#Spring Boot development
128 related articles
·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·2 min
Getting Started with ChatGPT Codex: Key Differences Between an AI Coding Agent and ChatGPT
Codex is OpenAI's AI coding agent that autonomously reads code, fixes bugs, and runs tests — far beyond ChatGPT's code generation. Learn the key differences.
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·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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·2 min
Codex Joins ChatGPT: A Beginner's Guide to AI Coding Agents
OpenAI integrates Codex into ChatGPT. Learn what AI coding agents can do, how Codex compares to Claude Code and Cursor, and how to get started today.
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·4 min
Claude Code Getting Started: Environment Setup, Core Commands, and Git Workflow Automation
A practical guide to Claude Code covering installation, Chinese LLM switching, project analysis, key commands, and conversational Git workflow automation for developers.
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·3 min
Java Developers Pivoting to AI: Why RAG Knowledge Bases Are the Best Entry Point
Traditional Java roles are shrinking while AI demand surges. Learn the three paths into AI for developers, and why RAG knowledge bases are the highest-ROI entry point for Java engineers.
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·4 min
Understanding CI/CD from Scratch: Core Principles and Practical Introduction to Automated Deployment
A clear, practical guide to CI/CD: from Waterfall to DevOps, manual vs. automated deployment, and a full Jenkins + RuoYi hands-on learning path for beginners.
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·3 min
Java Developers Transitioning to AI: A Practical Guide to Building RAG Knowledge Bases
How can Java developers break into AI? This guide covers the AI application engineer career path, RAG knowledge base fundamentals, vector database retrieval, and enterprise-grade RAG challenges.
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·3 min
Claude Code Enterprise Guide: Environment Setup, Git Workflows, and Switching to Chinese AI Models
Enterprise guide to Claude Code: CLI setup, switching to DeepSeek and other Chinese AI models, Git workflow automation, and bug fix loops to boost team productivity.
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·2 min
Spring AI Alibaba Graph in Action: Build an HR Recruitment Workflow Agent from Scratch
Build a full HR recruitment Workflow Agent with Spring AI Alibaba Graph: résumé scoring, interview generation, Human-in-the-Loop, and state rollback across 20 technical concepts.
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·2 min
Spring AI in Action: A Complete Guide to LLM Integration for Java Developers
Spring AI is Java's answer to LangChain — offering unified multi-model APIs, structured output, RAG, Tool Calling, and MCP protocol support for enterprise LLM development.
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·3 min
LangChain4j Getting Started Guide: A Java Engineer's Fast Track to AI Application Development
LangChain4j is the AI application development framework built for Java engineers. Integrate DeepSeek, Qwen, and more into Spring Boot — no Python required.
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·4 min
LangChain in Practice: Mastering the Four Core Modules of the Agent Ecosystem
A deep dive into LangChain's four core modules: LangChain components, LangGraph orchestration, Deep Agents, and LangSmith. Build your first Agent from scratch.
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MailFlow Open Source Email Client: A D…
·2 min
MailFlow Open Source Email Client: A Developer's Guide to Contribution and Value
MailFlow is an open source email client project built for developers, prioritizing privacy, self-hosting, and extensibility. Here's why it matters and how to evaluate whether to contribute.
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·2 min
Complete Spring AI Tutorial: ChatModel, RAG, and MCP Practical Guide
A complete Spring AI guide for Java developers covering ChatModel, EmbeddingModel, ChatMemory, Tool Calling, MCP protocol, and RAG with Milvus. Build LLM apps in Spring Boot.
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·3 min
LangChain Practical Guide: From RAG Applications to Intelligent Agent Development
A comprehensive guide to LangChain: core concepts, RAG applications, Agent development, version selection (0.3/1.0), and career opportunities for Java/Python developers entering LLM development.
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·3 min
Spring AI 2.0 Deep Dive: A Java Developer's Guide to LLM Application Development
A deep dive into Spring AI 2.0: provider-agnostic APIs, RAG with vector databases, and how Java developers can build LLM apps using the Spring ecosystem.
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·3 min
Sent Hundreds of Resumes with No Response? A Deep Dive into Tech Job Search Struggles and How to Break Through
Sending hundreds of resumes with no response? This article breaks down the core pain points in today's tech job market — ATS filtering, spray-and-pray fatigue, neglected referral channels — and offers actionable strategies to break through.
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·6 min
A Beginner's Guide to Claude Code: Why Is It More Powerful Than Cursor and Trae?
Claude Code is Anthropic's local AI programming assistant that reads your entire codebase, auto-debugs, and delivers far higher accuracy than Cursor and Trae. Here's why it's the strongest AI coding tool today.
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·4 min
Behind India's 5% AI Hiring Dip: Core Skill Demand Structure Remains Rock Solid
India's AI/data science postings hit 11,557 this week, down 5% from last week, but the skill demand structure barely changed. Python, ML, and SQL remain top skills while GenAI/LLM demand keeps rising.
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