The core team has gained many years of experience in high-frequency data recording, AI, and its implementation in market-making simulations and at practical level in trading. The team wants to take certain feats to the forefront of decentralized AI.

– (C++) The developed subnet validator logic in C++ is special on Bittensor; here, C++ code is applied to achieve the maximum level of efficiency and number of simulations. The team has played with similar latency sensitive matters before.

– (R&D) The team includes a high-frequency data expert with 20+ years of experience in agent-based modelling, trading, and market microstructure, plus others with a PhD. And the team is going to invest even more heavily into realistic liquidity modelling.

– (MBO) The simulations allow for L3 market-by-order (MBO) data to be used by the AI Agents, motivated by the fact that deep-level HF data may be useful in AI prediction. These type of HF data have been used heavily by the team in their prior operations.