170 related articles

Gaurav Sen reveals the fatal trap in AI learning: starting from ML fundamentals often leads to burnout. Learn the Onion Model approach—RAG, Agents first, Transformers next, math last.

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.

Frontend engineers pivoting to AI Agent development: TypeScript and Zod are now must-have skills. Explore the full progression from API calls to building LangGraph-style frameworks, and nail the 3 core interview topics.

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.

A ByteDance interviewer breaks down the 3-layer Vibe Coding interview framework: AI tool awareness, complex product engineering, and a 1-hour full-stack challenge. Architectural thinking wins.

Metaview engineer Nick Mayhew explains how to build self-evolving prompt systems: Markdown over rules, layered workflows to cut token costs, and agents that learn user preferences for human-centered AI recruiting.

A practical guide to OpenAI Codex: core features, Codex vs. Claude Code comparison, and why the Codex+DeepSeek combo doesn't work. Avoid common pitfalls and boost your coding productivity.

A Bilibili creator used Vibe Coding to independently build 'Breath Garden,' an AI sleep companion app, winning third place at the Tencent Music Hackathon. Full breakdown inside.

Vibe Coding lets anyone build apps using natural language — no coding required. Learn what it is, which tools to use, and see real stories of non-programmers shipping products.

Naval Ravikant says Vibe Coding lets one person do the work of 8 engineers. Learn what Vibe Coding is, which tools work in China, and how to start in 3 steps.
GitHub Daily · July 16: AI Agent Secur…
Today's GitHub Trending: AI Agent security tool destructive_command_guard surged +471 stars, hallmark's anti-AI-slop design pack jumped +1,277, and OpenCut leads as the open-source CapCut alternative.

Build a multi-scene life assistant Agent using ModelScope MCP Marketplace and Dify. Integrates Amap, LeetCode, recipe, and news MCP Servers with Qwen3 via Chatflow.

A structured AI Agent learning path covering core principles, prompt engineering, tool use, multi-agent systems, and frameworks like LangChain, CrewAI, and Dify for enterprise deployment.

How should test engineers choose AI tools? This guide breaks down the pitfalls of pure AI solutions and recommends a hybrid strategy using tools like DeepSeek, TRAE, Claude Code, and Skill encapsulation.

AI talent gap is widening fast. Learn LLMs from zero in 3 months: Python & Transformer basics → Agents & LLMs → fine-tuning & private deployment. Land your AI job.

A deep dive into AI-powered testing: Cursor Skills, Coze agents, and LangChain multi-agent systems for automated test case generation, BDD, and review workflows.

Calling an API isn't enough. This article breaks down the full AI application developer skill structure — Python, deep learning, fine-tuning, Agents, and enterprise projects — with a clear learning roadmap.

A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.

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.

As Vibe Coding rises, many developers can't write code without AI. We break down the risks, whether traditional coding skills still matter, and how to rebuild them.