Focused on AI programming, sharing ready-to-use development efficiency solutions.
The article explains how Spring AI’s .entity() method lets developers obtain structured Java objects such as POJOs, lists, enums, and response entities directly from LLM outputs, eliminating the need for manual JSON parsing and handling generic‑type issues with ParameterizedTypeReference.
The article explains how to move beyond static prompts in Spring AI by using System and User message roles, PromptTemplate placeholders with .param(), defaultSystem configuration for reusable role definitions, and independent PromptTemplate usage, providing concrete code examples for each technique.
This article explains how Spring AI's ChatClient serves as a high‑level façade over ChatModel, compares the synchronous call() method with the streaming stream() method, demonstrates retrieving plain text, full responses, or structured entities, and shows how to override model parameters such as temperature on a per‑request basis.
This article introduces Spring AI 2.0, explains why Java developers should adopt it, and walks through setting up a Spring Boot 3.x project with JDK 17, adding the DeepSeek starter, configuring properties, writing a simple ChatController, and running a curl request to see the model’s reply.
The article walks through installing Claude Code and its Figma plugin, then compares two methods—official MCP extraction and screenshot‑based visual reconstruction—to automatically generate uni‑app code from Figma designs, highlighting speed gains, precision differences, and common pitfalls when converting px to rpx.
Claude Code released ten versions in ten days, but only two new features—Agent View and the /goal command—fundamentally reshape how developers orchestrate AI‑generated code, turning the tool from a simple assistant into a manageable workflow partner.
The article explores Harness Engineering as a framework for creating reliable, long‑running AI agents, detailing a hands‑on project called SkillForge that transforms books into executable Skills, and outlines key components, iterative processes, documentation, and best practices for stable, self‑evolving AI workflows.
The article maps the four core mechanisms of Deep Agents—Skills, MCP, A2A, and ACP—explaining how Skills governs internal agent behavior while the three protocols handle external tool integration, agent‑to‑agent collaboration, and client‑to‑agent communication, and offers guidance on when to adopt each layer.
The article breaks down three complementary protocols—MCP for tool integration, A2A for agent‑to‑agent collaboration, and ACP for client‑to‑agent calls—explaining their layers, responsibilities, concrete examples, maturity levels, and a recommended learning order for developers building AI agents.
This article compares Model Context Protocol (MCP) and OpenAI‑style Function Calling, explaining their fundamental differences, runtime architectures, capability scopes, ecosystem effects, and providing concrete scenarios and a decision tree to help developers pick the most suitable approach for their AI applications.
