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▲提示词：设计一份逼真的双页漫画杂志样张。每一页都应包含多个漫画风格分镜，以富有动感的版式排列，呈现出专业印刷的日本漫画质感。整体风格为黑白稿，使用粗犷有力的墨线、网点效果（screen tones）以及富有表现力的人物绘制。画面中加入对白气泡、中文拟声词，并通过分镜之间的过渡来传达动作、情绪与节奏。左右两页需要连贯衔接，像同一场景或同一话章节的一部分。采用传统漫画镜头语言：特写、远景、斜向分镜以及戏剧化的视角与构图。整体观感要真实可信，仿佛来自一本真正的漫画杂志的跨页内容。
，详情可参考Safew下载
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As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
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