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Vibe Coding is trending, but can it replace solid fundamentals? A deep analysis of why core principles, systems thinking, and knowledge frameworks remain a developer's moat in the AI era.

How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.

SlopCodeBench sparks deep reflection on AI code evaluation. From benchmark contamination to pass-rate pitfalls, exploring why current benchmarks fail to measure real code quality.

Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.

Deep analysis of circular financing in NVIDIA's $750B partnership deals, examining real AI compute demand, self-reinforcing valuations, and key investor signals.

A detailed guide to OpenAI Codex coding agent: understand how it differs from ChatGPT, how AI agents autonomously generate, debug, and test code, and why developers need to learn this new paradigm.

Senior data scientist interviews are broad and multi-round. Learn an efficient evergreen fundamentals + targeted sprint strategy covering ML, SQL, system design, and mindset tips.

A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.

A detailed guide to OpenAI's Codex coding agent: understand what it is, how it differs from ChatGPT, and why developers should master AI coding agents now.

A detailed comparison of OpenAI Codex and Claude Code with hands-on testing. From AI agent concepts to account setup, helping developers quickly master AI coding agents.

AI coding tools are reshaping software development. Is learning to code still worthwhile? This article analyzes the challenges and opportunities of learning programming in the AI era.

Deep dive into Sebastian Lague's experiment building a graphics library from scratch, covering rasterization, depth buffering, texture mapping, and the educational value of software renderers.

GitHub found that giving Copilot more specialized tools actually degraded code review quality. By migrating to Unix-style composable tools and evidence-driven workflows, they cut costs and improved results.

Awesome Free AI Books is an open-source repo with 30+ legally free AI & ML classic textbooks covering deep learning, reinforcement learning, NLP, LLMs, and more — all linking to official sources with weekly automated link checks.

A complete guide to learning AI Agents: from large model fundamentals and core technologies to hands-on projects. Systematically outlines beginner methods and exposes crash-course marketing traps.

A creator spent 40 days and 80 billion tokens testing the real limits of Vibe Coding. This article dissects why AI programming crashes in production: complexity, context limits, and compression loss.

A comprehensive guide to three core AI tool types (personal assistant, CLI geek, AI IDE) in the testing era. Uncover the real challenges of AI test case generation and the new AI test development paradigm.

A complete guide to the three core categories of AI tools in the testing era (personal assistants, CLI geek tools, AI IDEs), revealing the real challenges of AI test case generation and the new AI test development paradigm.
Dive into LLMs: A Complete Guide to th…
"Dive into LLMs" is a 44,830-star Chinese LLM tutorial on GitHub. Using Jupyter Notebooks, it covers Transformers, LoRA fine-tuning, RAG, and Prompt Engineering.

A complete guide to Dify, the open-source AI application platform: its core positioning, key differences from Coze, workflow-building capabilities, and enterprise private deployment advantages.