90 related articles
Deep Dive into Three Major LLM Career …
Deep analysis of three core LLM roles—Application Engineer, Development Engineer, and Algorithm Engineer—covering technical requirements, salary thresholds, and career prospects including RAG, fine-tuning, and inference deployment.
Anthropic Co-founder's Vatican Speech:…
Anthropic's co-founder delivered a landmark Vatican speech, admitting AI companies face structural conflicts of interest, revealing emotion-like signals found inside AI models, and calling for society-wide participation in AI governance.
memU Memory Framework Explained: Unify…
Deep dive into the memU open-source memory framework: how it organizes Agent memory as a file system with three-layer semantic abstraction, dual-loop collaboration, and two retrieval modes.
Hermes Self-Evolution Framework: An Op…
Deep dive into NousResearch's open-source Hermes Agent self-evolution framework, using DSPy and GEPA for automated prompt optimization with five-layer safety mechanisms.
Why Qwen3 Is the Best Open-Source Mode…
Analysis of Qwen3's advantages for MCP agent development, comparing DeepSeek R1's lack of Function Calling, covering MoE architecture and thinking mode switching.
Interpreting OpenAI's Frontier Governa…
Deep analysis of OpenAI's Frontier Governance Framework, examining its core elements in AI safety and risk management, and how it aligns with the EU AI Act, California AI regulations, and global trends.
AI Is Getting More Expensive: The Indu…
From $1.3M monthly token bills to rising premium AI model prices, AI isn't becoming accessible. A deep dive into the industry's two price lists, centralization trends, and what it means for everyone.
TutorialsDeep dive into LangGraph multi-agent architecture covering Graph structure principles, MCP service integration, Time Travel debugging, and supervised multi-agent enterprise implementation patterns.
TutorialsDeep dive into Andrew Ng and Harrison Chase's LangChain course, covering the five core components—Models, Prompts, Indexes, Chains, and Agents—to help developers master LLM app development.
TutorialsA systematic four-stage learning roadmap for programmers transitioning to AI Agent development, covering core theory, ReAct and classic paradigms, Prompt engineering, and hands-on projects.
Tech FrontiersOpenAI releases GPT-5.2 with a 390x efficiency gain on ARC-AGI, beating Claude Opus 4.5. Deep analysis of the efficiency leap, user experience paradox, Disney's $1B deal, and the AI content quality crisis.
Tech FrontiersDeep dive into Google Gemini Omni's video style transfer: transform videos into watercolor, cyberpunk, or Ghibli styles using natural language. Explore its technology, workflow, and competitive landscape.
TutorialsA systematic breakdown of the Complete Guide to Claude Code course, covering context engineering, MCP protocol, claude.md configuration, multi-Agent architecture, and three progressive projects.
Product ReviewsDeep analysis of Moonshot AI's open-source Kimi K2.6 Agent orchestration: 300 sub-Agents executing 4000-step tasks, outperforming GPT-5.4 in coding benchmarks, LoRA fine-tuning on 2x RTX 4090s.
TutorialsHow can frontend engineers advance into AI Agent development? This guide covers LangGraph.js core architecture (state, nodes, edges), LangChain comparison, and workflow agent design with practical examples.
TutorialsA systematic guide to the relationships between AI, machine learning, deep learning, and large language models, helping developers build a clear knowledge framework and find an efficient learning path.
Product ReviewsIn-depth review of Kimi K2.6's coding, Agent collaboration, and visual development capabilities. #1 open-source on SWE-Bench Pro, 300 parallel sub-agents, API priced at 1/3 of competitors.
Running Qwen3.6-27B Locally on Mac: 4 …
Benchmarking 4 solutions for running Qwen3.6-27B locally on Mac: GGUF, MLX Diflash, and MTP-LX. MTP-LX 4bit leads at 43.6 tok/s with solid coding, writing, and reasoning quality.
TutorialsA complete beginner's guide to LLM application development: learn the three key directions (API calling, RAG, Agent), master frameworks like LangChain, and follow a step-by-step learning path to become an AI application developer.
TutorialsHow to start LLM application development from scratch? A complete roadmap covering Python basics, RAG knowledge bases, and Agent development with LangChain.