A cutting‑edge think tank for AI product innovators, focusing on AI technology, product design, and business insights. It offers deep analysis of industry trends, dissects AI product design cases, and uncovers market potential and business models.
The article analyzes Meta’s recent purchase of Chinese AI startup Manus, examines the Chinese government’s regulatory response, showcases a hands‑on test where Manus instantly generated a native Android app, and discusses the broader implications for Chinese AI companies navigating global deals.
The article explores how product managers must shift from deterministic PRDs to uncertainty‑driven specifications for AI chatbots, replacing exhaustive logic with value‑based constraints, fuzzy‑evaluation metrics, dynamic benchmarks, and sample‑based requirements to better align with probabilistic large‑model behavior.
The article explains that prompt engineering alone cannot guarantee reliable AI responses because models lack situational awareness, and introduces context engineering as a systematic approach that structures memory, manages context flow, and integrates RAG and evaluation to make large language models truly useful in real‑world applications.
Although AI breakthroughs capture headlines, the true obstacle for startups is not technology but building a sustainable business model that delivers continuous revenue, controls costs, and creates defensible advantages, as illustrated by examples like OpenAI and Midjourney.
Deploying large‑model AI in customer service can boost efficiency, but without proper boundaries, feedback loops, and emotional handling it often creates costly mistakes, brand damage, and poor user experience, as this article explains the six most frequent traps and how to sidestep them.
AI agents are reshaping enterprise workflows by replacing manual, tool‑based tasks with autonomous, event‑driven processes, as illustrated through customer service, design project acceleration, and cross‑department collaboration cases, and the article outlines why they work, how to implement them, and future digital‑twin trends.
At the 2025 Taipei International Computer Expo, Nvidia CEO Jensen Huang outlined the company's shift from a chipmaker to an AI infrastructure leader, introduced the concept of physical AI, and detailed upcoming hardware, software, and strategic initiatives that could reshape data centers, robotics, and autonomous driving.
The article introduces the OWL (Optimized Workforce Learning) general‑purpose AI agent, explains its six‑step architecture, benchmark performance surpassing OpenManus, and argues that its innovations represent genuine application‑level advancement rather than mere “shell‑wrapping,” while highlighting its multi‑agent collaboration framework.
This article walks through installing OpenManus locally using Python 3.12, cloning its GitHub repository, configuring DeepSeek LLM credentials, launching the service, and prompting the agent to generate a full WeChat mini‑program, while sharing observations on performance, token cost, and limitations.
This article explains the technical principles behind Function Call and ReAct in large language models, provides code samples, compares their strengths and limitations, and illustrates each approach with practical scenarios such as smart customer service and financial analysis assistants.
