AI

Andrew Ng proposes a 3-Layer Loop Engineering framework for AI Agent development, comprising micro-loops (model calls), meso-loops (tool use), and macro-loops (user feedback), offering B2B enterprises a systematic methodology for building reliable, iterable AI systems.

This article delves into the architecture design principles and implementation methods of agent skills systems, providing systematic guidance for B2B enterprises to build scalable and reusable AI agent capabilities.

多机构联合发布工业级异构Token工厂设计实践，拆解跨硬件推理服务的核心边界。

AutoResearch is an open-source project that achieves fully automated software development through multi-agent cross-review, covering requirement analysis, code generation, testing, and deployment, significantly boosting development efficiency.

ScrapingBee introduces a new Gemini API endpoint, allowing users to send prompts via existing accounts and receive structured AI responses (plain text, Markdown, citations), enabling AI analysis, summarization, and insight extraction in scraping workflows without separate infrastructure.

This article documents a complete R&D process led by AI agents, from PRD writing and goal setting to post-goal evaluation, demonstrating AI's autonomous decision-making and execution capabilities in engineering practice.

The author personally experiences a new type of job in the AI era—working for robots—revealing the absurdity and reality of this work model, and exploring its potential impact on the labor market and business operations.

This article reviews the Seed 2.1 Pro model, noting significant improvements in Agent tasks, making it capable of handling complex workflows.

Wispr Flow is an AI voice input product that significantly improves writing efficiency through real-time speech-to-text and intelligent editing. This article analyzes its technical features, impact on B2B enterprises, and recommended actions.

Drawing from experience building multiple viral AI Skills, the author reflects on the Skills ecosystem, user needs, and developer strategies, highlighting issues like homogenization and low retention, and recommending differentiation, contextualization, and deep integration.