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.
But look at what's happening now. Someone needs information, they open ChatGPT or Claude or Perplexity, they ask a question in natural language, they receive a comprehensive answer immediately with sources cited. No clicking through multiple websites. No comparing different perspectives. No scanning search results pages. The AI synthesizes information and delivers a direct answer, fundamentally changing the discovery process.
。safew官方版本下载对此有专业解读
选择模型类型 — 这将激活特殊的 FunctionGemma 解析器。，推荐阅读搜狗输入法2026获取更多信息
A decline in glycolytic ATP production is the fundamental mechanism limiting lifespan; species with an optimal rate of decline over time survived