents it can pull from the session history but also it can automatically keep track of rules You can add stuff in the profile for example

[11:00] You can give it lessons. You get the idea. So if you want it to always remember something, just tell it. It can automatically update this, or you can edit the file yourself, add the markdown, and it will remember. Now, just to clarify here, the memory that you see is self-learning.

[11:14] And that means that it's going to automatically remember what you tell it, and these files that you see here aren't really designed to be edited manually. It's more so that you can view them and you can see what code work has extracted and remembered.

[11:26] Now, another thing to note is that the memory is shared across harnesses. So that means you can easily switch between Hermes and Anton, and you keep the exact same memory, making it a lot easier to switch between harnesses.

[11:38] Okay, so that's kind of a general walkthrough. Of course, we have the scheduled tasks too, but I'm not going to show that one right now. What I want to do is show you a few legitimate use cases of what these types of tools can produce and why you would actually use them.

[11:50] Okay, so for the first example, I have this kind of Google sheet with a bunch of information, just like fake sponsorship data, right? So what I'm going to do is I'm actually going to have Anton convert this into a fully functioning dashboard and web user interface.

[12:03] And that's what this prompt does. It turns it into a legitimate database application that I can actually deploy, which is going to be a lot more visual than something like the sheet. So let's run this and let's see the output we get. Okay, so it took a few minutes here, but it looks like we've got the result.

[12:15] Let's check it out. And, okay, you know, that's not exactly perfect. It looks like there's a few issues. So what I'm going to do is just copy some of this, and I'll just tell it to fix this up. And look, guys, sometimes that happens when we're using AI agents.

[12:28] It's not always perfect, so let's give it another prompt to fix that up. Hey, there are some pretty big issues. The UI is not really rendering properly. There's all kinds of random text on it. Can you have a look at this again and redo it? Okay, and I'm just going to paste this in.

[12:41] This is an example of what I'm seeing on the actual screen. Again, the UI is pretty messed up. Okay, so just wrapping up now with a figure of the artifact. You can see now it's working. There we go. That's better. That's what I was looking for. And we get the full dashboard with all of the deals.

[12:54] We have any payments that came in, deliverables, sponsors. We effectively just took the Google Sheet and converted it into a real legitimate dashboard that's actually useful. Next, let's go to another use case.

[13:06] So something I've been doing a lot recently is actually making interactive presentations to go along with some of the videos that I have. So for example, I have this brand guide for my new school community called AIH Builders that has all of the information about it.

[13:19] So what I'm going to do is just pass this in as a file reference here to MindTag or Anton, and I'm going to ask you just to make a quick presentation about something, right, and then use this brand guide. So let me come up with a prompt, and I'll be right back.

[13:31] Okay, so I just asked it to make something about prompt injection. Let's see what it can do, and there's what we get. Okay, so just wrapped up here, if I go to the artifact, we can see this is what we've got. Prompt injection follows the guidelines exactly.

[13:43] We have, let me make it actually full screen here so we can see it a little bit bigger. Okay, there we go. You can see we have kind of a little box down here. We have the code snippets. We have real-world impact, defense layers, and it's exactly four slides just like I asked for.

[13:57] I guess it's not super complex, but the point is it follows the instructions, and I can add these documents and create something really meaningful. And look guys, there's so many other things you can do with tools like this, I'm going to leave it to your imagination. The main reason why I like a tool like this really is because I have full flexibility with all of the different models and the providers,

[14:14] and I can keep all of my data and information local and really have full control. So anyways, that's going to wrap it up. If you guys enjoyed the video, make sure you hit like, subscribe to the channel, and I will see you in the next one.

[14:32] Thank you.