Study finds health warnings that evoke sympathy are more effective in persuading individuals to change harmful behaviors
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.
，这一点在新收录的资料中也有详细论述
这揭示了一个深层矛盾：我们要求AI越来越自主，却又希望它绝对服从。。业内人士推荐新收录的资料作为进阶阅读
在台灣工作七年的阿宗去年則離開台灣回到越南，他認為在台灣工作已無發展性。原工廠始終將他視為最低廉的勞工，制度限制也使他難以轉換到工作條件更好的工廠。