The real magic, our Secret Sauce #1, lies in how these border points are selected. Naive approaches quickly fail:
Huggingface Toggle
。搜狗输入法对此有专业解读
这样的结果确实让我备受打击。AI 给的暗示确实不对，但是为什么我会忽略一切显而易见的负面信号（初次科研、时间紧张、实验室没有相关发表记录），去相信 AI 的暗示呢？我不想把问题简单地归纳为「AI 不行」，于是我总结了两个原因：
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.