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On May 31, the Islamic Revolutionary Guard Corps (IRGC) of Iran reported that a US MQ-1 drone entered Iranian territorial waters in the early hours of the day. The Iranian forces quickly detected it and shot it down using missiles. (Xinhua News Agency)

[IRGC Claims to Have Shot Down US MQ-1 Drone]On May 31, the Islamic Revolutionary Guard Corps (IRGC) of Iran reported that a US MQ-1 drone entered Iranian territorial waters in the early hours of the day. The Iranian forces quickly detected it and shot it down using missiles. (Xinhua News Agency)

IRGC Claims to Have Shot Down US MQ-1 Drone

According to monitoring by Beating, The Wall Street Journal revealed that significant interpersonal conflicts arose internally between OpenAI President Greg Brockman and the Amodei siblings (Dario Amodei and Daniela Amodei) before the mass exodus of Anthropic's core team. Brockman, known for his strong and overreaching 'bigfooting' management style, sought to avoid administrative meetings and aimed to spend 80% of his time coding, but often intervened forcefully in team projects without understanding the context, trampling on others' work and drawing numerous complaints from colleagues. Management friction peaked in the early days of OpenAI. Brockman's excessive interference in a project led to a top researcher being marginalized, which infuriated the then project leader and future Anthropic founder Dario Amodei. Amodei then took extreme precautionary measures, directly banning Brockman from intervening in the development of the predecessor project that led to the creation of ChatGPT. This workplace rift not only contributed to Amodei's team's later departure to establish Anthropic but also set the stage for future power struggles. During the Thanksgiving board 'coup' in 2023, one of the core reasons given for co-founder Ilya Sutskever's ousting of Sam Altman was that Altman 'could not control Brockman.' Their personal relationship was equally dramatic. In 2018, Brockman fell in love at first sight with Anna and began dating. Daniela Amodei, now the president of Anthropic, firmly believed they would not last and directly bet Brockman $100, insisting Anna was not his lifelong partner. A year later, the couple married at OpenAI headquarters, where a robotic arm served as the best man and delivered the wedding rings, while Daniela presented a $100 check as promised. After the split between the tech giants, their once-close relationship ultimately shattered. As Ilya Sutskever, who had been inseparable from Brockman, officially left OpenAI at the end of 2024, both parties engaged in extremely painful negotiations to retain each other. Ultimately, Brockman's wife Anna sent a cactus to Sutskever as a final farewell gift, symbolizing the end of an era.

[Past Conflicts Between Anthropic Founder and OpenAI President: Dario Banned Brockman from ChatGPT's Predecessor Development]According to monitoring by Beating, The Wall Street Journal revealed that significant interpersonal conflicts arose internally between OpenAI President Greg Brockman and the Amodei siblings (Dario Amodei and Daniela Amodei) before the mass exodus of Anthropic's core team. Brockman, known for his strong and overreaching 'bigfooting' management style, sought to avoid administrative meetings and aimed to spend 80% of his time coding, but often intervened forcefully in team projects without understanding the context, trampling on others' work and drawing numerous complaints from colleagues. Management friction peaked in the early days of OpenAI. Brockman's excessive interference in a project led to a top researcher being marginalized, which infuriated the then project leader and future Anthropic founder Dario Amodei. Amodei then took extreme precautionary measures, directly banning Brockman from intervening in the development of the predecessor project that led to the creation of ChatGPT. This workplace rift not only contributed to Amodei's team's later departure to establish Anthropic but also set the stage for future power struggles. During the Thanksgiving board 'coup' in 2023, one of the core reasons given for co-founder Ilya Sutskever's ousting of Sam Altman was that Altman 'could not control Brockman.' Their personal relationship was equally dramatic. In 2018, Brockman fell in love at first sight with Anna and began dating. Daniela Amodei, now the president of Anthropic, firmly believed they would not last and directly bet Brockman $100, insisting Anna was not his lifelong partner. A year later, the couple married at OpenAI headquarters, where a robotic arm served as the best man and delivered the wedding rings, while Daniela presented a $100 check as promised. After the split between the tech giants, their once-close relationship ultimately shattered. As Ilya Sutskever, who had been inseparable from Brockman, officially left OpenAI at the end of 2024, both parties engaged in extremely painful negotiations to retain each other. Ultimately, Brockman's wife Anna sent a cactus to Sutskever as a final farewell gift, symbolizing the end of an era.

Past Conflicts Between Anthropic Founder and OpenAI President: Dario Banned Brockman from ChatGPT's Predecessor Development

On May 31, according to monitoring by HyperInsight, as the market rebounded this morning, Maji chose to increase its Ethereum long positions again. As of the time of publication, its holding value has risen to $10.36 million, with a liquidation price of $1993.9, and the current price is less than 2% away from the liquidation price.

[Maji Increases Ethereum Long Positions, Holding Value Reaches $10.36 Million, Less Than 2% from Liquidation Price]On May 31, according to monitoring by HyperInsight, as the market rebounded this morning, Maji chose to increase its Ethereum long positions again. As of the time of publication, its holding value has risen to $10.36 million, with a liquidation price of $1993.9, and the current price is less than 2% away from the liquidation price.

Maji Increases Ethereum Long Positions, Holding Value Reaches $10.36 Million, Less Than 2% from Liquidation Price

According to monitoring by Dongcha Beating, Trajectory, in collaboration with UC Berkeley's Sky Computing Lab and Anyscale, has announced the launch of the open-source multi-LoRA reinforcement learning training platform SkyRL, along with the release of the concurrent training architecture Multi-LoRA Training that supports continuous learning for large models. In traditional large model fine-tuning experiments, developers must individually schedule GPU nodes for each fine-tuning task, frequently loading and unloading the same massive model on servers. For instance, training a giant model with 397 billion parameters traditionally requires multiple sets of computing nodes to repeatedly load hundreds of GB of weight files, resulting in significant waste of computational time and resources. The Multi-LoRA architecture completely eliminates the dilemma of repeated cold starts. The system opts to keep a shared model base online in GPU memory and manages multiple different fine-tuning experiments as lightweight 'adapter modules.' During the training phase, the system serially executes forward and backward propagation by quickly swapping in and out the states of each adapter between the CPU and GPU, thus avoiding the cold start overhead of repeatedly loading the giant base model. Combined with concurrent processing during the inference phase, the overall experimental throughput for multi-tasking is significantly enhanced. Test data shows that, while ensuring no degradation in large model performance, the end-to-end experimental throughput has increased by up to 2.81 times, and the throughput within absolute time on a single node has improved by approximately 3.25 times. The training code is now open-sourced in the SkyRL repository, aiming to assist developers in achieving hourly self-evolution of large models with minimal memory cost through real-time production data.

[Large Model Achieves Hourly Self-Evolution; Open Source SkyRL Concurrent Training Stack Delivers 2.8x Efficiency Boost]According to monitoring by Dongcha Beating, Trajectory, in collaboration with UC Berkeley's Sky Computing Lab and Anyscale, has announced the launch of the open-source multi-LoRA reinforcement learning training platform SkyRL, along with the release of the concurrent training architecture Multi-LoRA Training that supports continuous learning for large models. In traditional large model fine-tuning experiments, developers must individually schedule GPU nodes for each fine-tuning task, frequently loading and unloading the same massive model on servers. For instance, training a giant model with 397 billion parameters traditionally requires multiple sets of computing nodes to repeatedly load hundreds of GB of weight files, resulting in significant waste of computational time and resources. The Multi-LoRA architecture completely eliminates the dilemma of repeated cold starts. The system opts to keep a shared model base online in GPU memory and manages multiple different fine-tuning experiments as lightweight 'adapter modules.' During the training phase, the system serially executes forward and backward propagation by quickly swapping in and out the states of each adapter between the CPU and GPU, thus avoiding the cold start overhead of repeatedly loading the giant base model. Combined with concurrent processing during the inference phase, the overall experimental throughput for multi-tasking is significantly enhanced. Test data shows that, while ensuring no degradation in large model performance, the end-to-end experimental throughput has increased by up to 2.81 times, and the throughput within absolute time on a single node has improved by approximately 3.25 times. The training code is now open-sourced in the SkyRL repository, aiming to assist developers in achieving hourly self-evolution of large models with minimal memory cost through real-time production data.

Large Model Achieves Hourly Self-Evolution; Open Source SkyRL Concurrent Training Stack Delivers 2.8x Efficiency Boost

On May 31, local time May 30, US President Trump stated in an interview aired on Fox News that the US and Iran are close to reaching a 'very good agreement.' Trump also mentioned that we will get a 'better agreement,' which should have been achieved more quickly. However, he indicated that if an agreement cannot be reached, he would ask the 'War Department' (referring to the US Department of Defense) to intervene. Trump stated that if he does not get what he wants, 'we will end it another way.' He also emphasized that by signing an agreement, we can ensure the navigation of the Strait of Hormuz and ensure that Iran does not have nuclear weapons. When discussing the agreement with Iran, Trump stressed that he is not in a hurry. 'If you are in a hurry, you cannot reach a good agreement.' (CCTV News)

[Trump: If US-Iran Agreement Fails, 'War Department' Will Intervene]On May 31, local time May 30, US President Trump stated in an interview aired on Fox News that the US and Iran are close to reaching a 'very good agreement.' Trump also mentioned that we will get a 'better agreement,' which should have been achieved more quickly. However, he indicated that if an agreement cannot be reached, he would ask the 'War Department' (referring to the US Department of Defense) to intervene. Trump stated that if he does not get what he wants, 'we will end it another way.' He also emphasized that by signing an agreement, we can ensure the navigation of the Strait of Hormuz and ensure that Iran does not have nuclear weapons. When discussing the agreement with Iran, Trump stressed that he is not in a hurry. 'If you are in a hurry, you cannot reach a good agreement.' (CCTV News)

Trump: If US-Iran Agreement Fails, 'War Department' Will Intervene

According to monitoring by Dongcha Beating, Weijie Su, an alumnus of Peking University's School of Mathematical Sciences from the class of 2007, a PhD in Statistics from Stanford University, and a full professor at the Wharton School of the University of Pennsylvania, announced on social media platform X that he has officially joined OpenAI during his sabbatical from Wharton. Su stated that he will participate in AI model training upon returning to the Bay Area after ten years. Additionally, after a decade of academic efforts, Su has been officially promoted to full professor in the Department of Statistics and Data Science at the Wharton School. Su has made significant contributions in the fields of statistics, machine learning optimization, and theoretical safety of large language models (LLMs). He received the COPSS Presidents' Award in 2026, one of the highest honors in the international statistics field. The COPSS official commendation highly praised his work in establishing the statistical foundations of generative AI (including watermarking mechanisms for LLMs, human preference alignment, and ranking theory), advancing differential privacy data analysis methods in the 2020 U.S. Census, and foundational contributions to deep learning theory, convex optimization, and high-dimensional statistical inference. In academic research, Su has made several advancements in reshaping the underlying statistics of large models. In watermark detection for large models, he formalized watermarking techniques as statistical hypothesis testing problems and proposed the 'Truncated Goodness-of-Fit Test' (Tr-GoF). Tr-GoF enhances the robustness of watermark detection for modified and trimmed texts without relying on prior knowledge of human editing processes or precise probabilities. Regarding preference alignment (RLHF), Su pointed out that traditional RLHF, based on ranking optimization mechanisms, is prone to 'Preference Collapse,' which overlooks the preferences of minority groups and leads to a homogenized reward distribution across different prompts. To preserve response diversity, the team derived a 'Preference Matching' regularization scheme by solving ordinary differential equations (ODEs), maximizing human rewards while avoiding uniform responses, and explored a game-theoretic perspective of 'Nash Learning Based on Human Feedback' (NLHF) as an alternative to traditional scalar reward alignment. Su graduated with a bachelor's degree from Peking University's School of Mathematical Sciences and is referred to as part of the 'Golden Second Generation' (or 'Platinum First Generation') of Peking University's mathematics department by media and scholars. Compared to the 'Golden Generation' of the class of 2000, known for pure mathematical research (such as Yun Zhiwei, Zhang Wei, and Xu Chenyang), Su and his peers from the class of 2007 exhibit broader interdisciplinary characteristics, expanding the boundaries of mathematics into statistics, data science, and artificial intelligence.

[Weijie Su Joins OpenAI for Model Training and Promoted to Full Professor at Wharton]According to monitoring by Dongcha Beating, Weijie Su, an alumnus of Peking University's School of Mathematical Sciences from the class of 2007, a PhD in Statistics from Stanford University, and a full professor at the Wharton School of the University of Pennsylvania, announced on social media platform X that he has officially joined OpenAI during his sabbatical from Wharton. Su stated that he will participate in AI model training upon returning to the Bay Area after ten years. Additionally, after a decade of academic efforts, Su has been officially promoted to full professor in the Department of Statistics and Data Science at the Wharton School. Su has made significant contributions in the fields of statistics, machine learning optimization, and theoretical safety of large language models (LLMs). He received the COPSS Presidents' Award in 2026, one of the highest honors in the international statistics field. The COPSS official commendation highly praised his work in establishing the statistical foundations of generative AI (including watermarking mechanisms for LLMs, human preference alignment, and ranking theory), advancing differential privacy data analysis methods in the 2020 U.S. Census, and foundational contributions to deep learning theory, convex optimization, and high-dimensional statistical inference. In academic research, Su has made several advancements in reshaping the underlying statistics of large models. In watermark detection for large models, he formalized watermarking techniques as statistical hypothesis testing problems and proposed the 'Truncated Goodness-of-Fit Test' (Tr-GoF). Tr-GoF enhances the robustness of watermark detection for modified and trimmed texts without relying on prior knowledge of human editing processes or precise probabilities. Regarding preference alignment (RLHF), Su pointed out that traditional RLHF, based on ranking optimization mechanisms, is prone to 'Preference Collapse,' which overlooks the preferences of minority groups and leads to a homogenized reward distribution across different prompts. To preserve response diversity, the team derived a 'Preference Matching' regularization scheme by solving ordinary differential equations (ODEs), maximizing human rewards while avoiding uniform responses, and explored a game-theoretic perspective of 'Nash Learning Based on Human Feedback' (NLHF) as an alternative to traditional scalar reward alignment. Su graduated with a bachelor's degree from Peking University's School of Mathematical Sciences and is referred to as part of the 'Golden Second Generation' (or 'Platinum First Generation') of Peking University's mathematics department by media and scholars. Compared to the 'Golden Generation' of the class of 2000, known for pure mathematical research (such as Yun Zhiwei, Zhang Wei, and Xu Chenyang), Su and his peers from the class of 2007 exhibit broader interdisciplinary characteristics, expanding the boundaries of mathematics into statistics, data science, and artificial intelligence.

Weijie Su Joins OpenAI for Model Training and Promoted to Full Professor at Wharton

On May 31, recent DeFi attacks targeting protocols such as Thorchain, Drift, and KelpDAO have relied on flash loans, a mechanism that does not exist on the XRP Ledger. Due to the atomic nature of XRPL transactions and the inability to include composable calls within transactions, flash loan attacks are structurally impossible on this network. As XRPL advances its AMM upgrade and the tokenization of real-world assets continues to expand, institutional investors may weigh this built-in resistance to attacks against the deeper liquidity and more mature DeFi ecosystem of Ethereum.

[New Proposal on XRP Ledger to Prevent Flash Loan Attacks Causing Hundreds of Millions in DeFi Losses]On May 31, recent DeFi attacks targeting protocols such as Thorchain, Drift, and KelpDAO have relied on flash loans, a mechanism that does not exist on the XRP Ledger. Due to the atomic nature of XRPL transactions and the inability to include composable calls within transactions, flash loan attacks are structurally impossible on this network. As XRPL advances its AMM upgrade and the tokenization of real-world assets continues to expand, institutional investors may weigh this built-in resistance to attacks against the deeper liquidity and more mature DeFi ecosystem of Ethereum.

New Proposal on XRP Ledger to Prevent Flash Loan Attacks Causing Hundreds of Millions in DeFi Losses

genius Surges 10.06% in 24H, Currently at 0.4956 USDT

【genius currently trades at 0.4956 USDT, up 10.06% in 24h, according to HTX market data.】genius Surges 10.06% in 24H, Currently at 0.4956 USDT

genius currently trades at 0.4956 USDT, up 10.06% in 24h, according to HTX market data.

On May 31, according to Tokens on Solana, Strategy's perpetual preferred stock STRCx has been launched on Kamino, becoming the first lending market on Solana to support tokenized STRC.

[Strategy's Perpetual Preferred Stock STRCx Launched on Kamino]On May 31, according to Tokens on Solana, Strategy's perpetual preferred stock STRCx has been launched on Kamino, becoming the first lending market on Solana to support tokenized STRC.

Strategy's Perpetual Preferred Stock STRCx Launched on Kamino

According to BTC.com data, Bitcoin adjusted the mining difficulty at block height 756000, and the mining difficulty was lowered by 2.14% to 31.36T, the first time in the past two months. Bitcoin mining difficulty hit an all-time high of 32.05 T in the last adjustment.

Detail

【Bitcoin mining difficulty decreased by 2.14% to 31.36 T】According to BTC.com data, Bitcoin adjusted the mining difficulty at block height 756000, and the mining difficulty was lowered by 2.14% to 31.36T, the first time in the past two months. Bitcoin mining difficulty hit an all-time high of 32.05 T in the last adjustment.

Bitcoin mining difficulty decreased by 2.14% to 31.36 T

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Bitcoin mining difficulty decreased by 2.14% to 31.36 T

Disclaimer：Il contenuto di cui sopra non rappresenta le posizioni di HTX.，HTX non fornisce alcuna raccomandazione di trading.。

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