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Reflections on recent AI model experiences, prompt engineering principles, context design strategies, and philosophical considerations about AI anthropomorphism and human-AI interaction.

An exploration of coding model usage patterns, the differentiation and convergence of model ecosystems, and the evolving landscape of AI reasoning and agentic capabilities.

Two weeks after GPT-5 release, observations on AI coding model selection, metacognition capabilities, and product strategy in the AI era.

How do we design a runnable cognitive environment and operating system for AI? We need to know not only what to do, but also how to make AI understand.

The more intelligent the model, the worse instruction following (too autonomous)? Partner or tool?

Gall's Law tells us that complex systems must evolve from simple systems. The history of AI is precisely a path of inevitable technological accumulation that cannot be skipped.

The essence of AI is not a specific technology, but a projection of future capabilities. Or rather, it's a public cognitive label.

Deep learning is not merely an intermediate stage towards AGI, but is defining a new era of intelligent augmentation centered on data-driven, problem-oriented approaches.

We shouldn't use AI as a tool, but become part of the Nexus with it. A paradigm shift from Company to Nexus, where humans and AI collaborate as equal partners in an intelligent ecosystem.

Recommending Gemini as the architect and Claude as the engineer to achieve a balance between efficiency and innovation.

Intelligence craves higher intelligence, seeking higher-dimensional intelligence

Critical thinking is not about denying technology, but about using it better. Just like learning to distinguish between true and false information, this is an essential skill in the digital age.