This Dot·2yBuild Facial Recognition and Chatbot AIs using TypeScript with Jack Herrington
Learn how to build a voice-activated AI assistant using OpenAI's Whisper and TTS models combined with Meta's Llama 3.1, integrated within a Next.js application. The guide covers setting up the client-side using Next.js, recording and sending audio for processing, and handling voice responses. The backend setup includes using OpenAI SDK and Vercel AI SDK for processing audio and generating responses.
Faun·2yBuilding AI Agents and Workflow
With advancements in AI, creating AI Agents and AI-driven Workflows has become a growing interest. An AI Agent uses a Language Model (LLM) for reasoning and utilizes tools to perform tasks autonomously, while an Agentic Workflow combines these agents to execute a series of tasks. The post provides a step-by-step guide to build an AI Agent using langchain and OpenAI, including examples of sending emails and generating responses based on queries. The process involves defining tools, creating agents, setting up workflows, and executing tasks, with a focus on flexibility and extensibility.
Collections·2yBuild and Deploy Your Own RAG Chatbot with JavaScript
Learn to build and deploy a Retrieval Augmented Generation (RAG) chatbot using JavaScript, LangChain.js, Next.js, Vercel, and OpenAI through a 90-minute YouTube course by Ania Kubow on the freeCodeCamp.org channel. The course covers everything from integrating a vector database with DataStax, deploying to Vercel, and using a practical Formula 1 chatbot example for real-time data fetching. It's suitable for both beginners and experienced developers aiming to enhance their skill set.
Towards AI·2yBuilding Multi-Agent AI Systems From Scratch: OpenAI vs. Ollama
Multi-agent systems in AI enhance efficiency, accuracy, and reliability by distributing tasks among specialized agents such as summarizing texts and ensuring data privacy. This guide provides a comprehensive look at building such systems from scratch using Python, focusing on OpenAI’s GPT-4 model and Ollama’s LLaMA 3.2:3b model, without relying on existing frameworks.
portkey·2yClaude vs. ChatGPT: Comparison of Two Leading AI Models
ChatGPT and Claude are two leading AI models offering distinct capabilities. ChatGPT excels in structured and precise tasks, responding best to specific prompts and technical instructions. On the other hand, Claude is designed for natural, fluid interactions, making it ideal for creative and ethical tasks. Understanding their prompting styles and strengths can help optimize responses for various applications.
Towards AI·2yGraphRAG: Microsoft’s Open-Source Solution for Enhanced Document Understanding
GraphRAG is an open-source solution from Microsoft designed to enhance document understanding. It operates by indexing documents into smaller sub-documents, extracting entities and relationships to create a knowledge graph, and then structuring responses based on community detection. Though highly effective, running it on the OpenAI API, particularly GPT-4, can be costly, around $7 for a sample book. The setup involves creating a virtual environment, configuring necessary settings, and building the knowledge graph for querying.
Towards AI·2y#50 Why Do Neural Networks Hallucinate?
Towards AI offers a comprehensive course aimed at transforming students from beginners to advanced LLM developers. The course includes over 85 lessons and covers everything from choosing suitable LLM applications to advanced techniques and deployment. It uses popular tools like OpenAI, Llama 3, and Hugging Face. The course also emphasizes non-technical skills and offers instructor support on Discord. Additionally, this post features community projects, an AI poll, and articles on predictive modeling and AI hallucinations.
We Are .NET·1yHow to create your Own AI Bot on WhatsApp Using the ABP Framework
Learn how to create a WhatsApp bot incorporating ChatGPT's conversational AI by integrating an ABP.io application template with Twilio and the OpenAI API. The tutorial covers setting up the ABP.io project, configuring Twilio for WhatsApp messaging, and connecting to OpenAI’s API to enable a powerful AI-driven communication tool on WhatsApp. Key implementation steps, including setting up API credentials, creating service classes, handling WhatsApp messages, and deploying the bot, are detailed for an end-to-end solution.
freeCodeCamp·2yHow to Use LangChain and GPT to Analyze Multiple Documents
LangChain is a versatile project designed to provide easy integrations with various large language models (LLMs). This guide demonstrates how to use LangChain with GPT to programmatically access, summarize, and analyze online documents, with examples using financial reports from companies like Alphabet, Cisco, and IBM. The process involves setting up a Python environment, loading multiple PDF files, and utilizing tools such as PyPDFLoader, CharacterTextSplitter, and FAISS for text processing and embedding. The tutorial highlights the ability to automate AI tasks on real-world data efficiently.
Hacker News·2ygregpr07/browser-use
Browser-Use allows Language Models (LLMs) to interact with websites naturally, offering features like universal LLM support, smart element detection, multi-tab management, and vision model support. Users can customize browser interactions and persist browser states across multiple agents. It supports all LangChain chat models and provides examples and quick start guides to help users get started.
Community Picks·2yA Shiny New Programming Language
Mirror is an experimental programming language that uses AI to generate code from user-defined function signatures and examples. Developed by Austin Z. Henley of Carnegie Mellon University, Mirror leverages a large language model to convert input-output examples into JavaScript code. Currently in its early stages, Mirror is available for users to try via a GitHub-hosted playground.
Hacker News·2ycirclemind-ai/fast-graphrag: RAG that intelligently adapts to your use case, data, and queries
Fast GraphRAG is a streamlined and adaptable framework for high-precision, agent-driven retrieval workflows. It offers cost-efficiency, dynamic data handling, and interpretable knowledge graphs that support real-time updates. You can easily install it from PyPi or source and integrate it into your retrieval pipeline with full type support and asynchronous operations. The framework leverages PageRank-based graph exploration for accurate and dependable results. Contributions to this open-source project are encouraged, and a managed service option is available for ease of deployment.
Baeldung·2yAI Image Generation With OpenAI DALL·E 3 in Java
This tutorial outlines how to generate images using OpenAI's DALL·E 3 model with Spring AI in a Java application. It covers setting up the project, configuring dependencies and the OpenAI API key, setting default image options, creating and testing an ImageGenerator class, and overriding default image options dynamically.
Machine Learning News·2yA Comparison of Top Embedding Libraries for Generative AI
Text embeddings, which convert textual data into dense vector representations, are crucial for various AI tasks including text, image, and audio processing. This post compares 15 popular embedding libraries such as OpenAI, HuggingFace, Gensim, Facebook, and AllenNLP, highlighting their strengths and limitations. The choice of library depends on specific use cases, computational needs, and the extent of required customization.
Waseem .NET Newsletter·2ySetting Up OpenAI Chat in a .NET API
Learn how to integrate OpenAI's Chat API into your .NET application in three simple steps: obtaining your API key, installing the required NuGet package, and configuring your code. The post covers basic setup, methods and properties of the `ChatClient`, and best practices for robust implementation including handling models, securing API keys, and error management.
GoPenAI·1yStep-by-Step Guide to Integrating Gemini Models with OpenAI for Advanced AI Solutions
Integrating Google's Gemini models into the OpenAI library allows developers to leverage advanced AI capabilities without switching between tools. This seamless compatibility supports tasks like text and image processing, chat completions, and embeddings, enhancing applications ranging from customer support to data analysis. The step-by-step guide provides detailed instructions and examples to set up and utilize Gemini models via Python and REST APIs, showcasing their versatility and efficiency in real-time AI solutions.
.NET Blog·2yBuilding AI Applications from Scratch: A Hands-On Guide for .NET Developers
Jeremy and Lise from the intelligent apps team for .NET discuss building AI applications from scratch for .NET developers. They cover various aspects such as integrating AI-enhanced samples, Microsoft extensions, and community collaboration for a vibrant ecosystem. The discussion includes practical scenarios using AI for summarization, semantic search, classification, localization, and sentiment analysis. The post also touches upon advanced topics like retrieval augmented generation (RAG) for contextual data enhancement and responsible AI practices. Furthermore, it highlights the importance of evaluations in ensuring reliable AI outputs and demonstrates how to use built-in evaluators for AI applications.