Welcome to this Yocto Project tutorial, where we introduce a widely-used and flexible toolset for embedded Linux development. The Yocto Project is an open-source initiative that provides a comprehensive set of templates, tools, and methods for building custom Linux-based systems tailored for embedded products. Yocto Project enables you to build efficient, scalable, and production-ready systems from the ground up.It supports all major hardware architectures, giving developers the freedom to target a wide ran ...

Could you tell these receipts from real ones. These are actual examples of AI generated receipts that have been submitted by employees at work filing expenses and they're almost impossible to distinguish from legitimate ones. This is a massive problem for systems like Concur and Appzen who help many organizations with their expense processing.And you don't need Photoshop. You don't even need a printer. And if you look very closely at these examples, you'll see the details that are there.Wrinkles in the pape ...

Project Introduction - The Graph introduces the Token Metadata Foundational Store for Ethereum [1] - It provides a single, preprocessed source of truth for ERC-20 token data [1] Data Features - Includes names, symbols, decimals, and logos of ERC-20 tokens [1] - Data is updated block-by-block [1] Benefits - Eliminates redundant calls for token metadata [1] - Offers fast, reliable, and shared metadata for everyone [1]

Summary of NetApp Insight 2025 Conference Call Company Overview - **Company**: NetApp (NasdaqGS: NTAP) - **Event**: Insight 2025 Conference - **Date**: October 14, 2025 Key Industry Insights AI and Technology Trends - The emergence of AI has transformed all businesses into technology-driven entities, with a significant focus on leveraging data for operational improvements [8][9][10] - Companies are increasingly interested in utilizing AI for enhancing sales pipelines and adapting to regulatory changes in the financial sector [11][12] - The advancements in hardware, particularly by companies like NVIDIA, have facilitated a rapid increase in AI capabilities [14][15] Virtualization and Cloud Strategies - There is a notable shift towards prioritizing on-premises workloads, influenced by data sovereignty concerns and the desire for control over data [20][21] - The acquisition of VMware by Broadcom has led to a strategic focus on on-prem solutions, contrasting the previous trend of cloud migration [16][17] - The virtualization landscape is evolving with the rise of containerization and new alternatives like Proxmox gaining traction [22][23][24] Storage Solutions - The resurgence of block storage is attributed to improvements in protocols and networking, particularly with NVMe technologies [41][42][43] - The demand for low-latency and high-reliability storage solutions is increasing, especially for tier one workloads [42][43] - NetApp's ONTAP system is being positioned as a comprehensive solution for various workloads, emphasizing the importance of performance and capacity [71][73] Product Developments - NetApp has consolidated its licensing into ONTAP One, streamlining access to various features like backup, data protection, and cyber resilience [32][34] - The introduction of proactive ransomware detection capabilities within ONTAP is highlighted as a unique offering in the industry [35][36] Market Dynamics - The consumer technology growth is driving enterprise data demands, with an explosion of data from various devices and services [47][48][49] - The integration of AI into consumer products is expected to influence enterprise solutions, leading to more customized experiences and data utilization [48][49] Future Outlook - The conference anticipates significant advancements in AI and machine learning, with expectations for more integrated and user-friendly systems in the coming years [58][59] - The potential for operating systems to incorporate AI functionalities is discussed, suggesting a future where AI becomes a standard feature across platforms [52][54] Additional Notes - The conference emphasizes the importance of understanding data and its implications for businesses, highlighting NetApp's long-standing commitment to innovation in data management [120][121] - The event serves as a platform for showcasing new technologies and strategies that align with current market needs and future trends [118][119]

Welcome to Beyond Artificial, >> Jensen. >> Thank you. It's great to be here, Alex.>> Thank you very much. >> Nice to see you. >> Okay, I have to tell you how Infinia started this new product.2017, >> Nvidia said, "We want to stand up a reference architecture super pot. And we need the data part of it." And we walked away from that meeting. I said, "There's got to be a different architecture for AI." If Jensen's far-reaching vision turns into reality over the next decade or so, a completely different approa ...

Infinia Platform Overview - Infinia is a software-defined, metadata-driven, containerized, cloud-native data intelligence platform designed for scalability, performance, and efficiency across core, cloud, and edge environments [1] - The platform supports critical data protocols like object and block, integrating with AI data acceleration libraries like TensorFlow and PyTorch [1] - Infinia enhances AI execution engines by serving data in its native form, reducing the need for data conversion and speeding up applications [1] Metadata and Multi-Tenancy Capabilities - Infinia allows for tagging massive amounts of metadata to objects, enabling faster data discovery and processing, with no limitations on metadata capability [1] - The platform has built-in multi-tenancy capabilities, providing SLAs for individual tenants and sub-tenants on capacity and performance, ensuring quality of service [1] Scalability and Cloud Native Design - Infinia is fully containerized, allowing for scale-out at web scale, starting from a few terabytes and scaling to exabytes [1] - The product is designed to be cloud-native and will soon be available in leading cloud provider marketplaces [2] AI Data Challenges and Solutions - Infinia addresses the complexity of managing large amounts of distributed multimodal data across core, cloud, and edge environments by creating a unified platform [1] - It tackles the demand for extremely low latency required to run AI applications, as well as the high costs associated with running AI [1] - The platform ensures data protection at any time and at any scale [1] Performance Metrics - Infinia can deliver time to first byte in less than a microsecond [2] - It can deliver 30 to 40 million objects per second in list object operations [2] - Infinia can deliver terabytes per second throughput at large scale [2] Efficiency and Sustainability - Infinia can achieve 10x data reduction, fitting over 100 petabytes of storage into a single rack [2] - It can reduce the overall data center footprint by a quarter compared to competitors, saving 10x power and cooling costs [2] Security Features - Infinia focuses on security authentication and access control, preventing unauthorized data access [2] - Data is always encrypted, and all actions within the system are audited [2] - The platform provides 99.9999% uptime enabled by reliability-focused features [2] Key Business Outcomes - Infinia aims to reduce complexity and achieve more accurate results on a unified platform for AI inference, data analytics, and model training [2] - It accelerates innovation by running AI apps faster, enabling businesses to beat the competition [2] - The platform enables rapid deployment across the cloud core and the edge to increase productivity, boost efficiency, and maximize ROI [2]

Infinia Platform Overview - Infinia is a data intelligence platform, not just a traditional storage product, offering S3 object interface, CSI, Cinder, and file system interfaces [1] - The platform is designed for large-scale deployments, already tested at almost an exabyte in size across approximately 1,000 nodes [1][2] - Infinia is a pure software product that can be integrated into the cloud, with a system already running at GCP for testing [2] Key Features and Capabilities - Infinia supports extensive metadata tagging, allowing tens of thousands of metadata attributes per object for enhanced data discovery and enrichment [1][3] - The system is highly multi-tenant, enabling service providers to manage large-scale systems efficiently while providing SLAs for individual end-users [1][2] - Infinia offers quality of service (QoS) at the application level, allowing prioritization of performance for critical tasks [2] - The platform supports online upgrades, capacity expansion, and reduction without downtime [2] Data Intelligence and AI Workloads - Infinia can serve as a Lakehouse on-premise, providing object, block, and parallel file system access to the same data [2][3] - Remote bucket support allows Infinia to pull metadata from existing data sets, enabling querying and caching of data from external sources [2] - Native library support and SDK can provide up to 10x performance improvement for data ingestion with frameworks like Spark [3] Performance and Scalability - The system is designed for very large scale, with deployments reaching almost an exabyte of capacity [1][2] - Infinia can deliver millions of object operations per second with single-digit millisecond latency [4] - Client-side data reduction and erasure coding eliminate east-west traffic, improving overall performance [3] Resilience and Availability - The system demonstrated high resilience by maintaining operation with only a short IO delay (10-15 seconds) after an entire rack of 480 drives went offline [4]

Data Conversion - The core objective is converting data into actionable information [1][2] - AI faces challenges in processing vast amounts of multimodal data to extract relevant information [1] - Efficient data processing minimizes the need to move large datasets for every task [1] Metadata's Role - Metadata provides context and enables real-time data-to-information conversion [2] - The speed of converting data into actionable information is crucial [2] - Metadata is presented as an effective method for achieving rapid data conversion [2]

Multimodal AI & Metadata - Multimodal AI is essential for enterprises to truly benefit from AI [1] - Metadata tagging and movement are crucial for the economics of AI to work, given limitations in data center space and power [1] - Metadata attributes are very key to the success of AI initiatives [1] Data Intelligence & Business Value - The core focus should be on a metadatarrier infrastructure that can handle low-latency object transformation to gain insight [2] - The ultimate goal of bringing data into the environment to train models is to gain data intelligence [2] - AI initiatives must result in business value for enterprises and leisure value for consumers [3]

Core Technology - The company utilizes accelerated computing and artificial intelligence to learn from data [1] - The company transforms raw data into data intelligence [1] - The company embeds intelligence into models and extracts semantics, intelligence, and information from data [2] - Instead of serving raw data, the company serves metadata, knowledge, and insights [2] - The semantic layer of data is extremely compressed [2]