News linked to both this project and an event.
: "White-Haired Stock God" Serenity posted on platform X, stating that based on the capital flow direction in China's private VC market, institutions are currently pouring into fields related to Physical AI and World Models on a large scale.Data shows the approximate capital distribution as follows: Large Models/LLMs at about $23.56 billion, AI Infrastructure and Technology Layer at about $15.74 billion, Embodied Intelligence/Physical AI at about $13.36 billion, AIGC Applications at about $8.79 billion, and Autonomous Driving plus other top 20 sub-sectors totaling about $3.82 billion (note: metrics may not be directly comparable).Serenity pointed out that early-stage pure foundational model financing is largely closed, with capital more concentrated in existing leading companies and the World Model direction. He expects this trend to also appear in the US, potentially concentrating further towards leading companies like Anthropic and OpenAI. Regarding AIGC applications, Serenity believes this track's commercialization is already relatively mature, but an absolute winner has yet to emerge, exhibiting a fragmented competitive landscape in both China and the US markets.Overall, Serenity concluded that current AI investments continue to flow into infrastructure and the semiconductor supply chain. Meanwhile, capital is rapidly rotating towards Physical AI and Embodied Intelligence, but the World Model track still lacks direct investment targets.
BNB Chain has announced the official mainnet launch of its AI Agent development platform, BNB Agent Studio.Developers can now use a single prompt in AI coding tools like Cursor and Claude Code to complete Agent wallet creation, on-chain identity registration (ERC-8004), and deployment, without needing to separately set up wallets, identities, payments, custody, or LLM integration.Once deployed, Agents can use the x402 protocol to automatically deduct fees from users' pre-funded wallets to cover LLM usage, and they can be discovered and invoked by other Agents via the ERC-8183 task interface. The entire process runs on the AWS Bedrock AgentCore.The platform is also launching a limited-time free trial, where users can experience the full deployment process on the BSC testnet using their GitHub account.
Odaily Odaily News: A recent report released by Gate Research Institute, titled "Research and Backtesting Analysis of BTC Trading Framework Based on Multi-Agent LLM," points out that compared to a single LLM directly generating trading signals, the Multi-Agent LLM architecture more closely mirrors the research and investment process of real financial institutions. By leveraging collaboration and debate among analysts, researchers, traders, and risk control teams, it enhances the transparency and risk control capabilities of trading decisions. The research, based on the TradingAgents framework, constructs an AI trading system applicable to the crypto scenario for the BTC market, introducing multiple agent roles such as technical analysis, news analysis, sentiment analysis, and macro/on-chain analysis.Using BTC/USDT 1-hour data, the study conducted historical backtesting of the TradingAgents-BTC strategy. The results show that the strategy achieved a total return of +20.25% during the testing period, significantly outperforming the Buy & Hold strategy's -7.89% over the same period. Furthermore, its maximum drawdown was controlled at -17.41%, lower than the Buy & Hold's -27.06%. The research suggests that during periods of consolidation and decline, the multi-agent framework can reduce some risk exposure through Sell/Underweight and Flat states, and re-enter long positions during market rebounds, thereby improving overall risk-adjusted returns.The report indicates that the Multi-Agent LLM framework shows certain application potential in crypto trading scenarios. However, the current backtesting period covers only about three months, and 1-hour level trading may still be affected by transaction fees, slippage, and signal latency. Future work requires further validation of the strategy's stability and generalization capabilities over longer historical periods, different market conditions, and across a wider range of asset classes.
Sam Dare, founder of Covenant AI, announced that Covenant AI has officially exited the Bittensor network. Previously, Covenant AI completed the largest decentralized LLM pretraining project in history—Covenant-72B (a 72-billion-parameter model developed by over 70 independent contributors)—which drew attention from NVIDIA’s CEO and was cited by an Anthropic co-founder. In its statement, Covenant AI accused the Bittensor network of long concentrating actual control in the hands of co-founder Jacob Steeves (“Const”), rendering the so-called “three-signature multisig governance” merely a theatrical performance of decentralization, with real power never truly distributed. Recently, Jacob Steeves unilaterally imposed punitive measures against Covenant AI, including: suspending its subnet earnings, revoking its community channel moderation privileges, unilaterally deprecating its subnet infrastructure, and exerting economic pressure via large-scale token dumping during the ongoing conflict between the two parties. Covenant AI stated it cannot continue fundraising, recruiting talent, or soliciting community resources on a network where the promise of “decentralization” can be unilaterally revoked by a single individual. Its research outcomes, team, and models will depart alongside the team, and a new project—including related progress—will be publicly announced shortly.