Tutorials

Deepseek is the latest LLM to hit the digital shelves. It boasts high-quality reasoning at a fraction of the cost of current state-of-the-art models, OpenAI o1 and o3-mini, and Gemini 2.0 Flash Thinking. DeepSeek R1 is open-source, which means two things. First, developers can examine the model's architecture, training process, and weights directly, enabling a

Hand gesture recognition has become increasingly important in computer vision and human-computer interaction. With the rise of video conferencing and virtual interactions, there's a growing need for intuitive ways to control our digital environments. In this tutorial, we'll explore building a hand gesture detection system using TensorFlow.js that can recognize various hand poses in real-time

Chat has become a necessary feature in most products. Businesses want to integrate quick, real-time communication to connect with their users. It can be for HR team management, hiring, onboarding, or within existing teams – but chat is almost everywhere. If you are a developer or product manager looking to integrate a chat API into

Python and TypeScript-based AI agent frameworks, CrewAI, LangChain, Agno, and Vercel AI SDK allow developers to build AI applications with multiple agents to act as Computer-Using Agents, or Deep Research Agents to automate browser tasks like clicking, scrolling, ordering products on the web and performing complex and multi-step tasks. These multi-AI agents may be put

JavaScript is the foundation of the modern web. Without it, you don’t have the rich interaction you expect from websites, like real-time updates, video, or chat. But JS brings a problem: bloat. Modern web apps often ship megabytes of JavaScript to the browser. This slows initial page load and hurts performance, especially for mobile devices.

Many people (especially developers) want to use the new DeepSeek R1 thinking model but are concerned about sending their data to DeepSeek. Read this article to learn how to use and run the DeepSeek R1 reasoning model locally and without the Internet or using a trusted hosting service. You run the model offline, so your

AI agent chats are mostly a 1:1 experience. But that misses a clear opportunity--having an AI member of your team. If every team member was participating in a chat with AI, you could collaborate as a group, create shared knowledge bases, or solve problems together more efficiently. So, let's build that. We're going to extend

Integrating AI into enterprise applications often challenges getting accurate and efficient results from LLMs. The main reason is that LLMs are trained on large datasets rather than specifically on your enterprise's data. These challenges may usually include hallucinations, outdated information presentation, and more. This article explores the integration of AI agents, or, Agentic Retrieval Augmented