Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
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"So we want to just take advantage of this to set up both vendors for future success on a lunar landing," he said. "This is the proper way to do it, if it works out from a timing perspective, to be able to rendezvous and dock with both. ... This, again, is the right way to proceed in order to have a high confidence opportunity in '28 to land."
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Local sandboxing on developer machinesEverything above is about server-side multi-tenant isolation, where the threat is adversarial code escaping a sandbox to compromise a shared host. There is a related but different problem on developer machines: AI coding agents that execute commands locally on your laptop. The threat model shifts. There is no multi-tenancy. The concern is not kernel exploitation but rather preventing an agent from reading your ~/.ssh keys, exfiltrating secrets over the network, or writing to paths outside the project. Or you know if you are running Clawdbot locally, then everything is fair game.
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