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.
What were some of the first steps you took to get your side hustle off the ground? How much money/investment did it take to launch?
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Flexibility Clash: CH typically pre-calculates optimal paths. Supporting OsmAnd's 10+ routing parameters (leading to over 1024 combinations per profile!) would be impossible with standard CH.
Президент Соединенных Штатов Америки (США) Дональд Трамп перед поездкой в Техас сделал журналистам ряд ярких заявлений, одним из которых стало желание отменить санкции против России, но при одном условии.