Developers using the streams API are expected to remember to use options like highWaterMark when creating their sources, transforms, and writable destinations but often they either forget or simply choose to ignore it.
Around this time, my coworkers were pushing GitHub Copilot within Visual Studio Code as a coding aid, particularly around then-new Claude Sonnet 4.5. For my data science work, Sonnet 4.5 in Copilot was not helpful and tended to create overly verbose Jupyter Notebooks so I was not impressed. However, in November, Google then released Nano Banana Pro which necessitated an immediate update to gemimg for compatibility with the model. After experimenting with Nano Banana Pro, I discovered that the model can create images with arbitrary grids (e.g. 2x2, 3x2) as an extremely practical workflow, so I quickly wrote a spec to implement support and also slice each subimage out of it to save individually. I knew this workflow is relatively simple-but-tedious to implement using Pillow shenanigans, so I felt safe enough to ask Copilot to Create a grid.py file that implements the Grid class as described in issue #15, and it did just that although with some errors in areas not mentioned in the spec (e.g. mixing row/column order) but they were easily fixed with more specific prompting. Even accounting for handling errors, that’s enough of a material productivity gain to be more optimistic of agent capabilities, but not nearly enough to become an AI hypester.
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为了在相对公平的环境下对比，我决定将人工干预降到最低：只提供基础内容和最简单的指令，以此测试各家软件生成能力的「下限」。这不仅是因为（囊中羞涩）测试积分有限，更为了模拟真实的「开箱即用」场景——毕竟，作为普通用户，大多数人只想要一个能用的 PPT，而不是被强迫系统学习提示词工程。
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