A prolific open-source contributor who constantly starts new projects. Passionate about sharing software development insights to help developers improve their KPIs. Currently active in IDEs, graphics engines, and compiler technologies.
This article presents a detailed reference architecture for building LLM‑driven applications, using the ArchGuard Co‑mate project to illustrate layered design, local model integration, DSL‑based orchestration, and streaming LLM interfaces, complete with code examples and practical implementation notes.
The article outlines five practical patterns—natural‑language DSL, streaming DSL, DSL‑guided generation, explicit retry, and dynamic proxying—that enable developers to treat large‑language‑model interactions as first‑class APIs, improving efficiency, accuracy, and user experience across diverse scenarios.
This article outlines four foundational design principles—user‑intent‑driven design, context awareness, atomic capability mapping, and language‑API integration—for building LLM‑centric software architectures, illustrating each with DSL examples, Kotlin implementations, and practical insights on prompt engineering, dynamic context layering, and API evolution.
This article explains how ArchGuard 2.0 leverages large language models to transform tacit architectural knowledge into dynamic, DSL‑based governance functions, detailing the challenges of traditional tools, the design of the Co‑mate system, and the practical implementation using Kotlin REPL.
This comprehensive guide walks programmers through the fundamentals of large language model capabilities, prompt writing and management, new interaction and workflow designs, advanced scenario‑specific applications, and context engineering, offering practical strategies and architectural insights for AI‑native development.
This article reverse‑engineers GitHub Copilot's context construction, detailing visible and invisible processes, prompt composition, token allocation strategies, fast response mechanisms, and future directions for LLM context engineering.
This article explores four distinct fine‑tuned LLaMA/ChatGLM approaches for AI‑assisted code generation—example‑based, test‑driven, metadata‑augmented, and information‑matching—detailing their training data, prompts, sample inputs and outputs, and evaluating their strengths, limitations, and suitable application scenarios.
The article explores how AI‑driven prompt programming can transform traditional software development by converting natural‑language requirements directly into executable code, outlining its core features, DSL examples, DDD integration, practical demos, current challenges, and the future potential of real‑time software generation.
This article walks through a practical AIGC workflow—defining a three‑step human‑machine blueprint, crafting effective prompts and negative prompts, leveraging ControlNet and DreamBooth for custom models, and refining outputs with Inpaint—showing how to boost creative efficiency with open‑source AI tools.
This guide explores how to craft effective prompts for Stable Diffusion image creation, ChatGPT article writing, and GitHub Copilot code generation, covering prompt evolution, negative prompts, ControlNet enhancements, model selection, and practical tips for iterative refinement and context building.
