The Timeless Art of Vibe Coding 永恒的 Vibe Coding 艺术 Based on Lao Tzu. Adapted by Rick Rubin 依据老子，Rick Rubin改编 Each encounter I’ve had with Lao Tzu has pointed me to something new. Almost as if the book changes with every reading. I first picked up Stephen Mitchell’s translation 40 years ago at the Bodhi Tree bookstore in Los Angeles and my life has never been quite the same. — Rick Rubin ...
Reflections after 30 days of using GitHub Copilot Agent. Reflections after 30 days of using GitHub Copilot Agent. My vibe coding project: live App Agentic AI Apps. And code repo -> Agentic AI Apps Repo This project is a demonstration and experimental modern application of AI agents. It features a variety of agent types, including Instruction Agents, Function Call Agents, Workflow Agents, and MCP Agents. The project contains over 60 code files and uses a distributed deployment approach. The entire application development process was driven by me providing requirements in natural language, while GitHub Copilot Agent handled the coding. ...
This Framework is an advanced tool designed to evaluate and optimize the hardware requirements necessary for the training, fine-tuning, and deployment of large language models (LLMs). By combining techniques such as memory estimation, quantization, and GPU benchmarking, this framework provides developers and researchers with a comprehensive methodology to plan and allocate computational resources efficiently. As LLMs like GPT-3, LLaMA-2, and T5 continue to grow in size and complexity, the ability to precisely estimate GPU demand is critical for ensuring their accessibility and scalability in both research and industry contexts. ...
The Service as Software represents a transformative evolution in how software is designed, delivered, and consumed, building upon the foundational innovations of traditional software models and Software as a Service. Unlike earlier approaches, where software was either installed on-premises or accessed via cloud-hosted applications, the Service as Software model integrates advanced automation and artificial intelligence (AI) technologies to enable software to function autonomously. By proactively addressing tasks and resolving issues without direct human intervention, this model redefines the role of software, emphasizing efficiency, scalability, and proactive problem-solving capabilities.[1][2] ...
Explores the anticipated advancements, applications, and societal impacts of artificial intelligence (AI) in the near and distant future. By 2025, AI is expected to become a transformative force across industries, reshaping sectors such as healthcare, education, finance, transportation, and supply chain logistics. The rise of generative AI, autonomous systems, and collaborative AI networks is set to redefine traditional workflows, foster innovation, and enable personalized experiences, but also present significant challenges in areas like ethics, regulation, and workforce disruption. These trends mark the next stage of AI’s rapid evolution, which has already begun to alter economies, labor markets, and human-AI interaction on a global scale. ...
We are in the midst of a new wave of innovation driven by artificial intelligence (AI) applications. This trend promises to significantly accelerate automation, boost productivity, spark innovation, and improve both the quality of work and the experiences of employees and customers. However, companies that fail to act and adapt in time may find themselves left far behind. According to a recent global survey by McKinsey, employees are already far ahead of their organizations in adopting AI. An overwhelming 91% of respondents reported that they are using next-generation AI technologies in their work. Yet, alarmingly, most companies are lagging in this domain. Despite high adoption rates among employees, enterprises are falling short of expectations in their maturity of AI implementation. In McKinsey’s study, only 13% of respondents stated that their organizations had successfully implemented multiple AI use cases. ...
In the rapidly evolving landscape of artificial intelligence, the mantra “Model is not productivity, intelligence is” opens up a dialogue on the true essence of productivity in the digital age. The advent of generative AI applications marks a significant milestone in showcasing the pinnacle of productivity, leveraging the most advanced technologies to redefine the boundaries of what machines can accomplish. Productivity Trend of Gen AI era Productivity, in the context of AI, can be dissected into two pivotal dimensions, each offering unique insights into how AI can transcend traditional limitations and usher in a new era of efficiency and innovation. ...