4102 related articles

A clear explanation of how AI large models work: from concept hierarchy and Transformer mechanics to probabilistic traits, helping test engineers grasp AI testing.

A thorough explanation of the essence of AI large language models: from conceptual hierarchy and Transformer mechanics to probabilistic nature, helping test engineers understand LLM strengths and weaknesses.
Loving LLMs, Hating the Hype: How Engi…
Engineers love LLMs for real productivity gains but hate the hype around AGI narratives, glossed-over hallucinations, and valuation bubbles. Here's how to find the rational balance.

Confused about breaking into AI LLMs? This guide breaks down the two core career tracks — Engineering & Deployment vs. Algorithm Research — covering RAG, Agents, and more.

Learn how AI LLMs revolutionize JS reverse engineering—automating encryption cracking, signature reconstruction, and parameter analysis to boost freelance scraping efficiency by 10x.
TutorialsA systematic LLM engineer learning roadmap covering Transformer basics, prompt engineering, RAG, Agent development, API integration, fine-tuning, deployment, and project practice across six stages.
Industry InsightsIn-depth analysis of two core AI LLM career paths: engineering implementation vs. algorithm research. Compare education requirements, skills, and job prospects for programmers transitioning to AI.

Exposing the truth behind viral Codex 5-minute website videos: creators aren't building original sites with AI—they're copying shared prompts or scraping others' work. Learn AI coding tools' real limits.

A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.

Exposing the truth behind viral Codex 5-minute website videos: creators aren't building original sites with AI — they're copying shared prompts or scraping others' work.

A detailed guide on using AI Agents to build Research Logs for scientific experiments, covering skeleton structure, daily workflows, pre-experiment thinking standards, code change tracking, and Agent-researcher division of labor.

A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.

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.

How can traditional product managers transition to AI PM? This article analyzes the essential differences and details three must-have skills: AI product cognition, advanced Prompt engineering, and large model technical logic.

A detailed guide to ByteDance's Coze platform covering agent building, workflow orchestration, and knowledge base management to help beginners start AI app development with zero coding.

Learn how to use AI Agents to build a Research Log for scientific experiments, covering structure, daily workflows, pre-experiment thinking, code change tracking, and human-AI division of labor.

When your AI system underperforms, the problem is often not the model or algorithm — it's basic work like data cleaning, prompt writing, and evaluation that hasn't been done right.

Step-by-step guide to installing MCP connector in WorkBuddy and syncing Skills across Cursor and other AI workstations via AI conversation.

Discover how LiDAR laser technology penetrates deserts and jungles to reveal Sela's underground cistern system and Nan Madol's hidden structures, rewriting the history of lost civilizations.

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.