// Tech Observation
Topic Archive — 9 articles
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01
Who Gets to Train on Whom? Distillation and Data Boundaries in the Kimi–Claude Dispute
The Kimi–Claude dispute is not only about whether one model crossed a line. It is also about where users' prompts went.
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02
The Three Clocks of Robotics: The Gap From Demo to Productivity Is More Than a Model
Hardware, intelligence, and capital move at different speeds. Robotics becomes productive only when long-term reliability and operating metrics prove it.
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03
When a Technology Roadmap Becomes a “Law”: A Rational Look at Huawei's Tau Law Narrative
Huawei's Tau Law points to real post-Moore engineering problems, but media narratives can turn system-level optimization into a myth of invention from scratch.
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04
The Real Test for Embodied AI in China Is Not Acrobatics, but a Commercial Loop
The real test is not how impressive a demo video looks, but whether a company can close the loop across low-cost hardware, real scenarios, data feedback, model improvement, and customer repurchase.
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05
AI Will Not Erase White-Collar Workers, but It Will Erase Their False Security
AI is not simply replacing white-collar workers. It is breaking jobs apart and repricing routine, template-based, low-responsibility judgment work.
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06
After Google I/O, AI Competition Is Shifting From Model Capability to Workflow Control
The core signal from Google I/O 2026 is not a single model upgrade. It is Gemini being pushed into the task operating layer behind search, work, personal agents, and app entry points.
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07
The Essence of Engineering Growth Is Moving Up Abstraction Layers
Engineering growth is not just more experience. It is the ability to move between code, modules, systems, business, and organization.
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08
AI Weekly: Agent Platforms Scale Up, Models Keep Advancing, and AI Hardware Moves Toward the Edge
This week’s AI story is not just stronger models. Work models, Agent platforms, and edge AI are all moving further into product reality.
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09
The "Three-Month Rule" of Open Source: From Viral to Forgotten
Why do so many open source projects launch to massive fanfare, then fade into obscurity months later? Analyzing the hype cycle through projects like OpenClaw.
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