Edge-Veda
✨On-device full-stack AI SDK for Flutter with LLM, Vision, Speech, Image Gen, and RAG; features compute budget contracts and adaptive QoS with zero cloud dependency.
On-device full-stack AI SDK for Flutter with LLM, Vision, Speech, Image Gen, and RAG; features compute budget contracts and adaptive QoS with zero cloud dependency.
Official code repository for the O'Reilly book "Hands-On Large Language Models". Features 12 core chapters and bonus content covering Tokens, Transformers, RAG, and Fine-tuning. Includes 300+ illustrations and runnable Jupyter Notebooks optimized for Colab and local environments.
A comprehensive AI engineering hub featuring 93+ production-ready projects with in-depth tutorials and implementations for LLMs, RAGs, AI Agents, and MCP, covering beginner to advanced skill levels.
An AI agent framework built with Rust, powered by ICP blockchain and TEEs, designed to create a highly composable, autonomous, and perpetually memorizing network of AI agents.
A customizable AI desktop companion project with character settings, voice conversations, long-term memory capabilities, and sub-1-second response times. Integrates with Live2D models for visual presentation.
PageIndex is a vectorless, reasoning-based RAG system that builds hierarchical tree indexes from long documents and uses LLM reasoning for human-like retrieval, delivering superior performance in professional document analysis.
An LLM-powered retrieval engine designed to process extensive sources to collect comprehensive entity information, generating enriched tabular results rather than traditional research reports or answers.
A curated collection of resources that bridge the gap between Retrieval-Augmented Generation (RAG) and Reasoning in Large Language Models and Agents, featuring papers, tools, and implementations.
A collection of Jupyter notebooks accompanying the "Building LLMs for Production" book by Towards AI, providing practical tutorials on RAG, LLMs, prompt engineering, fine-tuning, and more.
A benchmark platform featuring 100 PhD-level research tasks across 22 distinct fields, systematically evaluating Deep Research Agents (DRAs) on report generation quality and information retrieval capabilities.
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