Masks and tracking
SAM 3 tracks a selected person on Runpod GPUs so we can cut them out of a clip. SAM 2 is a planned alternative on AWS.
ShareFuze turns NFL stats, game film and a creator's own takes into finished graphics, clips and posts. A human approves every post. We build it first for our own NFL channel, then open it to other creators.
Working tools today. Video models in testing. AWS plans below.
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Machine learning (ML) helps turn research into media. The War Room is our own working app for this studio. We test video jobs on Runpod GPUs (graphics processors) and fal.ai.
Live: works in our own app. In testing: a trial, not a finished feature. Planned on AWS: future work.
SAM 3 tracks a selected person on Runpod GPUs so we can cut them out of a clip. SAM 2 is a planned alternative on AWS.
Wan 2.1 and Wan 2.2 support person-swap trials through Runpod GPUs and fal.ai. Wan 2.1 VACE is a planned alternative on AWS.
We plan GPU jobs with LatentSync and MuseTalk to match mouth movements to speech. The app already has a local MuseTalk engine on the Mac.
We plan to use Chatterbox, CosyVoice and RVC for parody voices on AWS GPUs. Every parody voice must carry a clear label.
We plan to use Whisper to turn speech into timed captions. AWS GPUs will run this job, with the Mac as a local option.
Claude, GPT and Gemini models help draft scripts and story angles. The app compares numerical claims with saved nflverse evidence before human review.
These phases describe future work. We have not moved this pipeline to Amazon Web Services (AWS).
Amazon S3 will store all media. Claude on Amazon Bedrock will help write scripts and check facts.
Bedrock embeddings will turn clips and stats into searchable representations, so creators can find related material.
Amazon EC2 G6e and G6 instances, with NVIDIA L40S and L4 GPUs, will run mask, swap, lip-sync, voice and caption jobs.
AWS Batch will queue jobs. AWS Step Functions will chain the steps. We plan to use Spot capacity to reduce costs.
Amazon SageMaker training jobs will train small LoRA adapters, which adapt existing models, and a model that ranks clips.
The ranking model will learn from nflverse play-by-play data, including expected points added (EPA) and win probability, to find strong clips.
AWS would put storage, GPUs and models in one place, with Claude on Bedrock next to the media in S3. Managed job queues would help a one-person team avoid running servers. Credits would let us move the GPU work that runs on Runpod today.
These rules guide the working app and every planned feature.
These tools work in our own app today. Early access for other creators comes next.
Search and compare nflverse stats. Build your take from the numbers.
Choose from 77 graphics templates. Prepare graphics for OBS Studio, the recording app, or export them.
Control OBS Studio and recordings from the app. Follow live scores while you work.
Bring matchup research into one place before you record.
Ask Claude, GPT or Gemini for help. Check numerical claims against your saved evidence before you approve a post.
Prepare files and captions for X, YouTube, TikTok, Instagram, Facebook and Threads. You review the kit and post it yourself.
Research, make the media, then review it. The final publishing step belongs to the creator.
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