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Five paradigms—prompts/RAG/fine-tuning/knowledge graphs/context engineering—in three years. Models improve, yet we can't make agents work in products.

Coding agents cut MVP costs to a few hundred yuan a day, ending the old ideation funnel. The bottleneck is now hypothesis generation, not execution.

Mac permissions are for humans, Linux for programs. Agents are infrastructure, not apps. Running one on a Mac is like forcing a server into a laptop: it works, but feels wrong.

I switch models daily. Opus is reckless but strong, GPT 5.4 drifts, Gemini misses bugs, domestic models each have quirks. Switching beats prompt tuning.

I covered cost and control before; this post adds law. Cloud is rented, edge is sold, and that distinction matters once AI agents decide on their own.

After installing OpenClaw for dozens of people, I answer the top three questions: safety, cost, and Claude Code comparison — with domestic model pricing.

Edge devices shouldn't chase cloud LLMs. A Mac mini-class box with helper models—TTS, ASR, OCR, VLM—should sit in a corner like a router: boring is right.

Building beats meetings. As AI flattens skill gaps, collaboration may shift to passion-based roles, horse-race validation, and diversity as the edge.

MiniMax will surpass Baidu, which lacks a usable API. Traffic is moving from humans to Agents; infra not built for Agents will lose both users and machines.

The AI wave leaves one boat. I install OpenClaw for free not as charity: onboard people, train the agent, and make every setup strengthen the AI agent.

OpenClaw has real vulnerabilities, but your password may be 123456 and your face data already went to text-to-video AI apps. Your own habits may be the bigger risk.

After installing OpenClaw for others: its value hinges on the underlying model's execution power, and today's best experience sits in coding agents like Claude Code.