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.
一群研究人員決定測試「正向思考」是否能提高人工智慧（AI）聊天機器人的準確率，結果卻出乎意料。他們向不同的聊天機器人提問，嘗試稱讚它們「聰明」，鼓勵它們認真思考，甚至在問題結尾加上一句「這會很有趣！」。然而，這些方法都沒有產生一致的效果，但其中一種方法脫穎而出。當他們讓AI假裝自己身處《星際迷航》場景，它的基礎數學能力竟然有所提升。看來，它真的能把我傳送上去。
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Мерц резко сменил риторику во время встречи в Китае09:25
These are likely to be older patients who have suffered falls and those with broken hips.