import { ManimScene } from 'manim-web/vue';
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.
，这一点在爱思助手下载最新版本中也有详细论述
虽然Seedance 2.0在单次生成的十几秒内保持了较好的连贯性，但将时间尺度拉长，问题便开始浮现。目前所有视频模型都面临着“记忆衰减”的挑战。
数据为数字纪检监察体系提供“源头活水”。过去，在纪检监察工作中，不同部门、环节信息共享程度不够，在个别领域存在监管“盲区”。数字纪检监察体系建设能够有效打破部门间的信息壁垒，建立起覆盖全域的监督网络。此外，数字纪检监察体系能够对重复录入、格式错误、逻辑矛盾的数据，以及来源存疑、真伪难辨的数据，进行“技术筛查+人工复核”双重把关，防止被错误数据“带偏”。这样，数据从采集、筛选到存储、使用都有章可循，实现找得到、可信赖、能用好。