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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.
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Read's agency is currently funding various pioneering approaches to robotics, some of which involve actuators made of elastomers – like rubbery plastics. Such material might be sandwiched between electrodes so that they contract or expand as voltage is applied and removed, for example. Not unlike an animal muscle.
The answer is essentially hardware-level dependency injection. Before calling LD_DESCRIPTOR, the caller saves its desired test constant into a hardware latch using a micro-op called PTSAV (Protection Save). Within LD_DESCRIPTOR, another micro-op called PTOVRR (Protection Override) retrieves and fires the saved test.