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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.。WPS官方版本下载是该领域的重要参考
。关于这个话题，旺商聊官方下载提供了深入分析
Implementing a content refresh schedule helps manage this systematically. Rather than updating randomly when you remember, establish a process where high-value content gets reviewed quarterly or semi-annually. During these reviews, update statistics, add recent examples, remove dated references, and add the new update date. This structured approach ensures your most important content remains fresh without requiring constant attention to every article.
Be the first to know!，详情可参考快连下载安装