Parallel Web Systems Achieves $2 Billion Valuation Following $100 Million Series B Funding Round

Parallel Web Systems, an AI agent-tool startup founded by former Twitter CEO Parag Agrawal, has raised $100 million in a Series B funding round, achieving a valuation of $2 billion. The round was led by Sequoia, with participation from existing investors including Kleiner Perkins, Index Ventures, and Khosla Ventures. This funding comes just five months after the company secured $100 million in a Series A round at a $740 million valuation, bringing its total capital raised to $230 million. Parallel offers web search and research APIs tailored for AI agents, serving notable clients such as Clay, Harvey, Notion, and Opendoor, alongside over 100,000 developers utilizing its products. Agrawal's success follows a tumultuous period at Twitter, where he was part of a lawsuit against Elon Musk regarding severance payments.

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CybersecurityAI-powered defense+2

CybersecurityAI-powered defenseCritical systemsIntelligence Age

OpenAI Unveils Action Plan for Strengthening Cybersecurity in the Intelligence Age

OpenAI has announced a five-part action plan aimed at enhancing cybersecurity in the Intelligence Age. The plan emphasizes the importance of democratizing AI-powered cyber defense measures and safeguarding critical systems from emerging threats. By focusing on these key areas, OpenAI seeks to provide a robust framework for organizations to better protect their digital infrastructures.

Computer VisionFine-Tuning+2

Computer VisionFine-TuningNLPSafety

Innovative Real-Time Adaptive Traffic Signal System Enhances Safety for Vulnerable Road Users

Current pedestrian crossing signals are fixed and do not adapt to the behavior of vulnerable road users (VRUs), including the elderly and disabled, potentially leaving them stranded. The No Pedestrian Left Behind (NPLB) system has been developed to monitor VRUs in crosswalks and automatically extend signal timing when necessary. Evaluations of five leading object detection models on the BGVP dataset revealed that YOLOv12 achieved the highest mean Average Precision at 50% (mAP@0.5) of 0.756. NPLB combines the fine-tuned YOLOv12 with ByteTrack multi-object tracking and an adaptive controller, improving VRU safety by 71.4% through 10,000 Monte Carlo simulations, while reducing stranding rates from 9.10% to 2.60% with signal extensions required in only 12.1% of crossing cycles.

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FintechAI+2

FintechAIStartup FundingEnterprise Adoption

Founders of Rogo Technologies Develop $2 Billion AI Tool to Alleviate Junior Bankers' Grunt Work

John Willett, Gabe Stengel, and Tumas Rackaitis, the founders of Rogo Technologies, have created an AI tool valued at $2 billion aimed at reducing the repetitive tasks faced by junior bankers. This innovative solution addresses the industry's demand for efficiency and productivity, allowing less experienced employees to focus on more strategic activities. The development reflects a growing trend within the financial sector to leverage advanced technologies for operational improvements.

Bloomberg TechnologyRead →

AnthropicAI+2

AnthropicAIEnterprise AdoptionFinancial Services

Goldman Sachs Restricts Access to Anthropic's Claude AI for Hong Kong Bankers

Goldman Sachs has restricted its bankers in Hong Kong from accessing Anthropic's AI models, including Claude, as of a few weeks ago. This decision has led to employees being unable to utilize the company's AI capabilities in their work. Further details regarding the reasons behind this move have not been disclosed.

Financial Times TechRead →

FPGACNN+2

FPGACNNHealthcareSpace

Ultra-Low-Power FPGA-Based CNN Achieves Real-Time Cardiac Feature Extraction for Astronaut Health Monitoring

A new ultra-low-power Field-Programmable Gate Array (FPGA) solution has been developed for real-time Seismocardiography (SCG) feature classification, addressing the needs of wearable health sensors for astronauts. This approach utilizes Convolutional Neural Networks (CNNs) with quantization-aware training and a systolic-array accelerator, allowing efficient integer-only inference on the Lattice iCE40UP5K FPGA. The implementation demonstrates a validation accuracy of 98% while consuming just 8.55 mW and completing inference in 95.5 ms with minimal hardware resources. These findings indicate the feasibility of on-device cardiac feature extraction, paving the way for energy-efficient, autonomous health monitoring in space missions.

arXiv AIRead →

AIRegulation+2

AIRegulationTech PolicyInnovation

China Issues Warning to Tech Sector with Ban on Meta-Manus Deal

China has imposed a ban on the Meta-Manus deal, signaling a stern warning to its technology sector to prioritize domestic AI innovations. This decision reflects the government's intent to maintain control over AI developments and ensure that advancements remain within the country. The move comes amid growing concerns about foreign influence in China's tech landscape, particularly in the rapidly evolving field of artificial intelligence.

Financial TimesRead →