And while it used to be a pain to transition from Windows to Mac, it’s far easier these days, especially if you mainly rely on web apps. It also wouldn't be tough for Apple to make short tutorials to help Windows users get their bearings with the macOS basics, like installing apps and juggling app windows. Apple could also make a play for iPhone owners using Windows, who may not be aware of the many ways iOS and macOS are integrated. iPhone mirroring may be a huge draw on its own.
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In the months since, I continued my real-life work as a Data Scientist while keeping up-to-date on the latest LLMs popping up on OpenRouter. In August, Google announced the release of their Nano Banana generative image AI with a corresponding API that’s difficult to use, so I open-sourced the gemimg Python package that serves as an API wrapper. It’s not a thrilling project: there’s little room or need for creative implementation and my satisfaction with it was the net present value with what it enabled rather than writing the tool itself. Therefore as an experiment, I plopped the feature-complete code into various up-and-coming LLMs on OpenRouter and prompted the models to identify and fix any issues with the Python code: if it failed, it’s a good test for the current capabilities of LLMs, if it succeeded, then it’s a software quality increase for potential users of the package and I have no moral objection to it. The LLMs actually were helpful: in addition to adding good function docstrings and type hints, it identified more Pythonic implementations of various code blocks.
Unlike Outranking, the interface to Frase is very user-friendly and accessible.