A ubiquitous public account for pirate enthusiasts, regularly sharing curated experiences, tech learning, and growth insights. Currently publishing articles on AI RAG customer service, AI MCP technology, and open-source design. Personal free Knowledge Planet: Awakening New World Programmer.
This article walks through the MCP Server‑based chart generation service, covering its 25+ chart types, integration via AntV, configuration for Stdio and SSE modes, source code structure, and step‑by‑step private deployment using Docker and environment variables.
This article walks through installing Node.js with NVM, downloading the AIHub v1.8.9 source, installing dependencies, examining the package configuration, adding a custom Code Review navigation entry in App.vue, and running the Electron‑based desktop app to demonstrate a functional AI code review interface.
This article walks through the internal startup sequence and runtime configuration‑change mechanism of the open‑source DynamicTp dynamic thread‑pool, covering Spring Boot starter integration, @EnableDynamicTp annotation processing, Nacos‑driven property refresh, SPI extensibility, queue adaptation, and graceful shutdown, all illustrated with concrete code snippets and diagrams.
This article introduces DynamicTp, a lightweight, configuration‑center‑driven dynamic thread‑pool library for Java, explains why static pools are problematic, details its features such as runtime parameter adjustment, visual monitoring, smart alerts and middleware integration, and walks through a complete Spring Boot 2.7 demo with Nacos and Feishu notifications.
This article presents six practical AI Agent design patterns—chain, parallel, routing, coordinator‑executor, generator‑evaluator (reflection), and tool‑use—explaining their concepts, typical use cases, and workflow diagrams, and shows how they improve reliability, maintainability, and performance of LLM‑driven applications.
This article walks through a detailed Java interview simulation where the candidate designs a high‑throughput hotel pricing service, covering business decomposition, Kafka event‑driven pipelines, cache‑aside consistency, HikariCP tuning, fault‑tolerant microservices, multi‑region disaster recovery, and AI recommendation gray‑release strategies.
This article explains how to construct and manage various context data—such as historical review feedback, code diffs, file types, developer experience, commit messages, business scenarios, framework knowledge, and Git logs—to improve the quality of AI‑driven code reviews, and provides a concrete TypeScript implementation with modular context providers, prompt engineering, and integration with GitLab and OpenAI APIs.
The article tells of a developer whose code caused a CPU spike, shows how an AI code review tool caught an unnecessary database query, and then outlines sixteen practical ideas—including multi‑turn dialogue, diff analysis, domain fine‑tuning, CI/CD integration, template feedback, intent inference, automated test generation, security scanning, auto‑fix suggestions, performance analysis, readability scoring, and visual reporting—to improve AI‑assisted code review quality.
This article explains how to build a TypeScript PromptBuilder that supports variable placeholders, demonstrates three concrete AI code‑review prompt templates, and shows how these templates can be integrated into a code‑review workflow to produce structured, high‑quality feedback.
This article walks through using Vibe Coding’s AI‑driven workflow with the Trae tool to automatically generate a Vue 2‑based web management dashboard, detailing how to extract UI design rules, create project‑level prompts, run commands, troubleshoot, and refine the interface with iterative prompts.
