A knowledge base that logs daily experiences and learning journeys, sharing them with you to grow together.
This guide explains why universal rules are essential for consistent AI output, shows how to extract them from successful interactions, store them as PDFs for cross‑platform use, and turn them into custom assistants, complete with concrete examples and step‑by‑step instructions.
This article explains why settling for “good enough” with AI yields mediocre results and shows how a disciplined output‑iteration process—specific feedback, concrete examples, and the “canvas” editing mode—can transform AI‑generated content into polished, high‑impact outputs.
The article explains how to create a personal AI prompt—called a Master Prompt or Personal OS—by documenting identity, goals, style preferences, and using it each session, with step‑by‑step guidance, examples, and tips for automating the creation and adapting it to multiple roles.
This article explains why asking AI the right way matters, introduces a practical four‑step prompting framework—role, background, task, format—illustrates each step with concrete examples, reveals a hidden “sample” trick, and shows how iterative refinement can turn generic replies into precise, useful results.
This article dissects the architecture, workflow, and real‑world applications of Code Agent – an AI‑driven system that understands, generates, debugs, and optimizes code – comparing it with traditional assistants, showcasing concrete examples, code snippets, and future challenges for software development.
This article explains the self‑attention mechanism behind transformers, detailing its core components, mathematical formulation, step‑by‑step example, multi‑head extension, industry use cases, and a thorough comparison with RNN and CNN approaches, all supported by concrete numbers and citations.
This article explains fine‑tuning as a second‑stage training method that adapts large pre‑trained models to specific tasks, outlines the three‑phase workflow, compares it with prompt engineering and retrieval‑augmented generation, and provides four detailed case studies with complete code snippets and best‑practice tips.
This article explains Retrieval‑Augmented Generation (RAG), detailing its offline knowledge‑base construction and online retrieval‑enhanced generation workflow, comparing it with traditional and fine‑tuned models, and providing step‑by‑step LangChain implementations, advanced techniques, and practical use‑case demos.
This article explains AI agents as autonomous systems that perceive environments, set goals, and act, contrasting them with traditional AI, detailing their core definition, architecture, key components, practical applications, implementation steps, classification, technology stack, case studies, emerging trends, challenges, and future directions.
This guide walks you through using Tencent's CodeBuddy AI and the open‑source Spec‑Kit to adopt a spec‑driven development process, covering environment setup, spec creation, AI‑assisted coding, task generation, checklist validation, risk analysis, and implementation, all illustrated with concrete commands, examples, and workflow diagrams.
