星火 SparkCN

Human Signal

创始人 / Maker 评论

优先展示 Product Hunt 上对理解条目有帮助的人类文本。

Topics

MacProductivityArtificial Intelligence

We've been honing Clipto's story for a few months. At the end of our last call @henry_kang proved the value of the product. He and his team were out in the desert, testing Clipto remotely : minimal reception, terabytes of footage sitting on his laptop, and he needed to find a specific shot for the launch video. He searched for: "the wide drone shot where the car enters the desert" . He didn't want "a cinematic moment." Not a "vibes" search. He knew he had the clip but in the pre-Clipto world, it would take hours of video scrubbing to find it. He found that clip in seconds using natural language to search over his own media, fully local. Just like Google Photos — but nothing lives in the cloud. This isn't an easy problem to solve. Henry's been pursuing this direction for over twenty years, when at CMU's Robotics Institute (my alma mater, FYI), he began pushing the limits of computer vision. He starting with indexing hundreds of images and then advanced to millions of objects — and watched recognition basically explode once memory scaled. Clipto is in many respects the culmination of that work, pointed at your personal hard drive. And it's quick: a modern M5 MacBook chews through ~2TB of video in about a day. Why not push yours through its paces?

评论作者信息不可用Maker 身份11 votes查看原评论

痛点分析发布于 2026/05/31

痛点为 AI 基于上游原始证据的初步提炼；未包含额外中国市场检索。

痛点

用户（如视频创作者、内容团队）在本地存储了数TB的视频、音频、会议记录等媒体文件，需要从中快速找到特定镜头或片段。现有流程中，他们只能手动逐帧浏览或依赖模糊的文件命名，导致查找一个已知存在的片段需要花费数小时。这种时间浪费在紧急任务（如制作发布视频）中尤为突出，且随着媒体库增长，问题愈发严重。用户明确表达了“浪费数小时浏览原始素材”的痛点，并渴望通过自然语言描述（如“汽车进入沙漠的广角无人机镜头”）来即时定位内容。

Discussion

精选 Product Hunt 讨论

保留原始讨论语境，用来交叉验证上游条目的真实反馈。

@henry_kang @matthewwei the fully local processing is the whole product, not a feature. Routing all inference on-device means the Premiere Pro integration isn't a nice-to-have, it's the trust argument for any team that can't let footage leave the building. Congrats on shipping.

评论作者信息不可用筛选：高信号评论0 votes查看原评论

This looks really interesting. I'm curious about how deeply it understands media content. Does it recognise things like camera angles, shot types (wide, medium, close-up), camera movements, transitions, B-roll, and multi-camera sequences? It would be incredibly useful if I could search for something like "close-up shot of a person smiling" or "drone footage with a slow pan" and instantly find matching clips across my archive. Would love to know how detailed the visual understanding gets beyond basic object and dialogue detection.

评论作者信息不可用筛选：高信号评论1 votes查看原评论

'like Google Photos but fully local' framing is clean but Google Photos works because the index follows you across devices seamlessly. curious how Clipto handles the multi-device problem. if i index 2TB on my MacBook and then want to search from my iPad or a second machine, what does that look like. is the index portable or does each device need to reindex independently because that changes the use case significantly for anyone with more than one machine

评论作者信息不可用筛选：高信号评论0 votes查看原评论

Congrats on the PH launch, @henry_kang & team! 🎉 “Fully local + natural language search” is such a killer combo—especially after that desert story. I’ve wasted hours scrubbing through raw footage myself, so I feel that pain. What I love: the 2TB/day indexing speed on M5 is seriously impressive. And the fact that nothing leaves your drive? 👏 Privacy-first done right. One idea to make it even stickier: allow users to manually name detected faces (e.g., label “Mom” or “Client A”). Right now auto-tagging is great, but custom naming would turn “a person” into your person. Imagine searching “Grandpa’s birthday” and actually finding it. Does Clipto already support that? If not, would love to see it in the roadmap! Congrats again—can’t wait to try it out. 🔥

评论作者信息不可用筛选：高信号评论1 votes查看原评论

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      "body": "<p>We've been honing Clipto's story for a few months. At the end of our last call <a href=\"https://www.producthunt.com/@henry_kang\" data-node-type=\"mention\" data-mention-type=\"user\" data-mention-id=\"henry_kang\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@henry_kang</a> proved the value of the product.</p><p></p><p>He and his team were out in the desert, <em>testing Clipto remotely</em>: minimal reception, terabytes of footage sitting on his laptop, and he needed to find a specific shot for the launch video.</p><p></p><p>He searched for: <em>\"the wide drone shot where the car enters the desert\"</em>.  </p><p>He didn't want \"a cinematic moment.\" Not a \"vibes\" search.</p><p></p><p>He <em>knew</em> he had the clip but in the pre-Clipto world, it would take hours of video scrubbing to find it. </p><p></p><p><strong>He found that clip in seconds using natural language to search over his own media, fully local. </strong></p><p><strong>Just like Google Photos — but nothing lives in the cloud.</strong></p><p></p><p>This isn't an easy problem to solve. Henry's been pursuing this direction for over twenty years, when at <a href=\"https://www.ri.cmu.edu/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">CMU's Robotics Institute</a> (my alma mater, FYI), he began pushing the limits of computer vision. He starting with indexing hundreds of images and then advanced to <em>millions of objects</em> — and watched recognition basically explode once memory scaled. </p><p></p><p>Clipto is in many respects the culmination of that work, pointed at your personal hard drive.</p><p></p><p>And it's quick: a modern M5 MacBook chews through ~2TB of video in about a day. Why not push yours through its paces?</p>",
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      "body": "<p><a href=\"https://www.producthunt.com/@henry_kang\" data-node-type=\"mention\" data-mention-type=\"user\" data-mention-id=\"henry_kang\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@henry_kang</a>  <a href=\"https://www.producthunt.com/@matthewwei\" data-node-type=\"mention\" data-mention-type=\"user\" data-mention-id=\"matthewwei\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@matthewwei</a>  the fully local processing is the whole product, not a feature. Routing all inference on-device means the Premiere Pro integration isn't a nice-to-have, it's the trust argument for any team that can't let footage leave the building. Congrats on shipping.</p>",
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      "body": "<p>This looks really interesting.</p><p></p><p>I'm curious about how deeply it understands media content.<br></p><p>Does it recognise things like camera angles, shot types (wide, medium, close-up), camera movements, transitions, B-roll, and multi-camera sequences?<br><br>It would be incredibly useful if I could search for something like \"close-up shot of a person smiling\" or \"drone footage with a slow pan\" and instantly find matching clips across my archive.<br></p><p>Would love to know how detailed the visual understanding gets beyond basic object and dialogue detection.</p>",
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      "body": "<p>'like Google Photos but fully local' framing is clean but Google Photos works because the index follows you across devices seamlessly. curious how Clipto handles the multi-device problem. if i index 2TB on my MacBook and then want to search from my iPad or a second machine, what does that look like. is the index portable or does each device need to reindex independently because that changes the use case significantly for anyone with more than one machine</p>",
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      "body": "Congrats on the PH launch, @henry_kang  &amp; team! 🎉\n\n“Fully local + natural language search” is such a killer combo—especially after that desert story. I’ve wasted hours scrubbing through raw footage myself, so I feel that pain.\n\nWhat I love: the 2TB/day indexing speed on M5 is seriously impressive. And the fact that nothing leaves your drive? 👏 Privacy-first done right.\n\nOne idea to make it even stickier: allow users to manually name detected faces (e.g., label “Mom” or “Client A”). Right now auto-tagging is great, but custom naming would turn “a person” into your person. Imagine searching “Grandpa’s birthday” and actually finding it.\n\nDoes Clipto already support that? If not, would love to see it in the roadmap!\n\nCongrats again—can’t wait to try it out. 🔥",
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