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AI Tutorial: A Guide to AI LLMs, from Basics to Depth # This article takes you deep into the core concepts of large language models — from basic principles to vector representation — building a complete knowledge system step by step. 1. Foundations of AI Application Development # 1.1 Basic Principles and Concepts # In Plain Words # Core mechanism: predict the next word from the previous one — like a word-chain game How it works: output is generated token by token A Deeper Look # A large AI model involves two key stages:

Timeline Recorder: Requirements & Technical Architecture (MVP → Scalable) # Target: personal/small-team local or centralized deployment; web + CLI + TUI; text/images/linked audio and video; draft/publish; users may edit only their own content; everyone’s content viewable, with mute support; no built-in transcription; decentralization as an optional later capability. 0. Background and Goals # Goal: build a “local-first, centrally deployable” timeline recorder that guarantees data sovereignty and simple deployment; an extremely fast capture experience without sacrificing a clean timeline.