It’s nuts that all other rockets just smash back to Earth!“The Falcon has landed” almost 500 times.Tesla Owners Silicon Valley (@teslaownersSV):Elon Musk on Rocket Reusability:“Imagine a pallet of cash falling from space, about to burn up. Would you try to save it? Probably yes.”“So we want to get it back that way, we don’t have to make another one.”https://t.co/3K0a5BGNV3 ...

Core Viewpoint - CrowdStrike Holdings Inc. has experienced a significant decline in stock price, down over 14% in the last 30 days, despite a generally bullish earnings season for technology stocks [1] Company Performance - Despite the recent pullback, CRWD stock is still up 29.25% in 2025, over 99% in the last 12 months, and more than 285% in the last five years [2] - The company capitalized on a previous software update glitch to drive revenue growth by allowing customers to test its Falcon platform [3] - The latest quarter saw a 100% year-over-year gain in annual recurring revenue (ARR), although revenue expectations were missed [5] AI Integration - CrowdStrike's Falcon platform is cloud-native and AI-first, with the introduction of Charlotte AI to automate detection and response to emerging threats [4] Earnings Guidance - For the upcoming earnings report on August 27, CrowdStrike guided for revenue between $1.14 and $1.15 billion, indicating a 3% sequential improvement and an approximate 18% year-over-year increase [6] - Analysts forecast revenue at $1.15 billion, suggesting that even at the high end of guidance, CrowdStrike would only meet expectations [7] Market Sentiment - Analysts maintain a price target of $461.17 for CRWD stock, with a moderate buy rating based on 46 analyst ratings [10] - The stock has broken below its 100-day simple moving average, indicating potential resistance and increasing selling momentum [11] Options Market - The options chain for August 29 shows a bearish bias, with a 43% chance that the stock will close below $450 [14] Institutional Activity - Institutions may be preparing to buy CRWD stock at lower levels, potentially between $420 and $425, as they signal that the stock is currently expensive compared to its past and peers [16]

KEY FIGURES OF DASSAULT AVIATION CONSOLIDATED DATA H1 2025H1 2024Order intakeEUR 8,075 million 26 Rafale Export8 FalconEUR 5,134 million 18 Rafale Export 11 FalconAdjusted net salesEUR 2,847 million 7 Rafaleincl. 4 Export and 3 France 12 Falcon EUR 2,538 million 6 Rafale France 12 Falcon as of June 30, 2025as of December 31, 2024BacklogEUR 48,290 million 239 Rafaleincl. 186 Export and 53 France75 FalconEUR 43,224 million 220 Rafale incl.164 Export and 56 France79 Falcon <td style="width:207.94px;;borde ...

Core Viewpoint - The article introduces the LensLLM framework developed by Virginia Tech, which significantly enhances the efficiency of selecting large language models (LLMs) while reducing computational costs, thus addressing the challenges faced by researchers and developers in model selection [2][3][4]. Group 1: Introduction - The rapid advancement of LLMs has created a challenge in model selection, as traditional methods are resource-intensive and yield limited results [4]. Group 2: Theoretical Breakthrough of LensLLM - LensLLM is based on a novel PAC-Bayesian Generalization Bound, revealing unique dynamics in the relationship between test loss and training data size during LLM fine-tuning [6][10]. - The framework provides a first-principles explanation of the "phase transition" in LLM fine-tuning performance, indicating when data investment leads to significant performance improvements [12][16]. Group 3: LensLLM Framework - LensLLM incorporates Neural Tangent Kernel (NTK) to accurately capture the complex dynamics of transformer architectures during fine-tuning, establishing a precise relationship between model performance and data volume [15][16]. - The framework demonstrates impressive accuracy in curve fitting and test loss prediction across various benchmark datasets, outperforming traditional models [17][18]. Group 4: Performance and Cost Efficiency - LensLLM achieved a Pearson correlation coefficient of 85.8% and a relative accuracy of 91.1% on the Gigaword dataset, indicating its effectiveness in ranking models [21]. - The framework reduces computational costs by up to 88.5% compared to FullTuning, achieving superior performance with significantly lower FLOPs [23][25]. Group 5: Future Prospects - The research opens new avenues for LLM development and application, with potential expansions into multi-task scenarios and emerging model architectures like Mixture of Experts (MoE) [27][30]. - LensLLM is particularly suited for resource-constrained environments, accelerating model testing and deployment cycles while maximizing performance [31].

29 landings of single booster!SpaceX (@SpaceX):Falcon fleet leader touches down for its 29th launch and landing https://t.co/4ypDMyJQTs ...

Falcon fleet leader touches down for its 29th launch and landing https://t.co/4ypDMyJQTs ...

Group 1 - FutureHouse is an AI lab focused on "AI for Science," aiming to create AI systems that can autonomously ask questions, plan experiments, and iterate hypotheses [3][4][5] - The lab has launched four AI research agents: Crow (general intelligence), Falcon (automated literature review), Owl (research agent), and Phoenix (experimental agent), which can access full scientific literature and assess information quality [3][4] - FutureHouse's approach emphasizes scientific automation, transforming laboratories into "black box laboratories" and creating a software pipeline for research [4][5] Group 2 - FutureHouse is building a research API, focusing on automating scientific research through non-traditional mechanisms [19][22] - The founders aim to tackle "moonshot" challenges that require sustained investment and commercial strategies, with a focus on AI-driven scientific automation [22][23] - The ChemCrow project integrates language models and tools to achieve a complete scientific discovery process, demonstrating the value of scientific literature [23][24] Group 3 - The development of FutureHouse's research agents involves a clear distinction between agents and environments, with memory integrated into the agents for better performance [29][30] - The agents are designed to interact with their environments through language, observations, and actions, allowing for flexible combinations of different agents and environments [29][30] - The focus on full-text search and filtering relevant information is crucial for enhancing the performance of the research agents [32][33] Group 4 - FutureHouse believes that AI will not fully replace human involvement in scientific research, emphasizing the need for a semi-autonomous approach [46][47] - The complexity of biological systems requires human oversight, as AI cannot independently conduct experiments without human-defined frameworks [47][48] - The lab is exploring modular approaches to drug discovery and literature research, integrating human resources into the scientific process [51] Group 5 - AI technologies like AlphaFold and ESM-3 are expected to significantly enhance experimental efficiency, potentially increasing hit rates by tenfold or more [53] - The integration of computational predictions with experimental validation is becoming increasingly important in biological research [53][54] - Despite advancements, the complexity of biological systems means that experimental measurements remain the most reliable method for understanding biological mechanisms [55][56]

Core Viewpoint - The RoboSense Challenge 2025 aims to systematically evaluate the perception and understanding capabilities of robots in real-world scenarios, addressing the limitations of traditional perception algorithms in complex environments [1][44]. Group 1: Challenge Overview - The challenge is organized by multiple prestigious institutions, including National University of Singapore and University of Michigan, and is officially recognized as part of IROS 2025 [5]. - The competition will take place in Hangzhou, China, with key dates including registration starting in June 2025 and award decisions on October 19, 2025 [3][46]. Group 2: Challenge Tasks - The challenge includes five real-world tasks focusing on various aspects of robotic perception, such as language-driven autonomous driving, social navigation, sensor placement optimization, cross-modal drone navigation, and cross-platform 3D object detection [6][9]. - Each task is designed to test the robustness and adaptability of robotic systems under different conditions, emphasizing the need for innovative solutions in perception and understanding [44]. Group 3: Technical Features - The tasks require the development of end-to-end multimodal models that integrate visual sequences with natural language instructions, aiming for deep coupling between language, perception, and planning [7]. - The challenge emphasizes the importance of robust performance in dynamic environments, including the ability to handle sensor placement variations and social interactions with humans [20][28]. Group 4: Evaluation Metrics - The evaluation framework includes multiple dimensions such as perception accuracy, understanding through visual question answering (VQA), prediction of trajectories, and planning consistency with language commands [9][22]. - Baseline models and their performance metrics are provided for each task, indicating the expected computational resources and training requirements [13][19][39]. Group 5: Awards and Incentives - The challenge offers a total prize pool exceeding $10,000, with awards for first, second, and third places, as well as innovation awards for outstanding contributions in each task [40][41]. - All teams that complete valid submissions will receive official participation certificates, encouraging widespread engagement in the competition [41].

Congratulations @SpaceX team on 500 Falcon flights!SpaceX (@SpaceX):Falcon completes its 500th overall mission! Congratulations to the entire SpaceX team for making the impossible possible on the road to rapidly reusable rockets! https://t.co/znQPnAw6az ...

Core Insights - CrowdStrike Holdings (CRWD) shares have increased by 51.7% over the past three months, outperforming the Zacks Security industry's growth of 23.5% [1][9] - The stock has outperformed peers such as CyberArk Software (CYBR), Palo Alto Networks (PANW), and Check Point Software (CHKP), which saw gains of 29.1%, 14.8%, and 3.6% respectively [1][9] Financial Performance - CrowdStrike's quarterly revenues exceeded $1 billion for the third consecutive time in Q1 of fiscal 2026, reflecting a year-over-year growth of nearly 21% [5] - The company ended Q1 with an Annual Recurring Revenue (ARR) of $4.44 billion, up 22% year-over-year [11] - Non-GAAP earnings declined by 7.6% year-over-year to 73 cents per share due to rising costs [16] Subscription Model and Customer Adoption - The Falcon Flex subscription model has been a significant driver of customer growth, with over 820 customer accounts adopting it, leading to a total deal value of $3.2 billion [12][10] - Customers using six or more cloud modules represented 48% of total subscription customers, with 32% using seven or more modules [6] Strategic Partnerships and AI Integration - CrowdStrike has integrated its Falcon platform with NVIDIA's Enterprise AI Factory and partnered with Microsoft to standardize cyber threat attribution [8] - The company is focusing on expanding its capabilities through partnerships with other AI companies [7] Cost Structure and Investment - Sales and marketing expenses surged nearly ninefold to $1.52 billion in fiscal 2025 from $173 million in fiscal 2019, while R&D expenses increased 12-fold over the last six fiscal years [14][15] - In Q1 of fiscal 2026, sales and marketing and R&D expenses rose by 25.5% and 34.7% year-over-year, respectively [16] Valuation Metrics - CrowdStrike is trading at a high price-to-sales (P/S) ratio of 22.60X, significantly above the Zacks Security industry's ratio of 14.78X [19][22] - Compared to peers, CyberArk, Palo Alto Networks, and Check Point Software have P/S multiples of 13.96X, 12.94X, and 9.25X, respectively [22] Conclusion - The company is well-positioned in the AI-driven cybersecurity market, but shrinking profits and high valuation suggest a cautious approach for investors [23]