The opportunity is enormous right now precisely because it's so new. Early adopters are claiming top positions in AI responses while their competitors remain oblivious to this emerging channel. But this window won't stay open forever. As more people recognize the value of appearing in AI results, competition will increase and optimization will become more sophisticated. The time to understand and implement AIO strategies is now, while the landscape is still relatively uncrowded.
The very first thing I did was create a AGENTS.md for Rust by telling Opus 4.5 to port over the Python rules to Rust semantic equivalents. This worked well enough and had the standard Rust idioms: no .clone() to handle lifetimes poorly, no unnecessary .unwrap(), no unsafe code, etc. Although I am not a Rust expert and cannot speak that the agent-generated code is idiomatic Rust, none of the Rust code demoed in this blog post has traces of bad Rust code smell. Most importantly, the agent is instructed to call clippy after each major change, which is Rust’s famous linter that helps keep the code clean, and Opus is good about implementing suggestions from its warnings. My up-to-date Rust AGENTS.md is available here.
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