generative-ai Tools

WeKnora is an open-source platform that turns your raw documents and data into a powerful, queryable knowledge base. It solves the problem of scattered, static information by creating a dynamic system that can answer questions via RAG, reason autonomously, and maintain itself like a wiki. It's built for developers, teams, and organizations who need to unlock the value trapped in their documents and build intelligent knowledge applications.

This repository is a curated collection of extracted system prompts from leading AI models like Anthropic's Claude, OpenAI's ChatGPT, Google's Gemini, and others. It addresses the problem of opaque AI behavior by revealing the foundational instructions that guide these systems, providing transparency for researchers, developers, and enthusiasts. The collection is regularly updated, serving as a valuable resource for anyone interested in understanding, comparing, or reverse-engineering how these AI models are fundamentally directed to operate.

AI Engineering from Scratch is a comprehensive, open-source curriculum that provides the missing spine for AI education. It solves the problem of scattered, framework-first learning by having you implement every core algorithm—from backprop to transformers—using only math and code, before any libraries are introduced. It's designed for engineers and students who want deep, foundational understanding and the ability to build, debug, and ship AI systems with confidence.

Embabel is an Agentic AI framework designed for the JVM, enabling developers to move beyond AI experiments to build reliable, scalable, and production-ready AI agents. It solves the problem of Python's limitations in production environments by leveraging the type safety, performance, and mature ecosystem of Java and Kotlin. It's for JVM developers and teams who need to integrate robust, observable AI capabilities into their enterprise systems.

LTX provides open-weight diffusion transformer models for multimodal generation and simulation. It solves the problem of limited access and control over high-fidelity video, audio, and world simulation AI. It's designed for developers, researchers, and enterprises who need production-grade generative capabilities with full transparency and ownership.

Semantica is an open-source, graph-native infrastructure that provides AI systems with structured context, causal reasoning, and full decision provenance. It solves critical production AI failures like data silos and black-box decisions by making AI explainable, traceable, and accountable by design. It's built for developers and enterprises using AI agents, LangGraph, CrewAI, LlamaIndex, and other frameworks who need trust and auditability in production.

AirLLM is a library that enables efficient inference for large language models like the 70B parameter class using just a single 4GB GPU. It solves the problem of high hardware requirements and costs typically associated with running state-of-the-art LLMs. This makes advanced AI capabilities accessible to developers, researchers, and small teams with limited computational resources.

This is a beginner-friendly course designed to demystify Generative AI and provide practical building skills. It solves the problem of overwhelming complexity by breaking down AI concepts into 21 digestible lessons. It's for developers, students, and anyone new to AI who wants to move from theory to application and start creating their own AI-powered projects.