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Ever feel like you’re drowning in repetitive tasks? Sorting emails, posting updates, or organizing files can eat up hours of your day. But what if you could automate all of that — and have an AI assistant that actually understands what you need? That’s where n8n comes in. n8n is an open-source, low-code automation platform that not only helps you connect your favorite apps and tools but also gives you access to powerful AI features. Whether it's managing your to-do list or answering complex queries with a single prompt, n8n lets you build smart, automated workflows with ease.

Learn how to create a custom Typeform-style survey with Replit Agent, an advanced AI-coding agent, and Notion, a popular productivity note-taking application. This step-by-step guide teaches you how to go from an initial idea to an animated, conversational survey form in minutes, regardless of skill level.

Welcome to the second article in our Model Context Protocol (MCP) series! In the first article , we covered all the basics—what MCP is, how it works, and its key components. Not only that, we even built our first simple MCP server to put that knowledge into practice. If you’re new to this topic, I highly suggest you check out that first article before continuing here. It’ll give you a clear understanding of what’s going on and make this journey a lot smoother. Now, it’s time to step things up. In this article, we’re going to take what we’ve learned and build something more advanced – a custom MCP server from scratch. This is where things get interesting, because we’ll see just how flexible and powerful MCP can be with the new tooling we’re going to explore, and how we can shape MCP to fit our own needs. Let’s get into it! First, let’s quickly mention the tech stack that we’re going to work with. We’re going to use the same one from the previous article: TypeScript, Node.js and MacOS. If you're using a different tech stack, no worries, the key ideas will be the same. As an additional reference, you can also refer to documentation which includes basics for Python and Java too.

Large Language Models (LLMs) like DeepSeek are powerful tools, but they often come with built-in safety layers and censorship filters. These restrictions might block sensitive topics, controversial opinions, or even accurate historical facts — especially when it comes to politically sensitive regions like China. In our previous article , we explored how to jailbreak Large Language Models (LLMs) like DeepSeek, using prompt engineering and unlock restricted answers. Now, we’re diving into the most powerful and lasting approach: fine-tuning. With tools like LoRA and Unsloth on free platforms like Google Colab, we’ll show you how to tweak DeepSeek to provide accurate, uncensored historical answers about China—free from filters that might obscure the truth. Our goal is to make DeepSeek a reliable source for sensitive topics, where default restrictions can block factual responses. Fine-tuning lets us retrain the model on a custom dataset to soften those limits, and as we’ll see, it’s more accessible than ever with modern tools. Done responsibly, this can reveal what’s hidden without crossing ethical lines.

Learn how to prompt Replit Assistant, an advanced AI-coding assistant, to create your first Slack bot with Bolt, a framework for building Slack apps with Python (or JavaScript). Part I covers forking a Slack bot project template, the anatomy of a basic Slack bot, personalization of Replit Assistant, and prompting techniques for Replit Assistant.