PROMPT RADAR / VIDEO视频叙事
Netflix paid $587 million in cash for Ben Affleck's AI startup... i rebu
Netflix paid $587 million in cash for Ben Affleck's AI startup... i rebuilt my own version from scratch inside Claude Code, here's how you can start yours for free: > wired every video, image and music model into one sin
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编辑先看
它为什么值得进入灵感库
先别急着照抄整段 Prompt:真正有用的是主体、空间和镜头之间的约束关系。 把一段描述拆成起始状态、动作变化、镜头响应和结束画面,连续性比形容词更重要。
01 / 原始方法
动作与镜头 Prompt
Netflix paid $587 million in cash for Ben Affleck's AI startup... i rebuilt my own version from scratch inside Claude Code, here's how you can start yours for free: > wired every video, image and music model into one single agent > extracted a style contract from real cinema: a vision model names the lens, lighting, palette and grain of shots that already worked > Higgsfield Supercomputer generated frames at volume before any motion, characters and locations locked into reference sheets > the agent wrote every shot prompt itself from one locked template: blocking, camera, lighting, audio, same order every time > Seedance 2.0 animated each keyframe as an isolated 3-5 second shot: a named camera move, an event, no frozen figures > a subagent fleet ran the generations in parallel, throttled under the rate caps, every attempt logged > the final cut rendered autonomously: score first, per clip audio muted where it fights the strings, one 4K upscale at the end full system in the article below:
02 / 复刻路径
不要一次改完所有变量
用 Seedance 复刻时,建议先单独生成稳定首帧,再把动作拆成“开始、变化、收束”三段;每段只保留一个主要运动。
- 主体:Netflix paid $587 million in cash for Ben Affleck's AI startup... i rebuilt my own version from scratch inside Claude Code, here's how you can start yours for free: > wired every video, image and music model into one sin
- 视觉方向:实时 AI 视频案例;查看原帖媒体与作者说明
- 镜头与构图:按原帖镜头流程执行;查看原帖媒体与工作流
- 交付约束:auto,保留原作者方法与归属;复刻时避免水印、低清、畸变、主体漂移和无关文字。
负向约束
保留原作者方法与归属;复刻时避免水印、低清、畸变、主体漂移和无关文字。
模型建议
Seedance / Veo / Kling。先用原始 Prompt 建立基准结果,再根据模型对自然语言、镜头运动或文字排版的能力做局部改写。
来源与编辑原则