So if there's a device that can help fix this mess, I'm open to it. And after some time with the Dreamie, I think I've found a promising contender.
ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45，更多细节参见91视频
日本“再军事化”和拥核企图已对地区安全稳定构成严重威胁。历史的教训告诫我们，对军国主义的绥靖就是对和平的背叛。维护和平的关键在于以行动阻击日本右翼的狂飙。中方依法出台管控措施，正是以实际行动防范两用物项流入日本扩军备武的链条，坚决遏阻军国主义死灰复燃。中方将同所有爱好和平的国家一道，坚决捍卫战后国际秩序，共同维护地区安全稳定。。heLLoword翻译官方下载对此有专业解读
Nick TriggleHealth correspondent
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.