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 explains the design and implementation of the Codebase AI Assistant in the Chocolate Factory framework, covering its problem‑solving DSL, retrieval‑augmented generation pipeline, indexing and querying stages, prompt strategies, and code‑splitting rules that together enable efficient semantic code search.
This article shares practical insights from months of building LLM proof‑of‑concepts, covering language‑agnostic architectures, FFI integration, prompt engineering, RAG patterns, DSL design, and four core architectural principles for scalable AI applications.
CoUnit is an open‑source Rust‑based tool that uses local semantic search and LLMs to create a virtual team interface, enabling low‑cost, offline knowledge retrieval, API discovery, and cross‑team assistance for software development teams.
The article explains how to enhance the open‑source AutoDev coding assistant by configuring your own large language model, customizing IDE behavior, defining custom prompts and actions, and adjusting request/response handling via FastAPI, offering step‑by‑step JSON examples and guidance for advanced developers.
This article examines the open‑source AutoDev project, outlining its assumptions about private LLMs, the three assistance modes it provides for IDEs, technical implementation details, integration challenges, and broader thoughts on LLMs as developer copilots.
AutoDev is an AI‑assisted JetBrains IDE plugin that connects to issue trackers, automates prompt generation, enforces code standards, supports multiple languages and LLM providers, and offers smart prompt strategies, aiming to reduce developer interaction costs while improving generated code quality.
This article explores how large language models can be integrated into software architecture governance, turning architectural rules into code, enhancing design, development, and runtime phases, and improving code generation quality through explicit knowledge, DSLs, and full‑process AI assistance.
This article explores how large language models can standardize software engineering practices by converting requirements and designs into DSL formats, enabling more automated and efficient code generation, while discussing the challenges of dynamic context building, DSL specification, and the evolving role of LLMs in development pipelines.
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.
