Reading
An open-source interactive educational project that uses visual scrollytelling to help developers understand the internal workings of modern decoder-based language models, covering core concepts such as tokenization, embeddings, attention mechanisms, training, and inference.
Section 01
An open-source interactive educational project that uses visual scrollytelling to help developers understand the internal workings of modern decoder-based language models, covering core concepts such as tokenization, embeddings, attention mechanisms, training, and inference.
Section 02
Section 03
In the field of artificial intelligence, large language models (LLMs) are often seen as mysterious black boxes—input text, output results, but what happens in between? The Anatomy of an LLM project was created to demystify this. It's an open-source educational project for software developers that breaks down the internal mechanisms of modern decoder-based language models step by step through visual interactive scrollytelling.
Unlike traditional technical documents or academic papers, this project uses a progressive, visual teaching method. Users can scroll through the page, follow carefully designed interactive charts, and gradually understand how the model processes information, learns, and generates outputs starting from the most basic text input.
Section 04
This project covers almost all core concepts of modern LLMs, divided into 15 chapters along the learning path:
Section 05
The project starts with Tokenization, showing how raw text is converted into a sequence of numbers that the model can understand. It uses the real GPT tokenizer (tiktoken's o200k_base), allowing users to see how actual tokens are generated. Next, it introduces Vector Embeddings, explaining how these numerical tokens are mapped to a high-dimensional semantic space.
Section 06
It visually demonstrates single neuron activation and matrix operation flow in feedforward neural networks. Users can intuitively see how data flows between network layers and the core role of matrix multiplication in neural networks.
Section 07
It explains Logits, Softmax function, and sampling strategies in detail. This section answers the key question: "How does the model decide what the next word is?" and shows the complete process from raw scores to probability distribution, then to the final word selection.
Section 08
This is the core of the modern Transformer architecture. The project deeply explains the intuitive understanding of Query/Key/Value, the mathematical principles of multi-head attention mechanism, and how RoPE (Rotary Position Embedding) allows the model to understand positional information in sequences.
Keep going with more reads from the same topic.
Nornir MCP Server is an enterprise-level server based on the Model Context Protocol (MCP). It seamlessly integrates large language models (such as Claude) with the Nornir network automation framework, supporting natural language orchestration for multi-vendor network devices (Cisco, Arista, Juniper, etc.), and providing production-grade features like a dual-engine architecture (NAPALM + Netmiko), intelligent filtering, and a secure sandbox.
Bibliothèque Française LLM is a structured indexing and annotation project for French public domain literature designed specifically for large language models (LLMs). It integrates multiple authoritative sources such as DraCor, Common Corpus, and Wikisource, providing metadata indexing categorized by genre, author, and era, as well as in-depth annotations for dramatic texts (including characters, lines, stage directions, etc.). Its aim is to enable LLMs to efficiently read and understand classic French literary works.
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.