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On May 31, local time, during an interview aired on Fox News on the evening of May 30, U.S. President Donald Trump stated that the U.S. and Iran are close to reaching a "very good agreement." Trump also mentioned that they would get a "better agreement," which should have come sooner. However, he indicated that if an agreement cannot be reached, he would ask the "War Department" (i.e., the U.S. Department of Defense) to intervene. Trump stated that if he does not get what he wants, "we will end it in another way." He also emphasized that by signing an agreement, they can ensure the navigation of the Strait of Hormuz and ensure that Iran does not possess 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, during an interview aired on Fox News on the evening of May 30, U.S. President Donald Trump stated that the U.S. and Iran are close to reaching a "very good agreement." Trump also mentioned that they would get a "better agreement," which should have come sooner. However, he indicated that if an agreement cannot be reached, he would ask the "War Department" (i.e., the U.S. Department of Defense) to intervene. Trump stated that if he does not get what he wants, "we will end it in another way." He also emphasized that by signing an agreement, they can ensure the navigation of the Strait of Hormuz and ensure that Iran does not possess 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 30, local time, the situation between Lebanon and Israel deteriorated sharply. Following a day of unsuccessful military negotiations and Israel's refusal to withdraw its troops, the Israeli military continued to expand its military offensive across Lebanon on the 30th. In response, Hezbollah launched a large-scale counterattack. The Israeli Home Front Command announced on the 30th that it would tighten civil defense policies in northern Israel to address rocket and drone attacks from Hezbollah. Observations by reporters indicate that both sides are caught in a vicious cycle of stalled negotiations and escalating conflict, with the risk of regional conflict continuing to rise. Israeli media believe that the developments on the 30th indicate that Israel is no longer hiding its true intentions, showing an unwillingness to compromise at the negotiating table while maintaining military pressure on the battlefield. However, the cost to its domestic security is also increasing significantly. (CCTV International News)

[Israeli Military Offensive Faces Retaliation as Lebanon-Israel Conflict Escalates]On May 30, local time, the situation between Lebanon and Israel deteriorated sharply. Following a day of unsuccessful military negotiations and Israel's refusal to withdraw its troops, the Israeli military continued to expand its military offensive across Lebanon on the 30th. In response, Hezbollah launched a large-scale counterattack. The Israeli Home Front Command announced on the 30th that it would tighten civil defense policies in northern Israel to address rocket and drone attacks from Hezbollah. Observations by reporters indicate that both sides are caught in a vicious cycle of stalled negotiations and escalating conflict, with the risk of regional conflict continuing to rise. Israeli media believe that the developments on the 30th indicate that Israel is no longer hiding its true intentions, showing an unwillingness to compromise at the negotiating table while maintaining military pressure on the battlefield. However, the cost to its domestic security is also increasing significantly. (CCTV International News)

Israeli Military Offensive Faces Retaliation as Lebanon-Israel Conflict Escalates

On May 31, according to crypto analyst Alicharts, the 12-hour chart for Bitcoin has shown a TD Sequential buy signal, indicating a possible rebound to $75,000. TD Sequential is a momentum indicator developed by Tom DeMark, used to identify oversold reversal points. However, its signals need to be validated with trading volume, ETF fund flows, and the macro environment. Currently, Bitcoin's price is fluctuating in the $73,000-$74,000 range.

[Bitcoin Shows Buy Signal, Potential to Rise to $75,000]On May 31, according to crypto analyst Alicharts, the 12-hour chart for Bitcoin has shown a TD Sequential buy signal, indicating a possible rebound to $75,000. TD Sequential is a momentum indicator developed by Tom DeMark, used to identify oversold reversal points. However, its signals need to be validated with trading volume, ETF fund flows, and the macro environment. Currently, Bitcoin's price is fluctuating in the $73,000-$74,000 range.

Bitcoin Shows Buy Signal, Potential to Rise to $75,000

According to monitoring by Dongcha Beating, OpenAI's Chief Revenue Officer Denise Dresser has been aggressively advancing enterprise-level commercialization since joining the company last December. She has taken over the partnership and advertising negotiation business previously managed by former COO Brad Lightcap. In external competition, rival Anthropic completed a new funding round on Thursday with a valuation of $900 billion, surpassing OpenAI's current valuation of $730 billion for the first time, intensifying the competition between the two large model giants in the enterprise market. To accelerate the capture of corporate budgets, Dresser is rapidly assembling a large 'Salesforce and Slack team' within OpenAI. Former Slack Chief Customer Officer Peter Doolan joined in April as the Global Head of AI Transformation, and former ServiceNow Chief Marketing Officer and Salesforce Senior Vice President Colin Fleming also joined this month as Chief Marketing Officer for commercial business. Additionally, the team has brought in at least six senior sales and channel executives, including former Google Channel Vice President Colleen Kapase. Under Dresser's leadership, OpenAI has successfully streamlined the chaotic joint sales process in collaboration with Databricks and has signed benchmark clients such as car rental giant Hertz and consulting giant KPMG this year. The clash between the two large model vendors is not only a valuation battle but also a fierce competition of business models and infrastructure. Financial forecasts indicate that OpenAI expects to increase the proportion of enterprise revenue from 40% at the beginning of the year to 50% by the end of 2026; in contrast, over 80% of Anthropic's revenue comes from enterprise clients, achieving high corporate loyalty by focusing on optimizing database connections and long text parsing for commercial scenarios. In an internal memo, Dresser launched a fierce attack on the competitor, accusing Anthropic of limiting computing power purchases as a 'strategic mistake,' leading to frequent product throttling and stability issues, and criticized their marketing narrative as 'built on fear and limitations.'

[OpenAI's Chief Revenue Officer Expands Authority, Leads Salesforce Team Against Anthropic]According to monitoring by Dongcha Beating, OpenAI's Chief Revenue Officer Denise Dresser has been aggressively advancing enterprise-level commercialization since joining the company last December. She has taken over the partnership and advertising negotiation business previously managed by former COO Brad Lightcap. In external competition, rival Anthropic completed a new funding round on Thursday with a valuation of $900 billion, surpassing OpenAI's current valuation of $730 billion for the first time, intensifying the competition between the two large model giants in the enterprise market. To accelerate the capture of corporate budgets, Dresser is rapidly assembling a large 'Salesforce and Slack team' within OpenAI. Former Slack Chief Customer Officer Peter Doolan joined in April as the Global Head of AI Transformation, and former ServiceNow Chief Marketing Officer and Salesforce Senior Vice President Colin Fleming also joined this month as Chief Marketing Officer for commercial business. Additionally, the team has brought in at least six senior sales and channel executives, including former Google Channel Vice President Colleen Kapase. Under Dresser's leadership, OpenAI has successfully streamlined the chaotic joint sales process in collaboration with Databricks and has signed benchmark clients such as car rental giant Hertz and consulting giant KPMG this year. The clash between the two large model vendors is not only a valuation battle but also a fierce competition of business models and infrastructure. Financial forecasts indicate that OpenAI expects to increase the proportion of enterprise revenue from 40% at the beginning of the year to 50% by the end of 2026; in contrast, over 80% of Anthropic's revenue comes from enterprise clients, achieving high corporate loyalty by focusing on optimizing database connections and long text parsing for commercial scenarios. In an internal memo, Dresser launched a fierce attack on the competitor, accusing Anthropic of limiting computing power purchases as a 'strategic mistake,' leading to frequent product throttling and stability issues, and criticized their marketing narrative as 'built on fear and limitations.'

OpenAI's Chief Revenue Officer Expands Authority, Leads Salesforce Team Against Anthropic

According to monitoring by Dongcha Beating, Microsoft plans to unveil several self-developed AI models at its annual Build developer conference in San Francisco next week. To reclaim the AI coding market that has been encroached upon by competitors, Microsoft is focusing on launching self-developed programming models to enhance the competitiveness of GitHub Copilot and counter the market erosion caused by Cursor and Claude Code. In addition to programming models, Microsoft is also planning to introduce various self-developed large models with different parameter specifications, covering vertical tasks such as transcription, reasoning, speech, and image processing. Mustafa Suleyman, CEO of Microsoft AI, has seen his research and development team fail to produce a model that tops mainstream industry rankings in the past two years, making this release a significant test of the team's self-development capabilities. Previously, an exclusive agreement with OpenAI restricted Microsoft's ability to develop human-level AI until April 2026, which limited self-development efforts; it was only after the two companies revised their cooperation terms in April of this year that Microsoft regained its freedom to innovate. In software services, Microsoft has long relied on OpenAI's technology, which is licensed free of charge until 2032, as well as paid procurement of Anthropic models to drive GitHub Copilot and 365 Copilot. The high costs of external procurement have forced Microsoft to raise the prices of some GitHub Copilot packages and impose usage limits on Anthropic models. By distributing lower-cost self-developed models through the Azure cloud platform, Microsoft can offer developers an affordable alternative to Claude, thereby reducing the daily operational costs of AI features in Office software. More importantly, when the free license with OpenAI expires in about six years, self-developed large models will become a key asset for Microsoft to break free from high-priced suppliers and maintain core independence.

[Microsoft to Launch Self-Developed Programming Models Next Week to Counter Cursor and Claude's Encroachment on Copilot]According to monitoring by Dongcha Beating, Microsoft plans to unveil several self-developed AI models at its annual Build developer conference in San Francisco next week. To reclaim the AI coding market that has been encroached upon by competitors, Microsoft is focusing on launching self-developed programming models to enhance the competitiveness of GitHub Copilot and counter the market erosion caused by Cursor and Claude Code. In addition to programming models, Microsoft is also planning to introduce various self-developed large models with different parameter specifications, covering vertical tasks such as transcription, reasoning, speech, and image processing. Mustafa Suleyman, CEO of Microsoft AI, has seen his research and development team fail to produce a model that tops mainstream industry rankings in the past two years, making this release a significant test of the team's self-development capabilities. Previously, an exclusive agreement with OpenAI restricted Microsoft's ability to develop human-level AI until April 2026, which limited self-development efforts; it was only after the two companies revised their cooperation terms in April of this year that Microsoft regained its freedom to innovate. In software services, Microsoft has long relied on OpenAI's technology, which is licensed free of charge until 2032, as well as paid procurement of Anthropic models to drive GitHub Copilot and 365 Copilot. The high costs of external procurement have forced Microsoft to raise the prices of some GitHub Copilot packages and impose usage limits on Anthropic models. By distributing lower-cost self-developed models through the Azure cloud platform, Microsoft can offer developers an affordable alternative to Claude, thereby reducing the daily operational costs of AI features in Office software. More importantly, when the free license with OpenAI expires in about six years, self-developed large models will become a key asset for Microsoft to break free from high-priced suppliers and maintain core independence.

Microsoft to Launch Self-Developed Programming Models Next Week to Counter Cursor and Claude's Encroachment on Copilot

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

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

Clause de non-responsabilité：Le contenu ci-dessus ne représente pas les positions de HTX.，HTX ne fournit pas de recommandations de trading.。

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