Reading
Introducing the multimodal-wiki-react project, a React-rebuilt multimodal AI knowledge base that serves as a systematic knowledge organization platform covering cutting-edge fields like LLM, VLM, VLA, and world models.
Section 01
multimodal-wiki-react is a multimodal AI knowledge base rebuilt using React, aiming to systematically organize knowledge in cutting-edge fields such as LLM, VLM, VLA, and world models. The project addresses the problem of scattered knowledge about multimodal AI technologies, providing a structured and interactive knowledge platform through modern web technologies to serve researchers, developers, and learners.
Section 02
The field of artificial intelligence is shifting from single-modal to multimodal. Technologies like LLM, VLM, VLA, and world models are developing rapidly, but relevant knowledge is scattered across papers, blogs, code repositories, and other channels, lacking systematic organization. Thus, the multimodal-wiki-react project was born, aiming to build a structured and interactive knowledge platform covering core concepts, technical progress, and application practices of multimodal AI.
Section 03
The original Multimodal Wiki had limitations in interactivity and user experience. Reasons for choosing React for reconstruction include: 1. Component-based architecture, facilitating content maintenance and updates; 2. Dynamic interactive features (search, filtering, etc.) to improve browsing efficiency; 3. Rich UI component libraries supporting modern responsive interfaces; 4. Virtual DOM and rendering optimization to ensure performance.
Section 04
The project covers four core areas:
Section 05
Content Organization: Uses timeline view (technical evolution context), category browsing (technology type/scenario, etc.), association graph (model/technology/paper connections), and in-depth articles (principles + examples); Technical Implementation: Frontend uses React18+, TypeScript, React Router; content stored in Markdown/MDX; integrated full-text search (e.g., Algolia); deployed to static hosting services like Vercel.
Section 06
The community value of multimodal-wiki-react includes: lowering the learning threshold for multimodal AI; promoting knowledge dissemination; connecting academia and industry; tracking cutting-edge trends. It is a new paradigm for technical knowledge dissemination, making complex AI knowledge easier to understand and use.
Section 07
Future directions for the project: 1. Use LLM to assist content generation and translation; 2. Establish a community collaborative editing mechanism; 3. Provide multilingual versions; 4. Embed interactive code examples and model demos; 5. Personalized recommendations based on user interests.
Keep going with more reads from the same topic.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
Folkering OS is the world's first AI-native bare-metal operating system, entirely written in Rust no_std without relying on Linux, POSIX, or libc. It can generate commands from scratch, compile them into WASM, and run them in 10 seconds, achieving true self-evolution.
This project explores how to use Large Language Models (LLMs) to detect logical vulnerabilities in Ethereum smart contracts, especially business logic flaws that are difficult for traditional static analysis tools to capture, such as economic manipulation and execution order errors.
This article details how to quickly deploy a virtual machine equipped with 4 NVIDIA V100 GPUs on Azure using Terraform to enable local inference of large language models with over 70B parameters. It covers a complete practical guide from infrastructure deployment, Ollama/vLLM dual-engine comparison testing, to cost optimization and actual performance data.