Like Google Photos, but fully local. Turn the terabytes of video, audio, meetings, and files you work with into searchable memories, without uploading anything to the cloud. Clipto automatically tags people, dialogue, and scenes, so you can instantly find any moment buried in your media just by describing what you're looking for. It's fast too: on a MacBook Pro M5, Clipto indexed 2TB of videos in just 24 hours.
"Automatically tags people" — is that face recognition, voice matching, or something else? And when it misidentifies someone, is there a way to correct the label without re-indexing the entire library?
Can I drag and drop clips directly from the Clipto search window straight into my Premiere Pro or DaVinci Resolve timeline, or do I need to reveal in Finder first?
Since it’s 100% local, does the indexing process completely lock up the Mac, or can I still smoothly edit 4K video in the foreground while it indexes in the background?
the 'store everything but remember nothing' line is the whole thing imo. the part people underrate is that the hard bit was never the search, its doing the indexing on-device without melting the laptop or quietly shipping stuff to a server, which is exactly why most tools just punt it to the cloud. respect for taking the harder path. one thing im curious about: once the first 2TB is indexed, is re-indexing incremental as you add footage, or does it re-chew the whole library? thats kind of the thing that decides whether this stays usable for anyone whose archive keeps growing
Concept is really intresting and the smooth onboarding How did you get the idea to make that kind of stuff? what was your excatly the moments you think to create this.
I am not a creator , but I do have lots of personal photos stored in different locations on my device , will clipto be able to organise those for me ? And can it build a memory chart out if it. For me rather then searching I like what google shows to me on time to time , like memories.
But sometimes searching is also required.
Local-only across audio + video + files is the version of this I keep waiting for, congrats on shipping. The piece that usually breaks under real load is the indexing job, not the search itself. How are you handling the initial pass on someone with 5 years of meeting recordings? And does the index update incrementally or do new files queue behind the original backfill?
How does the search handle lighting conditions? If I search for 'forest at night' vs 'forest during the day,' is the vision model sensitive enough to distinguish the cinematic mood?
Congrats! I believe this product is very helpful for me! Clipto arrives at the intersection of three powerful trends: on-device AI, privacy-centric computing, and knowledge management. it has genuine disruptive potential.
For long-form team collaboration, is there a way to share the index file with another editor, or does each person need to re-index the same footage locally?
Does the natural language search get better over time through local fine-tuning, or is the model static upon installation?
I've got a lot of multi-cam footage and heavy ProRes files. Does the app struggle with professional codecs, or is it optimized for proxy-like speeds internally?
Love the local-first philosophy! Does the single license cover multiple Macs, or do I need a separate seat for my studio desktop and my travel MacBook?
On-device NL search over 2TB is the hard part — curious if you're embedding frames with a local CLIP-style model + ANN index, or sampling keyframes? And how does it stay incremental as the library grows?
This looks like a great concept. I'd definitely love to give it a try, especially since I spend a lot of time switching between notes, recordings, and transcripts. One question though; when can we expect support for Pixel phones? That would make it an easy download for me.
This is super cool, I wanted to ask you a question. How does it deal with hardware and devices that are very weak?
An absolute game-changer for the creator economy. Managing asset libraries is the unsexy part of the job that everyone hates. Thanks for fixing this!
This solves a problem I didn't even realize was draining my energy every day. No more hunting for files. Instantly installed.
This feels like a glimpse into the future of local file management. Huge congrats on the Product Hunt launch, Henry & team! Def trying this out today.
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About Clipto on Product Hunt
“Fully local, natural language search over terabytes of media”
Clipto launched on Product Hunt on May 31st, 2026 and earned 449 upvotes and 138 comments, earning #1 Product of the Day. Like Google Photos, but fully local. Turn the terabytes of video, audio, meetings, and files you work with into searchable memories, without uploading anything to the cloud. Clipto automatically tags people, dialogue, and scenes, so you can instantly find any moment buried in your media just by describing what you're looking for. It's fast too: on a MacBook Pro M5, Clipto indexed 2TB of videos in just 24 hours.
Clipto was featured in Mac (103.5k followers), Productivity (652.8k followers) and Artificial Intelligence (469.8k followers) on Product Hunt. Together, these topics include over 241.3k products, making this a competitive space to launch in.
Who hunted Clipto?
Clipto was hunted by Chris Messina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Clipto stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Chris Messina
"Automatically tags people" — is that face recognition, voice matching, or something else? And when it misidentifies someone, is there a way to correct the label without re-indexing the entire library?