Practical AI for solving real problems and creating value
This guide walks through four practical ways to use Gemini 3 Pro together with DrawIO, SVG, and AI coding IDEs to automatically create, edit, and refine high‑quality architecture diagrams, complete with prompt examples, installation steps, and post‑generation adjustments.
This article explains the fundamental differences between workflow‑style fixed pipelines and agent‑style dynamic LLM orchestration, compares their characteristics, reviews classic workflow patterns, and walks through a concrete implementation using the Kuzi platform with step‑by‑step screenshots.
This article explains the MCP protocol for AI agents, detailing why a universal tool‑calling standard is needed, how it solves the M×N integration nightmare, the roles and execution stages involved, and demonstrates its use with Cherry Studio while highlighting current limitations.
This article examines a curated GitHub repository of system prompts for leading AI coding assistants, distilling best‑practice principles such as explicit role definition, safety boundaries, structured output formats, context management, task decomposition, error recovery, testing, and secure sandbox workflows, with concrete examples from tools like Cursor, VSCode Agent, Augment Code, and Devin AI.
This article explains a step‑by‑step workflow for mitigating large‑language‑model hallucinations by picking low‑hallucination models, leveraging internet‑enabled search tools, rephrasing queries, and creating a dedicated verification assistant with concrete prompts and a Claude implementation.
This article explains the relationship between AI, machine learning, deep learning, and large language models, detailing their evolution, training stages, transformer architecture, attention mechanisms, inference APIs, and practical usage examples, while demystifying common misconceptions about LLM capabilities.
The author evaluates Google’s newly released Gemini 3 model through seven diverse cases—hand‑counting, macOS desktop simulation, a jump‑the‑gap game, lightweight Word, expert‑style explanations, SVG fan rendering, and video understanding—highlighting its multimodal reasoning, coding assistance, and remaining limitations.
This article surveys the rapidly expanding AI coding ecosystem, categorizing tools by cloud versus local deployment and interactive versus autonomous operation, then details major IDE plugins, CLI utilities, Vibe‑style products, and asynchronous agents, offering selection guidance and best‑practice tips for developers.
This article explains the concept of AI agents, contrasting them with chatbots, detailing their ability and structural layers, summarizing academic surveys and whitepapers, and illustrating how agents plan, perceive, and act to autonomously accomplish user‑defined goals.
The article examines the hype versus real value of AI tools, compares different models and platforms, shares concrete usage scenarios, and offers practical recommendations for selecting models, mastering prompt engineering, building personal AI agents, and adopting an AI‑first mindset.
