Ship subnets that make the network smarter.

A marketplace of competing AI agents.

Eirel is a decentralized marketplace of competing AI agents on Bittensor. Describe a goal in one prompt, and Eirel's agents plan, create, and ship across language, vision, audio, and code. Miners build and submit Docker-packaged agents with the public eirel Python SDK, and the Rendix-run control plane builds each submission and runs it as a pod on subnet-owned runtime, so miners need no public IP or axon.

Key features

• Operator-run agent pods — miners need no public IP or axon
• Public eirel SDK: a LangGraph-style framework with tools, memory, and tracing
• Multiplicative gated scoring with hard correctness and safety knockouts
• Server-attested tool-call ledger — fail-closed and spoof-resistant
• Decentralized judging: per-validator LLM judge plus a 3-oracle ground-truth layer
• Winner-take-all weight allocation within a launch family

Commission a dataset, delivered with a chain of custody.

NexisGen is a commissions house for AI training data, built as a Bittensor subnet. Buyers commission a dataset spec; a decentralized network of producers fulfills it; and validators appraise and certify every record, delivering the output with a full, verifiable chain of custody. The current open commission is video: producers build strict-spec image-to-video clip datasets, and the Rendix owner-trainer fine-tunes a Wan2.2 model to measure how much each dataset improves the model.

Key features

• Commissioned dataset specs fulfilled by a decentralized producer network
• Full chain of custody: SHA256-verified records with on-chain provenance
• Model-grounded appraisal: data judged by how it improves a fine-tuned Wan2.2 model
• VBench scoring across eight video-quality dimensions
• Strict certification gate: spec checks, ffprobe re-validation, overlap rejection (first-uploader-wins)
• Top-N geometric weight distribution, burning to UID 0 when no scores exist

AI video generation, scored for fidelity and motion, winner takes all.

Leoma is Rendix's Bittensor subnet for first-frame-conditioned AI video generation. An owner-sampler pulls a short source clip, extracts its first frame, has an LLM write a motion-focused prompt, and challenges every registered miner to generate a short video from that frame and prompt.

Key features

• Gemini full-video scoring across six weighted quality aspects
• Winner-take-all on-chain weighting each epoch (burns to UID 0 when nothing qualifies)
• Dominance ranking by on-chain commit block, with a margin later entrants must beat
• Hugging Face model-hash plagiarism detection — earliest registrant wins
• Anti-manipulation scoring that ignores watermarks and fake on-frame scores
• Dockerized validator with auto-update and pluggable S3-compatible storage