InfiniteVibe operates as a competitive marketplace for AI-generated video content on Subnet-89. The workflow is roughly as follows:

1. Task Submission – “Share Your Vision”: A user or client with a video idea or request submits a task to the subnet (for example, “Create a 30-second ad with futuristic city visuals and voiceover”). This could be done through the InfiniteVibe website or a dedicated platform. The project’s site encourages users to share their vision, which kicks off the process.
2. Distributed Content Creation by Miners: Once a task is posted, multiple miner nodes (AI creatives) on the subnet work to fulfill the request in parallel. Each miner can use any AI model or tools at their disposal to generate the video content. For instance, one miner might leverage generative models (e.g. text-to-video AI or image-to-video animation pipelines) to produce a clip, while another might use a combination of Stable Diffusion for imagery and AI voice synthesis for narration. The system does not prescribe a single algorithm; miners compete by applying diverse methods to best realize the prompt.
3. Submission of Outputs: Miners submit their AI-generated videos or media outputs for evaluation. There may be an internal format or platform (potentially TensorFlix, see below) where these submissions are collected. At this stage, the content can be viewed and compared either by the requesting user or by the community/audiences if made public.
4. Feedback and Quality Evaluation: Instead of using purely automated scoring of AI outputs, InfiniteVibe’s subnet relies heavily on real-world user feedback to judge quality. In fact, miner scoring and emissions are determined by real user metrics – for example, whether a user decides to pay for the output, or the level of views and engagement a video receives. This means that a video which genuinely impresses the client or goes viral with viewers will translate into higher on-chain rewards for the miner who created it. Conversely, low-quality outputs that garner little interest would yield minimal reward. This feedback-driven reward model incentivizes miners to produce content that is not just technically correct, but also engaging and useful to users.
5. “Pay When Happy” Client Payment: InfiniteVibe introduces a client-friendly payment model: clients only pay if satisfied with a video. In practice, after viewing the submitted videos, the client can approve and pay for the one that meets their needs (or possibly request tweaks). The platform highlights an “Introductory tier – only pay when you’re satisfied” approach. This likely involves a smart contract escrow or a simple honor system where payment (possibly in TAO or another agreed currency) is released to the winning miner once the client is happy with the result. If none of the outputs are acceptable, the client might not be charged at all under this model.
6. Reward Distribution: Miners earn TAO token rewards from two sources: (a) the subnet’s own TAO emission (which is allocated based on the competitive ranking of their work by the validators), and (b) any optional payment or bounty from the client for the chosen content. The Bittensor consensus will adjust each miner’s reputation score according to the feedback signals (e.g. a paid, accepted video indicates high quality) and allocate a larger share of the subnet’s inflation to those miners. This dual incentive ensures miners are motivated to satisfy users – the better their video performs with real people, the more TAO they earn.

Throughout this process, InfiniteVibe actively bridges on-chain AI work with off-chain audiences. The team has indicated plans to “funnel traffic to infinitevibe.ai and tensorflix.ai” – websites that serve as front-end hubs for the. TensorFlix appears to be envisioned as “the future of AI-generated cinema,” essentially a showcase or streaming platform for videos created by the subnet. In early stages, the InfiniteVibe team is populating a beta list of “organic” tasks on these platforms (likely interesting prompts or community-suggested video ideas) to continually challenge the miners. As a growth strategy, they even suggested initially rewarding miners based on engagement on the miners’ own social media posts, effectively encouraging each miner to share their AI creations on YouTube/Twitter etc. and get real viewer feedback. “As a way to grow awareness and attract the world’s best creatives to SN89,” the subnet planned to first reward content that proves popular on the creator’s own social channels. In parallel, official channels (the InfiniteVibe site and TensorFlix) would drive viewers to these AI videos and aggregate user tasks, until the subnet’s output quality is high enough for a full launch. This clever approach bootstraps the subnet with external engagement while gradually improving the models through competition.

Overall, InfiniteVibe’s operation combines crowdsourced AI creativity with human-in-the-loop evaluation. Clients get access to multiple AI-generated video renditions of their request and only pay for what they like, while AI miners compete to outdo each other in creativity and production value. The Bittensor network infrastructure handles the reward logic and ensures that those who deliver valued content (as evidenced by “likes, views, and $$”) are proportionally rewarded in cryptocurrency.

Technical Architecture

InfiniteVibe’s architecture builds on the Bittensor substrate and customizes it for the domain of video production. Key technical components and systems include:

Bittensor Subnet Infrastructure: Subnet-89 runs on Bittensor’s decentralized network (built on Substrate). All miners and validators register on-chain, and the Subtensor blockchain maintains their reputations and coordinates token emissions. The subnet’s parameters (like difficulty for mining, validator count, etc.) are configured specifically for its use case – for example, it might allow larger message sizes or longer response times given the complexity of video tasks. InfiniteVibe’s network UID is 89, and it operates in the Dynamic TAO regime (meaning it has its own token supply governed by the TAO token economics).

Miner Nodes (AI Video Engines): Each miner in SN-89 is essentially a node running an AI pipeline for video/media generation or editing. Unlike text-based subnets that might run language models, InfiniteVibe’s miners likely run a suite of models: e.g. text-to-image/video generators, image animation tools, deepfake or voice synthesis models, and video editing algorithms. They might use frameworks like Stable Diffusion (for generating frames or images), video diffusion models, GAN-based upscalers, or even custom 3D renderers – whatever is effective for producing the requested content. Miners are free to choose or even combine models (“any model or means at their disposal” is allowed). This flexibility is important, as video creation can be tackled with many approaches (from pure generative AI to hybrid human-AI editing). The miner software (likely provided in the Delta-Compute/infinitevibe GitHub, though details are sparse) would handle receiving a task from the network, executing the creative AI process, and returning a result. Given the large size of video data, the actual video file might be shared via an off-chain mechanism (such as a URL, IPFS hash, or posted to the InfiniteVibe platform) rather than directly through the blockchain.

Validator Nodes: Validators in InfiniteVibe are responsible for assessing the miners’ contributions and setting the scores that determine rewards. However, unlike a typical AI task (e.g. where a validator could automatically check if an answer is correct), here the “correctness” is subjective (how good or appealing a video is). InfiniteVibe’s design leans on external feedback signals for this. Validators likely monitor user interactions and outcomes for each task: for example, detecting which miner’s video the client accepted/purchased, or pulling metrics like view counts or likes if the videos are posted on a public portal. These signals can be fed into a scoring algorithm on-chain. In a simple scheme, the validator could assign the highest score to the miner whose video was chosen by the client (or had the highest engagement), and lower scores to others – thereby weighting the next TAO emission toward the “best” miner. The Bittensor consensus (Yuma Consensus with validator trust, etc.) would then adjust stakes accordingly. It’s worth noting that implementing this requires bridging off-chain data (user feedback) into the blockchain. This might be done via manual validator input or oracles that input engagement data. The system is essentially an example of “proof-of-value delivered” as opposed to proof-of-work; the real-world validation is what counts.

Front-End Platforms (InfiniteVibe.ai and TensorFlix): To facilitate user interaction, the team has built (or is building) front-end platforms. InfiniteVibe.ai is the official website, which presents the project’s value proposition and likely will serve as a client dashboard to submit tasks or review results. The website emphasizes features like AI-powered editing, automated post-production, smart transitions, and AI voiceovers – indicating a full pipeline for video editing is available. TensorFlix.ai appears to be a complementary platform described as “the future of AI-generated cinema”, where the general public can watch or discover AI-created videos. In practice, TensorFlix could function as a content hub showing the best creations from the subnet (imagine a decentralized “Netflix for AI videos”). The integration of these platforms with the Bittensor backend is crucial: they likely handle user logins, task submissions, content hosting/streaming, and feedback collection, then relay the necessary data (tasks and feedback outcomes) to the blockchain layer. For instance, when a user submits a task on infinitevibe.ai, the site would create a corresponding request that validators broadcast to miners on Subnet-89. When a user likes or pays for a video, the site can inform the validators so that it affects the on-chain scores.

Real-Time Processing and Streaming: InfiniteVibe’s marketing highlights “lightning-fast media processing and live streaming solutions powered by cutting-edge AI”, including real-time effects and cloud processing. This suggests the architecture is not limited to batch video generation; it may also target real-time video applications. For example, miners could eventually handle live input (like a livestream or video call) and apply AI effects or translations on the fly. Achieving this would require highly optimized models and probably a framework for streaming data through the subnet (which is an ambitious extension beyond the standard request-response model). It’s possible the project envisions use cases like virtual event production, live AR filters, or real-time multilingual dubbing for videos – all handled in a decentralized way. While these features are likely in development, the architecture is being built with cloud-based GPU processing to handle intensive tasks quickly, aiming for interactivity.

Workflow and Modules: Technically, an end-to-end pipeline for an AI video might involve multiple steps (scripting, rendering scenes, editing, audio generation, etc.). It’s not publicly detailed how InfiniteVibe coordinates these steps. One possibility is monolithic tasks – each miner does all steps internally however they see fit. Another is a modular pipeline where different miners specialize (one generates raw footage, another improves resolution, another adds sound). Given the current info, it leans toward the former (each miner produces a complete output). The “Automated Editing” and “Smart Transitions” features hint that the miner software might include modules for cutting and stitching video clips intelligently and inserting transitions, so that the output looks professionally edited without human intervention. Also, “AI Voice” implies text-to-speech capabilities are integrated, so miners can easily add narration or dialogue to videos with synthetic voices. All these components run in the miners’ local environments (or their cloud instances) but are orchestrated by the rules of the Bittensor subnet (who works on what, when submissions are due, how they are scored, etc.).

In summary, InfiniteVibe’s architecture marries blockchain-based coordination and incentive mechanisms with a cloud/distributed computing stack for AI video generation. Bittensor provides the consensus, security, and tokenomics layer, while the Delta-Compute team provides the AI models and web interfaces that make the actual video magic happen. The focus on cloud and 3D experiences is reflected in the tech stack, aligning with the team’s background in cloud-based human experiences. As the subnet matures, we can expect optimizations for heavy data (video) handling, possibly utilizing decentralized storage for outputs, and maybe interoperability with other subnets (for example, leveraging a text-generation subnet to help write scripts, or a translation subnet to handle multi-language subtitles). InfiniteVibe is essentially building a decentralized AI video production pipeline from scratch, which involves solving both AI challenges (making high-quality videos via AI) and distributed system challenges (coordinating many independent nodes to deliver a single creative product).