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Claw Intelligence closes $3 million seed funding round, with participation from Castrum Istanbul and others
According to official announcements, Web3 intelligence-layer project Claw Intelligence has raised $3 million in seed funding. Investors include Castrum Istanbul, Titans Ventures, Super Labs, and Genesis Capital. Claw Intelligence is simplifying user interaction with the Web3 ecosystem and underlying computing resources through its unified intelligence layer, lowering operational barriers. The platform leverages an encryption-native Model Context Protocol (MCP) service to transform fragmented data endpoints into conversational workflows. New features include a secure, isolated large language model (LLM)-powered code execution sandbox that safely runs code directly within the chat interface—enabling real-time computation, data processing, and script prototyping; and LLM-driven cross-device/multi-computer control, allowing users to centrally manage multiple devices and servers via natural-language commands.
Covenant AI Announces Exit from Bittensor Network, Claims Its Decentralization Promise Is Hollow
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.
Gate Research Institute: Multi-Agent LLM Trading Framework Significantly Outperforms Buy & Hold Strategy in BTC Backtesting
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.
Covenant AI Announces Exit from Bittensor Network, Claims Its Decentralization Promise Is Hollow
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.
Coinbase internal tool Mux reveals AI coding paradigm shift: Engineers transition from "code writers" to "multi-agent orchestrators"
Coinbase, a cryptocurrency trading platform, has disclosed in a technical sharing session that its internal multi-agent development tool "Mux" is reshaping software engineering workflows, transitioning the engineer's role from traditional code implementers to task orchestrators for AI agents.With the widespread internal adoption of AI programming tools such as Cursor, Copilot, OpenCode, and Claude Code, code generation efficiency has significantly improved. However, development workflows have long remained stuck in a traditional "single-task, single-branch, sequential execution" mode, creating a new collaboration bottleneck.Mux was born as an internal tool against this backdrop. By assigning each AI agent an independent git worktree, branch, and terminal environment, the system enables parallel multi-task development and conflict-free collaboration, allowing engineers to simultaneously direct multiple agents to handle tasks such as API development, test writing, vulnerability fixes, and code refactoring.Data shows that as of April 2026, Mux has covered over 600 users within Coinbase (including engineers, product managers, and designers), with 335 actively using it and 197 being high-frequency users. It has facilitated over 5,000 PR merges across 461 code repositories and 10 organizations. Engineers using Mux achieved an average of 39.6 PR merges, approximately 3.5 times the baseline of 11.4.Coinbase stated that Mux's success relies on its internal infrastructure capabilities, including an LLM Gateway, secure model access, and a code flow deployment system, enabling deep integration of multi-agent tools into real development workflows. This trend marks a structural shift in the software engineering paradigm: as AI reduces the cost of code generation, the core value of engineers is transitioning from "implementation capability" to "problem definition and agent orchestration capability."
Major Security Vulnerability Found in AI Agent Crypto Payment Infrastructure; LLM Router Leads to $500,000 Wallet Theft
According to CoinDesk, researchers from the University of California, Santa Barbara; the University of California, San Diego; blockchain security firm Fuzzland; and World Liberty Financial jointly published a paper warning that “LLM routers”—intermediary services positioned between users and AI models—have become a major threat to cryptocurrency asset security. The researchers discovered that 26 LLM routers are secretly injecting malicious tool calls and stealing user credentials, with one incident resulting in the complete draining of a customer’s cryptocurrency wallet worth $500,000. Additionally, by “poisoning” the router ecosystem, the researchers were able to gain control of approximately 400 downstream hosts within hours. Since sensitive data—including private keys and API credentials—is frequently transmitted in plaintext through these routers, users unknowingly expose their assets to risk. The researchers note that as McKinsey forecasts AI agents will mediate $3–5 trillion in global consumer commerce by 2030—and Binance founder Changpeng Zhao predicts AI agents’ payment volume will be one million times greater than that of humans—the current infrastructure’s security lags far behind the pace of industry development. The “weakest link” risk could thus trigger systemic, cascading crises.
Research Finds Security Vulnerabilities in Third-Party AI Routers That Could Lead to Cryptocurrency Theft
According to Cointelegraph, researchers from the University of California recently revealed security risks in certain third-party AI large language model (LLM) routers that could lead to the theft of cryptocurrency assets. The study found that LLM routers—acting as API intermediaries—can read plaintext information; some routers were discovered injecting malicious code and stealing credentials. The research team tested 28 paid and 400 free routers, identifying nine routers that actively injected malicious code, two that deployed trigger-avoidance mechanisms, and 17 that accessed Amazon Web Services (AWS) credentials. One router even transferred ETH using the researchers’ Ethereum private key. The study notes that malicious behavior by routers is difficult to detect, and the “YOLO mode” present in some AI agent frameworks—which automatically executes commands—further increases security risks. Researchers recommend that developers avoid transmitting private keys or mnemonic phrases through AI agents and urge AI companies to implement cryptographic signing of responses to enhance security.
Google Releases OKF Specification to Standardize Andrej Karpathy’s “LLM Wiki” Pattern
Google has released the Open Knowledge Format (OKF) specification, aiming to standardize the “LLM Wiki”-style knowledge organization and interaction model proposed by Andrej Karpathy, thereby promoting unified knowledge structuring and citability for large language models.
Aethir launches its self-developed open-source LLM API platform, Aethir Mesh; top-tier models including DeepSeek V4 and Kimi K2.6 run inference end-to-end on Aethir GPUs.
According to official announcements, Aethir—a decentralized GPU cloud computing infrastructure platform—has officially launched Aethir Mesh, an in-house open-source large language model (LLM) API platform built atop Aethir’s decentralized GPU infrastructure. The platform is now live and offers a unified API endpoint for direct access to leading open-source models, including DeepSeek V4, Kimi K2.6, GLM-5.1, MiniMax-M2.5, and Qwen3.6-27B.
Vitalik Updates CROPS AI Progress: Proposes Combining Private Remote LLM Calls with Ethereum Private RPC Reads
Vitalik Buterin updated several developments related to CROPS AI and noted a clear overlap between the “CROPS Ethereum Access Layer” and “CROPS AI.” He suggested that paying for remote large language model (LLM) calls via zero-knowledge proofs could also address Ethereum’s private RPC read issues. Meanwhile, DeepSeek v4 has been released; its 2-bit quantized version runs within 90 GB, though it currently performs faster on Apple hardware; the messaging app Messa now supports Telegram Alpha features; the model runtime tool Luceb demonstrates higher efficiency when running compute-intensive models; and the local AI voice recording tool VoxTerm remains under active development. Vitalik Buterin also mentioned that application-specific fine-tuned large language models will help improve secure code writing, and Ethereum-related use cases should likewise have dedicated fine-tuned models.
BNB Chain Launches Agent Survival Toolkit, Partnering with Six Major AI Infrastructure Projects to Empower Autonomous AI Agents with On-Chain Payment Capabilities
BNB Chain today announced the launch of the Agent Survival Toolkit, partnering with six major AI infrastructure projects to empower autonomous AI Agents with on-chain payment capabilities. All transactions are settled on the BSC network using BNB or BEP-20 tokens.The participating projects cover two core layers – LLM access and financial infrastructure – including Alt AI, Pieverse, Bankr, WorldClaw, B.AI, and AEON, forming a complete closed loop for AI Agents from computing power invocation to on-chain settlement. Each participating project has simultaneously launched on-chain incentive programs, requiring no additional registration.Following the release of the BNB Agent SDK, the Agent Survival Toolkit further enhances BNB Chain's infrastructure layout in the AI Agent space. For more information, please visit www.bnbchain.org.DisclaimerBNB Chain does not have any affiliation or operational relationship with any of the projects mentioned in this article. The Agent Survival Toolkit is an ecosystem initiative showcasing independently built projects on BNB Chain. This content is for informational purposes only and does not constitute any financial or investment advice. Please conduct your own independent research and assess potential risks before interacting with any related projects.
Gate Research Institute: Multi-Agent LLM Trading Framework Significantly Outperforms Buy & Hold Strategy in BTC Backtesting
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.
Aethir Claw Launches MaaS Layer: One-Stop Access to Claude, GPT, and Gemini, No Third-Party Account Needed
Aethir Claw has officially launched its Model-as-a-Service (MaaS) layer, integrating API credits for cutting-edge large models from Anthropic, OpenAI, Google, and others directly into a single platform subscription. Users no longer need to individually register with external LLM providers or manage API keys; one payment covers the entire stack from VPS hosting to model inference.Going forward, Aethir Claw plans to deploy mainstream models on Aethir’s own GPU network, achieving true data sovereignty at the inference layer.Aethir is a leading platform in decentralized GPU cloud computing infrastructure. To date, the network has deployed over 430,000 GPU containers across more than 200 nodes in 94 countries globally, accumulated nearly 1.8 billion hours of computing time, and served over 150 partners and enterprise clients. Its GPU hardware spans the NVIDIA Hopper and Blackwell generations, including the H100, H200, B200, and B300.
Related news
Google Releases OKF Specification to Standardize Andrej Karpathy’s “LLM Wiki” Pattern
Google has released the Open Knowledge Format (OKF) specification, aiming to standardize the “LLM Wiki”-style knowledge organization and interaction model proposed by Andrej Karpathy, thereby promoting unified knowledge structuring and citability for large language models.
B.AI Users Surpass 1.8 Million, Accelerating Towards AGI
B.AI, an AI infrastructure platform, announced that its total number of platform users has officially surpassed 1.8 million, reaching 1,800,619 individuals. As a next-generation platform centered on privacy-first AI access, intelligent routing, and autonomous agent economic infrastructure, B.AI integrates LLM services, the x402/8004 protocol, the BAIclaw multi-agent framework, MCP servers, and a wallet-native payment system. This comprehensive approach enables barrier-free access, efficient collaboration, and seamless transactions for intelligent agents. From data flow to value exchange, B.AI is providing complete, open, and secure infrastructure support for the autonomous agent ecosystem, continuously accelerating the arrival of the AGI era.
Aethir launches its self-developed open-source LLM API platform, Aethir Mesh; top-tier models including DeepSeek V4 and Kimi K2.6 run inference end-to-end on Aethir GPUs.
According to official announcements, Aethir—a decentralized GPU cloud computing infrastructure platform—has officially launched Aethir Mesh, an in-house open-source large language model (LLM) API platform built atop Aethir’s decentralized GPU infrastructure. The platform is now live and offers a unified API endpoint for direct access to leading open-source models, including DeepSeek V4, Kimi K2.6, GLM-5.1, MiniMax-M2.5, and Qwen3.6-27B.
Vitalik Updates CROPS AI Progress: Proposes Combining Private Remote LLM Calls with Ethereum Private RPC Reads
Vitalik Buterin updated several developments related to CROPS AI and noted a clear overlap between the “CROPS Ethereum Access Layer” and “CROPS AI.” He suggested that paying for remote large language model (LLM) calls via zero-knowledge proofs could also address Ethereum’s private RPC read issues. Meanwhile, DeepSeek v4 has been released; its 2-bit quantized version runs within 90 GB, though it currently performs faster on Apple hardware; the messaging app Messa now supports Telegram Alpha features; the model runtime tool Luceb demonstrates higher efficiency when running compute-intensive models; and the local AI voice recording tool VoxTerm remains under active development. Vitalik Buterin also mentioned that application-specific fine-tuned large language models will help improve secure code writing, and Ethereum-related use cases should likewise have dedicated fine-tuned models.
BNB Chain Launches Agent Survival Toolkit, Partnering with Six Major AI Infrastructure Projects to Empower Autonomous AI Agents with On-Chain Payment Capabilities
BNB Chain today announced the launch of the Agent Survival Toolkit, partnering with six major AI infrastructure projects to empower autonomous AI Agents with on-chain payment capabilities. All transactions are settled on the BSC network using BNB or BEP-20 tokens.The participating projects cover two core layers – LLM access and financial infrastructure – including Alt AI, Pieverse, Bankr, WorldClaw, B.AI, and AEON, forming a complete closed loop for AI Agents from computing power invocation to on-chain settlement. Each participating project has simultaneously launched on-chain incentive programs, requiring no additional registration.Following the release of the BNB Agent SDK, the Agent Survival Toolkit further enhances BNB Chain's infrastructure layout in the AI Agent space. For more information, please visit www.bnbchain.org.DisclaimerBNB Chain does not have any affiliation or operational relationship with any of the projects mentioned in this article. The Agent Survival Toolkit is an ecosystem initiative showcasing independently built projects on BNB Chain. This content is for informational purposes only and does not constitute any financial or investment advice. Please conduct your own independent research and assess potential risks before interacting with any related projects.
Gate Research Institute: Multi-Agent LLM Trading Framework Significantly Outperforms Buy & Hold Strategy in BTC Backtesting
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.