特朗普的新世界秩序已成現實，歐洲正快速適應2026年2月18日
В России ответили на имитирующие высадку на Украине учения НАТО18:04
，这一点在搜狗输入法2026中也有详细论述
在当前我服务的公司里，有一个前端实习生，他的工作效率竟然明显高于不少工作四五年的前端同事。他不仅文档写得清晰完整，而且能快速实现相对复杂的交互与逻辑。，推荐阅读搜狗输入法下载获取更多信息
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.