We are on building a multi-turn conversational assistant across instant and thinking modes. It is backed by owner-routed tool services, including web search, URL fetching, Python sandbox execution for verifiable computation, and RAG retrieval over per-run document corpora. We are now building the product and will soon release it with the agent code from the top miner. Stay tuned for the updates. #Bittensor $TAO

Since yesterday, miners have added 5 more hours of video generation data to NexisGen. That brings the total to 27 hours of video generation data now available. You can check our progress here: nexisgen.ai/catalogue/video_… Thanks to all the miners contributing. #Bittensor $TAO

We are starting with AI training datasets for video generation models, but this is only the first step. The current dataset includes structured video clips like this: - 1280×704 resolution - 24 FPS - 121 frames per clip - structured clip IDs, producers, and intervals - link: nexisgen.ai/catalogue/video_… In just a few days, we’ve already collected around 22 hours of video data, with a lot more coming soon. SN70 is building the data layer for decentralized AI training, starting with video. #Bittensor $TAO

All three @rendix_network subnets are live now. Subnet 70 - @nexisgen_ai is to get the large scale AI Training Data for various models. (x.com/nexisgen_ai/status/205…) Subnet 99 - @leoma_ai to build the best Video Generation Model. (x.com/leoma_ai/status/205278…) Subnet 36 - @eirel_ai is the execution layer of multimodal AI workflows. (x.com/eirel_ai/status/205494…) All three subnets are live and moving fast in parallel. The product needs high-quality output, and that comes from both a strong agent and a strong model. A strong model comes from a good training pipeline and high-quality data. That’s why we need all three subnets working together to build a product that can compete with SOTA. #Bittensor $TAO

Incentive Mechanism updates! Reward miners whose video datasets produce the best fine-tuned TI2V models. Quality is measured by training a LoRA on each miner's data and scoring its generations with VBench. How a cycle works: - Miners upload 400-clip datasets (1280×704, 24fps, 121 frames, captioned) to their own R2 buckets. - Owner-trainer picks eligible miners, validates the dataset spec, trains a LoRA on each, and publishes eval-prompt videos to a shared bucket. - Validators score every miner's videos with VBench across 8 dimensions, sign and submit scores to the subnet API. - API averages scores across validators → total_score.json. - Set-weight (every 300 blocks): top-5 miners get weights [1, 1/2, 1/4, 1/8, 1/16] normalized; no scores → burn to UID 0. VBench dimensions: i2v_subject, i2v_background, subject_consistency, background_consistency, motion_smoothness, dynamic_degree, aesthetic_quality, imaging_quality Aggregate = mean of the 8. Dataset must-haves (any failure → rejected): - Exactly 400 clips, 1280*704, 24fps, 121 frames - Non-empty per-clip caption - No internal (source_url, start_sec) duplicates - <=100 clips overlap with the global overlap index Anti-gaming: - Selected miners auto-marked invalid; re-entry requires a top-5 finish. - Global overlap index blocks resubmitting the same URLs. - Multiple independent validators average each score. Links: - Dashboard: nexisgen.ai/floor - Github: github.com/RendixNetwork/nex… bittensor:native Bittensor

Old datasets train yesterday’s problems. Static data pipelines move too slowly for what models need now. @nexisgen_ai is a live data layer built to generate high-quality training datasets beyond the limits of static pipelines: • failure cases current datasets miss • edge cases legacy data never captured • performance gaps limiting output Starting with video generation, built to scale across high-demand AI verticals. Only possible on Bittensor. Datasets - Subnet 70 Rendix Live data. Stronger models. $TAO - SN70 - #Bittensor

We're proud to announce @leoma_ai is the first subnet built and launched by Rendix Network on Bittensor. After months of development, the full subnet codebase is now live: production-ready from launch. AI video generation is exploding: ~67M monthly users today A market growing from $1.8B → $21B But current platforms remain closed systems with strict user limits and expensive pricing models. Leoma opens the market. Our focus at Rendix is simple: designing high-quality subnet architecture. Leoma is just the beginning. More subnets are already in development as we continue building across the Bittensor ecosystem. $TAO - #Bittensor - SN99