Jockey by TwelveLabs

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."This new launch by @TwelveLabs connects the dots. 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.

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!