Let AI Be Your Docs

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.

Building instant-on web applications at scale have been elusive. Real-world sites need tracking, analytics, and complex user interfaces and interactions. We always start with the best intentions but end up with a less-than-ideal site.
QwikCity is a new meta-framework that allows you to build large-scale applications with constant startup-up performance. We will look at how to build a QwikCity application and what makes it unique. The workshop will show you how to set up a QwikCitp project. How routing works with layout. The demo application will fetch data and present it to the user in an editable form. And finally, how one can use authentication. All of the basic parts for any large-scale applications.
Along the way, we will also look at what makes Qwik unique, and how resumability enables constant startup performance no matter the application complexity.

The modern web would be different without rich client-side applications supported by powerful frameworks: React, Angular, Vue, Lit, and many others. These frameworks rely on client-side JavaScript, which is their core. However, there are other approaches to rendering. One of them (quite old, by the way) is server-side rendering entirely without JavaScript. Let's find out if this is a good idea and how Remix can help us with it?
Prerequisites- Good understanding of JavaScript or TypeScript- It would help to have experience with React, Redux, Node.js and writing FrontEnd and BackEnd applications- Preinstall Node.js, npm- We prefer to use VSCode, but also cloud IDEs such as codesandbox (other IDEs are also ok)

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

Fastify is an HTTP framework for Node.js that focuses on providing a good developer experience without compromising on performance metrics. What makes Fastify special are not its technical details, but its community which is wide open for contributions of any kind. Part of the secret sauce is Fastify plugin architecture that enabled developers to write more than a hundred plugins.This hands-on workshop is structured around a series of exercises that covers from basics "hello world", to how to structure a project, perform database access and authentication.

https://github.com/nearform/the-fastify-workshop

This session will focus on Starbeam's universal building blocks. We'll use Starbeam to build a data library that works in multiple frameworks.We'll write a library that caches and updates data, and supports relationships, sorting and filtering.Rather than fetching data directly, it will work with asynchronously fetched data, including data fetched after initial render. Data fetched and updated through web sockets will also work well.All of these features will be reactive, of course.Imagine you filter your data by its title, and then you update the title of a record to match the filter: any output relying on the filtered data will update to reflect the updated filter.In 90 minutes, you'll build an awesome reactive data library and learn a powerful new tool for building reactive systems. The best part: the library works in any framework, even though you don't think about (or depend on) any framework when you built it.
Table of contents- Storing a Fetched Record in a Cell- Storing multiple records in a reactive Map- Reactive iteration is normal iteration- Reactive filtering is normal filtering- Fetching more records and updating the Map- Reactive sorting is normal sorting (is this getting a bit repetitive?)- Modelling cache invalidation as data- Bonus: reactive relationships