The workflow begins with identifying the queries you want to track. These are essentially "AIO keywords"—questions that people might ask AI models where your content should ideally appear in the answer. Unlike traditional SEO keywords, which are often short phrases, AIO queries tend to be longer, more conversational questions that reflect how people actually talk to AI assistants.
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There’s a secondary pro and con to this pipeline: since the code is compiled, it avoids having to specify as many dependencies in Python itself; in this package’s case, Pillow for image manipulation in Python is optional and the Python package won’t break if Pillow changes its API. The con is that compiling the Rust code into Python wheels is difficult to automate especially for multiple OS targets: fortunately, GitHub provides runner VMs for this pipeline and a little bit of back-and-forth with Opus 4.5 created a GitHub Workflow which runs the build for all target OSes on publish, so there’s no extra effort needed on my end.
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Visitors can also tour Twig's on-site museum to hear its story first-hand, and watch the handcrafted soda being made.