Subnet 85: Vidaio
Vidaio is Bittensor Subnet 85. Its on-chain description is “Next-Generation Video Processing Powered By AI,” and the project site is vidaio.io. The subnet turns video enhancement into a rewarded service: miners run AI models that improve and shrink video, and validators measure how good the results are so that creators, businesses, and developers can get video processing done through the network. The Vidaio README source describes the live task set as video upscaling and compression.
What Vidaio Rewards
The subnet focuses on two video tasks. The first is upscaling — using AI to raise a video’s resolution and visual quality. The second is compression — reducing a video’s file size while keeping it looking as close to the original as possible. Both are valuable because they make video cheaper to store and stream without an obvious drop in quality, and miners are rewarded for doing them well.
Quality is judged objectively rather than by opinion. For upscaling, the network can take a high-resolution video, shrink it down, and ask miners to reconstruct it — then compare each miner’s result against the known original to see how faithfully it was restored. For compression, miners are measured on how much smaller they can make a file while preserving its quality. Scoring relies on established video-quality metrics, so the reward follows measured output quality rather than claims.
Query Context
Vidaio’s public architecture separates controlled benchmark work from real user work. In synthetic queries, validators create tasks where the source material and expected comparison target are known: upscaling can start from a downscaled version of a high-resolution video, while compression can start from a high-quality source where the output is judged against the original.
Organic queries are different. They represent real video submitted for processing, where miners apply upscaling or compression based on the user’s request and the results are returned after processing. That separation lets the subnet use controlled tests to measure quality while still pointing the same miner capability at useful production-style video work.
Metric Context
The incentive mechanism source describes quality validation around VMAF and PIE-APP. VMAF is used as an objective video-quality comparison metric, especially for preserving quality during compression. PIE-APP is used for perceptual similarity in upscaling, where the question is how close the restored video is to the reference content.
These metrics matter because video processing can trade one visible property against another. A compressed video can be very small but visibly degraded, and an upscaled video can be sharp while introducing artifacts. By tying rewards to objective quality checks, the subnet can reward miners for outputs that preserve or improve the viewer-facing result rather than for simply producing a file with the requested label.
Miner and Validator Roles
Miners are the video processors. They run or develop AI models that handle upscaling and compression requests, aiming for the highest-quality output, and a registered hotkey on netuid 85 is what ties their work back to them. The same miners can serve both controlled test tasks and real video sent in by users.
Validators are the quality check. They benchmark miners with controlled tasks where the correct answer is known, route real user video through the network, and grade each processed result using objective quality measures. From those scores they set weights on the network, which is how reward flows through Yuma Consensus. At the article level the split is straightforward: miners process the video, while validators measure how good each result is and weight each miner accordingly.
Source and Live Data
The codebase is maintained in the vidaio-subnet/vidaio-subnet repository. Live SN85 data is available on TaoStats. The mechanism details in this article are tied to the public README and incentive documentation rather than to live identity fields.
Relationship to Yuma Consensus
Subnet 85 uses Yuma Consensus to convert the video-quality weight vectors that validators submit into the emission shares distributed to miners and validators within the subnet each tempo. The Yuma Consensus documentation describes how validator weight submissions are aggregated into consensus weights for each miner registered on the subnet.
In Vidaio’s context, validators benchmark miners with controlled video tasks where the source material and quality target are known, scoring each processed result using objective metrics such as VMAF for compression quality and PIE-APP for upscaling perceptual similarity. The Emission documentation describes how those consensus weights determine each participant’s share of the subnet’s accumulated emission each tempo.
Development Stage Context
The Introduction to Bittensor describes subnet development as moving from localnet to testnet and then mainnet. For Subnet 85, that sequence applies to the standard Bittensor lifecycle: localnet for isolated development, testnet for shared non-production testing, and mainnet for live operation with real emissions.
On mainnet, Subnet 85 is registered as the live production subnet at netuid 85. The Bittensor Networks reference separates mainnet, testnet, and localnet. Participation examples or emission outcomes from one environment should not be read as representing production subnet performance in another environment.
Reader Boundary
Subnet 85 Vidaio should not be read as generic Bittensor subnet documentation, a consumer video-editing app, or a guarantee of output quality. It names one subnet’s AI video-processing task — upscaling and compression scored on objective video-quality metrics — on netuid 85 (Vidaio repository, Understanding Subnets, Glossary: Netuid).
A miner’s reward reflects measured reconstruction or compression quality against a known reference, so it should not be read as a subjective quality promise for any particular video (Vidaio site — vidaio.io).