准确把握中国与世界关系新方位。“世界好，中国才能好；中国好，世界才更好。”这是中国发展同世界关系的底层逻辑。面对外部环境的严峻挑战，中国以强大韧性、充沛活力为促进共同发展注入强劲动能，以自信自立、敢斗善斗为维护国际公平正义提供有力支撑，以中国式现代化成功探索为广大发展中国家独立自主迈向现代化拓宽路径，成为变乱交织的世界中举足轻重的和平力量、发展力量、进步力量。越来越多国家看重中国作用、中国机遇，希望了解和借鉴中国之治、中国之理，但也有一些势力散布各种“中国威胁论”。习近平外交思想深刻揭示中国同世界关系发生的历史性新变化：中国已经能够更多把握历史主动、更大程度影响世界发展方向，中国式现代化打破“现代化=西方化”的迷思，中华民族伟大复兴势不可挡；明确中国特色大国外交将进入一个可以更有作为的新阶段，要为以中国式现代化全面推进强国建设、民族复兴伟业营造更有利国际环境、提供更坚实战略支撑；强调前进道路不可能一马平川，必定会有艰难险阻，可能遇到风高浪急甚至惊涛骇浪的重大考验，必须以越是艰险越向前的精神奋勇搏击、迎难而上，务必敢于斗争、善于斗争，依靠顽强斗争打开事业发展新天地。习近平总书记提出的一系列重大论断，为我们制定对外战略、谋划对外工作提供了科学指南。
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4. MidJourney: AI Art GenerationWhat Makes It Special: Midjourney V6 has redefined AI image generation by mastering the nuances of professional photography and artistic style. Its ability to understand and execute complex creative directions – from specific lighting conditions to branded visual styles – while maintaining consistent quality across multiple generations makes it the go-to tool for creators who need stunning visuals that align perfectly with their brand identity.
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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.