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.
讲述人：中国人民银行丽水市分行副行长 吴炜玲
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在生活服务领域，数据也在重新定义我们的日常生活。地图导航、网约车、餐饮外卖……这些便捷服务的背后，是海量数据与信息的精准匹配与供给。在医疗领域，影像、病理等数据的深度分析，让智能辅助诊疗走向现实，为健康护航。公共数据通过授权运营等方式有序流向社会，相关主体据此开发出丰富多样的数智化产品。例如，一些地方整合司法、民政等多方数据，在用户授权下即可核验家政人员资质与信用，破解了行业信息不对称的问题。。夫子对此有专业解读
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"It's too far away and train prices are expensive. But with somewhere this local, it's really accessible and I think that's important with the music industry at the moment."