18 related articles
Product Reviews"It's Ready" is an AI hotpot timer mini app — just voice your order and it auto-generates countdown cards that turn green when each dish is perfectly cooked.

Skip the dry theory and get hands-on! This article demonstrates step by step how to build a working AI Agent from scratch in 30 minutes using AI coding tools—covering the agent skeleton, tool system, memory mechanism, Flask web UI, and DeepSeek API integration.

A deep dive into two frontier dense retrieval works: Hobbit uses gradient analysis to automatically construct hard batches; Disco replaces single-document competition with submodular collaborative coverage, reshaping Top-K retrieval.

Local LLM tool Ollama closes a $65M Series B, bringing total funding to $88M. With 9M developers and 85% of Fortune 500 having deployed internally, this deep dive explores why enterprises embrace local LLMs: compliance, Agent cost savings, and open-source ecosystem.

A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.

A systematic four-stage roadmap for AI Agent development: fundamentals, core principles, enhancement, and real-world deployment. Build complete Agent skills.

What is an AI Agent? This article systematically explains the core architecture of AI agents (LLM + Planning + Memory + Tools), how they differ from ChatGPT, their combination with robots, and why developers must master Agent development skills.

By introducing an engineered verification loop reasoning framework, DeepSeek's effective pass rate on complex tasks can improve ~4x, matching Claude Opus at one-seventh the cost. A deep dive into verification loops, test-time compute scaling, and their practical implications.

Cut through the Agentic AI hype to see the real value of agentic applications. Based on Andrew Ng's course, learn why Evals and error analysis—not framework choice—separate top developers.

Step-by-step guide to installing Claude Code Desktop, enabling developer mode for account-free use, integrating DeepSeek via CC Switch, Chinese localization, and custom Skills in ten minutes.

A deep dive into Loop Engineering for AI Agents — what loop feedback mechanisms are, how they differ from Harness Engineering, and a complete guide from principles to production implementation.

A deep dive into AI Agent architecture and enterprise deployment. From LangChain and ReAct design to dynamic tool calling and multi-task recognition — build autonomous enterprise AI assistants.

Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.

Beyond beef hotpot: explore Chaoshan's porcelain mosaic temples, licorice fruits, Yingge war dance, and 80-year-old overseas Chinese letters across Chaozhou, Shantou, and Jieyang.

How Daily Food Journal accumulated 30-40 million followers over a decade using cinema-grade visuals, saturation shooting, and meticulous sound design. A deep dive into their methodology.
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.
TutorialsDeep dive into LangChain Agent Executor internals, covering the ReAct reasoning-action-observation loop, tool choice strategies, and building a custom Agent Executor from scratch.
Deep DivesDeep analysis of the 58K-Star open-source project learn-claude-code: building a Claude Code-like programming agent from scratch. Deconstruct the Agent Harness, ReAct loop pattern, and Bash execution layer.