intermediateTutorials

In this article, we will create a data visualization that animates the changes in the stargazer counts of popular front-end library/framework GitHub repositories over the past 15 years. Which front-end libraries/frameworks currently dominate the web development landscape? Which front-end libraries/frameworks used to dominate web development landscape?

As AI continues to evolve, we’re constantly finding new ways how to improve and to use it. Today, AI has gone much further being just a chat tool. And one of these significant evolutionary steps is the creation and adoption of AI agents. With agents, you can deploy AI solutions that autonomously perform real-world tasks, for example: managing customer support, processing large amounts of information in real-time, and much more! Basically, any task that benefits from working with real-time data and reasoning capabilities. This series of articles will help you not only to grasp the fundamentals of AI agents, but also to get a practical experience of building one yourself. Covering crucial theoretical knowledge and concepts, as well as also learning how to properly apply them in the real world.

Learn about Replit Agent, an advanced AI-coding agent that’s capable of building apps from scratch. Through natural language interactions and real-time assistance, Replit Agent sets up environments, writes code, and deploys apps, all done within minutes.

Learn how to create your first React Native mobile app with Expo and Replit Agent, an advanced AI-coding agent. This step-by-step guide teaches you how to go from an initial idea to a cross platform, mobile app in minutes, regardless of skill level.

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 create your first Chrome extension with Replit Agent, an advanced AI-coding agent. This step-by-step guide teaches you how to go from an initial idea to a fully functional Chrome extension in minutes, regardless of skill level.

Today we often hear about incredible AI advancements that promise to make our lives easier. But besides developing and improving new AI models, we also find new ways to use them and utilize their full potential. One exciting feature of LLMs AI Retrieval-Augmented Generation, or RAG for short. This system connects real time data to the power of AI models. And knowing how RAG works really raises the ceiling of your expertise as an AI engineer. So, in this opening article let's make sure to cover all the core fundamental concepts. And in the upcoming articles we will build exciting applications to apply our knowledge in practice. Large language models (LLMs) generate text by predicting the most probable next word, but without access to real-time or domain-specific information, they produce errors, outdated answers, and hallucinations.

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.

In the last article we covered statistical metrics like Perplexity, BLEU, ROUGE and more, as well as some of the statistical concepts that underpin them, their strengths (accuracy, reliability) and weaknesses (no subjective focus, use of reference texts. Between human evaluation (manual testing) and statistical measures we get a mix of high-value qualitative assessment on a small part of the test surface, and a rigorous but limited view on a wider area. That still leaves a lot of middle ground uncovered! That’s why there’s been a push the last few years to get coverage for the space between - something that has a level of subjectivity and nuance but that also scales up. This is where LLM-as-a-Judge comes in. In our manual testing for LLMs article I compared this to a kind of ouroboros where AI validates AI - and rightly so, that isn’t necessarily a bad thing. LLMs are able to do some things better than humans and LLM-as-a-Judge plays to those strengths - but it does not replace the need for human oversight and statistical assessment. There’s also metrics that combine LLM-as-a-Judge with statistical metrics - but we’ll talk more about that later.

Learn how to use Zapier and ChatGPT to identify customer pain points from Reddit and automatically generate a report that summarizes and prioritizes key issues. 

Imagine having the power to gather data from any website—product prices, news articles, or social media posts—with just a few lines of code. Maybe you’ve tried building web scrapers before — only to hit endless walls with anti-bot protections, slow speeds, or messy data formats. In today’s AI-driven world, simply grabbing raw HTML isn’t enough. We need clean, structured, and meaningful data that’s ready for machine learning models, data pipelines, and AI agents to understand. That’s where Crawl4AI steps in. In this beginner-friendly guide, we’ll walk you through the basics of Crawl4AI, an open-source Python tool designed to scrape and extract web data effortlessly. You’ll learn what it is, why it’s a game-changer, and how to set up your first web crawler step by step.