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.
Developers in regions with limited access to Google’s registration infrastructure，推荐阅读夫子获取更多信息
，更多细节参见heLLoword翻译官方下载
4. For the Z80 implementation, I did zero steering. For the Spectrum implementation I used extensive steering for implementing the TAP loading. More about my feedback to the agent later in this post.
音頻加註文字，黎智英國安法案遭判20年 《蘋果》員工判囚6年9個月至10年，推荐阅读搜狗输入法2026获取更多信息