A few of the biggest takeaways from our H3 conversation on ThursdAI 🎥 Also, huge thank you to @altryne for having us and for bringing together such a great discussion. 🔓 Open video is reaching the frontier. The discussion didn’t frame H3 as simply “good for an open model.” It was compared directly alongside Seedance, FLUX and WAN, with @altryne calling H3 the “leading open weights open model.” And the distinction was immediate: “Open weights, hostable by yourself, finetunable.” ⚡ Open weights compound incredibly fast. Within ~48 hours of launch, the community had already brought LoRA support, Apple Silicon/MLX, ComfyUI quantization and optimizations across new hardware. That’s exactly why we open-weighted H3: putting frontier capabilities in developers’ hands means the ecosystem can take the model places we never could alone. 🎭 Omni Reference + character consistency stood out as a major leap. @blizaine called H3 “so flexible compared to a lot of the previous open-weight models” and said it was “as good as anything I’ve seen” for recreating a character across environments. Images, voices, audio and video can all become references — making control and consistency increasingly as important as raw generation quality. 🛠️ Local and hosted workflows can complement each other. We also discussed Context-IR + Regenerate-2K: generate locally with H3, optimize multimodal context when needed, then regenerate a 768p result at 2K using the original references rather than simply upscaling it. 📈 The broader video capability curve is moving incredibly fast. @arena perspective summed it up well: “the changes we’ve seen in the fidelity and the quality and the sound is just unbelievable.” Open models are no longer sitting on a separate curve — they’re increasingly competing at the frontier itself. And a fitting note to end on from our host @altryne: “Thank you guys for open weighting the models. We expect more.” 🙌 We hear you. 🔓