01版 - 我国稳居全球最大苹果生产国与消费国

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去年の落とし物 都内。关于这个话题,同城约会提供了深入分析

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In a 2023 living note from Shalizi, it's proposed that LLMs are Markov. Therefore there's nothing special about them other than being large; any other Markov model would do just as well. Shalizi therefore proposes Large Lempel-Ziv: LZ78 without dictionary truncation. This is obviously a little silly, because Lempel-Ziv dictionaries don't scale; we can't just magically escape asymptotes. Instead, we will do the non-silly thing: review the literature, design novel data structures, and demonstrate a brand-new breakthrough in compression technology.

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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.