This article details how to implement Generative User Interfaces (Generative UI) using LangGraph, specifically focusing on integrating React components with LangGraph graphs to create dynamic and interactive applications.
Ollogger is a powerful, flexible logging application that helps users create custom AI-powered logging assistants. Built with React, TypeScript, and modern web technologies.
The article provides an overview of 12 essential Web APIs every developer should know, including Storage API, Payment Request API, DOM API, HTML Sanitizer API, Canvas API, History API, Clipboard API, Fullscreen API, FormData API, Fetch API, Drag and Drop API, and Geolocation API. Each API is explained with code examples.
A comprehensive overview of React concepts, covering components, JSX, fragments, props, state, lifecycle methods, purity, Strict Mode, hooks, Context API, lists and keys, forms, and React Router.
Learn about JSON Web Tokens (JWT) authentication, its working, components, benefits, and implementation with examples using Node.js and Express. This guide covers the process, key components, and security of JWT.
With such a wide variety of JavaScript libraries available, selecting the right one can be daunting. This article highlights the top 10 JavaScript libraries to use in 2024, examining their key features, performance, and specific use cases.
The article discusses the importance of cheat sheets for frontend developers and provides a list of the seven best cheat sheets to help with various frontend development tasks.
- Codecademy FrontEnd Cheat Sheets
- The Frameworks Cheatsheet
- SheCodes CSS Cheat Sheet
- Responsive Web Design Cheat Sheet
- Flexbox Cheatsheet Cheatsheet
- HTML5 Canvas Cheat Sheet
- Sass Cheat Sheet
- Introduces embeviz - a simple side project for exploring text embeddings
- Uses backend API with GoFiber framework and frontend UI with React and React Router
- Provides interactive charts for visualizing computed projections
- Can label texts and select options for both projections and chunking
- Offers swagger docs for the API, in-memory data store, and persistent data store with QDrant
The article discusses the use of large language models (LLMs) as reasoning engines for powering agent workflows, focusing specifically on ReAct agents. It explains how these agents combine reasoning and action capabilities and provides examples of how they function. Challenges faced while implementing such agents are also mentioned, along with ways to overcome them. Additionally, the integration of open-source models within LangChain is highlighted.
