An Aldi staff member in Sunderland is celebrating her 20 year milestone with the supermarket chain. Aylsa Wilson first joined Aldi back in 2003, and she has worked her way up from store assistant ...

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Weltschmerz, literally “world pain” in German, arises when we come face-to-face with the unknowable “why” of evil and suffering, and from feeling the existential weight that our ideals and expectations of how the world ought to be don’t line up with the reality of our lived experience.

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Carely

carely

Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 5. MACHINE LEARNING USE CASES In this section, we review the applications of Deep Lake. A typical scenario in a Deep Learning application starts with (1) A raw set of files that is collected on an object storage bucket. It might include images, videos, and other types of multimedia data in their native formats such as JPEG, PNG or MP4. (2) Any associated metadata and labels stored on a relational database. Optionally, they could be stored on the same bucket along with the raw data in a normalized tabular form such as CSV, JSON, or Parquet format. As shown in Fig. 4, an empty Deep Lake dataset is created. Then, empty tensors are defined for storing both raw data as well as metadata. The number of tensors could be arbitrary. A basic example of an image classification task would have two tensors, • images tensor with htype of 𝑖𝑚𝑎𝑔𝑒 and sample compression of JPEG • labels tensor with htype of 𝑐𝑙𝑎𝑠𝑠_𝑙𝑎𝑏𝑒𝑙 and chunk compression of LZ4. After declaring tensors, the data can be appended to the dataset. If a raw image compression matches the tensor sample compression, the binary is directly copied into a chunk without additional decoding. Label data is extracted from a SQL query or CSV table into a categorical integer and appended into labels tensor. labels tensor chunks are stored using LZ4 compression. All Deep Lake data is stored in the bucket and is self-contained. After storage, the data can be accessed in a NumPy interface or as a streamable deep learning dataloader. Then, the model running on a compute machine iterates over the stream of image tensors, and stores the output of the model in a new tensor called predictions. Furthermore, we discuss below how one can train, version control, query, and inspect the quality of a Deep Lake dataset. 5.1 Deep Learning Model Training Deep learning models are trained at multiple levels in an organization, ranging from exploratory training occurring on personal computers to large-scale training that occurs on distributed machines involving many GPUs. The time and effort required to bring the data from long-term storage to the training client are often comparable to the training itself. Deep Lake solves this problem by enabling rapid streaming of data without bottlenecking the downstream training process, thus avoiding the cost and time required to duplicate data on local storage. 5.2 Data Lineage and Version Control Deep learning data constantly evolve as new data is added and existing data is quality controlled. Analytical and training workloads occur in parallel while the data is changing. Hence, knowing which data version was used by a given workload is critical to understand the relationship between the data and model performance. Deep Lake enables deep learning practitioners to understand which version of their data was used in any analytical workload and to time travel across these versions if an audit is required. Since all data is mutable, it can be edited to meet compliance-related privacy requirements. Like Git for code, Deep Lake also introduces the concept of data branches, allowing experimentation and editing of data without affecting colleagues’ work. 5.3 Data Querying and Analytics Training of deep learning models rarely occurs on all data collected by an organization for a particular application. Training datasets are often constructed by filtering the raw data based on conditions increasing model performance, which often includes data balancing, eliminating redundant data, or selecting data that contains specific features. Deep Lake provides the tools to query and analyze data so that deep learning engineers can create datasets yielding the highest accuracy models. 5.4 Data Inspection and Quality Control Though unsupervised learning is becoming more applicable in realworld use cases, most deep learning applications still rely on supervised learning. Any supervised learning system is only as good as the quality of its data, often achieved by manual and exhaustive inspection of the data. Since this process is time-consuming, it is critical to provide the humans in the loop with tools to examine vast amounts of data very quickly. Deep Lake allows inspecting deep learning datasets of any size from the browser without any setup time or need to download data. Furthermore, the tools can be extended for comparing model results with ground truth. Combined with querying and version control, this can be applied to the iterative improvement of data to achieve the best possible model. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 5. MACHINE LEARNING USE CASES In this section, we review the applications of Deep Lake. A typical scenario in a Deep Learning application starts with (1) A raw set of files that is collected on an object storage bucket. It might include images, videos, and other types of multimedia data in their native formats such as JPEG, PNG or MP4. (2) Any associated metadata and labels stored on a relational database. Optionally, they could be stored on the same bucket along with the raw data in a normalized tabular form such as CSV, JSON, or Parquet format. As shown in Fig. 4, an empty Deep Lake dataset is created. Then, empty tensors are defined for storing both raw data as well as metadata. The number of tensors could be arbitrary. A basic example of an image classification task would have two tensors, • images tensor with htype of 𝑖𝑚𝑎𝑔𝑒 and sample compression of JPEG • labels tensor with htype of 𝑐𝑙𝑎𝑠𝑠_𝑙𝑎𝑏𝑒𝑙 and chunk compression of LZ4. After declaring tensors, the data can be appended to the dataset. If a raw image compression matches the tensor sample compression, the binary is directly copied into a chunk without additional decoding. Label data is extracted from a SQL query or CSV table into a categorical integer and appended into labels tensor. labels tensor chunks are stored using LZ4 compression. All Deep Lake data is stored in the bucket and is self-contained. After storage, the data can be accessed in a NumPy interface or as a streamable deep learning dataloader. Then, the model running on a compute machine iterates over the stream of image tensors, and stores the output of the model in a new tensor called predictions. Furthermore, we discuss below how one can train, version control, query, and inspect the quality of a Deep Lake dataset. 5.1 Deep Learning Model Training Deep learning models are trained at multiple levels in an organization, ranging from exploratory training occurring on personal computers to large-scale training that occurs on distributed machines involving many GPUs. The time and effort required to bring the data from long-term storage to the training client are often comparable to the training itself. Deep Lake solves this problem by enabling rapid streaming of data without bottlenecking the downstream training process, thus avoiding the cost and time required to duplicate data on local storage. 5.2 Data Lineage and Version Control Deep learning data constantly evolve as new data is added and existing data is quality controlled. Analytical and training workloads occur in parallel while the data is changing. Hence, knowing which data version was used by a given workload is critical to understand the relationship between the data and model performance. Deep Lake enables deep learning practitioners to understand which version of their data was used in any analytical workload and to time travel across these versions if an audit is required. Since all data is mutable, it can be edited to meet compliance-related privacy requirements. Like Git for code, Deep Lake also introduces the concept of data branches, allowing experimentation and editing of data without affecting colleagues’ work. 5.3 Data Querying and Analytics Training of deep learning models rarely occurs on all data collected by an organization for a particular application. Training datasets are often constructed by filtering the raw data based on conditions increasing model performance, which often includes data balancing, eliminating redundant data, or selecting data that contains specific features. Deep Lake provides the tools to query and analyze data so that deep learning engineers can create datasets yielding the highest accuracy models. 5.4 Data Inspection and Quality Control Though unsupervised learning is becoming more applicable in realworld use cases, most deep learning applications still rely on supervised learning. Any supervised learning system is only as good as the quality of its data, often achieved by manual and exhaustive inspection of the data. Since this process is time-consuming, it is critical to provide the humans in the loop with tools to examine vast amounts of data very quickly. Deep Lake allows inspecting deep learning datasets of any size from the browser without any setup time or need to download data. Furthermore, the tools can be extended for comparing model results with ground truth. Combined with querying and version control, this can be applied to the iterative improvement of data to achieve the best possible model. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Lake, a Lakehouse for Deep Learning: Machine Learning Use Cases

ডিপ লেক, গভীর শিক্ষার জন্য একটি লেকহাউস: মেশিন লার্নিং ব্যবহারের ক্ষেত্রে

Deep Lake, ein Lakehouse für Deep Learning: Anwendungsfälle für maschinelles Lernen

Deep Lake, una casa de lago para el aprendizaje profundo: casos de uso del aprendizaje automático

Deep Lake, un Lakehouse pour le Deep Learning : cas d'utilisation du Machine Learning

डीप लेक, डीप लर्निंग के लिए एक लेकहाउस: मशीन लर्निंग उपयोग के मामले

ディープラーニングのためのレイクハウス、Deep Lake: 機械学習のユースケース

딥 러닝을 위한 레이크하우스인 Deep Lake: 머신 러닝 사용 사례

Deep Lake, um lago para aprendizado profundo: casos de uso de aprendizado de máquina

Deep Lake, дом для глубокого обучения: примеры использования машинного обучения

Deep Lake, Derin Öğrenme için Bir Göl Evi: Makine Öğrenimi Kullanım Örnekleri

Deep Lake, Lakehouse cho Deep Learning: Các trường hợp sử dụng Machine Learning

Deep Lake，深度学习的 Lakehouse：机器学习用例

Meet the Winners of Startups in North and Central America

Building Scalable E-commerce Infrastructure on Magento

Best Practices for Securing Cloud Environments Against Cyber Threats

TOKYO, Japan, October 31st, 2024/Chainwire/--Mawari Network a spatial computing DePIN (Decentralized Physical Infrastructure Network), has announced a limited node license sale. Already a pioneer in immersive content, the node sale will make Mawari’s 3D streaming technology more scalable and accessible for content creators and developers. This node sale will help create a global, decentralized spatial computing infrastructure as the market for extended reality devices like the Apple Vision Pro, Meta Orion, and Meta Quest 3 is projected to reach more than 100 million units in the next five years. As demand for immersive experiences grows, Mawari is building a network that can grow with it. Mawari’s infrastructure is optimized for the demanding needs of immersive experiences, positioning Mawari as a key actor in the emerging multibillion-dollar spatial computing market. Called “a sleeping lion” by Samsung Next, Mawari is redefining and building the future of spatial computing. Its solution comprises the Spatial Streaming SDK and the Mawari Network. The SDK, supporting the Unity and Unreal engines, empowers developers to create seamless, immersive experiences. The Mawari Network, working in parallel, leverages a globally distributed network of GPU nodes to ensure low latency and optimal performance. The node license sale's objective is to help scale the network, optimize its global performance, and ensure its resiliency. “This node sale allows node operators to help bring 3D content to the world, and be rewarded for their efforts,” Luis Oscar Ramirez, Mawari’s Founder & CEO said. “The current cloud services didn’t allow us to scale our proprietary spatial streaming technology, we researched ways to scale immersive content and found DePIN was clearly the best option. Now, we can allow our global community of node operators to participate in building out our critical node network alongside our community of developers and content creators.” Mawari has worked with industry leaders, such as Deutsche Telekom, KDDI, Telefonica, Magic Leap, Netflix, Qualcomm, T-Mobile and dozens of other clients. The Solana Foundation recently selected Mawari for a grant, endorsing its roadmap for decentralized spatial computing. Backed by investors including Anfield LTD, Borderless Capital, 1k(x), and Samsung Next, and with veteran DePIN and web3 operator, Sean Carey, a co-founder of Helium, also joining its board, Mawari is the clear category leader. Node Sale Details The node sale is scheduled for Q4 2024, building out the Mawari Network’s ability to perform compute-intensive tasks and efficiently deliver immersive 3D content on a global scale. The Mawari Network will be leveraging specialized GPUs around the world for advanced computation and streaming tasks, but a set of Guardian Nodes are needed to verify their work and the performance of the network overall as it serves multiple simultaneous immersive experiences. The node sale is for licenses to operate these critical Guardian Nodes. More specifically, Guardian Nodes’ responsibilities include assessing critical performance indicators like latency, bandwidth, and content quality to maintain the high standards necessary for immersive, real-time experiences. By monitoring the Mawari network, the community-run Guardian Nodes will make it more performant and more resilient, ensuring it can scale securely as it grows. Details of the node license sale will be announced soon. About Mawari Mawari is setting the standard for decentralized spatial computing and immersive content delivery. The Mawari Network powers real-time streaming of immersive content through a global network of compute nodes. Mawari is optimizing XR for awe, creating unforgettable experiences and revolutionizing how creators engage audiences at scale. Mawari Network built the foundational technologies to power the next generation of interactive media, fully realizing the possibilities of virtual worlds existing seamlessly alongside our physical reality. Its core technology, the Mawari Engine and Spatial Streaming SDK, runs on the Mawari Network — the world’s first and only DePIN (Decentralized Physical Infrastructure Network) that orchestrates storage, bandwidth, and rendering in real-time for spatial computing. Bypassing the rendering limitations of existing mobile devices and content delivery networks, Mawari’s global network of compute nodes has already powered activations from digital fashion shows to concert visualizations. The Mawari Network’s peer-to-peer architecture was purpose-built to scale spatial media streaming requirements with a community-centered ethos. Website X (Twitter) [Discord](https://: https://discord.gg/mawari) Contact Will Haskins will@serotonin.co This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here TOKYO, Japan, October 31st, 2024/Chainwire/--Mawari Network a spatial computing DePIN (Decentralized Physical Infrastructure Network), has announced a limited node license sale. Already a pioneer in immersive content, the node sale will make Mawari’s 3D streaming technology more scalable and accessible for content creators and developers. This node sale will help create a global, decentralized spatial computing infrastructure as the market for extended reality devices like the Apple Vision Pro, Meta Orion, and Meta Quest 3 is projected to reach more than 100 million units in the next five years. As demand for immersive experiences grows, Mawari is building a network that can grow with it. Mawari’s infrastructure is optimized for the demanding needs of immersive experiences, positioning Mawari as a key actor in the emerging multibillion-dollar spatial computing market. Called “a sleeping lion” by Samsung Next , Mawari is redefining and building the future of spatial computing. Its solution comprises the Spatial Streaming SDK and the Mawari Network. The SDK, supporting the Unity and Unreal engines, empowers developers to create seamless, immersive experiences. Samsung Next Samsung Next The Mawari Network, working in parallel, leverages a globally distributed network of GPU nodes to ensure low latency and optimal performance. The node license sale's objective is to help scale the network, optimize its global performance, and ensure its resiliency. “This node sale allows node operators to help bring 3D content to the world, and be rewarded for their efforts,” Luis Oscar Ramirez, Mawari’s Founder & CEO said. “This node sale allows node operators to help bring 3D content to the world, and be rewarded for their efforts,” Luis Oscar Ramirez, Mawari’s Founder & CEO said. “The current cloud services didn’t allow us to scale our proprietary spatial streaming technology, we researched ways to scale immersive content and found DePIN was clearly the best option. Now, we can allow our global community of node operators to participate in building out our critical node network alongside our community of developers and content creators.” “The current cloud services didn’t allow us to scale our proprietary spatial streaming technology, we researched ways to scale immersive content and found DePIN was clearly the best option. Now, we can allow our global community of node operators to participate in building out our critical node network alongside our community of developers and content creators.” Mawari has worked with industry leaders, such as Deutsche Telekom, KDDI, Telefonica, Magic Leap, Netflix, Qualcomm, T-Mobile and dozens of other clients. The Solana Foundation recently selected Mawari for a grant, endorsing its roadmap for decentralized spatial computing. Backed by investors including Anfield LTD, Borderless Capital, 1k(x), and Samsung Next, and with veteran DePIN and web3 operator, Sean Carey, a co-founder of Helium, also joining its board, Mawari is the clear category leader. Node Sale Details The node sale is scheduled for Q4 2024, building out the Mawari Network’s ability to perform compute-intensive tasks and efficiently deliver immersive 3D content on a global scale. The Mawari Network will be leveraging specialized GPUs around the world for advanced computation and streaming tasks, but a set of Guardian Nodes are needed to verify their work and the performance of the network overall as it serves multiple simultaneous immersive experiences. The node sale is for licenses to operate these critical Guardian Nodes. More specifically, Guardian Nodes’ responsibilities include assessing critical performance indicators like latency, bandwidth, and content quality to maintain the high standards necessary for immersive, real-time experiences. By monitoring the Mawari network, the community-run Guardian Nodes will make it more performant and more resilient, ensuring it can scale securely as it grows. Details of the node license sale will be announced soon. About Mawari Mawari is setting the standard for decentralized spatial computing and immersive content delivery. The Mawari Network powers real-time streaming of immersive content through a global network of compute nodes. Mawari is optimizing XR for awe, creating unforgettable experiences and revolutionizing how creators engage audiences at scale. Mawari Mawari Mawari Network built the foundational technologies to power the next generation of interactive media, fully realizing the possibilities of virtual worlds existing seamlessly alongside our physical reality. Its core technology, the Mawari Engine and Spatial Streaming SDK, runs on the Mawari Network — the world’s first and only DePIN (Decentralized Physical Infrastructure Network) that orchestrates storage, bandwidth, and rendering in real-time for spatial computing. Bypassing the rendering limitations of existing mobile devices and content delivery networks, Mawari’s global network of compute nodes has already powered activations from digital fashion shows to concert visualizations. The Mawari Network’s peer-to-peer architecture was purpose-built to scale spatial media streaming requirements with a community-centered ethos. Website Website X (Twitter) X (Twitter) [Discord](https://: https://discord.gg/mawari) Contact Will Haskins will@serotonin.co This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

Mawari Announces Node Sale To Bring Immersive Content To The World

Mawari kondig node-verkoping aan om meeslepende inhoud na die wêreld te bring

ማዋሪ አስማጭ ይዘትን ወደ አለም ለማምጣት የመስቀለኛ መንገድ ሽያጭን አስታወቀ

أعلنت شركة ماواري عن بيع عقدة لتقديم محتوى غامر للعالم

Mawari oznamuje prodej uzlu, aby světu přinesl pohlcující obsah

Mawari anuncia la venta de Node para llevar contenido inmersivo al mundo

मावारी ने दुनिया भर में इमर्सिव कंटेंट लाने के लिए नोड सेल की घोषणा की

Mawari Umumkan Penjualan Node untuk Hadirkan Konten Imersif ke Seluruh Dunia

Mawari מכריזה על מכירת צומת כדי להביא תוכן סוחף לעולם

Mawari、没入型コンテンツを世界に届けるためノードセールを発表

Mawari Asakoli Vente Ya Node Pona Ko Mema Contenu Immersive Na Mokili

Mawari tillkännager Node-försäljning för att ge uppslukande innehåll till världen

ماواری نے دنیا میں عمیق مواد لانے کے لیے نوڈ سیل کا اعلان کیا۔

Tokyo-based Mawari Network has raised $10.8 million in a strategic funding round, positioning the company at the forefront of the rapidly evolving spatial computing industry. The investment, announced on September 26, 2024, was led by Anfield LTD, Borderless Capital, and 1kx, with participation from a diverse group of venture capital firms and angel investors. Mawari, founded in 2017, has developed a decentralized content delivery network (CDN) specifically designed for immersive 3D experiences. The company's technology enables real-time streaming of 3D content to spatial computing devices such as the Apple Vision Pro, Meta Quest 3, and Meta Orion AR Glasses. Luis Oscar Ramirez, Founder & CEO of Mawari, stated, "Our vision has always been to bring immersive experiences to everyone, and this investment will unlock the platform for a broader community to build the 3D Internet with us." The funding round attracted notable investors from both Web2 and Web3 sectors, highlighting the growing interest in spatial computing technologies. Sean Carey, Helium Co-Founder and Partner at Borderless Capital, praised Mawari's innovation, saying, "Every time I see what Mawari is building, I am reminded of how wild the future of spatial computing will be." Mawari's technology consists of two key components: The Spatial Streaming SDK: A toolkit that integrates with popular development environments like Unity and Unreal Engine, allowing creators to focus on content creation without worrying about backend complexities.


The Mawari Network: A decentralized, GPU-powered content delivery network optimized for spatial computing, leveraging a global network of nodes to ensure low latency and high performance. The company has already secured three patents, with 11 more in process, and boasts a client roster of 40 global brands, including T-Mobile, Sapporo, KDDI, Netflix, and BMW. Prior to this funding round, Mawari had been bootstrapped, averaging $1.5 million in annual revenue. Christopher Heymann, Founding Partner at 1kx, noted, "Mawari is not only an early pioneer in spatial computing but is also one of the first of a new cohort of successful Web2 businesses harnessing blockchain and decentralization to drive innovation, efficiency, and improved user experience." The new funds will be used to expand into new markets, enhance the Spatial Streaming SDK, and continue research and development efforts. Mawari also plans to conduct a node license sale in Q4 of 2024 to further decentralize and scale its spatial computing network. As the spatial computing industry continues to grow, Mawari's innovative approach to content delivery and its strong patent portfolio position the company as a key player in shaping the future of immersive digital experiences. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Tokyo-based Mawari Network has raised $10.8 million in a strategic funding round, positioning the company at the forefront of the rapidly evolving spatial computing industry. The investment, announced on September 26, 2024, was led by Anfield LTD, Borderless Capital, and 1kx, with participation from a diverse group of venture capital firms and angel investors. Mawari, founded in 2017, has developed a decentralized content delivery network (CDN) specifically designed for immersive 3D experiences. The company's technology enables real-time streaming of 3D content to spatial computing devices such as the Apple Vision Pro, Meta Quest 3, and Meta Orion AR Glasses. Luis Oscar Ramirez, Founder & CEO of Mawari, stated, "Our vision has always been to bring immersive experiences to everyone, and this investment will unlock the platform for a broader community to build the 3D Internet with us." The funding round attracted notable investors from both Web2 and Web3 sectors, highlighting the growing interest in spatial computing technologies. Sean Carey, Helium Co-Founder and Partner at Borderless Capital, praised Mawari's innovation, saying, "Every time I see what Mawari is building, I am reminded of how wild the future of spatial computing will be." Mawari's technology consists of two key components: The Spatial Streaming SDK: A toolkit that integrates with popular development environments like Unity and Unreal Engine, allowing creators to focus on content creation without worrying about backend complexities. The Mawari Network: A decentralized, GPU-powered content delivery network optimized for spatial computing, leveraging a global network of nodes to ensure low latency and high performance. The Spatial Streaming SDK: A toolkit that integrates with popular development environments like Unity and Unreal Engine, allowing creators to focus on content creation without worrying about backend complexities. The Spatial Streaming SDK: A toolkit that integrates with popular development environments like Unity and Unreal Engine, allowing creators to focus on content creation without worrying about backend complexities. The Mawari Network: A decentralized, GPU-powered content delivery network optimized for spatial computing, leveraging a global network of nodes to ensure low latency and high performance. The Mawari Network: A decentralized, GPU-powered content delivery network optimized for spatial computing, leveraging a global network of nodes to ensure low latency and high performance. The company has already secured three patents, with 11 more in process, and boasts a client roster of 40 global brands, including T-Mobile, Sapporo, KDDI, Netflix, and BMW. Prior to this funding round, Mawari had been bootstrapped, averaging $1.5 million in annual revenue. Christopher Heymann, Founding Partner at 1kx, noted, "Mawari is not only an early pioneer in spatial computing but is also one of the first of a new cohort of successful Web2 businesses harnessing blockchain and decentralization to drive innovation, efficiency, and improved user experience." The new funds will be used to expand into new markets, enhance the Spatial Streaming SDK, and continue research and development efforts. Mawari also plans to conduct a node license sale in Q4 of 2024 to further decentralize and scale its spatial computing network. As the spatial computing industry continues to grow, Mawari's innovative approach to content delivery and its strong patent portfolio position the company as a key player in shaping the future of immersive digital experiences. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Mawari Secures $10.8 Million to Advance Spatial Computing Technology

Мавары забяспечвае 10,8 мільёна долараў на развіццё тэхналогіі прасторавых вылічэнняў

Mawari obtiene 10,8 millones de dólares para impulsar la tecnología de computación espacial

Mawari משיגה 10.8 מיליון דולר לקידום טכנולוגיית מחשוב מרחבית

Mawari osigurava 10,8 milijuna dolara za unaprjeđenje tehnologije prostornog računalstva

Mawari Sekirize $10.8 Milyon Pou Avanse Teknoloji Espasyal Computing

マワリ、空間コンピューティング技術の進歩に向けて1,080万ドルを調達

Мавари орон зайн тооцооллын технологийг хөгжүүлэхийн тулд 10.8 сая долларыг барьцаалж байна

Mawari asigură 10,8 milioane de dolari pentru a promova tehnologia de calcul spațial

ماواری نے مقامی کمپیوٹنگ ٹیکنالوجی کو آگے بڑھانے کے لیے $10.8 ملین کا تحفظ کیا۔

Mavari fazoviy hisoblash texnologiyasini rivojlantirish uchun 10,8 million dollarni kafolatlaydi

Mawari 获 1080 万美元融资，用于推进空间计算技术

20 Crazy Stories On Cyber Reputation

SILVER SPRING, Maryland, September 19th, 2024/CyberNewsWire/--Aembit, the non-human identity and access management (IAM) company, today released its 2024 Non-Human Identity Security Report, a definitive survey highlighting how organizations currently manage and protect non-human identities (NHIs) – such as applications, scripts, and service accounts. The report reveals a stunning, widespread reliance on outdated methods and manual practices that fail to provide adequate protection against the reality of increased NHI-focused breaches. As non-human identities (NHIs) rapidly proliferate in modern IT environments, driven by the shift from monolithic to distributed architectures, widespread cloud adoption, and increasing automation, the report reveals a chasm between non-human and user identity security practices, with most organizations acknowledging their efforts to secure non-human identities are either lagging or struggling to keep pace. The survey of IT and security professionals also shows that careless habits, such as storing long-term credentials directly in code, relying on spreadsheets for manual input, and sharing sensitive information via collaboration tools, are still prevalent. Additionally, many organizations face difficulty in securing NHIs in complex, multi-cloud environments, with concerns about inconsistent access management and unclear ownership of security processes. Key findings of the survey include: IAM Maturity Gap: 88.5% of organizations admitted that their non-human IAM practices lag behind or are on par with their user IAM efforts.
Low Confidence: Only 19.6% of respondents expressed strong confidence in their non-human IAM practices.
Insecure Practices: 30.9% of respondents store long-term credentials in code and 23.7% share secrets through copying and pasting, such as via email or messaging apps.
Outmoded Methods: 38.9% of respondents still use less-secure methods like secrets managers for non-human workload-to-workload authentication.
Cloud Complexity: 35.6% of organizations struggle to manage non-human identity security across hybrid and multi-cloud environments.
Blind Spots: 23.5% of organizations are not sure of the biggest threat to their non-human identities. “Organizations are starting to recognize that non-human identities are more than just background tools. As businesses rapidly automate, NHIs play a critical role in digital ecosystems and often handle sensitive data,” said David Goldschlag, co-founder and CEO of Aembit. “But, as our survey shows, NHI security remains very much a work in progress. While awareness is growing, most organizations still have significant shortfalls in how they secure these identities and the vital connections between them. It’s time to elevate non-human IAM to the same level of importance as user IAM.” The survey, which included responses from 110 professionals, from developers to identity architects to CISOs, also revealed a growing need for more holistic approaches to managing non-human identities. As businesses expand across cloud environments, managing workload identities has become increasingly complex, with many organizations struggling to keep up due to piecemeal or legacy approaches. Those interested can read the full survey by downloading it here. About Aembit Aembit is the non-human identity and access management platform that secures access between workloads across clouds, SaaS, and data centers. With Aembit’s identity control plane, DevSecOps can fully automate secretless, policy-based, and Zero Trust workload access with MFA-strength capabilities. For more information, users can visit https://aembit.io/ and follow Aembit on LinkedIn. Contact CMO Apurva Davé Aembit info@aembit.io This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here SILVER SPRING, Maryland, September 19th, 2024/CyberNewsWire/--Aembit, the non-human identity and access management (IAM) company, today released its 2024 Non-Human Identity Security Report, a definitive survey highlighting how organizations currently manage and protect non-human identities (NHIs) – such as applications, scripts, and service accounts. The report reveals a stunning, widespread reliance on outdated methods and manual practices that fail to provide adequate protection against the reality of increased NHI-focused breaches. increased NHI-focused breaches. increased NHI-focused breaches. As non-human identities (NHIs) rapidly proliferate in modern IT environments, driven by the shift from monolithic to distributed architectures, widespread cloud adoption, and increasing automation, the report reveals a chasm between non-human and user identity security practices, with most organizations acknowledging their efforts to secure non-human identities are either lagging or struggling to keep pace. The survey of IT and security professionals also shows that careless habits, such as storing long-term credentials directly in code, relying on spreadsheets for manual input, and sharing sensitive information via collaboration tools, are still prevalent. Additionally, many organizations face difficulty in securing NHIs in complex, multi-cloud environments, with concerns about inconsistent access management and unclear ownership of security processes. Key findings of the survey include: IAM Maturity Gap: 88.5% of organizations admitted that their non-human IAM practices lag behind or are on par with their user IAM efforts. Low Confidence: Only 19.6% of respondents expressed strong confidence in their non-human IAM practices. Insecure Practices: 30.9% of respondents store long-term credentials in code and 23.7% share secrets through copying and pasting, such as via email or messaging apps. Outmoded Methods: 38.9% of respondents still use less-secure methods like secrets managers for non-human workload-to-workload authentication. Cloud Complexity: 35.6% of organizations struggle to manage non-human identity security across hybrid and multi-cloud environments. Blind Spots: 23.5% of organizations are not sure of the biggest threat to their non-human identities. IAM Maturity Gap: 88.5% of organizations admitted that their non-human IAM practices lag behind or are on par with their user IAM efforts. Low Confidence: Only 19.6% of respondents expressed strong confidence in their non-human IAM practices. Insecure Practices: 30.9% of respondents store long-term credentials in code and 23.7% share secrets through copying and pasting, such as via email or messaging apps. Outmoded Methods: 38.9% of respondents still use less-secure methods like secrets managers for non-human workload-to-workload authentication. Cloud Complexity: 35.6% of organizations struggle to manage non-human identity security across hybrid and multi-cloud environments. Blind Spots: 23.5% of organizations are not sure of the biggest threat to their non-human identities. “Organizations are starting to recognize that non-human identities are more than just background tools. As businesses rapidly automate, NHIs play a critical role in digital ecosystems and often handle sensitive data,” said David Goldschlag, co-founder and CEO of Aembit. “Organizations are starting to recognize that non-human identities are more than just background tools. As businesses rapidly automate, NHIs play a critical role in digital ecosystems and often handle sensitive data,” said David Goldschlag, co-founder and CEO of Aembit. “But, as our survey shows, NHI security remains very much a work in progress. While awareness is growing, most organizations still have significant shortfalls in how they secure these identities and the vital connections between them. It’s time to elevate non-human IAM to the same level of importance as user IAM.” “But, as our survey shows, NHI security remains very much a work in progress. While awareness is growing, most organizations still have significant shortfalls in how they secure these identities and the vital connections between them. It’s time to elevate non-human IAM to the same level of importance as user IAM.” The survey, which included responses from 110 professionals, from developers to identity architects to CISOs, also revealed a growing need for more holistic approaches to managing non-human identities. As businesses expand across cloud environments, managing workload identities has become increasingly complex, with many organizations struggling to keep up due to piecemeal or legacy approaches. Those interested can read the full survey by downloading it here . downloading it here downloading it here About Aembit Aembit is the non-human identity and access management platform that secures access between workloads across clouds, SaaS, and data centers. With Aembit’s identity control plane, DevSecOps can fully automate secretless, policy-based, and Zero Trust workload access with MFA-strength capabilities. For more information, users can visit https://aembit.io/ and follow Aembit on LinkedIn . Aembit Aembit https://aembit.io/ https://aembit.io/ follow Aembit on LinkedIn follow Aembit on LinkedIn Contact CMO Apurva Davé Aembit info@aembit.io This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here

Aembit Unveils 2024 Survey Report Highlighting Major Gaps In Securing Non-Human Identities

Aembit onthul 2024-opnameverslag wat groot leemtes in die beveiliging van nie-menslike identiteite beklemtoon

Aembit ukax 2024 maran yatxatäw yatiyäw uñacht’ayi, ukax jach’a ch’amanchawinak uñacht’ayi, jan jaqinakan identidad ukanakar jark’aqañataki

Aembit odhaluje zprávu o průzkumu z roku 2024, která zdůrazňuje hlavní mezery v zabezpečení nelidských identit

Aembit afslører 2024-undersøgelsesrapport, der fremhæver store huller i sikringen af ikke-menneskelige identiteter

Aembit presenta el informe de la encuesta de 2024 que destaca las principales deficiencias en la protección de las identidades no humanas

Aembit presenta o informe da enquisa de 2024 que destaca as principais lagoas na garantía de identidades non humanas

Aembit、非人間IDのセキュリティ確保における大きなギャップを浮き彫りにした2024年調査レポートを発表

Aembit dia manambara ny tatitra fanadihadiana 2024 izay manasongadina ny banga lehibe amin'ny fiarovana ny maha-olona tsy maha-olombelona

Aembit 2024 watapi tapukuymanta willakuyta riqsichin, chaypim qawachin hatun mana allin kaqkunata mana Runa kaqkunata waqaychaypi

Aembit Inofumura 2024 Survey Report Inosimbisa Mapundu Makuru Mukuchengetedza Zvisizvo Zvevanhu

Aembit, İnsan Olmayan Kimliklerin Güvenliğini Sağlamadaki Büyük Boşlukları Vurgulayan 2024 Anket Raporunu Açıkladı

Aembit оприлюднила звіт про опитування за 2024 рік, у якому висвітлюються основні прогалини в захисті нелюдських ідентичностей

Good Managers Know to Not Make These Mistakes

ভাল পরিচালকরা এই ভুলগুলি করতে জানেন না

Dobri menadžeri znaju da ne prave ove greške

Los buenos gerentes saben que no deben cometer estos errores

Kudeatzaile onek badakite akats hauek ez dituztela egiten

I buoni manager sanno che non devono commettere questi errori

優秀なマネージャーはこれらのミスをしないことを知っています

კარგმა მენეჯერებმა იციან, რომ არ დაუშვან ეს შეცდომები

ښه مدیران پوهیږي چې دا غلطۍ نه کوي

Bons gerentes sabem que não devem cometer esses erros

Allin Kamachiqkunam Yachanku Mana Kay Pantaykunata

ንፉዓት ኣካየድቲ ነዚ ጌጋታት ከምዘይፍጽሙ ይፈልጡ እዮም።

Abaphathi Abahle Bayazi Ukungawenzi Lawa Maphutha

In one of the many collaborative sessions with various flavors of LLMs - in this case, Claude - I arrived at this list of heuristics. It seems like once you reach some early-mid point of your career in tech, you create a portfolio page (still to be done), and you put a post with your heuristics on it (now, done). They lived in my personal notes for a few weeks, never to be published, but I caught myself coming back to them almost daily at work. All of them meta-game the approach to working with emerging tech; it seems they deserve to be here. Just Another Interview Question Any professional conundrum can be framed as a case study interview question, and those - being hypothetical and emotionless - are much easier to handle than real-life scenarios. Apply to get yourself out of a conundrum with cold logic, experience, and without an emotional load clouding your judgment. Friction Fulcrum Friction in a process increases overall quality. Too little leads to carelessness, too much is gridlock. Apply for a little extra motivation when dealing with pushback from stakeholders, an unreceptive market, or other ✨offputting✨ circumstances. Cognitive Compost Some ideas are scraps and peels. They need to decompose in the brain’s background processes to resurface as something innovative and useful. Apply when frustrated about wasted work that ended up going nowhere. Skill Scaffolding Learning is modular, especially in tech. Any new skills, even seemingly specific, are a modular block to quickly adapt to the next knowledge gap. Apply when unmotivated to learn a new skill that seems too specific or niche, or when demotivated by having to pivot to a new field. Innovation Inertia The bigger the past successes (or market share) of the organization, the bigger the resistance to change. A flavor of The Innovator’s Dilemma. Apply to protect sanity when building experimental projects in large organizations. Collaborative Crystallization Loose ideas align and form a cohesive strategy in a group setting. Apply when debating whether the half-baked idea is worth bringing up to the team. Expertise Erosion In the long run, overfocusing on one field of expertise - especially coupled with staying out of the loop on emerging tech and changing markets - makes you relatively obsolete. Apply when rolling your eyes at yet another AI headline. Decisional Doppler Effect The closer you are to decisions, the higher their urgency and importance seem to be. They’re usually not that important, or that urgent, and benefit from taking a breath and looking at the broader context. Apply when getting stressed out about work. The next three posts, currently sitting defeated in my half-baked-and-unfinished queue: The intersection of the California AI bill, and the impact of regulation on past technological breakthroughs
Innovating from first principles and this batch of AI products
Calling The Peak of Inflated Expectations for AI If you reply or comment with your pick, I will (most likely) actually finish it. In one of the many collaborative sessions with various flavors of LLMs - in this case, Claude - I arrived at this list of heuristics. It seems like once you reach some early-mid point of your career in tech, you create a portfolio page (still to be done), and you put a post with your heuristics on it (now, done). collaborative They lived in my personal notes for a few weeks, never to be published, but I caught myself coming back to them almost daily at work. All of them meta-game the approach to working with emerging tech; it seems they deserve to be here. Just Another Interview Question Any professional conundrum can be framed as a case study interview question, and those - being hypothetical and emotionless - are much easier to handle than real-life scenarios. Apply to get yourself out of a conundrum with cold logic, experience, and without an emotional load clouding your judgment. Friction Fulcrum Friction in a process increases overall quality. Too little leads to carelessness, too much is gridlock. Apply for a little extra motivation when dealing with pushback from stakeholders, an unreceptive market, or other ✨offputting✨ circumstances. Cognitive Compost Some ideas are scraps and peels. They need to decompose in the brain’s background processes to resurface as something innovative and useful. Apply when frustrated about wasted work that ended up going nowhere. Skill Scaffolding Learning is modular, especially in tech. Any new skills, even seemingly specific, are a modular block to quickly adapt to the next knowledge gap. Apply when unmotivated to learn a new skill that seems too specific or niche, or when demotivated by having to pivot to a new field. Innovation Inertia The bigger the past successes (or market share) of the organization, the bigger the resistance to change. A flavor of The Innovator’s Dilemma . Apply to protect sanity when building experimental projects in large organizations. The Innovator’s Dilemma Collaborative Crystallization Loose ideas align and form a cohesive strategy in a group setting. Apply when debating whether the half-baked idea is worth bringing up to the team. Expertise Erosion In the long run, overfocusing on one field of expertise - especially coupled with staying out of the loop on emerging tech and changing markets - makes you relatively obsolete. Apply when rolling your eyes at yet another AI headline. Decisional Doppler Effect The closer you are to decisions, the higher their urgency and importance seem to be. They’re usually not that important, or that urgent, and benefit from taking a breath and looking at the broader context. Apply when getting stressed out about work. The next three posts, currently sitting defeated in my half-baked-and-unfinished queue: The intersection of the California AI bill, and the impact of regulation on past technological breakthroughs Innovating from first principles and this batch of AI products Calling The Peak of Inflated Expectations for AI The intersection of the California AI bill, and the impact of regulation on past technological breakthroughs Innovating from first principles and this batch of AI products Calling The Peak of Inflated Expectations for AI If you reply or comment with your pick, I will (most likely) actually finish it.

Stuck? Here Are Some Heuristics to Apply Liberally

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