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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。关于这个话题，heLLoword翻译官方下载提供了深入分析
Sora和Veo追求的是“模拟一个真实的世界”，而Seedance 2.0追求的是“构建一个可控的片场”。对于需要快速产出、对真实感要求极高的短内容，Veo 3.1的原生音画同步可能是更优选。但对于需要精细控制角色表演、镜头语言和艺术风格的专业创作者，Seedance 2.0提供的“导演模式”无疑更具吸引力。
tasks = append(tasks, t)
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