Jockey is the first AI that understands your entire media library across photos and video. It analyzes thousands of hours of footage so you can find and connect people, moments and context across videos. Use it in Claude or ChatGPT, or build with the API/SDK. Ingest YouTube, TikTok and Instagram Reels links; assemble content into MP4s; and create and share video-driven artifacts with playable clips. Every TwelveLabs model upgrade makes Jockey better, without re-integration.
We're launching the general availability of Jockey today, and it's shaped by what we learned during the research preview. We heard three things loud and clear:
People needed the connectors to work better. The strongest reactions came through Claude and ChatGPT. So we prioritized ChatGPT support for GA and made the MCP experience seamless. If you want to query your library, your chat interface should be the last friction point, not the first.
Builders needed output they could control. Developers told us a black-box "final edit" wasn't what they wanted. They needed structured clips, JSON, artifacts they could manipulate. So we built durable artifacts that can be saved, shared, revoked, or deleted and ensured the API gives you timestamped, actionable output you own, not a render you're stuck with.
Users wanted deep social intelligence, not just basic video search. Creators and marketers kept asking us: "Why is this video trending? What specific hooks are holding attention? What common patterns make these reels go viral?" We listened, and built social intelligence features that analyze video hooks, breakdown visual and audio pacing, and explain what actually works across trending content. We also added social ingestion of URL links from YouTube, TikTok, and Instagram Reels, so users can analyze what's working in the wild and search performance alongside content without having to upload videos. It's a whole new way to deconstruct viral video performance.
Library-level understanding is the point. You point Jockey at thousands of videos and images, and it reasons across all of them, not just one. That's why we built it this way, and why every model upgrade makes it better without you lifting a finger.
If you're building a video app, organizing footage, or trying to find something in thousands of hours of content, Jockey is for you. Try Jockey and let us know you feedback and we will fix it!
Congrats for amazing product launch. I want to know your hidden limits and retention policy of data.
Honestly the main thing I use this for is to find clips of the cat sleeping in the background of family vacation videos. It's a little annoying because the system sometimes sees him as a pillow but it still helps me avoid going through three years of video just to find something strange.
I expect there are hidden limits hidden behind the ChatGPT plugin that gets a lot of promotion. After ten hours they will definitely push for an upgrade fee. I couldn't find any information about how the data is kept for the reels we upload which makes me really unsure about giving them access to all my videos.
oh wow, searchable videos? I have a ton of use cases for these. Congrats on the launch.
handling thousands of hours of video across chat interfaces is impressive, but does the MCP setup ever run into context window bottlenecks when a query spans too many disparate clips in a massive library??
Video is the one area that I feel AI hasn't yet reached its potential in and this tool seems to go in that direction.
Congrats on the launch team!
Congrats Aiden and the TwelveLabs team on taking Jockey to GA. Pairing your own library with a social corpus of trending Reels and TikToks is the part I want to try, since most video search tools only see what you uploaded. When Jockey explains why a hook works, is that grounded in performance data from the corpus or in Pegasus reasoning about the clip itself, and can a user tell which one an answer came from?
What is the workflow of a standard Claude or ChatGPT, and how big a library does it take before searches become slow? Additionally, Congratulations @jaelee_ , @aiden_lee7 and team Jockey by TwelveLabs 🚀✌️
Congrats @aiden_lee7 and team. Eight launches and still shipping from user feedback is the part I respect most. The social ingestion from YouTube and Reels links is a smart addition; no upload step is exactly the kind of friction people quit on.
How well does the search handle really specific moments in longer videos?
Congrats @aiden_lee7 & team!
Thanks for sharing @fmerian 🙌🏻
@jaelee_@aiden_lee7 How well does TwelveLabs understand long videos and find specific movement?
Congrats on the launch! Being able to find specific people and moments across thousands of hours of footage and turn them into shareable clips sounds really useful.
Great that Jockey understands my whole photo and video library, so I can describe a clip in plain words and actually find the right footage.
Congrats on the launch! I've always wanted videos to be searchable! Although currently my footage as scattered all over the place. Time to get organized.
Keeping timestamped clips and JSON in the output gives developers something they can actually build on. The ability to revoke shared results is a thoughtful detail too, especially when the footage isn't meant to stay public.
"Marengo finds it, Pegasus describes it, and Jockey turns raw footage into something you can query."
Marengo (3.0) launched 10 months ago, ranked among the Top Products of the Day. Pegasus launched 6 months ago (1.5) and 1 week ago (1.6), and again, both ranked among the Top Products of the Day.
S/O to ?makers for their consistency. It pays off. Keep launching.
About Jockey by TwelveLabs on Product Hunt
“Your entire video library: organized, searchable, understood”
Jockey by TwelveLabs launched on Product Hunt on October 16th, 2026 and earned 421 upvotes and 29 comments, earning #1 Product of the Day. Jockey is the first AI that understands your entire media library across photos and video. It analyzes thousands of hours of footage so you can find and connect people, moments and context across videos. Use it in Claude or ChatGPT, or build with the API/SDK. Ingest YouTube, TikTok and Instagram Reels links; assemble content into MP4s; and create and share video-driven artifacts with playable clips. Every TwelveLabs model upgrade makes Jockey better, without re-integration.
Jockey by TwelveLabs was featured in Productivity (663.7k followers), Artificial Intelligence (481.7k followers) and Video (2k followers) on Product Hunt. Together, these topics include over 308.4k products, making this a competitive space to launch in.
Who hunted Jockey by TwelveLabs?
Jockey by TwelveLabs was hunted by fmerian. 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 Jockey by TwelveLabs stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
fmerian
We're launching the general availability of Jockey today, and it's shaped by what we learned during the research preview. We heard three things loud and clear:
People needed the connectors to work better. The strongest reactions came through Claude and ChatGPT. So we prioritized ChatGPT support for GA and made the MCP experience seamless. If you want to query your library, your chat interface should be the last friction point, not the first.
Builders needed output they could control. Developers told us a black-box "final edit" wasn't what they wanted. They needed structured clips, JSON, artifacts they could manipulate. So we built durable artifacts that can be saved, shared, revoked, or deleted and ensured the API gives you timestamped, actionable output you own, not a render you're stuck with.
Users wanted deep social intelligence, not just basic video search. Creators and marketers kept asking us: "Why is this video trending? What specific hooks are holding attention? What common patterns make these reels go viral?" We listened, and built social intelligence features that analyze video hooks, breakdown visual and audio pacing, and explain what actually works across trending content. We also added social ingestion of URL links from YouTube, TikTok, and Instagram Reels, so users can analyze what's working in the wild and search performance alongside content without having to upload videos. It's a whole new way to deconstruct viral video performance.
Library-level understanding is the point. You point Jockey at thousands of videos and images, and it reasons across all of them, not just one. That's why we built it this way, and why every model upgrade makes it better without you lifting a finger.
If you're building a video app, organizing footage, or trying to find something in thousands of hours of content, Jockey is for you. Try Jockey and let us know you feedback and we will fix it!