#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)

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)

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

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.

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 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.

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.