YouTubeGPT • AI Chat with 100+ videos ft. YouTuber Marques Brownlee (@ MKBHD) ⚡️🔴🤖💬
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Updated
Jan 10, 2024 - TypeScript
YouTubeGPT • AI Chat with 100+ videos ft. YouTuber Marques Brownlee (@ MKBHD) ⚡️🔴🤖💬
AI chat over the US Constitution 📜 💬 🇺🇸
YouTubeGPT • AI Chat with 100+ videos ft. YouTuber Matt Wolfe (@mreflow) 🐺🟣🤖💬
UC Berkeley CS186 AI Chatbot 🤖 🖥️ 🐻
AI Chat with The ₿itcoin Whitepaper
AI pipeline built with the honc and workers-ai. vector embeddings, web scraping and processing with Cloudflare Workflows (beta)
SoulCare is a mental health app using NLP to analyze social media sentiment, track symptoms, and offer AI-driven support with personalized reports, document uploads, and symptom-based prioritization.
UC Berkeley EE16B AI Chatbot 🤖 🖥️ 🐻
Semantic search with openai's embeddings stored to pineconedb (vector database)
Developed an AI-powered note-taking application using Next.js 14, ChatGPT API, vector embeddings, Pinecone, TailwindCSS, Shadcn UI, and TypeScript.
Build an AI-powered chatbot that is able to access external data to provide the most accurate answer
Intelligent note taking Web App with AI Integration
Memomind is a sleek note-taking app built with React 18, Next.js 14, and TypeScript. It features a chat-based RAG workflow, AI-powered insights with Langchain and Llama3, and secure authentication via Clerk. It uses Tailwind CSS for styling and Shadcn-UI for components.
Vector embeddings with Deno, Drizzle and sqlite
AI chat with Tim Ferriss or any of his past guests
It's (SaaS) platform focused on advanced task and project management. This SaaS solution offers a comprehensive set of features including task tracking, real-time communication through audio and video calls, and a design collaboration tool inspired by Figma. By integrating these functionalities into a single user
A powerful tool that can scrape, store, and answer questions about web page content using OpenAI's embeddings and ChromaDB for vector storage.
Knowledge based AI chatbot
A simple, general-purpose Retrieval-Augmented Generation (RAG) system for Markdown-based documentation
Add a description, image, and links to the vector-embeddings topic page so that developers can more easily learn about it.
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