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For implementers, this promise-heavy design constrains optimization opportunities. The spec mandates specific promise resolution ordering, making it difficult to batch operations or skip unnecessary async boundaries without risking subtle compliance failures. There are many hidden internal optimizations that implementers do make but these can be complicated and difficult to get right.

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One of the criticisms about AI generated code is that it “just regurgitates everything on GitHub” but by construction, if the code is faster than what currently exists, then it can’t have been stolen and must be an original approach. Even if the explicit agentic nature of rustlearn makes it risky to adopt downstream, the learnings from how it accomplishes its extreme speed are still valuable.

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Building this system requires understanding of Make.com's interface and basic automation concepts, but it's accessible to anyone willing to invest a few hours in setup. The difficulty level sits at intermediate—more complex than basic automation but far simpler than custom programming. Once configured, the system runs automatically on whatever schedule you set, collecting data and building a historical record of your AIO performance.