Last May, I wrote a blog post titled As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often as a contrasting response to the hype around the rising popularity of agentic coding. In that post, I noted that while LLMs are most definitely not useless and they can answer simple coding questions faster than it would take for me to write it myself with sufficient accuracy, agents are a tougher sell: they are unpredictable, expensive, and the hype around it was wildly disproportionate given the results I had seen in personal usage. However, I concluded that I was open to agents if LLMs improved enough such that all my concerns were addressed and agents were more dependable.
在苹果的这套 AI 新硬件体系中，三款硬件分工明确：
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Cursor uses Apple’s Seatbelt (sandbox-exec) on macOS and Landlock plus seccomp on Linux. It generates a dynamic policy at runtime based on the workspace: the agent can read and write the open workspace and /tmp, read the broader filesystem, but cannot write elsewhere or make network requests without explicit approval. This reduced agent interruptions by roughly 40% compared to requiring approval for every command, because the agent runs freely within the fence and only asks when it needs to step outside.
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