
Rayon Labs
91 posts

Rayon Labs
@rayon_labs
Cracked team collaborating with the best subnets on the Bittensor Network to further decentralised AI



Ventura Labs Ep. 57 - Christopher Subia-Waud Christopher (@wanderinweights) is the primary contributor to Gradients (@gradients_ai) Timestamps 01:11 - Introduction 02:05 - First contact with Bittensor 04:51 - Subnet 19’s verification problem 05:14 - Next-token probability checks 07:44 - Verifying images vs. text (diffusion nuances) 08:33 - How Bittensor reshaped his view 12:13 - NeurIPS credentials vs. real-world impact 14:00 - Why Bittensor moves faster than academia 15:51 - Gradients (Subnet 56) overview 18:14 - Tasks covered: Instruct, DPO, GRPO, Diffusion 20:34 - Winner-take-all incentives & emissions design 22:37 - Hyperparameters and search space 26:04 - Benchmarks vs. HF/Databricks/GCP/Together 31:10 - Best-performing sizes (7B–8B) 33:23 - Iterative training on large datasets 35:18 - Model merging vs. sequential approach 36:09 - Customer demand 38:20 - Gradients 5.0: open-source tournament 43:29 - Biggest surprise from open-sourcing 47:59 - Attracting and retaining top miners 50:20 - Miner stories and preserving the hobbyist spirit 52:57 - Rayon Labs partnership (Chutes + Gradients) 55:53 - Thriving under dTAO; emissions growth and why 58:01 - Bittensor-suited problems ahead 01:00:36 - Building toward “best AI” 01:04:58 - Risks of embedded ads in model outputs 01:07:30 - Advice to AI devs skeptical of crypto






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