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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.

It’s undeniable that AI has - for better or worse - already had a huge impact on the software industry, from its practical applications at the technology level, to the changing demand from skills and experience, to the layoffs linked to AI processes taking over. One area we haven’t really talked about in our articles yet is recruitment: it’s no secret that AI is being used to scan CVs and automatically filter candidates. Several of my developer friends have been talking about this recently, and with a slew of layoffs in tech sending a lot of IT folks on the hunt for new jobs, I thought this would be the perfect time to dive into how AI is being used to filter candidates, and what you can do to stand out and get past the filters. Forbes estimates that 65% of employers will use AI tools to reject candidates in 2025. That’s a staggeringly high number, but for folks who work in hiring or are currently job hunting it’s not a surprising one. The article offers a further breakdown of how employers plan to use–or are already using–AI in their hiring process.

Learn how to create your first Discord bot with Replit Agent, an advanced AI-coding agent, and discord.js, a Node.js-based Discord API library. This step-by-step guide teaches you how to go from an initial idea to a 24/7 Discord bot in minutes, regardless of skill level.