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.
h-next_free = 0;
，详情可参考WPS官方版本下载
当AI能够以趋近于零的成本生成文本、代码和视觉素材时，个体的溢价能力体现在如何将复杂的业务需求拆解为AI可理解的逻辑结构，即“提示工程（Prompt Engineering）”的直觉化应用 [4, 22]。此外，跨行业技能的融合成为上升的捷径，例如，非技术背景的行政人员利用AI进行初级数据建模，或非设计人员生成专业级的营销内容，这种“跨界替代”能力在2026年具有极高的市场需求 [4, 25]。，更多细节参见WPS下载最新地址
9月10日——罗永浩吐槽西贝预制菜事件，这一点在雷电模拟器官方版本下载中也有详细论述
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