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.
There was debate among the modders about where Silgrid Tower should be located and which faction would have controlled it. This eventually led to an acrimonious split between the two groups. “The Silgrid Tower team was eventually put to the choice of either having to delete their work and restart it or, you know, leave the project. So they left the project,” said Sultan of Rum.
，更多细节参见搜狗输入法2026
按照小鹏的技术解读，称传统的VLA需要先把视觉信号翻译成机器语言，才能让机器识别。VLA2.0则省去了翻译这一过程，使得智驾链路缩短，当然技术难度也可想而知。
Throughout the development of Towerborne, we maintained our individual backend service codebases in various Azure DevOps (ADO) git repositories. For each service, we split out the codebase between a web and library project.
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