RT Linea.eth (@LineaBuild)What @nansen_ai is building is a game-changer for DeFi, a new kind of trading platform powered by data, UX, and onchain speed.Built on @LineaBuild.https://t.co/HBCfOIHeJC ...

Company Focus - Ant Group is leveraging its extensive data resources and AI technology to enter the global cash management sector [1] Business Strategy - The company is targeting the global cash management market, which is considered an overlooked but profitable area [1]

Core Insights - Capgemini has announced the availability of a bond prospectus for a new bond issuance, which has been approved by the French financial authority (AMF) [4][6]. Group 1: Company Overview - Capgemini is a global business and technology transformation partner with a workforce of 350,000 employees across more than 50 countries [5]. - The company reported global revenues of €22.1 billion for the year 2024 [5]. Group 2: Bond Issuance Details - The bond issuance includes several tranches: - €1 billion notes with a maturity of 2 years and a floating rate of 3-month Euribor plus 0.30% (2027 Bonds) [7]. - €500 million notes with a maturity of 3 years and a coupon of 2.50% (2028 Bonds) [7]. - €1.25 billion notes with a maturity of 6 years and a coupon of 3.125% (2031 Bonds) [7]. - €1.25 billion notes with a maturity of 9 years and a coupon of 3.50% (2034 Bonds) [7].

Learning System Design Philosophy - Traditional learning system design focuses on logic, data, dashboards, and methodology [1] - A key insight is that invitation and permission are more important than instructions in learning [3][4] - True learning starts from desire, not direction, emphasizing intrinsic motivation over external rewards like badges [5] - The learning process should prioritize curiosity, exploration outside of guidance, time for reflection, awareness, and creation [19] - Learning is not a straight line but a cyclical process of uploading and downloading knowledge [22] Challenges in Guidance and Data - Over-reliance on external guidance (like GPS or AI) can lead to negative outcomes [7][8] - Data used in learning systems may be incomplete, biased, or not reflective of real-world nature [14][15] - Personal experience significantly shapes perception and interpretation of data [15] - It's important to pause and reflect before making decisions, trusting subconscious signals [12] New Learning Formula - The industry should shift from a focus on content completion and compliance to curiosity, exploration, time, awareness, and creation [19] - The goal is to unlock the "black box" of each individual, collecting natural data and emotional signals for personalized learning [20][21]

💡 From defense to healthcare, Palantir runs on data. Now you can run trades on $PLTR Stock Futures ⚡✅ USDT settlement✅ Deep liquidity✅ 5× leverage👇 Plug into the future with PLTRUSDT today: ...

The e-commerce firm Pattern going public. It's happening today right here on the NASDAQ. The company helps brands uh uh resell goods on marketplaces like Amazon and Walmart. Joining us right now is David Wright, Pattern CEO. Good morning to you. Congratulations with Big Day. >> Go back a dozen years and tell us what was the initial idea for this company and what is it now on a relative basis to what you originally thought? >> Oh yeah, it's I mean significantly different. You know, the the way it started is ...

🇺🇸 POWELL: "Data we get from BLS is still good enough to do our work."Lol ...

Core Insights - The article discusses the significant changes in the open-source AI model ecosystem, highlighting a shift towards a more competitive and rapidly evolving landscape, particularly in the AI Agent and Model Serving sectors [4][9][61]. Group 1: Ecosystem Changes - The latest version of the open-source landscape includes 114 projects, a decrease of 21 from the previous version, with 39 new projects and 60 projects that have disappeared, indicating a significant reshuffling in the ecosystem [7][10]. - The average lifespan of projects in the AI model ecosystem is only 30 months, with 62% of projects emerging after the "GPT moment" in October 2022, showcasing a high turnover rate [10][11]. - TensorFlow has been overtaken by PyTorch, which now dominates the landscape, marking a dramatic shift in the competitive dynamics [8]. Group 2: Key Trends - The article identifies three main areas of focus: AI Coding, Model Serving, and LLMOps, which are emerging as the primary tracks in the evolving landscape [29][61]. - AI Coding has transitioned from merely assisting in code writing to becoming a comprehensive lifecycle engine, indicating a significant increase in its capabilities and market potential [43][44]. - The AI Data sector remains relatively stable but is expected to evolve as new challenges arise in the native large model era, suggesting a potential for future growth [82][88]. Group 3: Global Contributions - The United States and China contribute over 55% of the total developer population in the open-source AI space, with the U.S. leading at 37.41% [17][20]. - In specific areas, the U.S. has a dominant position in AI Infrastructure and AI Data, with contributions significantly higher than those from China [19][23]. Group 4: Licensing Trends - There is a noticeable trend towards more restrictive open-source licenses, with many new projects adopting custom agreements that allow for greater control by the license holders [90][92]. - This shift raises questions about the definition of "open source" in the current competitive environment, as some projects that are popular on platforms like GitHub are not fully open-source [94].

RT Sei (@SeiNetwork)United States Department of Commerce data is coming to Sei.The @chainlink data standard will deliver U.S. GDP and other U.S. government macroeconomic indicators to Sei - powering institutional-grade markets with secure, trusted data.Markets Move Faster on Sei. ($/acc) https://t.co/jDOQ2msKTG ...

Ready to build with better data? ➡️ https://t.co/bhs3bMkobu ...