72 related articles

Pure frontend roles are shrinking; AI Agent development is the high-salary divide. This guide breaks down the full skill tree for frontend engineers pivoting to AI: TypeScript, frameworks, AI productivity, and Agent core concepts (MCP, Tool Calling, Skill).

Mixar is an AI-native fork of Blender 5.0 that embeds AI into the kernel layer. This hands-on review tests texture baking, LOD generation, mood boards, image-to-3D, and more, comparing it to MCP. Fully open source and free.

Ternlight is a 7MB WebAssembly-based browser-side text embedding model requiring no server or GPU. Explore its tech, use cases, and tradeoffs for private, offline semantic search.

Over-reliance on LLMs is an overlooked pitfall in AI development. Explore the hidden costs of Token economics, the boundaries between LLMs and deterministic code, and how hybrid architectures balance flexibility and reliability.

Deep dive into AI Agent Skills: SKILL.md file structure, four component modules, differences from prompts, and practical scenarios for frontend generation, PPT creation, and more.

Deep dive into LangChain 1.0's architecture: LangChain framework, LangGraph multi-Agent orchestration, and LangSmith observability platform, with hands-on RAG and intelligent customer service projects.

Anthropic releases Claude Sonnet 5, its most agentic mid-tier model with planning, browser/terminal tool use, and autonomous execution—bringing flagship Agent capabilities at significantly lower cost.

In-depth analysis of OpenAI Codex's four usage forms, comparing Codex, Claude Code, and Cursor across price, stability, and frontend/backend fit to help developers choose the right AI programming tool.

Learn AI Agent development from scratch. This tutorial covers LLMs and prompts, then builds a conversational agent in Python using the DeepSeek API with multi-turn dialogue and system prompts.

Coding alone isn't enough anymore. Learn the 5 key steps to commanding AI Agents—define outcomes, split tasks, provide context, iterate small, and keep humans in the loop.

Deep dive into Agent Loop mechanics: the think-act cycle, how agents differ from LLMs, termination conditions, and design principles for building autonomous AI Agent systems.

Deep analysis of multi-agent system cost optimization: why the 'expensive commander + cheap workers' combination outperforms all-frontier fleets, covering decision-intent cost logic and Sonnet 5 tokenizer traps.

A deep dive into AI Agent architecture and engineering practices, covering tool design, ReAct execution patterns, Vercel deployment, and production considerations to bridge the prototype-to-production gap.

Deep dive into Agent Skills: how they solve React Agent pain points through on-demand loading and capability modularization, improving decision precision for production-ready enterprise AI Agents.

Three practical cases—RNA-seq setup, GATK reference data download, and SRA batch download—demonstrate using Claude Code with DeepSeek v4 Flash for bioinformatics environment configuration.

Hands-on test of Doubao AI for debugging hidden integration bugs: parameter casing mismatches, JSON structure misalignment, and more — paste raw logs and code to get precise root cause analysis and fixes.

A deep dive into AI engineering with Codex and Claude Code: Vibe Coding limitations, Chinese LLM rankings, Skill-driven development, and enterprise project practices.

Deep dive into the AI coding paradigm shift: from hand-crafted prompts to self-prompting agent loops. Learn how agent self-review and proactive context fetching enable scalable, high-quality AI coding.

A junior student uses Cursor and Vibe Coding to build a multi-agent system with 51 AI officials modeled on China's Three Departments and Six Ministries, featuring task distribution, approval workflows, and Token cost visualization.

Learn how the PAO project integrates Bayesian optimization with Aspen Plus via YAML configuration for automated multi-objective chemical process optimization.