According to @gensynai, the Gensyn network has surpassed 45 million transactions and reached 130,000 users, signaling substantial on-chain activity that traders can track for adoption momentum. According to @gensynai, product-level usage includes 122,500 BlockAssist sessions and 26,000 RL Swarm nodes connected, highlighting broad participation across the ecosystem.

According to @gensynai, the Gensyn testnet has recorded 40,535,515 transactions, 128,293 users, a 21,000-node RL Swarm, and 27,835 models trained via BlockAssist; source: gensyn (@gensynai) on X, Aug 23, 2025. Based on these figures, average transactions per reported user are roughly 316 (40,535,515 divided by 128,293), a usage intensity that traders can track as an on-chain adoption proxy for decentralized AI compute; source: calculation using data from gensyn (@gensynai) on X, Aug 23, 2025. The scale of nodes and completed model trainings indicates available compute supply and workload throughput that can inform positioning across AI-DePIN narratives; source: data from gensyn (@gensynai) on X, Aug 23, 2025.

According to @gensynai, RL Swarm has integrated with Weights & Biases, enabling direct terminal log uploads and real-time monitoring of key training metrics such as loss, learning rate, rewards, and GPU utilization (source: https://twitter.com/gensynai/status/1927789712938602649). This upgrade streamlines AI model optimization for algorithmic trading, giving crypto traders faster feedback loops and improved transparency for developing high-frequency trading bots.

According to gensyn (@gensynai), RL Swarm has expanded its decentralized network by adding Metal support, enabling Mac users with Apple silicon (M2, M3, M4) to train AI models significantly faster (source: @gensynai, May 28, 2025). This upgrade increases the network's computational capacity, potentially making decentralized AI training more efficient and accessible. For crypto traders, this enhancement may boost interest in RL Swarm-related tokens and decentralized AI infrastructure projects, as faster training could drive adoption and ecosystem growth.