219 related articles

Learn how AI Skills are transforming software testing. This guide covers Skill architecture, learning paths, and real-world applications in API automation and WebApp testing.

Real freelance case study: how to use DeepSeek and LLMs to reverse-engineer SM2/SM4 encrypted API parameters in 30 minutes, covering anti-debug bypass, call stack backtrace, and AI-assisted deobfuscation.

Deep dive into AI-era automated testing: using Pytest + Playwright + MCP for stable automation, constraining code conventions with Skills, avoiding non-determinism and high token costs. Includes real debugging war stories.

Learn how to use Trae, ByteDance's free AI coding tool, to build a Flask-based visual database query system (EasyQuery) in just 20 minutes. Full walkthrough included.

A complete guide to Java AI development: Spring AI, LangChain4j, Spring AI Alibaba, and AgentScope4j — framework comparisons, selection tips, and a clear learning path.

Many CS students use AI to learn programming but later feel they didn't truly learn. This article breaks down the two AI learning traps and offers Socratic questioning, the Feynman Technique, and more to turn AI into a real learning accelerator.

An electronics engineering student who hates hardware wants to pivot to backend dev, facing a dilemma between a "guaranteed" degree and a third-tier BCA. We break down the degree vs. skills tradeoff, how to explain gaps, and self-study paths.

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.

Overwhelmed by ML math courses? This guide maps out linear algebra, calculus, and probability into a practical learning path — from core courses to reference books.
Building AI Engineering Skills from Sc…
A deep dive into 'ai-engineering-from-scratch,' the GitHub project with 38K+ stars that helps developers build real AI engineering skills through a Learn-Build-Ship methodology.

A college student's MLOps 100-day challenge documents the full journey from Python engineering and Git to Docker, model deployment, and monitoring. A practical roadmap for data scientists transitioning to ML engineering.

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.

Why do AI results vary so dramatically? LangChain V1.3 reveals the answer: engineering mindset. Covers LangGraph, Deep Agent, RAG, Time Travel, and more.

A real-world retrospective on AI-assisted Python reverse engineering: from JS obfuscation tracing and SM2/SM4 key extraction to generating decryption code with DeepSeek. An honest assessment of LLM value and legal risks.

A developer used GPT and Codex to build a real-time typhoon detection system with 3D maps, timeline playback, and risk analysis — all in just 20 minutes.

Transitioning from software dev to AI/ML is hard to do alone. Discover why finding a study buddy beats picking the perfect course — and how peer accountability solves the consistency, judgment-free questioning, and foundation-building challenges.

A structured zero-to-one roadmap for AI Agent development: Phase 1 covers Python & LLM basics, Phase 2 tackles five core Agent capabilities and LangChain/LangGraph, Phase 3 delivers hands-on RAG projects.

A complete 5-stage AI large model learning roadmap — from Python basics and prompt engineering to RAG pipelines, Agent development, and private model deployment.

Learn LangChain 1.3 core concepts including LLM model abstraction, RAG retrieval-augmented generation, and Agent orchestration. Build a Deep Agent with planners, tools, and reflection modules.
GitHub Daily · July 17: AI Coding Infr…
AI coding infrastructure explodes on GitHub: context management, code graphs, and vector indexes become the new battleground as the community shifts from apps to underlying capabilities.