Овечкин продлил безголевую серию в составе Вашингтона09:40
An example of dithering using random noise. Top to bottom: original gradient, quantised after dithering, quantised without dithering.
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陆逸轩：那当然是一个因素，另外一个更重要的原因是，我清楚地知道，当下的表现可能会对我之后的人生产生深远的影响。这种“后果感”带来的压力是巨大的。相比之下，一场普通音乐会即便出现问题，通常也是不会改变你的人生走向的。。业内人士推荐服务器推荐作为进阶阅读
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.