"The best training I ever had for being a commander was being a parent - because you have to learn how to say no to people."
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.
，更多细节参见雷电模拟器官方版本下载
Snapshotting is a feature worth noting. You can capture a running VM’s state including CPU registers, memory, and devices, and restore it later. This enables warm pools where you boot a VM once, install dependencies, snapshot it, and restore clones in milliseconds instead of booting fresh each time. This is how some platforms achieve incredibly fast cold starts even with full VM isolation.。heLLoword翻译官方下载对此有专业解读
Step through the Python implementation. Watch the algorithm decide which branches to visit and which to prune: