NexisGen AI | SN70

88,000 video clips. 123 hours of training data for video generation. Built for training video generation models. @leoma_ai miners, this dataset is ready for you to use. At a simple $1 per minute benchmark, this dataset represents over $7,000 worth of generated training data. Now available on the Nexisgen catalogue. #AI #Video @huggingface $TAO #Bittensor nexisgen.ai/catalogue/vide…

NexisGen just published one of the first Bittensor-native, open, training-ready video generation datasets on Hugging Face. 50,000 standardized clips. - Not raw footage. - Not source URLs. - Not timestamps. - Not a dataset that needs days of preprocessing. Every clip is already prepared for training: - 1280 × 704 - 24 FPS - 121 frames - Pre-clipped - Consistent format This is what open video generation has been missing: clean, model-trainable video data at scale. Links: - huggingface.co/datasets/nexis… - nexisgen.ai/catalogue/vide… $TAO

Building AI models? Need high-quality training data? Whether you're an AI engineer pushing the frontiers of #MachineLearning or a company scaling your next-gen models, quality data is your biggest bottleneck. At NexisGen, we specialize in custom AI training data collection. Just tell us exactly what you need — domain, format, volume, annotation type — and our expert network will deliver it. Submit your data requirements here: nexisgen.ai/commission From computer vision & NLP to multimodal, robotics, or specialized industry datasets — we got you. Let’s accelerate your AI development together. $TAO #Bittensor #AI #TrainingData #DataForAI #GenerativeAI #LLM #MachineLearning #ArtificialIntelligence #AITraining

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/vide… Thanks to all the miners contributing. #Bittensor $TAO

Nowadays, most AI companies already have the model architecture, training pipelines, and GPU clusters ready. The real bottleneck is high-quality training data. That's why Bittensor subnet 70 - @nexisgen_ai matters. We've already collected 22+ hours of video training data, starting with video generation datasets and this is only the beginning. Video generation companies like @grok , #Seedance, @runwayml , @pika_labs , @LumaLabsAI , @Hailuo_AI , @Kling_ai , @GoogleDeepMind , @HaiperGenAI , @ViggleAI , @HeyGen , and @OpenAI should take a look. If you need high-quality video generation training data for your models, reach out me. Dataset preview: - 1280×704 resolution - 24 FPS - 121 frames per clip - structured clip IDs, producers, and intervals - nexisgen.ai/catalogue/vide… I’m ready to share this dataset with you for free, @elonmusk , for @grok.

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/vide… 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/st…) Subnet 99 - @leoma_ai to build the best Video Generation Model. (x.com/leoma_ai/statu…) Subnet 36 - @eirel_ai is the execution layer of multimodal AI workflows. (x.com/eirel_ai/statu…) 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/… bittensor:native Bittensor

Subnet 70 is live. NexisGen is a subnet built by @rendix_network, delivering on-chain verified datasets that solve the billion-dollar dataset problem across multiple AI verticals. Miners generate segmented, interval-based datasets. Validators verify quality of datasets producing reproducible, production-ready data. Top datasets feed AI pipelines: decentralized, high-value datasets powering real-world AI. Better data → better models → faster AI results. $TAO – #Bittensor – SN70