Tutorials on Ai

Large language models (LLMs) are very powerful tools that can help us with a wide range of tasks. These models are usually built with safety features meant to stop them from generating harmful, inappropriate, or otherwise restricted content. However, over time, researchers and enthusiasts have discovered ways to bypass these safeguards—a process known as jailbreaking. In this series of articles, we’re going to show you how to jailbreak one of the most popular open-source models out there: DeepSeek R1. In this opening article, we'll start with prompt jailbreaking. But don’t worry—we’re not just jumping straight into prompt examples. First, we’ll explain what jailbreaking really is, why people do it, and some of the tricky parts you should know about. Sound good? Let’s dive in! DISCLAIMER. This article is for learning and research only. The methods shared here should be used responsibly to test AI, improve security, or understand how these systems work. Please don't use them for anything harmful or unethical.

In this article, let’s continue to explore Cursor even further. Our first article ( which you can find here ) covered Cursor’s basics and the easiest-to-understand features, such as Rules for AI , Tab autocompletion and the Chat feature. So, if you’re new to Cursor, I highly recommend you check the previous article. In this “Part 2” article, we’ll go over the Cmd+K , Composer , and Agent features, including some use cases. So, get ready to learn how to use Cursor to its fullest potential and save an enormous amount of time. Starting from version 0.46, Cursor includes a lot of UI changes for the AI side panel. That’s why, if you’re currently using an older version, the UI elements mentioned in this article might not look the same for you. That’s completely fine, but I highly recommend you update to the latest version so we’re on the same page.

Welcome! This is part 5 of our course on how to build fullstack apps with Bolt and Supabase If you’re just joining, I highly recommend you take the course in the correct order before diving into this one. Here you can find Part 1 , Part 2 , Part 3 , and Part 4 .

If you’re into AI agent development, you’ve probably started hearing more and more about a new emerging protocol – Model Context Protocol ( MCP ). In essence, this protocol simplifies how AI agents connect to the data and tools they need. By standardizing these connections, MCP reduces the extra work developers usually have to deal with. Essentially, it replaces the need to directly manage multiple APIs in your AI agent with one unified protocol. And lets you to add/remove any external tools for your agent with incredible ease. Making it more convenient to build complex and flexible AI systems. In this article, we’ll walk you through everything you need to know about MCP—from its core components and main concepts to practical implementations. We will focus specifically on building an MCP server, as it is likely the most useful and frequently used part of the MCP architecture that you will want to implement. So, let’s go! The Model Context Protocol, or MCP, is a simple standard, designed and open sourced by Anthropic to help AI tools talk to the systems where data lives. Think of it like a USB-C port, but for AI applications. Just as a USB-C port lets you connect different devices with one common plug, MCP lets AI models easily connect to various data sources and tools without needing custom code for every connection.

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.

Welcome! This is part 4 of our course on how to build fullstack apps with Bolt and Supabase If you’re just joining, I highly recommend you take the course in the correct order before diving into this one. Here you can find Part 1 , Part 2 , Part 3 .

Hello and welcome! This is the 3rd lesson in our series about how to build complete fullstack applications in less than an afernoon with Bolt and Supabase. In the first 2 lessons, we talked about what exactly is Bolt in the first place, and what’s Supabase. If you want to read those first, here is Part 1 and Part 2 .

What if I told you that 2-3 hours from now you could have taken your app idea and transformed it into a beautiful, production-level full stack application, deployed and available on the internet, for everyone to use? If I told you something like this a couple of years ago, you’d laugh and scoff and dismiss everything I just said. In fact, this was my reaction too when I first heard someone from Supabase talk about what Bolt and Supabase combined could achieve.

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.

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.

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.

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.

Imagine a world where you could build an entire web application simply by describing it. Well, it’s not a fantasy anymore. Here, I’ll show you how Vercel’s v0 has revolutionized web development. By the end of this article, you’ll learn how to turn your written ideas into production-ready web applications! Vercel’s v0 is a generative AI tool with an ambitious goal – to help people build websites and web applications more efficiently. You can think of it as the ChatGPT for web developers, primarily focusing on building UI components and logic for web applications. It allows you to quickly turn your ideas into a live web app that people can interact with. For context, Vercel is the name of the company that created v0. It’s a cloud platform that provides hosting services as well as other useful tools for developers, including v0. The basic version of v0 is free (with some limitations), but it should be more than enough to get to know the tool. If you're interested in pricing, you can find it – here .

Welcome to our opening article about Cursor (AI IDE). In this article, we would cover all the basic and core knowledge that you need to know about it. And which you would be using most of the time. We’ll do it in depth, without cutting any corners! And in our next article , we'll get into even more complex topics, like advanced tips, Cursor’s Cmd+K, Composer, and Agent features. But first, we’ll start from building a really solid background. So, let's jump right in to it! Nowadays, tech grows and moves so fast that sometimes it's hard to keep up. To stay on top, we as the developers, always have to be ready embrace new tools, which can increase our productivity 10X while saving us a lot of time. One of such tools is Cursor, an IDE powered by AI that’s transforming how developers write, debug, and optimize their code. Cursor combines artificial intelligence with the standard features of an IDE to help you easily debug your code, provide smart code autocompletion, and offer many other features that can boost your productivity. Cursor is forked/built from VSCode, one of the most popular IDEs among developers. And it retains not only the familiar user-friendly interface, but large ecosystem of VS Code extensions as well. This foundation means that those already familiar with VSCode will find it relatively easy to transition to Cursor.

Welcome to the LLM model that's been absolutely everywhere on the Internet and news headlines in recent days – DeepSeek-R1! In this article, we take a comprehensive look at this new, industry-disrupting LLM. We'll investigate if it’s truly deserving of all the noise around it, or if there's something (i.e. censorship and GPT-4 references) more sinister going on beneath the buzz. So, brew some tea and settle in, because this is going to be an interesting ride. We're going to cover: