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On May 14, U.S. Treasury Secretary Becerra stated that there may be 'hot' inflation data in the coming one to two months. Following this period, a decline in inflation is expected. (Jin Shi)

[Becerra: 'Hot' Inflation Data Expected in the Coming One to Two Months]On May 14, U.S. Treasury Secretary Becerra stated that there may be 'hot' inflation data in the coming one to two months. Following this period, a decline in inflation is expected. (Jin Shi)

Becerra: 'Hot' Inflation Data Expected in the Coming One to Two Months

On May 14, U.S. Treasury Secretary Bessent stated that he believes Walsh will maintain an open attitude regarding personnel changes at the Federal Reserve.

[Bessent: Believes Walsh Will Maintain an Open Attitude Regarding Fed Personnel Changes]On May 14, U.S. Treasury Secretary Bessent stated that he believes Walsh will maintain an open attitude regarding personnel changes at the Federal Reserve.

Bessent: Believes Walsh Will Maintain an Open Attitude Regarding Fed Personnel Changes

CUDIS fluctuated by 51.64% within 24 hours, current price $0.008363

【CUDIS fluctuates over 50% in 24 hours!】CUDIS fluctuated by 51.64% within 24 hours, current price $0.008363

CUDIS fluctuates over 50% in 24 hours!

On May 14, market news: Anthropic has launched Claude aimed at small businesses.

[Anthropic Launches Claude for Small Businesses]On May 14, market news: Anthropic has launched Claude aimed at small businesses.

Anthropic Launches Claude for Small Businesses

According to monitoring by Dongcha Beating and reported by Bloomberg, sources have revealed that DayOne Data Centers is considering expanding its Series C funding to over $4 billion amid a surge in demand for related infrastructure assets driven by the AI boom. The new funding round is expected to be approximately double the amount raised in the previous round that ended in January of this year, with the majority of funds coming from existing investors. As discussions are still in a private phase, the sources requested anonymity.

[Data Center Giant DayOne Considers Expanding Series C Funding to $4 Billion]According to monitoring by Dongcha Beating and reported by Bloomberg, sources have revealed that DayOne Data Centers is considering expanding its Series C funding to over $4 billion amid a surge in demand for related infrastructure assets driven by the AI boom. The new funding round is expected to be approximately double the amount raised in the previous round that ended in January of this year, with the majority of funds coming from existing investors. As discussions are still in a private phase, the sources requested anonymity.

Data Center Giant DayOne Considers Expanding Series C Funding to $4 Billion

According to monitoring by Dongcha Beating, GPU computing and deployment platform Modal is in discussions for a new round of financing. As reported by The Information, its target valuation is approximately $4.5 billion, representing an 80% premium over the previous funding round a few months ago. The company expects to raise between $150 million and $250 million, with existing investors Accel and Redpoint Ventures participating in the negotiations. The high valuation is directly supported by a surge in revenue, with Modal's annualized income reaching around $300 million, five times that of last fall. This growth is primarily driven by the demand for AI Agent sandboxes, which provide developers with an isolated software environment to safely execute code without disrupting the main system. As companies like Ramp and Lovable extensively use sandboxes to develop Agents and conduct reinforcement learning, Modal has directly benefited from the explosion in application layer demand. However, behind this high growth, the significant demand for computing power from leading companies like OpenAI has driven up spot prices for GPUs, which may severely compress the profit margins for platforms like Modal that rent GPUs from large cloud providers and then lease them to developers.

[Modal Seeks $4.5 Billion Valuation in New Funding Round as Annual Revenue Soars to $300 Million]According to monitoring by Dongcha Beating, GPU computing and deployment platform Modal is in discussions for a new round of financing. As reported by The Information, its target valuation is approximately $4.5 billion, representing an 80% premium over the previous funding round a few months ago. The company expects to raise between $150 million and $250 million, with existing investors Accel and Redpoint Ventures participating in the negotiations. The high valuation is directly supported by a surge in revenue, with Modal's annualized income reaching around $300 million, five times that of last fall. This growth is primarily driven by the demand for AI Agent sandboxes, which provide developers with an isolated software environment to safely execute code without disrupting the main system. As companies like Ramp and Lovable extensively use sandboxes to develop Agents and conduct reinforcement learning, Modal has directly benefited from the explosion in application layer demand. However, behind this high growth, the significant demand for computing power from leading companies like OpenAI has driven up spot prices for GPUs, which may severely compress the profit margins for platforms like Modal that rent GPUs from large cloud providers and then lease them to developers.

Modal Seeks $4.5 Billion Valuation in New Funding Round as Annual Revenue Soars to $300 Million

According to monitoring by Dongcha Beating, AI evaluation agency Vals AI has released the second generation of its financial agent benchmark test (Finance Agent v2). This is an end-to-end test simulating the workflow of a junior financial analyst, consisting of 927 expert-reviewed questions. The difficulty of the new test has significantly increased, with GPT 5.5 achieving only 51.76% accuracy, narrowly surpassing Claude Opus 4.7 (51.51%) and Claude Sonnet 4.6 (51.03%) in a tightly contested score. Unlike single-turn Q&A, this test requires the model to autonomously find relevant paragraphs within hundreds of pages of 10-K and 10-Q financial reports, handle adjustments across multiple years of financial statements, and complete multi-step calculations with precise intermediate figures. Vals AI revealed that under a strict scoring standard of 'must be completely correct,' all leading models fell below 40% accuracy; in the most challenging categories of 'financial modeling' and 'precedent analysis,' the highest score was only 23%. Among other models, Kimi K2.6 ranked fifth with 44.87%, making it the highest-scoring domestic model; it was followed closely by GLM 5.1 (44.79%) and DeepSeek V4 (44.08%). Additionally, the official 'fastest speed' label was awarded to Claude Opus 4.7 (taking 360 seconds per instance), while GLM 5.1 received the 'most budget-friendly' label (costing $0.62 per instance). The collective drop in scores (the previous generation test saw Opus 4.7 scoring 64.4%) underscores one point: current AI can handle simple retrieval tasks, but in the complex financial sector, which requires adherence to specific industry practices and high precision in numerical accuracy, it is still far from replacing human analysts.

[Can AI Replace Financial Analysts? Vals AI's New Test Fails Across the Board, GPT 5.5 Barely Passes 50% Accuracy]According to monitoring by Dongcha Beating, AI evaluation agency Vals AI has released the second generation of its financial agent benchmark test (Finance Agent v2). This is an end-to-end test simulating the workflow of a junior financial analyst, consisting of 927 expert-reviewed questions. The difficulty of the new test has significantly increased, with GPT 5.5 achieving only 51.76% accuracy, narrowly surpassing Claude Opus 4.7 (51.51%) and Claude Sonnet 4.6 (51.03%) in a tightly contested score. Unlike single-turn Q&A, this test requires the model to autonomously find relevant paragraphs within hundreds of pages of 10-K and 10-Q financial reports, handle adjustments across multiple years of financial statements, and complete multi-step calculations with precise intermediate figures. Vals AI revealed that under a strict scoring standard of 'must be completely correct,' all leading models fell below 40% accuracy; in the most challenging categories of 'financial modeling' and 'precedent analysis,' the highest score was only 23%. Among other models, Kimi K2.6 ranked fifth with 44.87%, making it the highest-scoring domestic model; it was followed closely by GLM 5.1 (44.79%) and DeepSeek V4 (44.08%). Additionally, the official 'fastest speed' label was awarded to Claude Opus 4.7 (taking 360 seconds per instance), while GLM 5.1 received the 'most budget-friendly' label (costing $0.62 per instance). The collective drop in scores (the previous generation test saw Opus 4.7 scoring 64.4%) underscores one point: current AI can handle simple retrieval tasks, but in the complex financial sector, which requires adherence to specific industry practices and high precision in numerical accuracy, it is still far from replacing human analysts.

Can AI Replace Financial Analysts? Vals AI's New Test Fails Across the Board, GPT 5.5 Barely Passes 50% Accuracy

According to monitoring by Dongcha Beating, LangChain announced two core upgrades aimed at addressing the challenges of Agent debugging at its Interrupt conference: the new underlying database SmithDB and the automated troubleshooting tool LangSmith Engine. The old underlying architecture struggled to handle the increasing volume of trace data. The newly released SmithDB abandons traditional local disk solutions in favor of an object storage-based approach. This change has improved query performance for core workloads by up to 15 times. Built on this faster foundation, LangSmith Engine automates the process of 'bug fixing.' It continuously monitors the production environment in the background, automatically categorizing failed calls and pinpointing which segment of code is problematic. Furthermore, it drafts pull requests for developers to fix vulnerabilities, along with corresponding test sets (evals). For complex Agents, manually sifting through vast amounts of trace data to identify patterns has become the biggest efficiency bottleneck. This update from LangChain effectively creates a fully automated pipeline for the debugging loop of 'error discovery - code localization - automatic repair - supplementary testing.'

[LangChain Launches Fully Automated Bug Detection Tool: Automatically Locates Issues and Pushes Code Fixes to Users]According to monitoring by Dongcha Beating, LangChain announced two core upgrades aimed at addressing the challenges of Agent debugging at its Interrupt conference: the new underlying database SmithDB and the automated troubleshooting tool LangSmith Engine. The old underlying architecture struggled to handle the increasing volume of trace data. The newly released SmithDB abandons traditional local disk solutions in favor of an object storage-based approach. This change has improved query performance for core workloads by up to 15 times. Built on this faster foundation, LangSmith Engine automates the process of 'bug fixing.' It continuously monitors the production environment in the background, automatically categorizing failed calls and pinpointing which segment of code is problematic. Furthermore, it drafts pull requests for developers to fix vulnerabilities, along with corresponding test sets (evals). For complex Agents, manually sifting through vast amounts of trace data to identify patterns has become the biggest efficiency bottleneck. This update from LangChain effectively creates a fully automated pipeline for the debugging loop of 'error discovery - code localization - automatic repair - supplementary testing.'

LangChain Launches Fully Automated Bug Detection Tool: Automatically Locates Issues and Pushes Code Fixes to Users

cc Hits New High of 0.1601 USDT, Up 5.05% in 24H

【cc soared to 0.1601 USDT, a new high with a 24-hour increase of 5.05%. It currently trades at 0.1601 USDT on HTX.】cc Hits New High of 0.1601 USDT, Up 5.05% in 24H

cc soared to 0.1601 USDT, a new high with a 24-hour increase of 5.05%. It currently trades at 0.1601 USDT on HTX.

According to Businesswire, Core Scientific, Inc. (NASDAQ: CORZ) ("Core Scientific" or “the Company”), a leader in high-performance blockchain computing data centers and software solutions, today announced production and operational updates for October 2022.
As of month-end, the Company operated approximately 243,000 ASIC servers for both colocation and self-mining, representing a total of 24.4 EH/s. The Company’s Georgia, Kentucky, North Carolina, and North Dakota data center facilities continue to operate at capacity. The Company has completed 287 MW of its data center build out in Texas.
During the month of October, the Company sold 2,285 Bitcoins at an average price of $19,639 per bitcoin for total proceeds of approximately $44.8 million. As of October 31, 2022, the Company held 62 bitcoins and approximately $32 million in cash.

Detail

【Core Scientific announced production and operational updates for October 2022】According to Businesswire, Core Scientific, Inc. (NASDAQ: CORZ) ("Core Scientific" or “the Company”), a leader in high-performance blockchain computing data centers and software solutions, today announced production and operational updates for October 2022.
As of month-end, the Company operated approximately 243,000 ASIC servers for both colocation and self-mining, representing a total of 24.4 EH/s. The Company’s Georgia, Kentucky, North Carolina, and North Dakota data center facilities continue to operate at capacity. The Company has completed 287 MW of its data center build out in Texas.
During the month of October, the Company sold 2,285 Bitcoins at an average price of $19,639 per bitcoin for total proceeds of approximately $44.8 million. As of October 31, 2022, the Company held 62 bitcoins and approximately $32 million in cash.

Core Scientific announced production and operational updates for October 2022

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Core Scientific announced production and operational updates for October 2022

Disclaimer：The content above does not represent HTX's positions.，HTX does not provide any trading recommendations.。

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