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. 

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the most probable next word, but without access to real-time or domain-specific information, they produce errors, outdated answers, and hallucinations.

In a relatively short space of time, Large-Language Models (LLMs) seemingly took over everything. You can’t throw a rock without hitting a workflow that’s been altered by the ubiquity of chatbots - everything from coding with AI assistants to healthcare, energy, and even the service sector have been affected. In some cases, this has been a boon to workers and helped to remove manually intensive data tasks, in others, the results have not been so fruitful - take for example the recent (as of Feb 2024) case where a lawyer used ChatGPT to draft legal documents for a lawsuit against an airline, only for the LLM to hallucinate examples of case law . LLMs can be useful tools when used correctly, but it’s unsurprising that many are resentful and mistrustful of LLMs. But regardless of our personal views on the matter, they have already taken root in nearly every industry and are being embedded deep into our daily work lives. This article isn’t going to make a case for whether this is a good or bad thing - we’re simply going to focus on how to get the most out of these tools when using, and delve into one of the big buzzwords in the industry: Prompt Engineering. In this article we’re going to cover the basics of prompt engineering,

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 future articles, we'll get into even more complex topics, like advanced tips or Cursor’s “Composer” feature. 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.

Everyone knows chatgpt, but how do modern large language models fully work? The fundamentals start at the transformer. This workshop is a workshop to dymstify the transformer and be able to run through concept to code on how the transformer work. This workshops combines concept at an intutive level, to code, to math all with the intent at providing an end to end understanding at the fundamentals of large language models.

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Large Language Models are reshaping industries, yet integrating them into real-time streaming UIs presents unique challenges. In this course we will learn how to seamlessly integrate LLM APIs into applications and build AI-powered streaming text and chat UIs with TypeScript, React, and Python. Step-by-step, we will build a full-stack AI application with quality code and very flexible implementation. The LLM application in this course includes: - Completion Use-Case (english to emojis) - Chat - Retrieval Augmented Generation use-case - AI Agent Use-Cases (code execution, data-Analyste agent) This app can be used as a starting point in most projects, saving a huge amount of time, and its flexibilty allows new tools to be added as needed. At the end of this course, you will have mastered end-to-end implementation of a flexible and high-quality LLM application. This course will also equip you with the knowledge and skills necessary to create sophisticated LLM solutions of your own.

This course will teach you how to fetch in a performant way for React without relying on a third-party library or the useEffect hook. We will build a custom data fetching library from scratch that will prefetch, cache, and address all the common issues with fetching data.

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