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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.
Овечкин продлил безголевую серию в составе Вашингтона09:40。业内人士推荐WPS下载最新地址作为进阶阅读
组成计税价格=成本×（1+成本利润率）+消费税税额，更多细节参见safew官方版本下载
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Over time, this authentic participation creates a distributed network of references to your expertise and content across platforms that AI models access. These organic mentions, especially when they're accompanied by positive community response, signal that you're a legitimate authority worth citing. The impact accumulates gradually but compounds over months as you build a presence in relevant communities.