Your entire video library: organized, searchable, understood
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!
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.
On the analytics side, Jockey by TwelveLabs competes within Productivity, Artificial Intelligence and Video — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Jockey by TwelveLabs performed against the three products that launched closest to it on the same day.
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.
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!