
In this lecture, we take a practical walkthrough of the Cloudbot website to understand what the platform actually offers and how people are using it in real scenarios. You’ll explore key features like local installation, privacy advantages, and integrations with tools like WhatsApp, Telegram, Slack, and more. We’ll also look at real examples of how Cloudbot can automate tasks such as email handling, reminders, and syncing across platforms. By the end, you’ll have a clearer picture of what’s possible with Cloudbot and how you can start thinking about your own use cases before moving into the setup and installation process.
In this lecture, you’ll go through the full process of installing Clawbot on a Windows machine, starting from checking system requirements like Python, pip, and Git, all the way to running your first setup command. You’ll also learn how to connect your bot to OpenAI using an API key, choose the right model based on cost and performance, and integrate Telegram to communicate with your bot in real time. By the end, you’ll have a fully installed and working Clawbot environment, ready to start building real automation workflows.
In this lecture, you’ll move beyond local setup and deploy Clawbot to the cloud so it can run 24/7 and be accessible from anywhere. You’ll learn why cloud deployment is essential for real-world automation and how to use DigitalOcean to create a droplet (your cloud server). We’ll explore two approaches—manual installation and the faster marketplace method—and focus on the easiest way to get your bot up and running quickly. By the end, you’ll have your environment ready for a fully online Clawbot ready for continuous automation.
In this lecture, you’ll take Clawbot to the next level by deploying it in the cloud using DigitalOcean. Running your bot in the cloud means it stays online 24/7 and can be accessed from anywhere, instead of being limited to your personal machine. You’ll learn what a droplet is, explore both manual and one-click deployment approaches, and focus on the faster marketplace method to save time and effort. By the end, you’ll have a live cloud environment ready, setting you up to run Clawbot continuously and build more advanced automation workflows.
In this lecture, you’ll explore the online version of Clawbot, which allows you to build and manage your bot directly from the browser without any installation. You’ll see how the interface mirrors other environments like Windows and VPS, making it easier to understand the platform regardless of where you run it. We’ll walk through creating a bot, connecting Telegram as a communication channel, and using the automatic deployment feature. You’ll also get familiar with the main dashboard, including Overview, Models, Channels, and Security sections, so you’re fully prepared to start interacting with your bot in the next lecture.
In this lecture, you’ll learn how to connect Clawbot to Gmail using Matin, a no-code integration platform that removes the complexity of traditional API setups. Instead of going through long authentication processes, you’ll connect Gmail in just a few steps and allow your agent to read, summarize, and send emails automatically. You’ll also understand why using the internal model simplifies configuration and how to switch to other models later. By the end, you’ll test the integration by summarizing emails and sending messages directly through Telegram, confirming that your agent is fully connected and working.
In this lecture, you’ll shift from reactive interactions to proactive automation by using cron jobs in Clawbot. Instead of manually sending messages every time, you’ll learn how to schedule tasks so the agent runs them automatically in the background. You’ll understand how cron jobs work, when to use them, and how they enable use cases like checking emails, reviewing your calendar, or running periodic reports. By the end, you’ll be able to turn your agent into a system that works for you continuously, without needing constant input.
This course will take you from zero to building real AI agents that can automate your daily tasks and work without constant input. Instead of focusing on theory or simple chatbots, you’ll learn how to build practical systems using Clawbot (also known as Moltbot), a platform designed to create agents that connect to real services and perform actions automatically. You will explore how agents can check emails, manage calendars, and run workflows across different tools.
This course will take you from zero to building real AI agents that can automate your daily work and operate without constant input. Instead of learning theory or simple chatbots, you’ll build practical automation systems using Clawbot (also known as Moltbot), a powerful platform for creating intelligent agents that connect to real services and perform actions automatically. You will learn how to design agents that can handle tasks like checking emails, managing calendars, generating summaries, and executing workflows across multiple platforms. Throughout the course, we focus on real-world scenarios, not abstract concepts, so every lecture moves you closer to building something useful and deployable.
We will start by understanding how Clawbot works, then move into setting up your environment locally and in the cloud. You’ll explore how to connect your agent with external services using simple integrations, without dealing with complex APIs. You will also learn how to automate actions using cron jobs, allowing your agent to work in the background without manual input. By the end, you’ll have a complete system that behaves like a real AI assistant—proactive, connected, and capable of handling tasks independently across different tools and environments.We will start by understanding the core concept of Clawbot, then move into setting it up locally and in the cloud. You’ll learn how to connect your agent to external services using simple integrations, without dealing with complex APIs. The course also covers how to automate tasks using cron jobs, so your agent can run in the background at scheduled times. By the end, you’ll have a working AI agent that behaves like a real assistant—connected, automated, and capable of handling useful tasks independently.