Clipto - Fully local, natural language search over terabytes of media

@chrismessina 

Thanks Chris. 🙏

One thing we’ve learned from today’s discussions is that people aren’t really looking for “AI magic.”

They already know the clip exists.

They already own the footage.

They just need a reliable way to find it.

Whether it’s:

• the exact moment a decision was made in a meeting
• a specific quote from a podcast recorded months ago
• a particular shot buried in terabytes of footage

the common problem is the same:

our computers store everything, but remember nothing.

That’s ultimately what we’re building toward: a local memory layer for the media people already own.

@ansari_adin That’s a very insightful question.

Today, Clipto works independently on each machine. The index is built locally and stays local, so if you index 2TB of media on your MacBook, that index currently doesn’t automatically appear on another device.

We made that tradeoff intentionally because our first priority was privacy, local ownership, and offline usability.

That said, we completely agree that long-term memory becomes much more valuable when it can follow you across devices. Cross-device memory and synchronization are already on our roadmap, and we’re actively exploring ways to do that while preserving the local-first principles that make Clipto unique.

In many ways, this is one of the most interesting problems for us: how do you build a Google Photos-like memory layer without giving up control of your data to the cloud?

@pradeepmalakar That's a really professional cinematography question. We're working hard to enrich our understanding of cinematic language to better serve professional video creators — here's what we can reliably recognize today:

• Shot Type: Wide Shot, Medium Shot — e.g. "wide shot of a city street" or "medium shot interview"

• Camera Angle: High Angle, Overhead/Top-down — e.g. "overhead shot of a table" or "high angle crowd scene"

• Framing & Composition: Landscape — e.g. "landscape framing outdoor scene"

• Scene & Setting: Urban/City, Green Screen/Studio, Day — e.g. "studio interview daytime" or "urban street scene"

• Technical Specs: AV1, Rec.709, 4:2:0, 8-bit, 25FPS — e.g. filter footage by codec or color space when you need format consistency in an edit

• Focus & Quality: Out of Focus — e.g. quickly filter out unusable takes

...and more, these are just a few examples across the many dimensions Clipto tags. Sorry I can't list them all here! Every case shown in our demo video is a real.

Camera movements, transitions, B-roll classification, and multi-camera sequences — those are on the roadmap and we're heads-down on it.

Would love to hear what specific search queries matter most to your workflow — it really helps us understand what to build next:)

@artstavenka1 Great question.

Yes, Clipto watches the local filesystem and keeps the index in sync.

If you rename or move a file after it’s been indexed, Clipto will detect the change and update its references automatically, so the media doesn’t need to be re-indexed from scratch.

The heavy lifting (transcripts, visual understanding, embeddings, etc.) is already done, so we’re simply updating the file mapping rather than reprocessing the entire asset.

We designed it this way because media libraries are constantly evolving. People reorganize folders, rename projects, move files between drives, and we don’t want that to break search. Local-first only works if the index evolves with your library.

@joe_rucker Great question. It’s not just metadata, transcripts, or simple keyframe extraction.

We combine multiple signals, including metadata, speech transcripts, visual understanding, and information extracted directly from the video stream.

That said, we also don’t do naive frame-by-frame analysis across every frame. At scale, that becomes extremely expensive while often adding little value. Instead, we use a more selective approach to identify and analyze the most informative moments within a video.

Our current focus is helping users find the right moments and clips from large media libraries as quickly as possible.

The workflow you described, where AI reviews multiple takes, ranks them, and suggests the best cuts, is a fascinating direction. While Clipto doesn’t currently optimize for edit recommendations, many of the underlying building blocks are already there.

Out of curiosity, what’s your current editing workflow today? Are you using Premiere, Final Cut, Resolve, or something else? And how much of the selection process is still manual versus AI-assisted?

We’re spending a lot of time thinking about how AI agents and creative tools could work together, so I’d love to learn how you’re approaching it.

@carlvert That's exactly right for 100% on-device processing, no data leaves your machine, no telemetry, nothing. That's the whole point of that mode.

If you choose Hybrid mode, some minimal data is used to enable cloud features like sync and collaboration — but that's opt-in, and clearly labeled when you set it up. Your choice, fully in your control：）

@jocky Thanks! Today, most users run Clipto across their personal devices without friction.

We’re still refining some of the licensing and account management details as the product grows, especially for creators and teams who work across multiple machines.

Our goal is to make legitimate personal use feel simple, not burdensome.

Out of curiosity, when you switch between your studio desktop and travel MacBook, are you typically working with the same media library or different projects on each machine?

Thanks @sandy_liusy Sandy! 🙏

Search has definitely become the heart of the product. There’s something magical about finding a specific moment from terabytes of media in seconds.

To be honest, I’m still surprised by some of the details Clipto catches. Every now and then it finds something in a frame that I completely missed while filming. 😄

@jaredl Absolutely. Video gets most of the attention, but Clipto works with audio just as well.

Podcasts, interviews, meetings, voice recordings, and other audio files are all indexed and made searchable. You can search across transcripts using natural language and jump directly to the relevant moments.

In fact, if you’re sitting on hundreds of hours of recorded interviews, that’s one of the strongest use cases for Clipto. Those recordings often contain valuable insights that are almost impossible to rediscover later without a system like this.

We’d love to hear how you’re currently managing and searching those interviews today.