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.
characters each, onto the middle of the card. The machines didn't actually punch
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Super Bowl LX was a two-score game with less than five minutes remaining. New England had the ball on the Seahawks’ 44-yard line and – after reaching the end zone in the fourth quarter, finally – that familiar sense of possibility. But that quickly vaporized when Devon Witherspoon knifed in on a corner blitz and jarred the ball loose from the Patriots quarterback, Drake Maye, mid-throw. Uchenna Nwosu snatched it in stride and rumbled 45 yards to the end zone, sealing Seattle’s 29‑13 victory.，详情可参考heLLoword翻译官方下载
該用戶還要求ChatGPT協助潤色一份範圍更廣的定期行動報告，目的是「發現、施壓、干擾及噤聲」異見人士。，推荐阅读搜狗输入法2026获取更多信息