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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.

On May 14, Intel's stock fell over 4% in pre-market trading, currently priced at $115.4.

[Intel Stock Drops Over 4% in Pre-Market Trading]On May 14, Intel's stock fell over 4% in pre-market trading, currently priced at $115.4.

Intel Stock Drops Over 4% in Pre-Market Trading

On May 14, JPMorgan announced that it has raised the price target for Cisco (CSCO.O) from $96 to $120. (Jin Shi)

[JPMorgan Raises Cisco Price Target from $96 to $120]On May 14, JPMorgan announced that it has raised the price target for Cisco (CSCO.O) from $96 to $120. (Jin Shi)

JPMorgan Raises Cisco Price Target from $96 to $120

According to monitoring by Dongcha Beating, the hacker group TeamPCP, responsible for the npm supply chain poisoning incident, has open-sourced the complete source code of the involved worm Mini Shai-Hulud on GitHub under the MIT license. Security personnel discovered that this malware comes with a 'kill switch'; if affected developers revoke their stolen GitHub or cloud service credentials without thoroughly cleaning up residual files, the malicious program will immediately wipe the entire home directory of the computer. Researchers confirmed that the worm installs a daemon in the background on macOS or Linux, checking every minute whether the stolen credentials are still valid. Once it detects that the credentials have been rejected by the server (indicating that the victim has executed a credential rotation), the worm will immediately invoke the system's underlying shred command to irreversibly destroy and overwrite all writable files in the current user's home directory. This directly undermines conventional security response processes: the usual first reaction for companies facing credential leaks is to immediately revoke keys, but in this attack, it would directly trigger the destruction of local data. Currently, the worm has been confirmed to have infected nearly 400 versions of over 170 package libraries, including TanStack, UiPath, and Mistral AI. After the hacker publicly released the code under the account name PedroTortoriello and mocked 'open-sourcing this massacre', a third party even submitted a PR to add FreeBSD support. Microsoft has swiftly banned this account and removed all GitHub repositories and fork branches, but the leaked source code continues to circulate through other channels.

[Revocation of Credentials Triggers Physical Liquidation? TanStack's Stealth Worm Open-Sourced with Irreversible Kill Switch]According to monitoring by Dongcha Beating, the hacker group TeamPCP, responsible for the npm supply chain poisoning incident, has open-sourced the complete source code of the involved worm Mini Shai-Hulud on GitHub under the MIT license. Security personnel discovered that this malware comes with a 'kill switch'; if affected developers revoke their stolen GitHub or cloud service credentials without thoroughly cleaning up residual files, the malicious program will immediately wipe the entire home directory of the computer. Researchers confirmed that the worm installs a daemon in the background on macOS or Linux, checking every minute whether the stolen credentials are still valid. Once it detects that the credentials have been rejected by the server (indicating that the victim has executed a credential rotation), the worm will immediately invoke the system's underlying shred command to irreversibly destroy and overwrite all writable files in the current user's home directory. This directly undermines conventional security response processes: the usual first reaction for companies facing credential leaks is to immediately revoke keys, but in this attack, it would directly trigger the destruction of local data. Currently, the worm has been confirmed to have infected nearly 400 versions of over 170 package libraries, including TanStack, UiPath, and Mistral AI. After the hacker publicly released the code under the account name PedroTortoriello and mocked 'open-sourcing this massacre', a third party even submitted a PR to add FreeBSD support. Microsoft has swiftly banned this account and removed all GitHub repositories and fork branches, but the leaked source code continues to circulate through other channels.

Revocation of Credentials Triggers Physical Liquidation? TanStack's Stealth Worm Open-Sourced with Irreversible Kill Switch

On May 14, Tencent Cloud officially announced the open sourcing of TencentDB Agent Memory, which provides short-term memory compression and long-term personalized memory capabilities for Agent long-task scenarios. According to the introduction, TencentDB Agent Memory utilizes the technology of 'Context Offloading + Mermaid Task Canvas' to offload complete information to external storage while retaining key states and execution paths in a structured task graph. This allows the Agent to maintain a lightweight context during long tasks, while also supporting the layered tracing and recovery of original information. In experiments with continuous multi-task sessions, this solution achieved a maximum reduction of 61% in token consumption, while also improving the success rate of tasks in long-task scenarios.

[Tencent Open Sources Agent Memory, Reducing Token Consumption by Up to 61%]On May 14, Tencent Cloud officially announced the open sourcing of TencentDB Agent Memory, which provides short-term memory compression and long-term personalized memory capabilities for Agent long-task scenarios. According to the introduction, TencentDB Agent Memory utilizes the technology of 'Context Offloading + Mermaid Task Canvas' to offload complete information to external storage while retaining key states and execution paths in a structured task graph. This allows the Agent to maintain a lightweight context during long tasks, while also supporting the layered tracing and recovery of original information. In experiments with continuous multi-task sessions, this solution achieved a maximum reduction of 61% in token consumption, while also improving the success rate of tasks in long-task scenarios.

Tencent Open Sources Agent Memory, Reducing Token Consumption by Up to 61%

Solana Status posted on twitter that the Solana mainnet activated a new feature during the last epoch boundary that caused 30-50% of RPC nodes in v1.8 to be forked forcibly. RPC Nodes that had already upgraded to v1.9 were not affected.

Detail

【Solana Mainnet Update Causes Some RPC Nodes to Fork Away】Solana Status posted on twitter that the Solana mainnet activated a new feature during the last epoch boundary that caused 30-50% of RPC nodes in v1.8 to be forked forcibly. RPC Nodes that had already upgraded to v1.9 were not affected.

Solana Mainnet Update Causes Some RPC Nodes to Fork Away

Article

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Solana Mainnet Update Causes Some RPC Nodes to Fork Away

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