But what followed after spades hit the ground was a string of delays and last-minute cancellations.
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.
The number of gallbladder surgeries recorded by NHS England in 2024-25 was at its highest peak in the past decade.，更多细节参见safew官方版本下载
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OpenAI gave fewer details on the Nvidia partnership, but said it had committed to using “3GW of dedicated inference capacity and 2GW of training on Vera Rubin systems” as part of the deal.
Nano Banana 2 上线：高画质与高速生成首次兼得，这一点在91视频中也有详细论述