#artificial intelligence

Knowledge of AI tooling is critical for future-proofing the careers of React developers, and the Vercel suite of AI tools is an approachable on-ramp. In this course, we’ll take a closer look at the Vercel AI SDK and how this can help React developers build streaming interfaces with JavaScript and Next.js. We’ll also incorporate additional 3rd party APIs to build and deploy a music visualization app.
Topics:- Creating a React Project with Next.js- Choosing a LLM- Customizing Streaming Interfaces- Building Routes- Creating and Generating Components - Using Hooks (useChat, useCompletion, useActions, etc)

In this workshop, we discuss the merits of serverless architecture and how it can be applied to the AI space. We'll explore options around building serverless RAG applications for a more lambda-esque approach to AI. Next, we'll get hands on and build a sample CRUD app that allows you to store information and query it using an LLM with Workers AI, Vectorize, D1, and Cloudflare Workers.

In this workshop, you'll learn how to create a GitHub Copilot Extension. We'll explore GitHub's new AI-powered tool ecosystem and guide you through building your own extension from scratch. By the end of this session, you'll understand how to leverage Copilot Extensions to enhance your development workflow and integrate with third-party services.In This Workshop, You'll Discover:What GitHub Copilot is and its capabilitiesWhat GitHub Copilot Extensions areThe anatomy of a Copilot ExtensionHow to build your own Copilot ExtensionHands-on Development:Clone the template repository: nickytonline/copilot-extension-templateInstall dependenciesExpose your local development serverCreate and configure your GitHub ApplicationUse the Copilot Extensions Preview SDKDebug and test your extensionBy the end of this workshop, you'll have created a working GitHub Copilot Extension that you can use immediately in your development environment. You'll be able to extend this foundation to build more complex integrations and custom AI-powered tools tailored to your specific needs.This workshop is ideal for:Developers looking to enhance their productivity with AI toolsTeams wanting to build custom integrations with GitHub CopilotAnyone interested in creating AI-powered developer tools

The way we write code is fundamentally changing. Instead of getting stuck in nested loops and implementation details, imagine focusing purely on architecture and creative problem-solving while your AI pair programmer handles the execution. In this hands-on workshop, I'll show you how to leverage Cline (an autonomous coding agent that recently hit 1M VS Code downloads) to dramatically accelerate your development workflow through a practice we call "vibe coding" - where humans focus on high-level thinking and AI handles the implementation.You'll discover:The fundamental principles of "vibe coding" and how it differs from traditional developmentHow to architect solutions at a high level and have AI implement them accuratelyLive demo: Building a production-grade caching system in Go that saved us $500/weekTechniques for using AI to understand complex codebases in minutes instead of hoursBest practices for prompting AI agents to get exactly the code you wantCommon pitfalls to avoid when working with AI coding assistantsStrategies for using AI to accelerate learning and reduce dependency on senior engineersHow to effectively combine human creativity with AI implementation capabilitiesWhether you're a junior developer looking to accelerate your learning or a senior engineer wanting to optimize your workflow, you'll leave this workshop with practical experience in AI-assisted development that you can immediately apply to your projects. Through live coding demos and hands-on exercises, you'll learn how to leverage Cline to write better code faster while focusing on what matters - solving real problems.

In this workshop I’ll cover a repeatable process on how to spin up full stack apps in Cursor.  Expect to understand techniques such as using GPT to create product requirements, database schemas, roadmaps and using those in notes to generate checklists to guide app development.  We will dive further in on how to fix hallucinations/ errors that occur, useful prompts to make your app look and feel modern, approaches to get every layer wired up and more!  By the end expect to be able to run your own AI generated full stack app on your machine!
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In this workshop we'll dive deep into integrating vector search into your applications. Over the course of this workshop, you'll not only grasp the core principles behind vector search but also cut through the buzzwords to understand its practical applications. We'll explore when vector search is the right tool for the job—and when it's not—so you can make informed decisions in your projects.By the end of the workshop, you'll have hands-on experience as we build a fully functioning application together, leveraging vector search to elevate the user experience. We'll also cover how to effectively test and optimize your implementation to ensure it meets real-world demands. Whether you're new to the concept or looking to refine your skills, this workshop will equip you with the knowledge and tools to integrate vector search confidently into your development workflow.Key takeaways:- Cut through the buzz and understand vector search's real-world applications to make informed choices in your projects.- Gain hands-on experience building an application with vector search to enhance user experience practically.- Learn best practices for testing and optimizing vector search implementations for robust, real-world performance.

In this webinar I’ll cover a repeatable process on how to spin up full stack apps in Cursor.  Expect to understand techniques such as using GPT to create product requirements, database schemas, roadmaps and using those in notes to generate checklists to guide app development.  We will dive further in on how to fix hallucinations/ errors that occur, useful prompts to make your app look and feel modern, approaches to get every layer wired up and more!  By the end expect to be able to run your own ai generated full stack app on your machine!

Code Less, Create More: AI-Driven App Development for React Devs.Join us for a workshop designed for React developers eager to harness the power of AI to streamline their app development process. In "Code Less, Create More," you'll learn how to build applications using the Cursor IDE with the power of AI and minimal manual coding.Explore how Cursor, an AI-enhanced code editor, revolutionizes your coding experience by seamlessly integrating with your existing workflow. Uncover its advanced capabilities: predicting your next edits, offering real-time code suggestions, and comprehending your project's architecture. This allows you to channel your energy into creativity and innovation rather than grappling with syntax and repetitive tasks.We'll guide you through initializing a Next.js project, demonstrating how to utilize AI tools to efficiently craft dynamic, responsive applications. By the workshop's conclusion, you'll possess the skills to construct sophisticated software with reduced coding effort, empowering you to become a more productive and innovative developer.Whether you're a seasoned React developer or new to AI-assisted coding, this workshop will equip you with the knowledge and tools to elevate your development projects to the next level.

Today, most of the AI applications send data to LLM cloud providers like OpenAI, raising privacy concerns. This workshop an alternative and privacy focused way to build AI applications by running LLMs locally with Ollama that keep everything local on your computer. This approach allows to avoid sending sensitive information to external servers. The workshop also highlights LangChain's ability to create versatile AI agents capable of handling tasks autonomously by creating embeddings for the data. So come learn how can you build the next gen, privacy focused React application powered by Local LLMs.

The workshop covers the following topics:

1.Overview of cloud-based LLMs privacy issues and the importance of running Local LLM inferencing.

2.Detailed insights into generating embeddings with tools like Ollama and demonstrating how LangChain agents can perform tasks such as document summarisation and API interactions, all while maintaining data privacy in a NextJS / React application.

3.Discovering practical use-cases for this approach.

Join Nathan and Alexa in this hands-on session where you will first learn at a high level what large language models (LLMs) are and how they work. Then dive into an interactive coding exercise where you will implement LLM functionality into a basic example application. During this exercise you will get a feel for key skills for working with LLMs in your own applications such as prompt engineering and exposure to OpenAI's API.

After this session you will have insights around what LLMs are and how they can practically be used to improve your own applications.

GenAI agents are one of the most promising directions for complex GenAI based applications. These agents can search the web, code, and carry complex tasks completely autonomously for the user. In this workshop we will learn the basics of GenAI agents. Define the basic terms and frameworks and understand how they differ from traditional use of LLMs.We will understand prompting techniques for LLM agents and specifically the React prompting technique for AI agents (not to be confused with React the programming language). We will build, from scratch, our own GenAI agent that can interact with the user, perform web searches and code and execute in Javascript.Table of contents:- Introduction to GenAI agents- Understanding the React framework- Hands-on Building of simple GenAI agent- Deployment of the Agent with streamlit- Tips, and frameworks for developing GenAI agents

Join Nathan in this hands-on session where you will first learn at a high level what large language models (LLMs) are and how they work. Then dive into an interactive coding exercise where you will implement LLM functionality into a basic example application. During this exercise you will get a feel for key skills for working with LLMs in your own applications such as prompt engineering and exposure to OpenAI's API.
After this session you will have insights around what LLMs are and how they can practically be used to improve your own applications.
Table of contents: - Interactive demo implementing basic LLM powered features in a demo app- Discuss how to decide where to leverage LLMs in a product- Lessons learned around integrating with OpenAI / overview of OpenAI API- Best practices for prompt engineering- Common challenges specific to React (state management :D / good UX practices)

Join our dynamic workshop to craft an AI-powered documentation portal. Learn to integrate OpenAI's ChatGPT with Next.js 14, Tailwind CSS, and cutting-edge tech to deliver instant code solutions and summaries. This hands-on session will equip you with the knowledge to revolutionize how users interact with documentation, turning tedious searches into efficient, intelligent discovery.
Key Takeaways:
- Practical experience in creating an AI-driven documentation site.- Understanding the integration of AI into user experiences.- Hands-on skills with the latest web development technologies.- Strategies for deploying and maintaining intelligent documentation resources.
Table of contents:- Introduction to AI in Documentation- Setting Up the Environment- Building the Documentation Structure- Integrating ChatGPT for Interactive Docs

Feeling the limitations of LLMs? They can be creative, but sometimes lack accuracy or rely on outdated information. In this workshop, we’ll break down the process of building and easily deploying a Retrieval-Augmented Generation system. This approach enables you to leverage the power of LLMs with the added benefit of factual accuracy and up-to-date information.

Today every developer is using LLMs in different forms and shapes, from ChatGPT to code assistants like GitHub CoPilot. Following this, lots of products have introduced embedded AI capabilities, and in this workshop we will make LLMs understandable for web developers. And we'll get into coding your own AI-driven application. No prior experience in working with LLMs or machine learning is needed. Instead, we'll use web technologies such as JavaScript, React which you already know and love while also learning about some new libraries like OpenAI, Transformers.js

Generative AI is exciting tech enthusiasts and businesses with its vast potential. In this session, we will introduce Retrieval Augmented Generation (RAG), a framework that provides context to Large Language Models (LLMs) without retraining them. We will guide you step-by-step in building your own RAG app, culminating in a fully functional chatbot.
Key Concepts: Generative AI, Retrieval Augmented Generation
Technologies: OpenAI, LangChain, AstraDB Vector Store, Streamlit, Langflow

Today every developer is using LLMs in different forms and shapes. Lots of products have introduced embedded AI capabilities, and in this workshop you’ll learn how to build your own AI application. No experience in building LLMs or machine learning is needed. Instead, we’ll use web technologies such as JavaScript, React and GraphQL which you already know and love.

In this workshop we'll take a tour of applied AI from the perspective of front end developers, zooming in on the emerging best practices when it comes to working with LLMs to build great products. This workshop is based on learnings from working with the OpenAI API from its debut last November to build out a working MVP which became PowerModeAI (A customer facing ideation and slide creation tool).
In the workshop they'll be a mix of presentation and hands on exercises to cover topics including:
- GPT fundamentals- Pitfalls of LLMs- Prompt engineering best practices and techniques- Using the playground effectively- Installing and configuring the OpenAI SDK- Approaches to working with the API and prompt management- Implementing the API to build an AI powered customer facing application- Fine tuning and embeddings- Emerging best practice on LLMOps

Have you wanted to explore AI, but didn't want to learn Python to do it? Tensorflow.js lets you use AI and deep learning in javascript – no python required!
We'll take a look at the different tasks AI can help solve, and how to use Tensorflow.js to solve them. You don't need to know any AI to get started - we'll start with the basics, but we'll still be able to see some neat demos, because Tensorflow.js has a bunch of functionality and pre-built models that you can use on the server or in the browser.
After this workshop, you should be able to set up and run pre-built Tensorflow.js models, or begin to write and train your own models on your own data.