Instrumental was founded by ex-Apple product design engineers Anna-Katrina Shedletsky and Samuel Weiss with a mission to eliminate $2T in electronic manufacturing waste. We have built the leading Manufacturing AI and Data Platform for brands and manufacturers developing and producing consumer and mission-critical electronics. The platform optimizes assembly, operations, and quality control processes to accelerate time-to-market, improve yields, eliminate rework, and save engineering time. We support the global engineering and operations of companies like Axon, Bose, Cisco, Meta, SolarEdge, and ChargePoint as they eliminate waste and deliver higher-quality products faster than ever before.

Instrumental

instrumental

Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi, a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. 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. Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi , a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. Rahul Chaturvedi Rahul Chaturvedi With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. Rahul Chaturvedi's contributions Rahul Chaturvedi's contributions The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Chaturvedi's work Chaturvedi's work Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. 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

Mastering Real-Time Data: Rahul Chaturvedi's Strategies for Building Reliable Data Platforms

রিয়েল-টাইম ডেটা আয়ত্ত করা: নির্ভরযোগ্য ডেটা প্ল্যাটফর্ম তৈরির জন্য রাহুল চতুর্বেদীর কৌশলগুলি

Echtzeitdaten beherrschen: Rahul Chaturvedis Strategien zum Aufbau zuverlässiger Datenplattformen

Dominar los datos en tiempo real: estrategias de Rahul Chaturvedi para crear plataformas de datos confiables

Maîtriser les données en temps réel : les stratégies de Rahul Chaturvedi pour créer des plates-formes de données fiables

रियल-टाइम डेटा में महारत हासिल करना: विश्वसनीय डेटा प्लेटफ़ॉर्म बनाने के लिए राहुल चतुर्वेदी की रणनीतियाँ

リアルタイム データの習得: 信頼性の高いデータ プラットフォームを構築するための Rahul Chaturvedi の戦略

실시간 데이터 마스터하기: 안정적인 데이터 플랫폼 구축을 위한 Rahul Chaturvedi의 전략

Dominando dados em tempo real: estratégias de Rahul Chaturvedi para construir plataformas de dados confiáveis

Освоение данных в реальном времени: стратегии Рахула Чатурведи по созданию надежных платформ данных

Gerçek Zamanlı Verilerde Uzmanlaşmak: Rahul Chaturvedi'nin Güvenilir Veri Platformları Oluşturma Stratejileri

Làm chủ dữ liệu thời gian thực: Chiến lược xây dựng nền tảng dữ liệu đáng tin cậy của Rahul Chaturvedi

掌握实时数据：Rahul Chaturvedi 构建可靠数据平台的策略

Telegram: Crypto Island's Bridge to the Mainland

টেলিগ্রাম: ক্রিপ্টো দ্বীপের মূল ভূখণ্ডের সেতু

Telegram: Die Brücke der Kryptoinsel zum Festland

Telegram: el puente de Crypto Island hacia el continente

Télégramme : le pont de Crypto Island vers le continent

टेलीग्राम: क्रिप्टो द्वीप का मुख्य भूमि से पुल

Telegram: クリプト島と本土を結ぶ橋

텔레그램: 크립토 아일랜드에서 본토로 연결되는 다리

Telegram: a ponte da Crypto Island para o continente

Telegram: мост Крипто-острова на материк

Telegram: Kripto Adası'nın anakaraya olan köprüsü

Telegram: Cầu nối đảo Crypto vào đất liền

Telegram：加密岛通往大陆的桥梁

The Crowded AI Spaces Still Have Room for One More Genius

জনাকীর্ণ এআই স্পেসগুলিতে এখনও আরও একটি প্রতিভা থাকার জায়গা রয়েছে

In den überfüllten KI-Räumen ist noch Platz für ein weiteres Genie

Los abarrotados espacios de IA todavía tienen espacio para un genio más

Les espaces IA bondés ont encore de la place pour un génie supplémentaire

भीड़-भाड़ वाले एआई स्पेस में अभी भी एक और प्रतिभा के लिए जगह है

混雑したAI空間にはまだもう一人の天才の余地がある

혼잡한 AI 공간에는 아직 천재가 한 명 더 들어갈 여지가 있습니다

Os lotados espaços de IA ainda têm espaço para mais um gênio

В переполненных пространствах ИИ все еще есть место для еще одного гения

Kalabalık Yapay Zeka Alanlarında Hala Bir Dahiye Daha Yer Var

Không gian AI đông đúc vẫn còn chỗ cho một thiên tài nữa

拥挤的人工智能领域仍有空间容纳更多天才

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 23 of 26. FIRST CLAIM FOR RELIEF - Violation of Exchange Act Section 15(a) 329. The Commission re-alleges and incorporates by reference here the allegations in paragraphs 1 through 329. 330. By engaging in the acts and conduct described in this Complaint, Consensys, a person other than a natural person under the Exchange Act, is a broker and made use of the mails and the means and instrumentalities of interstate commerce to effect transactions in, or to induce or attempt to induce the purchase or sale of, securities for the account of others, without registering as a broker, and without having an exemption or exception from such registration. 331. By reason of the foregoing, Consensys violated, and, unless enjoined, will continue to violate Exchange Act Section 15(a) [15 U.S.C. § 78o(a)]. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 23 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 23 of 26. HackerNoon’s Legal PDF Series here FIRST CLAIM FOR RELIEF - Violation of Exchange Act Section 15(a) 329. The Commission re-alleges and incorporates by reference here the allegations in paragraphs 1 through 329. 330. By engaging in the acts and conduct described in this Complaint, Consensys, a person other than a natural person under the Exchange Act, is a broker and made use of the mails and the means and instrumentalities of interstate commerce to effect transactions in, or to induce or attempt to induce the purchase or sale of, securities for the account of others, without registering as a broker, and without having an exemption or exception from such registration. 331. By reason of the foregoing, Consensys violated, and, unless enjoined, will continue to violate Exchange Act Section 15(a) [15 U.S.C. § 78o(a)]. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

SEC Alleges Consensys Violated Exchange Act in Brokerage of Crypto Securities

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 21 of 26. V. CONSENSYS WAS REQUIRED TO, BUT DID NOT, REGISTER AS A BROKER WITH RESPECT TO METAMASK SWAPS. 323. As fully set forth in the preceding paragraphs, Consensys, through MetaMask Swaps, used the means and instrumentalities of interstate commerce to engage in the business of effecting transactions in securities for the account of others by, for example, soliciting potential investors in crypto asset securities, holding itself out as a place to buy and sell crypto assets (including crypto asset securities), providing investment advice by highlighting the “best” prices or “best” value, and otherwise facilitating trading in crypto asset securities by creating customer wallets (i.e., “accounts”), routing customer orders, handling customer crypto asset securities through Consensys-operated smart contract addresses, facilitating order execution by submitting blockchain transactions to a Consensys node, and receiving transaction-based compensation for doing so. Consensys was herefore required to register with the SEC as a broker or operate pursuant to an exemption or exception but did not do so. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 21 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 21 of 26. HackerNoon’s Legal PDF Series here V. CONSENSYS WAS REQUIRED TO, BUT DID NOT, REGISTER AS A BROKER WITH RESPECT TO METAMASK SWAPS. 323. As fully set forth in the preceding paragraphs, Consensys, through MetaMask Swaps, used the means and instrumentalities of interstate commerce to engage in the business of effecting transactions in securities for the account of others by, for example, soliciting potential investors in crypto asset securities, holding itself out as a place to buy and sell crypto assets (including crypto asset securities), providing investment advice by highlighting the “best” prices or “best” value, and otherwise facilitating trading in crypto asset securities by creating customer wallets (i.e., “accounts”), routing customer orders, handling customer crypto asset securities through Consensys-operated smart contract addresses, facilitating order execution by submitting blockchain transactions to a Consensys node, and receiving transaction-based compensation for doing so. Consensys was herefore required to register with the SEC as a broker or operate pursuant to an exemption or exception but did not do so. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

Consensys Faces SEC Probe for Alleged Unlicensed Brokerage via MetaMask

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 13 of 26. A. MATIC 142. “MATIC” is the native token of the Polygon blockchain. Polygon, originally called the Matic Network and rebranded as Polygon in 2021, is a blockchain platform created in 2017 in Mumbai, India by, among others, Jaynti Kanani, Sandeep Nailwal, and Anurag Arjun. Since its creation, Polygon’s founders have remained actively involved with Polygon through “Polygon Labs” (“Polygon”), an entity they also founded for “the development and growth of Polygon.” 143. According to the Polygon website, https://polygon.technology/, the Polygon network is an Ethereum scaling platform that enables developers to build scalable user-friendly dApps with low transaction fees, purportedly by hosting “sidechains” that run alongside the Ethereum blockchain, and allows users to process transactions and initiate the transfer of assets and technology development on Polygon’s supposedly less congested sidechain network. 144. Polygon issued a fixed supply of 10 billion MATIC tokens. MATIC holders can earn additional MATIC for staking their MATIC on the Polygon platform and becoming a validator, from delegating their MATIC to other validators in return for a portion of the fees collected from validating transactions, or from staking their MATIC with other third parties, such as crypto asset platforms that offer staking services. 145. According to the initial whitepaper for MATIC, “Matic Tokens [we]re expected to provide the economic incentives … on the Matic Network [now Polygon] … [W]ithout the Matic Token, there would be no incentive for users to expend resources to participate in activities or provide services for the benefit of the entire ecosystem on the Matic Network.” 146. In or around 2018, Polygon sold approximately 4 percent of the total supply of MATIC in two early rounds of sales raising $165,000 at a price of $0.00079 USD per 1 MATIC and $450,000 at a price of $0.00263 USD per 1 MATIC. In April 2019, Polygon sold another 19% of the total supply of MATIC to the public through a so-called “initial exchange offering” (or “IEO”— essentially, an initial offer and sale of a crypto asset security on a crypto trading platform) on the Binance.com crypto asset trading platform at a price of $0.00263 USD per 1 MATIC, raising an additional $5 million to fund development of the network. 147. From the time of its offering, MATIC was offered and sold as an investment contract and therefore a security. 148. The price of all MATIC tokens goes up or down together. 149. MATIC has been available for buying and selling through the brokerage services offered by MetaMask Swaps since at least July 2021. 150. The information Polygon publicly disseminated would lead a reasonable investor, including those who purchased MATIC since October 2020, to view MATIC as an investment. Specifically, MATIC holders would reasonably expect to profit from Polygon’s efforts to grow the Polygon protocol because this growth would in turn increase the demand for and the value of MATIC. 151. For example, Polygon stated publicly, including in the whitepaper, that it would pool investment proceeds through its private and public fundraising to develop and grow its business. 152. Following the IEO, moreover, Polygon engaged in additional MATIC sales, stating publicly that it was doing so in order to raise the funds needed to support the growth of its network. On February 7, 2022, Polygon reported on its blog that it raised about $450 million through a purportedly private sale of its native MATIC token in a funding round to several prominent venture capital firms. Polygon reported, “[w]ith this warchest, the core team can secure Polygon’s lead in paving the way for mass adoption of Web3 applications, a race that we believe will result in Ethereum prevailing over alternative blockchains.” 153. Polygon has also reported fundraising from other marquee and celebrity investors. 154. Polygon also stated that it would reserve roughly 67% of MATIC to support the Polygon ecosystem, the Foundation, and network operations. Another 20% of MATIC was further reserved to compensate the Polygon team members and advisors, aligning their fortunes with investors’ with respect to MATIC. 155. In addition, the Polygon blog provides frequent updates on network growth and developments at Polygon, including, prior to December 2022, weekly statistics on active wallets and transactions per day, as well as financial metrics such as revenue per day and total network revenue. 156. Polygon has also routinely announced when crypto asset trading platforms have made MATIC available for trading. 157. Polygon has explicitly encouraged MATIC purchasers to view MATIC as an investment in other ways. For example, in a February 5, 2021 tweet, 14 months after MATIC’s single biggest price drop, Nailwal compared the token to a prize fighter that came back from defeat to become a champion: 158. Also, on November 3, 2022, Nailwal stated on Twitter: “I will not rest till @0xPolygon gets its well-deserved ‘Top 3’ spot alongside BTC & ETH. No other project comes even close.” In a May 24, 2022 “Fireside Chat” with CNBC posted on YouTube, Bejelic described part of “what’s different about Polygon” as: “[w]e are as a team very, very committed, we have a very hands on approach with all the projects out there, we are working around the clock on adoption and that is why we are currently the most adopted scaling infrastructure platform.” Into 2023, the founders of Polygon continued to promote the platform through various social media. For example, on February 21, 2023, Nailwal tweeted, and Kanani retweeted, “Polygon has grown exponentially. To continue on this path of stupendous growth we have crystallized our strategy for the next 5 yrs to drive mass adoption of web3 by scaling Ethereum. Our treasury remains healthy with a balance of over $250 million and over 1.9 billion MATIC.” 159. Since January 2022, Polygon has also marketed that it “burns” MATIC tokens accumulated as fees, indicating that the total supply of MATIC would decrease. For example, in January 2022, Polygon emphatically announced a protocol upgrade that enabled burning in a blogpost titled, “Burn, MATIC, Burn!” As Polygon explained in another blog post on its website around the same time, “Polygon’s MATIC has a fixed supply of 10 billion, so any reduction in the number of available tokens will have a deflationary effect.” As of March 28, 2023, Polygon had burned approximately 9.6 million MATIC tokens. This marketed burning of MATIC as part of the Polygon’s network’s “deflationary effect” has led investors reasonably to view their purchase of MATIC as having the potential for profit to the extent there is a built-in mechanism to decrease the supply and therefore increase the price of MATIC. 160. In another white paper, Nailwal and others recently announced a revised Polygon protocol where a new token, POL, would succeed MATIC “as the native token of the Polygon ecosystem.” The white paper states that: “As the successor of MATIC, POL is envisioned to become an instrumental tool for coordination and growth of the Polygon ecosystem and the main driver of the vision of Polygon as the Value Layer for the Internet.” 161. The whitepaper lays out a model “to simulate important performance indicators of the POL-powered ecosystem.” The model estimates a $5 average POL price during the proposed 10-year period, a significant increase from the current price of MATIC. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 13 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 13 of 26. HackerNoon’s Legal PDF Series here A. MATIC 142. “MATIC” is the native token of the Polygon blockchain. Polygon, originally called the Matic Network and rebranded as Polygon in 2021, is a blockchain platform created in 2017 in Mumbai, India by, among others, Jaynti Kanani, Sandeep Nailwal, and Anurag Arjun. Since its creation, Polygon’s founders have remained actively involved with Polygon through “Polygon Labs” (“Polygon”), an entity they also founded for “the development and growth of Polygon.” 143. According to the Polygon website, https://polygon.technology/, the Polygon network is an Ethereum scaling platform that enables developers to build scalable user-friendly dApps with low transaction fees, purportedly by hosting “sidechains” that run alongside the Ethereum blockchain, and allows users to process transactions and initiate the transfer of assets and technology development on Polygon’s supposedly less congested sidechain network. 144. Polygon issued a fixed supply of 10 billion MATIC tokens. MATIC holders can earn additional MATIC for staking their MATIC on the Polygon platform and becoming a validator, from delegating their MATIC to other validators in return for a portion of the fees collected from validating transactions, or from staking their MATIC with other third parties, such as crypto asset platforms that offer staking services. 145. According to the initial whitepaper for MATIC, “Matic Tokens [we]re expected to provide the economic incentives … on the Matic Network [now Polygon] … [W]ithout the Matic Token, there would be no incentive for users to expend resources to participate in activities or provide services for the benefit of the entire ecosystem on the Matic Network.” 146. In or around 2018, Polygon sold approximately 4 percent of the total supply of MATIC in two early rounds of sales raising $165,000 at a price of $0.00079 USD per 1 MATIC and $450,000 at a price of $0.00263 USD per 1 MATIC. In April 2019, Polygon sold another 19% of the total supply of MATIC to the public through a so-called “initial exchange offering” (or “IEO”— essentially, an initial offer and sale of a crypto asset security on a crypto trading platform) on the Binance.com crypto asset trading platform at a price of $0.00263 USD per 1 MATIC, raising an additional $5 million to fund development of the network. 147. From the time of its offering, MATIC was offered and sold as an investment contract and therefore a security. 148. The price of all MATIC tokens goes up or down together. 149. MATIC has been available for buying and selling through the brokerage services offered by MetaMask Swaps since at least July 2021. 150. The information Polygon publicly disseminated would lead a reasonable investor, including those who purchased MATIC since October 2020, to view MATIC as an investment. Specifically, MATIC holders would reasonably expect to profit from Polygon’s efforts to grow the Polygon protocol because this growth would in turn increase the demand for and the value of MATIC. 151. For example, Polygon stated publicly, including in the whitepaper, that it would pool investment proceeds through its private and public fundraising to develop and grow its business. 152. Following the IEO, moreover, Polygon engaged in additional MATIC sales, stating publicly that it was doing so in order to raise the funds needed to support the growth of its network. On February 7, 2022, Polygon reported on its blog that it raised about $450 million through a purportedly private sale of its native MATIC token in a funding round to several prominent venture capital firms. Polygon reported, “[w]ith this warchest, the core team can secure Polygon’s lead in paving the way for mass adoption of Web3 applications, a race that we believe will result in Ethereum prevailing over alternative blockchains.” 153. Polygon has also reported fundraising from other marquee and celebrity investors. 154. Polygon also stated that it would reserve roughly 67% of MATIC to support the Polygon ecosystem, the Foundation, and network operations. Another 20% of MATIC was further reserved to compensate the Polygon team members and advisors, aligning their fortunes with investors’ with respect to MATIC. 155. In addition, the Polygon blog provides frequent updates on network growth and developments at Polygon, including, prior to December 2022, weekly statistics on active wallets and transactions per day, as well as financial metrics such as revenue per day and total network revenue. 156. Polygon has also routinely announced when crypto asset trading platforms have made MATIC available for trading. 157. Polygon has explicitly encouraged MATIC purchasers to view MATIC as an investment in other ways. For example, in a February 5, 2021 tweet, 14 months after MATIC’s single biggest price drop, Nailwal compared the token to a prize fighter that came back from defeat to become a champion: 158. Also, on November 3, 2022, Nailwal stated on Twitter: “I will not rest till @0xPolygon gets its well-deserved ‘Top 3’ spot alongside BTC & ETH. No other project comes even close.” In a May 24, 2022 “Fireside Chat” with CNBC posted on YouTube, Bejelic described part of “what’s different about Polygon” as: “[w]e are as a team very, very committed, we have a very hands on approach with all the projects out there, we are working around the clock on adoption and that is why we are currently the most adopted scaling infrastructure platform.” Into 2023, the founders of Polygon continued to promote the platform through various social media. For example, on February 21, 2023, Nailwal tweeted, and Kanani retweeted, “Polygon has grown exponentially. To continue on this path of stupendous growth we have crystallized our strategy for the next 5 yrs to drive mass adoption of web3 by scaling Ethereum. Our treasury remains healthy with a balance of over $250 million and over 1.9 billion MATIC.” 159. Since January 2022, Polygon has also marketed that it “burns” MATIC tokens accumulated as fees, indicating that the total supply of MATIC would decrease. For example, in January 2022, Polygon emphatically announced a protocol upgrade that enabled burning in a blogpost titled, “Burn, MATIC, Burn!” As Polygon explained in another blog post on its website around the same time, “Polygon’s MATIC has a fixed supply of 10 billion, so any reduction in the number of available tokens will have a deflationary effect.” As of March 28, 2023, Polygon had burned approximately 9.6 million MATIC tokens. This marketed burning of MATIC as part of the Polygon’s network’s “deflationary effect” has led investors reasonably to view their purchase of MATIC as having the potential for profit to the extent there is a built-in mechanism to decrease the supply and therefore increase the price of MATIC. 160. In another white paper, Nailwal and others recently announced a revised Polygon protocol where a new token, POL, would succeed MATIC “as the native token of the Polygon ecosystem.” The white paper states that: “As the successor of MATIC, POL is envisioned to become an instrumental tool for coordination and growth of the Polygon ecosystem and the main driver of the vision of Polygon as the Value Layer for the Internet.” 161. The whitepaper lays out a model “to simulate important performance indicators of the POL-powered ecosystem.” The model estimates a $5 average POL price during the proposed 10-year period, a significant increase from the current price of MATIC. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

Polygon's MATIC Token Under SEC Spotlight: What Investors Need to Know

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 4 of 26. JURISDICTION AND VENUE 18. This Court has jurisdiction over this action pursuant to 28 U.S.C. § 1331, Sections 20(b), 20(d), and 22 of the Securities Act [15 U.S.C. §§ 77t(b), 77t(d), and 77v], and Sections 21(d), 21(e), and 27 of the Exchange Act [15 U.S.C. §§ 78u(d), 78u(e), and 78aa]. 19. Defendant, directly and indirectly, has made use of the means or instrumentalities of interstate commerce or of the mails in connection with the transactions, acts, practices, and courses of business alleged herein. 20. Venue lies in this District under Securities Act Section 22(a) [15 U.S.C. § 77v(a)] and Exchange Act Section 27 [15 U.S.C. § 78aa]. Defendants may be found in, are inhabitants of, or transact business in the Eastern District of New York, and certain of the acts, practices, transactions, and courses of business alleged in this Complaint occurred within this District. Prior to February 2024, Consensys was based in Brooklyn, New York, and still maintains offices in Brooklyn. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 4 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 4 of 26. HackerNoon’s Legal PDF Series here JURISDICTION AND VENUE 18. This Court has jurisdiction over this action pursuant to 28 U.S.C. § 1331, Sections 20(b), 20(d), and 22 of the Securities Act [15 U.S.C. §§ 77t(b), 77t(d), and 77v], and Sections 21(d), 21(e), and 27 of the Exchange Act [15 U.S.C. §§ 78u(d), 78u(e), and 78aa]. 19. Defendant, directly and indirectly, has made use of the means or instrumentalities of interstate commerce or of the mails in connection with the transactions, acts, practices, and courses of business alleged herein. 20. Venue lies in this District under Securities Act Section 22(a) [15 U.S.C. § 77v(a)] and Exchange Act Section 27 [15 U.S.C. § 78aa]. Defendants may be found in, are inhabitants of, or transact business in the Eastern District of New York, and certain of the acts, practices, transactions, and courses of business alleged in this Complaint occurred within this District. Prior to February 2024, Consensys was based in Brooklyn, New York, and still maintains offices in Brooklyn. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

Consensys Faces Federal Court in NY Over Securities Violations

Businesses increasingly recognize the potential of generative artificial intelligence (GenAI). However, many organizations find themselves trapped in a cycle of endless pilot projects, unable to fully realize the benefits of GenAI in production environments. The AI control platform Aporia is changing how enterprises adopt and scale GenAI solutions, pushing them to break free from pilot limbo and achieve live success. "At Aporia, our mission is to maximize the immense potential of GenAI and the real-world challenges that hinder its widespread adoption," says Liran Hason, Chief Executive Officer (CEO) of Aporia, who coined the term “GenAI Chasm,” referring to the pilot phase AI often gets caught in before being able to go live due to security and reliability issues. With its guardrails and comprehensive risk mitigation strategies, the company is opening new avenues for seamless GenAI deployment across industries. Aporia's Robust Guardrails for Seamless GenAI Adoption One of the primary challenges in GenAI deployment is the lack of adequate control and risk management. Aporia’s comprehensive suite of tools is meant to ensure safe, responsible, and compliant AI interactions. These guardrails seamlessly integrate with existing AI workflows, providing a powerful layer of protection against potential risks such as data leakage, hallucinations, and prompt injection attacks. "Aporia's guardrails are the cornerstone of our platform," explains Hason. "They empower businesses to adopt GenAI with confidence, secure in the knowledge that their applications are reliable, secure, and aligned with their core values and objectives.” Mitigating Hallucinations and Prompt Injection Risks Hallucinations and prompt injection attacks pose threats to the integrity and reliability of GenAI applications. Aporia addresses these challenges with a multi-faceted approach that combines advanced detection algorithms, real-time actions, and proactive mitigation strategies. "Hallucinations and prompt injections can erode trust in AI systems and lead to severe consequences," notes Hason. "Our comprehensive approach ensures that these risks are identified and mitigated before they can impact end-users or the business." With Aporia's robust safeguards, organizations can confidently deploy GenAI applications, knowing their systems are protected against these critical threats. Ensuring Compliance and Ethical AI Deployment with Aporia's Solutions GenAI is becoming more prevalent, and compliance with regulations and ethical standards is paramount. Aporia's solutions are designed to help businesses deal with this complex landscape easily and to cross the GenAI chasm with confidence. "Compliance and ethics are not mere afterthoughts for us. They are embedded in the very fabric of our platform," states Hason. "We firmly believe that the long-term success of GenAI hinges on its ability to operate within a framework of trust, transparency, and accountability from both the user and business perspective." Pioneering Research for Secure and Reliable GenAI Applications Aporia Labs, Aporia's dedicated research and development division, is advancing the platform’s security and reliability. Staffed by a team of world-class AI researchers, security experts, and data scientists, Aporia Labs conducts constant research to identify emerging threats and develop services. "The work we do at Aporia Labs is instrumental to the future of GenAI. It's shaping the very trajectory of the industry," says Hason. Aporia Labs makes way for secure and reliable GenAI applications through its pioneering research. How Aporia Streamlines the GenAI Development Lifecycle Aporia's AI control platform is designed to streamline the entire GenAI development lifecycle, from ideation to deployment and beyond. A unified environment for building, testing, and monitoring GenAI applications enables teams to deliver better results. Furthermore, the platform's intuitive interface and powerful automation capabilities make it easy for businesses to bring their GenAI visions to life without compromising quality or security. "We've witnessed firsthand the transformative impact of Aporia on the GenAI development process," shares Hason. With Aporia, organizations can streamline their GenAI development efforts, accelerating time-to-market and maximizing the return on their AI investments. The Key to Unlocking GenAI's Full Potential in Enterprise Settings For enterprises seeking to harness GenAI's full potential, Aporia's AI control platform is the key to success. With its comprehensive feature set, scalable architecture, and enterprise-grade security, the platform is able to meet large-scale AI deployments' unique needs and challenges. From customizable guardrails to advanced analytics and reporting, Aporia provides the tools and insights to drive GenAI adoption and return on investment (ROI) across the organization. "Enterprises face complex challenges when it comes to scaling GenAI, from ensuring security and governance to driving organization-wide adoption," notes Hason. "Aporia's platform is purpose-built to address these challenges head-on, enabling enterprises to unlock the full potential of GenAI and drive transformative business outcomes." Bridging the Gap: Aporia's Proven Strategies for Successful GenAI Pilot-to-Production Transitions Transitioning GenAI projects from pilot to production is a critical milestone many businesses struggle with. Aporia bridges this gap with proven strategies and best practices that have helped countless organizations successfully deal with this journey. Aporia remains a leading enabler for businesses to unlock the full potential of this advanced technology. With Aporia, businesses can confidently solve the challenges of GenAI deployment, harnessing its immense potential to drive growth and become a more competitive advantage in an AI-driven future. This piece was published as a part of HackerNoon’s Business Blogging Program. For more details, please click here. Businesses increasingly recognize the potential of generative artificial intelligence (GenAI). However, many organizations find themselves trapped in a cycle of endless pilot projects, unable to fully realize the benefits of GenAI in production environments. Businesses increasingly recognize the potential of generative artificial intelligence (GenAI). However, many organizations find themselves trapped in a cycle of endless pilot projects, unable to fully realize the benefits of GenAI in production environments. The AI control platform Aporia is changing how enterprises adopt and scale GenAI solutions, pushing them to break free from pilot limbo and achieve live success. Aporia Aporia " At Aporia, our mission is to maximize the immense potential of GenAI and the real-world challenges that hinder its widespread adoption, " says Liran Hason, Chief Executive Officer (CEO) of Aporia, who coined the term “GenAI Chasm,” referring to the pilot phase AI often gets caught in before being able to go live due to security and reliability issues. With its guardrails and comprehensive risk mitigation strategies, the company is opening new avenues for seamless GenAI deployment across industries. At Aporia, our mission is to maximize the immense potential of GenAI and the real-world challenges that hinder its widespread adoption, Aporia's Robust Guardrails for Seamless GenAI Adoption Aporia's Robust Guardrails for Seamless GenAI Adoption One of the primary challenges in GenAI deployment is the lack of adequate control and risk management. Aporia’s comprehensive suite of tools is meant to ensure safe, responsible, and compliant AI interactions. These guardrails seamlessly integrate with existing AI workflows, providing a powerful layer of protection against potential risks such as data leakage, hallucinations, and prompt injection attacks. "Aporia's guardrails are the cornerstone of our platform," explains Hason. " They empower businesses to adopt GenAI with confidence, secure in the knowledge that their applications are reliable, secure, and aligned with their core values and objectives.” "Aporia's guardrails are the cornerstone of our platform," They empower businesses to adopt GenAI with confidence, secure in the knowledge that their applications are reliable, secure, and aligned with their core values and objectives.” Mitigating Hallucinations and Prompt Injection Risks Mitigating Hallucinations and Prompt Injection Risks Hallucinations and prompt injection attacks pose threats to the integrity and reliability of GenAI applications. Aporia addresses these challenges with a multi-faceted approach that combines advanced detection algorithms, real-time actions, and proactive mitigation strategies. "Hallucinations and prompt injections can erode trust in AI systems and lead to severe consequences," notes Hason. " Our comprehensive approach ensures that these risks are identified and mitigated before they can impact end-users or the business." With Aporia's robust safeguards, organizations can confidently deploy GenAI applications, knowing their systems are protected against these critical threats. "Hallucinations and prompt injections can erode trust in AI systems and lead to severe consequences," Our comprehensive approach ensures that these risks are identified and mitigated before they can impact end-users or the business." Ensuring Compliance and Ethical AI Deployment with Aporia's Solutions Ensuring Compliance and Ethical AI Deployment with Aporia's Solutions GenAI is becoming more prevalent, and compliance with regulations and ethical standards is paramount. Aporia's solutions are designed to help businesses deal with this complex landscape easily and to cross the GenAI chasm with confidence. "Compliance and ethics are not mere afterthoughts for us. They are embedded in the very fabric of our platform," states Hason. " We firmly believe that the long-term success of GenAI hinges on its ability to operate within a framework of trust, transparency, and accountability from both the user and business perspective." "Compliance and ethics are not mere afterthoughts for us. They are embedded in the very fabric of our platform," We firmly believe that the long-term success of GenAI hinges on its ability to operate within a framework of trust, transparency, and accountability from both the user and business perspective." Pioneering Research for Secure and Reliable GenAI Applications Pioneering Research for Secure and Reliable GenAI Applications Aporia Labs, Aporia's dedicated research and development division, is advancing the platform’s security and reliability. Staffed by a team of world-class AI researchers, security experts, and data scientists, Aporia Labs conducts constant research to identify emerging threats and develop services. "The work we do at Aporia Labs is instrumental to the future of GenAI. It's shaping the very trajectory of the industry," says Hason. Aporia Labs makes way for secure and reliable GenAI applications through its pioneering research. "The work we do at Aporia Labs is instrumental to the future of GenAI. It's shaping the very trajectory of the industry," How Aporia Streamlines the GenAI Development Lifecycle How Aporia Streamlines the GenAI Development Lifecycle Aporia's AI control platform is designed to streamline the entire GenAI development lifecycle, from ideation to deployment and beyond. A unified environment for building, testing, and monitoring GenAI applications enables teams to deliver better results. Furthermore, the platform's intuitive interface and powerful automation capabilities make it easy for businesses to bring their GenAI visions to life without compromising quality or security. "We've witnessed firsthand the transformative impact of Aporia on the GenAI development process," shares Hason. With Aporia, organizations can streamline their GenAI development efforts, accelerating time-to-market and maximizing the return on their AI investments. "We've witnessed firsthand the transformative impact of Aporia on the GenAI development process," The Key to Unlocking GenAI's Full Potential in Enterprise Settings The Key to Unlocking GenAI's Full Potential in Enterprise Settings For enterprises seeking to harness GenAI's full potential, Aporia's AI control platform is the key to success. With its comprehensive feature set, scalable architecture, and enterprise-grade security, the platform is able to meet large-scale AI deployments' unique needs and challenges. From customizable guardrails to advanced analytics and reporting, Aporia provides the tools and insights to drive GenAI adoption and return on investment (ROI) across the organization. "Enterprises face complex challenges when it comes to scaling GenAI, from ensuring security and governance to driving organization-wide adoption," notes Hason. "Aporia's platform is purpose-built to address these challenges head-on, enabling enterprises to unlock the full potential of GenAI and drive transformative business outcomes." "Enterprises face complex challenges when it comes to scaling GenAI, from ensuring security and governance to driving organization-wide adoption," "Aporia's platform is purpose-built to address these challenges head-on, enabling enterprises to unlock the full potential of GenAI and drive transformative business outcomes." Bridging the Gap: Aporia's Proven Strategies for Successful GenAI Pilot-to-Production Transitions Bridging the Gap: Aporia's Proven Strategies for Successful GenAI Pilot-to-Production Transitions Transitioning GenAI projects from pilot to production is a critical milestone many businesses struggle with. Aporia bridges this gap with proven strategies and best practices that have helped countless organizations successfully deal with this journey. Aporia remains a leading enabler for businesses to unlock the full potential of this advanced technology. With Aporia, businesses can confidently solve the challenges of GenAI deployment, harnessing its immense potential to drive growth and become a more competitive advantage in an AI-driven future. This piece was published as a part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as a part of HackerNoon’s Business Blogging Program. For more details, please click here . here

How Aporia Enables GenAI Deployment From Pilot Limbo to Live Success

অ্যাপোরিয়া কীভাবে পাইলট লিম্বো থেকে লাইভ সাফল্যের জন্য GenAI স্থাপনাকে সক্ষম করে

Wie Aporia die GenAI-Bereitstellung von der Pilotphase bis zum Live-Erfolg ermöglicht

Cómo Aporia permite la implementación de GenAI desde el limbo piloto hasta el éxito en vivo

Comment Aporia permet le déploiement de GenAI des limbes pilotes au succès réel

एपोरिया कैसे जेनएआई की तैनाती को पायलट लिम्बो से लाइव सफ़लता तक सक्षम बनाता है

Aporia が GenAI の導入をパイロット段階から実際の成功まで可能にする方法

Aporia가 파일럿 림보(Pilot Limbo)에서 실제 성공까지 GenAI 배포를 활성화하는 방법

Como Aporia permite a implantação de GenAI do piloto Limbo ao sucesso ao vivo

Как Aporia обеспечивает развертывание GenAI от экспериментального неопределенности до реального успеха

Aporia, Pilot Araf'tan Canlı Başarıya Kadar GenAI Dağıtımını Nasıl Etkinleştiriyor?

Cách Aporia cho phép triển khai GenAI từ thí điểm Limbo đến thành công trực tiếp

Aporia 如何使 GenAI 部署从试点走向成功

Author is leading Developer Productivity GTM at Microsoft Western Europe. GitHub Copilot is a part of this business area. GitHub Copilot Chat is an incredibly powerful and useful feature that allows you to chat with or about your code. Even though it’s 100% natural language-friendly (i.e., you can send your messages without using any specific syntax), leveraging some special chat capabilities can unlock new AI-assisted development scenarios and significantly boost your productivity. These powerful features, which you can use by applying special syntax, include chat participants, slash commands, and context variables. Note that the described features are available in VS Code and might not be fully supported in other IDEs where GitHub Copilot Chat is available. Target Your Question or Request by Messaging One of the Available Chat Participants In GitHub Copilot Chat, you can reference one of the AI-powered “domain experts” using conventional chat syntax—by prepending @ to the participant name. The currently available chat participants are: @workspace: Knows everything about the code in your currently open workspace. This is the chat participant you will most likely communicate with frequently. @terminal: Knows all about the integrated terminal shell, its contents, and its buffer. @vscode: Knows about the VS Code editor, its commands, and features. Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace chat participant right after we open the project folder in VS Code. In this particular case, you don’t even need to have files open in your editor. Compare this with the response you get without tagging @workspace: The @workspace chat participant is instrumental for all solution-wide queries where you want all code to be considered for the chat response. However, this doesn't mean that all code will be used and sent as part of the prompt. The GitHub Copilot Chat extension in VS Code does its best to determine relevant files and parts of these files using local knowledge and intelligence first. You can check which files and code lines were used for the prompt by expanding the “Used references” line: Productivity hint: Use Ctrl-Enter (Cmd-Enter) instead of just Enter after typing your message, and the @workspace string will be inserted into your message automatically before sending. Be Precise in Setting the Context Using Chat Variables In many cases, considering the full solution as the context for your question or request (by using @workspace) is overkill. You might want to point to specific files or even parts of the files in your message. Chat variables can help! Use # to call one from this list: #file: Points to a specific file in your workspace. #codebase: All content of the open workspace. It’s similar to using @workspace and might be useful when you chat with another agent (like @terminal) but still want to reference the full solution. #editor: Source code in the editor’s viewport (visible part). #git: Current git repository: branch, remotes, path, etc. #selection: The currently selected code. #terminalLastCommand: Last run command in the editor’s terminal. #terminalSelection: Selection in the editor's terminal. Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Productivity hint: Use the up and down keyboard arrows to pick the chat variable you need after typing #. In the case of #file, use keyboard navigation again to pick one of the suggested files. Call the Most Often Used Actions Quickly With Slash Commands Chatting with your code using natural language is fun, but having the option to call often-used actions using handy shortcuts is even better. Compare typing the full message “Explain how selected code works” versus typing “/”, then using keyboard arrows to pick /explain from the popup overlay. Another benefit of using the predefined syntax for commands is the confidence that GitHub Copilot understands our intent 100% correctly (natural language might have some ambiguity). There are a bunch of slash commands available. You can use them in conjunction with referencing the chat participant to provide the desired scope. Some of the commands are: /help: Help with available slash commands, chat participants, chat variables, and more. /doc: Generate documentation for the code. /explain: Explain how the code works (or get help with terminal commands if you prepend @terminal). /fix: Optimize and/or fix issues in the code. /tests: Create unit tests for the code. /new: Scaffold a new workspace. Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “/explain. Productivity hint: Try GitHub Copilot Chat in inline mode instead of having the chat always open in the side pane. Press Ctrl-I (Cmd-I) and type your message in the small overlay dialog that appears right above the line where your cursor is in the code window. Summary Use chat participants, chat variables, and slash commands to maintain full control over the conversation context, ensure correct and consistent understanding of your intentions, and ultimately chat and code faster! Start your free GitHub Copilot trial here: https://aka.ms/try-github-copilot References https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands


https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/


https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants


https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/


https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements


https://code.visualstudio.com/updates/v1_84#_chat-agents Author is leading Developer Productivity GTM at Microsoft Western Europe. GitHub Copilot is a part of this business area. Author is leading Developer Productivity GTM at Microsoft Western Europe. GitHub Copilot is a part of this business area. GitHub Copilot Chat is an incredibly powerful and useful feature that allows you to chat with or about your code. Even though it’s 100% natural language-friendly (i.e., you can send your messages without using any specific syntax), leveraging some special chat capabilities can unlock new AI-assisted development scenarios and significantly boost your productivity. These powerful features, which you can use by applying special syntax, include chat participants, slash commands, and context variables. Note that the described features are available in VS Code and might not be fully supported in other IDEs where GitHub Copilot Chat is available. Target Your Question or Request by Messaging One of the Available Chat Participants In GitHub Copilot Chat, you can reference one of the AI-powered “domain experts” using conventional chat syntax—by prepending @ to the participant name. The currently available chat participants are: @workspace: Knows everything about the code in your currently open workspace. This is the chat participant you will most likely communicate with frequently. @workspace : Knows everything about the code in your currently open workspace. This is the chat participant you will most likely communicate with frequently. @workspace @terminal: Knows all about the integrated terminal shell, its contents, and its buffer. @terminal : Knows all about the integrated terminal shell, its contents, and its buffer. @terminal @vscode: Knows about the VS Code editor, its commands, and features. @vscode : Knows about the VS Code editor, its commands, and features. @vscode Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace chat participant right after we open the project folder in VS Code. Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace chat participant right after we open the project folder in VS Code. Example: Let’s get information about the backend part of the project we’ve just been assigned to by asking the @workspace In this particular case, you don’t even need to have files open in your editor. Compare this with the response you get without tagging @workspace : @workspace The @workspace chat participant is instrumental for all solution-wide queries where you want all code to be considered for the chat response. However, this doesn't mean that all code will be used and sent as part of the prompt. The GitHub Copilot Chat extension in VS Code does its best to determine relevant files and parts of these files using local knowledge and intelligence first. You can check which files and code lines were used for the prompt by expanding the “Used references” line: @workspace Productivity hint: Use Ctrl-Enter (Cmd-Enter) instead of just Enter after typing your message, and the @workspace string will be inserted into your message automatically before sending. Productivity hint: Use Ctrl-Enter (Cmd-Enter) instead of just Enter after typing your message, and the @workspace string will be inserted into your message automatically before sending. Be Precise in Setting the Context Using Chat Variables In many cases, considering the full solution as the context for your question or request (by using @workspace ) is overkill. You might want to point to specific files or even parts of the files in your message. Chat variables can help! Use # to call one from this list: @workspace #file: Points to a specific file in your workspace. #file : Points to a specific file in your workspace. #file #codebase: All content of the open workspace. It’s similar to using @workspace and might be useful when you chat with another agent (like @terminal) but still want to reference the full solution. #codebase : All content of the open workspace. It’s similar to using @workspace and might be useful when you chat with another agent (like @terminal ) but still want to reference the full solution. #codebase @workspace @terminal #editor: Source code in the editor’s viewport (visible part). #editor : Source code in the editor’s viewport (visible part). #editor #git: Current git repository: branch, remotes, path, etc. #git : Current git repository: branch, remotes, path, etc. #git #selection: The currently selected code. #selection : The currently selected code. #selection #terminalLastCommand: Last run command in the editor’s terminal. #terminalLastCommand : Last run command in the editor’s terminal. #terminalLastCommand #terminalSelection: Selection in the editor's terminal. #terminalSelection : Selection in the editor's terminal. #terminalSelection Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Example: Let’s get help on improving method names in a specific file (and we want to ensure that the whole content of the file is taken into consideration). Productivity hint: Use the up and down keyboard arrows to pick the chat variable you need after typing #. In the case of #file , use keyboard navigation again to pick one of the suggested files. Productivity hint: Use the up and down keyboard arrows to pick the chat variable you need after typing #. In the case of #file , use keyboard navigation again to pick one of the suggested files. Call the Most Often Used Actions Quickly With Slash Commands Chatting with your code using natural language is fun, but having the option to call often-used actions using handy shortcuts is even better. Compare typing the full message “Explain how selected code works” versus typing “/”, then using keyboard arrows to pick /explain from the popup overlay. /explain Another benefit of using the predefined syntax for commands is the confidence that GitHub Copilot understands our intent 100% correctly (natural language might have some ambiguity). There are a bunch of slash commands available. You can use them in conjunction with referencing the chat participant to provide the desired scope. Some of the commands are: /help: Help with available slash commands, chat participants, chat variables, and more. /help : Help with available slash commands, chat participants, chat variables, and more. /help /doc: Generate documentation for the code. /doc : Generate documentation for the code. /doc /explain: Explain how the code works (or get help with terminal commands if you prepend @terminal). /explain : Explain how the code works (or get help with terminal commands if you prepend @terminal). /explain /fix: Optimize and/or fix issues in the code. /fix : Optimize and/or fix issues in the code. /fix /tests: Create unit tests for the code. /tests : Create unit tests for the code. /tests /new: Scaffold a new workspace. /new : Scaffold a new workspace. /new Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “/explain. Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “ /explain . Example: Let’s get an explanation for one of the regular expressions in our code. Select the code line, and use the slash command “ /explain Productivity hint: Try GitHub Copilot Chat in inline mode instead of having the chat always open in the side pane. Press Ctrl-I (Cmd-I) and type your message in the small overlay dialog that appears right above the line where your cursor is in the code window. Productivity hint: Try GitHub Copilot Chat in inline mode instead of having the chat always open in the side pane. Press Ctrl-I (Cmd-I) and type your message in the small overlay dialog that appears right above the line where your cursor is in the code window. Summary Use chat participants, chat variables, and slash commands to maintain full control over the conversation context, ensure correct and consistent understanding of your intentions, and ultimately chat and code faster! Start your free GitHub Copilot trial here: https://aka.ms/try-github-copilot https://aka.ms/try-github-copilot References https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_84#_chat-agents https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2024-01-30-code-faster-and-better-with-github-copilots-new-features-in-visual-studio/#slash-commands https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://github.blog/changelog/2023-11-30-github-copilot-november-30th-update/ https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://code.visualstudio.com/docs/copilot/copilot-chat#_chat-participants https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://devblogs.microsoft.com/visualstudio/copilot-chat-slash-commands-and-context-variables/ https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_85#_terminal-agent-and-command-suggestion-improvements https://code.visualstudio.com/updates/v1_84#_chat-agents https://code.visualstudio.com/updates/v1_84#_chat-agents https://code.visualstudio.com/updates/v1_84#_chat-agents

How to Leverage GitHub Copilot Chat Syntax: Chat Participants, Chat Variables, and Slash Commands

কিভাবে গিটহাব কপিলট চ্যাট সিনট্যাক্স ব্যবহার করবেন: চ্যাট অংশগ্রহণকারী, চ্যাট ভেরিয়েবল এবং স্ল্যাশ কমান্ড

So nutzen Sie die Chat-Syntax von GitHub Copilot: Chat-Teilnehmer, Chat-Variablen und Schrägstrichbefehle

Cómo aprovechar la sintaxis del chat de GitHub Copilot: participantes del chat, variables del chat y comandos de barra diagonal

Comment tirer parti de la syntaxe de discussion GitHub Copilot : participants au chat, variables de chat et commandes Slash

GitHub Copilot चैट सिंटैक्स का लाभ कैसे उठाएं: चैट प्रतिभागी, चैट वैरिएबल और स्लैश कमांड

GitHub Copilot チャット構文を活用する方法: チャット参加者、チャット変数、スラッシュコマンド

GitHub Copilot 채팅 구문을 활용하는 방법: 채팅 참가자, 채팅 변수 및 슬래시 명령

Como aproveitar a sintaxe de bate-papo do GitHub Copilot: participantes do bate-papo, variáveis de bate-papo e comandos de barra

Как использовать синтаксис чата GitHub Copilot: участники чата, переменные чата и команды слэша

GitHub Copilot Sohbet Sözdiziminden Nasıl Yararlanılır: Sohbet Katılımcıları, Sohbet Değişkenleri ve Eğik Çizgi Komutları

Cách tận dụng Cú pháp trò chuyện Copilot của GitHub: Người tham gia trò chuyện, Biến trò chuyện và Lệnh gạch chéo

如何利用 GitHub Copilot 聊天语法：聊天参与者、聊天变量和斜线命令

Operationalizing Diversity and Inclusion Requirements for AI Systems: Results 

The role of a site reliability engineer (SRE) is not to be understated. With the recent wave of developments in cloud computing and artificial intelligence (AI), scalability, security, and the actual reliability of these technologies can come into question. One tenured senior site reliability engineer is Chaitanya Kanth Tummalachervu, who has provided his services across sectors ranging from medical insurance, finance, retail, the automobile industry, and even video communications. In these fields, Tummalachervu’s principal focus lies in DevOps – or developer operations – where he supports teams of coders as they work to create, support, and protect AI-powered products for businesses and consumers alike. DevOps: How Automation, Security, and Deployment Are Perfected The inner workings of a developer team can vary greatly between projects. While small, agile teams may not run into issues, more complex workplaces or large-scale projects can often be mired in clutter - both digitally and physically. Site reliability engineers, such as Tummalachervu himself, help reduce the strain on these teams by creating systems tailored to the needs of each operation. He says, “The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7.” Central to his work, the field of digital artificial intelligence products can be as demanding as it is expansive. With the complexity of advanced neural networks, operations can be bogged down by miscommunication, collapsed dependencies, and delays in the development pipeline. Even worse, unsecured systems of such importance are also a prime target for malicious actors, furthering the burden on the teams supporting it. “It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams,” explains Tummalachervu. Indeed, his focus on software-driven automation has reportedly increased developer productivity by 40%, with incident response time also seeing a 20% reduction as well. Expertise Delivered in Contemporary AI Platforms Of particular note is Tummalachervu’s current project, where his integration of strategic team collaboration and choice software systems has secured a premiere AI-based video streaming platform. Here, a combination of software development and IT team coordination is employed to enable update releases in a timely and secure manner. Based on cloud infrastructure accessible to the public, Tummalachervu and his team contributed through his implementation of “DevSecOps” - a sub-field of DevOps focused on ensuring full security throughout the development process. “We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date,” says Tummalachervu. In addition to security and team coordination, Tummalachervu has also been commended for his utilization of Infrastructure as Code (IAC). A modern strategy involving the use of code to support computing infrastructure rather than manual tweaks and settings adjustments, IAC is seeing widespread adoption across many enterprise-level systems. It is most effective when deployed in cloud environments, as IAC-enabling tools such as Terraform, Helm, and Cloudformation are wielded by Tummalachervu to great effect. “I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” elaborates Tummalachervu. “With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use.” Prior to working on said cloud-based infrastructure itself, Tummalachervu was instrumental in migrating systems and data therein, keeping his signature focus on strategy throughout each project. “One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams,” he says. Beyond the Clouds: Contributions and Teachings In addition to his technical expertise, Tummalachervu is highly regarded in the IT community for his extensive body of work, which has been published in renowned journals throughout the academic and technology spheres. His contributions to these communities extend beyond publications; he has also conducted numerous faculty development programs, directly benefiting nearly 150 PhD scholars, professors, university students, and colleagues. “Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles,” Tummalachervu says. “I’ve even been asked to judge at a few hackathons and international award ceremonies.” Central to the digital world, site reliability engineers like Chaitanya Tummalachervu act as pillars for what will become the future of technology: cloud-based environments. Secured, stabilized, and maintained by SREs and their teams, the future can rest easy knowing that the global infrastructure is supported by these skilled engineers, armed with nothing but best practices and sheer determination. The role of a site reliability engineer (SRE) is not to be understated. With the recent wave of developments in cloud computing and artificial intelligence (AI), scalability, security, and the actual reliability of these technologies can come into question. One tenured senior site reliability engineer is Chaitanya Kanth Tummalachervu , who has provided his services across sectors ranging from medical insurance, finance, retail, the automobile industry, and even video communications. In these fields, Tummalachervu’s principal focus lies in DevOps – or developer operations – where he supports teams of coders as they work to create, support, and protect AI-powered products for businesses and consumers alike. Chaitanya Kanth Tummalachervu Chaitanya Kanth Tummalachervu DevOps: How Automation, Security, and Deployment Are Perfected The inner workings of a developer team can vary greatly between projects. While small, agile teams may not run into issues, more complex workplaces or large-scale projects can often be mired in clutter - both digitally and physically. Site reliability engineers, such as Tummalachervu himself, help reduce the strain on these teams by creating systems tailored to the needs of each operation. He says, “ The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7. ” The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7. Central to his work, the field of digital artificial intelligence products can be as demanding as it is expansive. With the complexity of advanced neural networks, operations can be bogged down by miscommunication, collapsed dependencies, and delays in the development pipeline. Even worse, unsecured systems of such importance are also a prime target for malicious actors, furthering the burden on the teams supporting it. “ It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams, ” explains Tummalachervu. Indeed, his focus on software-driven automation has reportedly increased developer productivity by 40%, with incident response time also seeing a 20% reduction as well. It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams, Expertise Delivered in Contemporary AI Platforms Of particular note is Tummalachervu’s current project, where his integration of strategic team collaboration and choice software systems has secured a premiere AI-based video streaming platform. Here, a combination of software development and IT team coordination is employed to enable update releases in a timely and secure manner. Based on cloud infrastructure accessible to the public, Tummalachervu and his team contributed through his implementation of “DevSecOps” - a sub-field of DevOps focused on ensuring full security throughout the development process. “ We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date, ” says Tummalachervu. We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date, In addition to security and team coordination, Tummalachervu has also been commended for his utilization of Infrastructure as Code (IAC). A modern strategy involving the use of code to support computing infrastructure rather than manual tweaks and settings adjustments, IAC is seeing widespread adoption across many enterprise-level systems. It is most effective when deployed in cloud environments, as IAC-enabling tools such as Terraform, Helm, and Cloudformation are wielded by Tummalachervu to great effect. “ I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” elaborates Tummalachervu. “ With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use. ” I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use. Prior to working on said cloud-based infrastructure itself, Tummalachervu was instrumental in migrating systems and data therein, keeping his signature focus on strategy throughout each project. “ One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams, ” he says. One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams, Beyond the Clouds: Contributions and Teachings In addition to his technical expertise, Tummalachervu is highly regarded in the IT community for his extensive body of work, which has been published in renowned journals throughout the academic and technology spheres. His contributions to these communities extend beyond publications; he has also conducted numerous faculty development programs, directly benefiting nearly 150 PhD scholars, professors, university students, and colleagues. “ Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles, ” Tummalachervu says. “ I’ve even been asked to judge at a few hackathons and international award ceremonies. ” Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles, I’ve even been asked to judge at a few hackathons and international award ceremonies. Central to the digital world, site reliability engineers like Chaitanya Tummalachervu act as pillars for what will become the future of technology: cloud-based environments. Secured, stabilized, and maintained by SREs and their teams, the future can rest easy knowing that the global infrastructure is supported by these skilled engineers, armed with nothing but best practices and sheer determination.

Cloud Expert Chaitanya Kanth Tummalachervu Uses Complex DevOps and Cloud Architecture on AI Products

Hello everyone, I'm Nils, an Agile coach, and today I want to share a story of frustration, innovation, and transformation. For years, I grappled with the complexities of scaling Agile frameworks like SAFe. Despite their promises, these approaches often left my team and me feeling overwhelmed and disconnected. The endless layers of processes, the bureaucratic overhead, and the constant struggle to manage dependencies took a toll on our productivity and morale. One particularly challenging day, after yet another frustrating backlog refinement session, I realized we needed a change. We needed a tool that could cut through the chaos, bring clarity to our processes, and truly empower our team. That's when I, along with some dedicated colleagues, invented the Dependency Discovery Deck. This innovative card deck has since revolutionized our backlog refinement process, making it more structured, collaborative, and, most importantly, fun. Here's how it all happened. The Chaos of Dependency Management Before discovering the Dependency Discovery Deck, our backlog refinement sessions were a nightmare. Dependencies were often overlooked until they caused bottlenecks, leading to delays and frustration. Our team struggled to visualize the interconnections between various tasks, resulting in missed deadlines and scope creep. Communication gaps and misaligned priorities further exacerbated the issue, making it difficult to maintain a clear and consistent workflow. Every meeting felt like a battle against an invisible enemy, with team members growing increasingly disheartened. We needed a lifeline, a way to turn these sessions into something productive and engaging. The Birth of the Dependency Discovery Deck One evening, after a particularly draining day, we gathered for an informal brainstorming session. Fueled by coffee and a shared determination to find a better way, we started sketching out ideas. We wanted something simple yet effective, something that could bring a sense of play into our work. That's when the idea of the Dependency Discovery Deck was born. The Dependency Discovery Deck is a collection of gamified cards designed to help Agile teams systematically identify and manage dependencies during backlog refinement sessions. Each card represents a specific strategy or tool to uncover hidden dependencies and ensure that all potential blockers are addressed before they impact the project timeline. How the Deck Transformed Our Process Structured Framework:
The Dependency Discovery Deck provided us with a structured framework to approach dependency management. Instead of ad-hoc discussions, we now had a clear process to follow, ensuring that no stone was left unturned. The cards guided us through various aspects of dependency identification, from analyzing stakeholder relationships to mapping out technical requirements.


Enhanced Collaboration:
Using the deck fostered a more inclusive and collaborative environment. Each team member was encouraged to actively participate in discussions, share insights, and contribute ideas. The gamified nature of the cards made the process engaging and interactive, ensuring that everyone’s voice was heard. This newfound engagement not only improved our sessions but also fostered a sense of shared responsibility and ownership.


Improved Planning and Estimation:
By systematically identifying dependencies early in the refinement process, we were able to create more accurate and realistic project plans. This proactive approach allowed us to allocate resources more effectively, manage risks better, and set achievable milestones. Our planning accuracy improved significantly, leading to more predictable and reliable project outcomes.


Visualization of Dependencies:


The visual aspect of the Miro Board, combined with the Dependency Discovery Deck, made it easier to map out and visualize dependencies. We could see how different tasks and features were interconnected, helping us to anticipate and mitigate potential blockers. This clarity was instrumental in maintaining a smooth workflow and avoiding last-minute surprises.


Empowering the Team:
One of the most significant benefits of using the Dependency Discovery Deck was the empowerment it brought to the team. By providing a clear and structured approach to dependency management, the deck instilled confidence in our ability to handle complex projects. Team members felt more equipped to tackle challenges and were motivated to contribute to the collective success of the project. Conclusion The Dependency Discovery Deck has been a game-changer for our Agile team. By providing a structured framework, enhancing collaboration, improving planning and estimation, and empowering the team, it has transformed our backlog refinement process. For any team struggling with dependency management, I highly recommend exploring this innovative tool in the Miroverse. Give it a try, and watch how it can revolutionize your Agile workflow just as it did for us. Have fun with the cards and the board, and check out other card decks like the Dependency Discovery Deck in the Miroverse to further enhance your Agile practices! Here's to turning frustration into innovation and finding joy in the journey of Agile development. Links Free Card set on GitHub: https://github.com/nilsbert/Dependency-Discovery-Deck/ Free Miro Template: https://miro.com/miroverse/dependency-discovery-deck/ Cards on Etsy https://agilegames.etsy.com/listing/1712190266 Hello everyone, I'm Nils, an Agile coach, and today I want to share a story of frustration, innovation, and transformation. For years, I grappled with the complexities of scaling Agile frameworks like SAFe. Despite their promises, these approaches often left my team and me feeling overwhelmed and disconnected. The endless layers of processes, the bureaucratic overhead, and the constant struggle to manage dependencies took a toll on our productivity and morale. One particularly challenging day, after yet another frustrating backlog refinement session, I realized we needed a change. We needed a tool that could cut through the chaos, bring clarity to our processes, and truly empower our team. That's when I, along with some dedicated colleagues, invented the Dependency Discovery Deck. This innovative card deck has since revolutionized our backlog refinement process, making it more structured, collaborative, and, most importantly, fun. Here's how it all happened. The Chaos of Dependency Management Before discovering the Dependency Discovery Deck, our backlog refinement sessions were a nightmare. Dependencies were often overlooked until they caused bottlenecks, leading to delays and frustration. Our team struggled to visualize the interconnections between various tasks, resulting in missed deadlines and scope creep. Communication gaps and misaligned priorities further exacerbated the issue, making it difficult to maintain a clear and consistent workflow. Every meeting felt like a battle against an invisible enemy, with team members growing increasingly disheartened. We needed a lifeline, a way to turn these sessions into something productive and engaging. The Birth of the Dependency Discovery Deck The Birth of the Dependency Discovery Deck One evening, after a particularly draining day, we gathered for an informal brainstorming session. Fueled by coffee and a shared determination to find a better way, we started sketching out ideas. We wanted something simple yet effective, something that could bring a sense of play into our work. That's when the idea of the Dependency Discovery Deck was born. The Dependency Discovery Deck is a collection of gamified cards designed to help Agile teams systematically identify and manage dependencies during backlog refinement sessions. Each card represents a specific strategy or tool to uncover hidden dependencies and ensure that all potential blockers are addressed before they impact the project timeline. How the Deck Transformed Our Process Structured Framework:
The Dependency Discovery Deck provided us with a structured framework to approach dependency management. Instead of ad-hoc discussions, we now had a clear process to follow, ensuring that no stone was left unturned. The cards guided us through various aspects of dependency identification, from analyzing stakeholder relationships to mapping out technical requirements. Enhanced Collaboration:
Using the deck fostered a more inclusive and collaborative environment. Each team member was encouraged to actively participate in discussions, share insights, and contribute ideas. The gamified nature of the cards made the process engaging and interactive, ensuring that everyone’s voice was heard. This newfound engagement not only improved our sessions but also fostered a sense of shared responsibility and ownership. Improved Planning and Estimation:
By systematically identifying dependencies early in the refinement process, we were able to create more accurate and realistic project plans. This proactive approach allowed us to allocate resources more effectively, manage risks better, and set achievable milestones. Our planning accuracy improved significantly, leading to more predictable and reliable project outcomes. Visualization of Dependencies: The visual aspect of the Miro Board, combined with the Dependency Discovery Deck, made it easier to map out and visualize dependencies. We could see how different tasks and features were interconnected, helping us to anticipate and mitigate potential blockers. This clarity was instrumental in maintaining a smooth workflow and avoiding last-minute surprises. Empowering the Team:
One of the most significant benefits of using the Dependency Discovery Deck was the empowerment it brought to the team. By providing a clear and structured approach to dependency management, the deck instilled confidence in our ability to handle complex projects. Team members felt more equipped to tackle challenges and were motivated to contribute to the collective success of the project. Structured Framework:
The Dependency Discovery Deck provided us with a structured framework to approach dependency management. Instead of ad-hoc discussions, we now had a clear process to follow, ensuring that no stone was left unturned. The cards guided us through various aspects of dependency identification, from analyzing stakeholder relationships to mapping out technical requirements. Structured Framework: The Dependency Discovery Deck provided us with a structured framework to approach dependency management. Instead of ad-hoc discussions, we now had a clear process to follow, ensuring that no stone was left unturned. The cards guided us through various aspects of dependency identification, from analyzing stakeholder relationships to mapping out technical requirements. Structured Framework: Enhanced Collaboration:
Using the deck fostered a more inclusive and collaborative environment. Each team member was encouraged to actively participate in discussions, share insights, and contribute ideas. The gamified nature of the cards made the process engaging and interactive, ensuring that everyone’s voice was heard. This newfound engagement not only improved our sessions but also fostered a sense of shared responsibility and ownership. Enhanced Collaboration: Using the deck fostered a more inclusive and collaborative environment. Each team member was encouraged to actively participate in discussions, share insights, and contribute ideas. The gamified nature of the cards made the process engaging and interactive, ensuring that everyone’s voice was heard. This newfound engagement not only improved our sessions but also fostered a sense of shared responsibility and ownership. Enhanced Collaboration: Improved Planning and Estimation:
By systematically identifying dependencies early in the refinement process, we were able to create more accurate and realistic project plans. This proactive approach allowed us to allocate resources more effectively, manage risks better, and set achievable milestones. Our planning accuracy improved significantly, leading to more predictable and reliable project outcomes. Improved Planning and Estimation: By systematically identifying dependencies early in the refinement process, we were able to create more accurate and realistic project plans. This proactive approach allowed us to allocate resources more effectively, manage risks better, and set achievable milestones. Our planning accuracy improved significantly, leading to more predictable and reliable project outcomes. Improved Planning and Estimation: Visualization of Dependencies: Visualization of Dependencies: Visualization of Dependencies: The visual aspect of the Miro Board, combined with the Dependency Discovery Deck, made it easier to map out and visualize dependencies. We could see how different tasks and features were interconnected, helping us to anticipate and mitigate potential blockers. This clarity was instrumental in maintaining a smooth workflow and avoiding last-minute surprises. The visual aspect of the Miro Board, combined with the Dependency Discovery Deck, made it easier to map out and visualize dependencies. We could see how different tasks and features were interconnected, helping us to anticipate and mitigate potential blockers. This clarity was instrumental in maintaining a smooth workflow and avoiding last-minute surprises. Empowering the Team:
One of the most significant benefits of using the Dependency Discovery Deck was the empowerment it brought to the team. By providing a clear and structured approach to dependency management, the deck instilled confidence in our ability to handle complex projects. Team members felt more equipped to tackle challenges and were motivated to contribute to the collective success of the project. Empowering the Team: One of the most significant benefits of using the Dependency Discovery Deck was the empowerment it brought to the team. By providing a clear and structured approach to dependency management, the deck instilled confidence in our ability to handle complex projects. Team members felt more equipped to tackle challenges and were motivated to contribute to the collective success of the project. Empowering the Team: Conclusion The Dependency Discovery Deck has been a game-changer for our Agile team. By providing a structured framework, enhancing collaboration, improving planning and estimation, and empowering the team, it has transformed our backlog refinement process. For any team struggling with dependency management, I highly recommend exploring this innovative tool in the Miroverse. Give it a try, and watch how it can revolutionize your Agile workflow just as it did for us. Have fun with the cards and the board, and check out other card decks like the Dependency Discovery Deck in the Miroverse to further enhance your Agile practices! Here's to turning frustration into innovation and finding joy in the journey of Agile development. Links Free Card set on GitHub: https://github.com/nilsbert/Dependency-Discovery-Deck/ https://github.com/nilsbert/Dependency-Discovery-Deck/ Free Miro Template: https://miro.com/miroverse/dependency-discovery-deck/ https://miro.com/miroverse/dependency-discovery-deck/ Cards on Etsy https://agilegames.etsy.com/listing/1712190266 https://agilegames.etsy.com/listing/1712190266

Unveiling the Power of the Dependency Discovery Deck: A Game-Changer for Agile Teams

What the Heck Is Stacks? An Indepth Look Into One of the Largest L2 in the Bitcoin Ecosystem

**SINGAPORE, Singapore, June 24th, 2024/Chainwire/--**In an industry where traditional barriers often exclude the average investor from high-value assets, iRA Blocks emerges as a game-changer, leveraging blockchain to bring real-world assets within reach of the masses. The financial industry has long been characterized by its exclusivity, particularly when it comes to investing in high-value assets like real estate, luxury items, and aviation. Historically, these investment opportunities have been accessible only to a select few. iRA Blocks is set to change this narrative by introducing fractional ownership of tokenized assets. This innovative approach breaks down these assets into smaller, affordable units, allowing a broader range of investors to participate. For instance, iRA Blocks enables investors to own a fraction of a luxury yacht or a piece of prime real estate, starting with investments as low as $100. Digital Frontier for RWAs One of the standout features of iRA Blocks is its ability to bring liquidity to traditionally illiquid markets. In real estate, for example, the cumbersome processes involved in buying and selling properties have always been a challenge. With iRA Blocks' tokenization, transactions become simpler and faster, giving investors more flexibility in managing their portfolios. The platform uses smart contracts to automate many aspects of asset management, including dividend distributions and voting rights. This ensures that fractional owners have a say in the management of the assets they've invested in, proportional to their ownership stake. Empowering Web3 Users and Enterprises For Web3 users, iRA Blocks offers a seamless integration of blockchain with tangible asset investment. It bridges the gap between digital and physical assets, providing a secure and efficient way to diversify portfolios. The IRB Token, the platform's native utility token, facilitates smooth transactions and offers staking mechanisms, rewarding active participants and fostering a vibrant community. The platform also features a secondary market where tokenized assets can be traded, providing liquidity even for traditionally illiquid assets. This feature allows investors to easily enter or exit investments based on their financial goals and market conditions. Enterprises, especially those in real estate, aviation, and luxury goods, also stand to benefit from iRA Blocks. Tokenizing their assets allows them to attract a broader investor base, enhancing capital inflow and operational liquidity. This democratization of asset ownership opens up new revenue streams and paves the way for more innovative financial products and services. A Sustainable and Inclusive Approach iRA Blocks also emphasizes sustainability in its investment strategy. By supporting projects that contribute to a sustainable future, such as green real estate developments or eco-friendly luxury items, the platform aligns with the growing demand for responsible investing. This focus on environmental consciousness not only appeals to ethically-minded investors but also ensures that the platform's growth is in harmony with global sustainability goals. The Visionary Team Behind iRA Blocks The visionary team behind iRA Blocks is instrumental in driving its mission forward. Sandeep Mule, along with co-founders Dr. Anil Mundhe and Prakash Shinde, brings a wealth of experience in blockchain and asset management. Their strategic insights and commitment to innovation are critical in navigating the complexities of integrating blockchain with traditional asset investment. Unearthing Web3's Boundless Potential As the blockchain ecosystem continues to evolve, platforms like iRA Blocks are at the forefront of this transformation. By making high-value assets accessible to everyday investors and enhancing liquidity and transparency, iRA Blocks is poised to reshape the investment landscape fundamentally. The future of investment lies in inclusivity and accessibility, and iRA Blocks is leading the charge. For investors, enterprises, and Web3 users alike, this platform represents a significant step towards a more democratized and transparent financial ecosystem. About iRA Blocks iRA Blocks is a cutting-edge blockchain platform enabling fractional ownership of high-value real-world assets. By tokenizing assets such as real estate, art, and luxury goods, the platform democratizes investment opportunities, allowing a wider range of investors to participate in previously inaccessible markets. For more information, visit iRA Blocks' Official Website | Telegram | Twitter Contact IRA BLOCKS Revolutionising RWAs Team IRA Blocks IRA BLOCKS irablocks0@gmail.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SINGAPORE, Singapore, June 24th, 2024/Chainwire/--**In an industry where traditional barriers often exclude the average investor from high-value assets, iRA Blocks emerges as a game-changer, leveraging blockchain to bring real-world assets within reach of the masses. The financial industry has long been characterized by its exclusivity, particularly when it comes to investing in high-value assets like real estate, luxury items, and aviation. Historically, these investment opportunities have been accessible only to a select few. iRA Blocks is set to change this narrative by introducing fractional ownership of tokenized assets. This innovative approach breaks down these assets into smaller, affordable units, allowing a broader range of investors to participate. For instance, iRA Blocks enables investors to own a fraction of a luxury yacht or a piece of prime real estate, starting with investments as low as $100. iRA Blocks iRA Blocks Digital Frontier for RWAs One of the standout features of iRA Blocks is its ability to bring liquidity to traditionally illiquid markets. In real estate, for example, the cumbersome processes involved in buying and selling properties have always been a challenge. With iRA Blocks' tokenization, transactions become simpler and faster, giving investors more flexibility in managing their portfolios. The platform uses smart contracts to automate many aspects of asset management, including dividend distributions and voting rights. This ensures that fractional owners have a say in the management of the assets they've invested in, proportional to their ownership stake. Empowering Web3 Users and Enterprises For Web3 users, iRA Blocks offers a seamless integration of blockchain with tangible asset investment. It bridges the gap between digital and physical assets, providing a secure and efficient way to diversify portfolios. The IRB Token, the platform's native utility token, facilitates smooth transactions and offers staking mechanisms, rewarding active participants and fostering a vibrant community. iRA Blocks iRA Blocks The platform also features a secondary market where tokenized assets can be traded, providing liquidity even for traditionally illiquid assets. This feature allows investors to easily enter or exit investments based on their financial goals and market conditions. Enterprises, especially those in real estate, aviation, and luxury goods, also stand to benefit from iRA Blocks. Tokenizing their assets allows them to attract a broader investor base, enhancing capital inflow and operational liquidity. This democratization of asset ownership opens up new revenue streams and paves the way for more innovative financial products and services. A Sustainable and Inclusive Approach iRA Blocks also emphasizes sustainability in its investment strategy. By supporting projects that contribute to a sustainable future, such as green real estate developments or eco-friendly luxury items, the platform aligns with the growing demand for responsible investing. This focus on environmental consciousness not only appeals to ethically-minded investors but also ensures that the platform's growth is in harmony with global sustainability goals. The Visionary Team Behind iRA Blocks The visionary team behind iRA Blocks is instrumental in driving its mission forward. Sandeep Mule, along with co-founders Dr. Anil Mundhe and Prakash Shinde, brings a wealth of experience in blockchain and asset management. Their strategic insights and commitment to innovation are critical in navigating the complexities of integrating blockchain with traditional asset investment. Unearthing Web3's Boundless Potential As the blockchain ecosystem continues to evolve, platforms like iRA Blocks are at the forefront of this transformation. By making high-value assets accessible to everyday investors and enhancing liquidity and transparency, iRA Blocks is poised to reshape the investment landscape fundamentally. The future of investment lies in inclusivity and accessibility, and iRA Blocks is leading the charge. For investors, enterprises, and Web3 users alike, this platform represents a significant step towards a more democratized and transparent financial ecosystem. About iRA Blocks iRA Blocks is a cutting-edge blockchain platform enabling fractional ownership of high-value real-world assets. By tokenizing assets such as real estate, art, and luxury goods, the platform democratizes investment opportunities, allowing a wider range of investors to participate in previously inaccessible markets. For more information, visit iRA Blocks' Official Website | Telegram | Twitter iRA Blocks iRA Blocks Contact IRA BLOCKS Revolutionising RWAs Team IRA Blocks IRA BLOCKS irablocks0@gmail.com 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 here

iRA Blocks Unveils Vision To Democratize Real-World Asset Investment

iRA ব্লক রিয়েল-ওয়ার্ল্ড অ্যাসেট ইনভেস্টমেন্টকে গণতান্ত্রিক করার দৃষ্টিভঙ্গি উন্মোচন করে

iRA Blocks stellt Vision zur Demokratisierung von Investitionen in reale Vermögenswerte vor

iRA Blocks revela una visión para democratizar la inversión en activos del mundo real

iRA Blocks dévoile sa vision visant à démocratiser l'investissement dans les actifs du monde réel

iRA ब्लॉक्स ने वास्तविक दुनिया में परिसंपत्ति निवेश को लोकतांत्रिक बनाने के लिए अपने विजन का अनावरण किया

iRA Blocks、現実世界の資産投資を民主化するビジョンを発表

iRA Blocks, 실제 자산 투자를 민주화하기 위한 비전 공개

iRA Blocks revela visão para democratizar o investimento em ativos do mundo real

iRA Blocks представляет концепцию демократизации инвестиций в реальные активы

iRA Blocks, Gerçek Dünyada Varlık Yatırımını Demokratikleştirme Vizyonunu Açıkladı

Khối iRA tiết lộ tầm nhìn dân chủ hóa đầu tư tài sản trong thế giới thực

iRA Blocks 公布了实现现实世界资产投资民主化的愿景

Effortlessly Launch LangChain APIs with LangServe and MinIO Integration

অনায়াসে LangServe এবং MinIO ইন্টিগ্রেশন সহ LangChain API চালু করুন

Müheloses Starten von LangChain-APIs mit LangServe- und MinIO-Integration

Lance las API de LangChain sin esfuerzo con la integración de LangServe y MinIO

Lancez sans effort les API LangChain avec l'intégration LangServe et MinIO

LangServe और MinIO एकीकरण के साथ LangChain API को आसानी से लॉन्च करें

LangServe と MinIO の統合により LangChain API を簡単に起動

LangServe 및 MinIO 통합을 통해 LangChain API를 손쉽게 실행

Inicie APIs LangChain sem esforço com integração LangServe e MinIO

Легко запускайте API-интерфейсы LangChain с интеграцией LangServe и MinIO.

LangServe ve MinIO Entegrasyonu ile LangChain API'lerini Zahmetsizce Başlatın

Dễ dàng khởi chạy API LangChain với tích hợp LangServe và MinIO

通过 LangServe 和 MinIO 集成轻松启动 LangChain API

As cyberattacks become increasingly sophisticated, traditional security measures like passwords and firewalls are no longer enough to protect sensitive data. The convergence of two powerful technologies — behavioral biometrics and artificial intelligence (AI) — holds significant promise for creating a more robust and resilient cybersecurity framework. Understanding behavioral biometrics in cybersecurity At its core, biometric security aims to improve protection by using parts of the human body that are much more difficult to replicate. Advanced systems like quantum computing and machine learning (ML) may be able to guess a password but will likely struggle to emulate your fingerprints and facial features. Behavioral biometrics take this security measure up a notch by using data related to your unique behaviors to authenticate and verify your identity. Examples include: Mouse dynamics: This metric measures the speed and direction of mouse movements, as well as how you click and scroll when interacting with a page.
Keystroke dynamics: This covers how you type on a keyboard, including the typing speed and rhythm.
Voice recognition: Factors such as the tone, pitch, and rhythm of your voice serve as a unique identifier for establishing accessibility protocols.
Gait analysis: The length of your stride, the angle of your feet, and the rhythm of your steps are incredibly difficult to replicate, making them effective authentication measures.
Eye movement analysis: How you move your eyes, including how many times you blink, how you focus your gaze, and the speed and direction of each movement are unique behavioral markers. By analyzing these distinct patterns, cybersecurity systems can validate whether a user is who they claim to be and better detect suspicious activity. Studies show that implementing AI-enabled behavioral biometrics provides an additional security layer beyond traditional verification methods, improving the robustness and accuracy of human activity recognition datasets. Creating an AI-driven behavior biometrics framework Many organizations already implement AI-powered security systems. Recent statistics show that 51% of business owners use AI for cybersecurity and fraud prevention. AI algorithms and ML can supercharge existing digital security systems through real-time threat detection, analysis, and automated responses. A few years ago, this approach would have been sufficient to protect against cyber threats. However, the landscape has evolved quickly, as hackers have also weaponized AI to launch more sophisticated attacks. For instance, they can use advanced ML tools to identify patterns that reveal weaknesses in an organization’s security programs. Some threat actors leverage AI’s generative features to initiate deepfake video and voice duplication scams. Integrating behavioral biometrics' advanced authentication features into their cybersecurity framework allows individuals and organizations to stay ahead of rapidly evolving cyber issues. This process comprises several steps depending on their current security posture and financial capabilities. Defining security objectives As with any integration, it’s important to first outline the goals of combining behavioral biometrics and AI for cybersecurity. Is it to address a recurring threat? Perhaps you just want to enhance your overall data management process. Identifying your objectives is crucial to determining what behavioral biometric indicators to implement. For instance, eye movement dynamics can be largely effective in healthcare identity management since it already involves medically relevant information. Data collection and storage AI models can automate the behavioral data collection and storage processes. For instance, IoT sensors can capture and transmit real-time information about user behavior, making the procedure considerably faster with less specialized infrastructure. Processing and adaptive learning An AI-driven infrastructure is essential for processing the vast amount of captured behavioral data from multiple sources. AI and ML algorithms can also speed up baseline establishment, which the security system will use to measure the variance when authenticating users. Advanced pattern recognition models can assign risk scores to different interactions, flagging potential fraudulent behavior based on the deviation level. Innovative ML will be instrumental in refining the cybersecurity system’s understanding of normal user behavior. For example, the algorithms can accommodate a loop mechanism where they learn from user feedback to continuously update verification processes and minimize false positives. Balancing security and user experience As cybersecurity becomes an integral business imperative, user experience considerations take the spotlight. According to a recent survey, 85% of CISOs regard cybersecurity as a crucial goal, similar to revenue growth and market share. However, the deployment method must center around convenience and confidentiality. The complexity of collecting behavioral data from users depends on the type of indicator being integrated. Keystroke dynamics may require the user to simply type a few random sentences within a given timeframe. It gets more complicated when you ask users to walk around for a few minutes while the system captures their gait dynamics. How many people have the patience to go through such procedures? If a company must collect complex behavioral data, gamifying the process could be a solution. Making the procedure fun and rewarding provides the right mix of incentives to motivate users to comply with the request. Excessive friction in the authentication process can also be problematic. Imagine how frustrating it would be if a system required you to move your mouse repeatedly to verify your identity, but the process kept failing. Organizations can address this challenge by leveraging AI to adjust verification requirements dynamically — low-risk transactions may require minimal authentication, while high-risk activities trigger additional security steps. Integrate behavioral biometrics into AI-assisted cybersecurity. Integrating behavioral biometrics and AI represents the next step in the evolution of global cybersecurity measures and best practices. By introducing the ability to passively verify users based on their natural behaviors, organizations can stay ahead of sophisticated adversaries. Sources: https://expertinsights.com/insights/a-guide-to-behavioral-biometrics/
https://cybersecurity.asee.io/blog/what-is-behavioral-biometrics-authentication/
https://explodingtopics.com/blog/companies-using-ai
https://www.logsign.com/blog/the-science-of-behavioral-biometrics-in-ueba/
https://hal.science/hal-03281104/document
https://www.sciencedirect.com/science/article/abs/pii/S0952197623014021
https://www.uxdesigninstitute.com/blog/gamification-in-ux-design/
https://cyberseniors.org/uncategorized/how-hackers-and-scammers-use-ai-artificial-intelligence/
https://www.researchgate.net/publication/367302654_Artificial_Intelligence-Based_Behavioral_Biometrics As cyberattacks become increasingly sophisticated, traditional security measures like passwords and firewalls are no longer enough to protect sensitive data. The convergence of two powerful technologies — behavioral biometrics and artificial intelligence (AI) — holds significant promise for creating a more robust and resilient cybersecurity framework. Understanding behavioral biometrics in cybersecurity Understanding behavioral biometrics in cybersecurity At its core, biometric security aims to improve protection by using parts of the human body that are much more difficult to replicate. Advanced systems like quantum computing and machine learning (ML) may be able to guess a password but will likely struggle to emulate your fingerprints and facial features. Behavioral biometrics take this security measure up a notch by using data related to your unique behaviors to authenticate and verify your identity. Examples include: Mouse dynamics: This metric measures the speed and direction of mouse movements, as well as how you click and scroll when interacting with a page. Keystroke dynamics: This covers how you type on a keyboard, including the typing speed and rhythm. Voice recognition: Factors such as the tone, pitch, and rhythm of your voice serve as a unique identifier for establishing accessibility protocols. Gait analysis: The length of your stride, the angle of your feet, and the rhythm of your steps are incredibly difficult to replicate, making them effective authentication measures. Eye movement analysis: How you move your eyes, including how many times you blink, how you focus your gaze, and the speed and direction of each movement are unique behavioral markers. Mouse dynamics: This metric measures the speed and direction of mouse movements, as well as how you click and scroll when interacting with a page. Keystroke dynamics: This covers how you type on a keyboard, including the typing speed and rhythm. Voice recognition: Factors such as the tone, pitch, and rhythm of your voice serve as a unique identifier for establishing accessibility protocols. Gait analysis: The length of your stride, the angle of your feet, and the rhythm of your steps are incredibly difficult to replicate, making them effective authentication measures. Eye movement analysis: How you move your eyes, including how many times you blink, how you focus your gaze, and the speed and direction of each movement are unique behavioral markers. By analyzing these distinct patterns, cybersecurity systems can validate whether a user is who they claim to be and better detect suspicious activity. Studies show that implementing AI-enabled behavioral biometrics provides an additional security layer beyond traditional verification methods, improving the robustness and accuracy of human activity recognition datasets. improving the robustness and accuracy improving the robustness and accuracy Creating an AI-driven behavior biometrics framework Creating an AI-driven behavior biometrics framework Many organizations already implement AI-powered security systems. Recent statistics show that 51% of business owners use AI for cybersecurity and fraud prevention. AI algorithms and ML can supercharge existing digital security systems through real-time threat detection, analysis, and automated responses. 51% of business owners 51% of business owners A few years ago, this approach would have been sufficient to protect against cyber threats. However, the landscape has evolved quickly, as hackers have also weaponized AI to launch more sophisticated attacks. For instance, they can use advanced ML tools to identify patterns that reveal weaknesses in an organization’s security programs. Some threat actors leverage AI’s generative features to initiate deepfake video and voice duplication scams. leverage AI’s generative features leverage AI’s generative features Integrating behavioral biometrics' advanced authentication features into their cybersecurity framework allows individuals and organizations to stay ahead of rapidly evolving cyber issues. This process comprises several steps depending on their current security posture and financial capabilities. Defining security objectives Defining security objectives As with any integration, it’s important to first outline the goals of combining behavioral biometrics and AI for cybersecurity. Is it to address a recurring threat? Perhaps you just want to enhance your overall data management process. Identifying your objectives is crucial to determining what behavioral biometric indicators to implement. For instance, eye movement dynamics can be largely effective in healthcare identity management since it already involves medically relevant information. Data collection and storage Data collection and storage AI models can automate the behavioral data collection and storage processes. For instance, IoT sensors can capture and transmit real-time information about user behavior, making the procedure considerably faster with less specialized infrastructure. Processing and adaptive learning Processing and adaptive learning An AI-driven infrastructure is essential for processing the vast amount of captured behavioral data from multiple sources. AI and ML algorithms can also speed up baseline establishment, which the security system will use to measure the variance when authenticating users. Advanced pattern recognition models can assign risk scores to different interactions, flagging potential fraudulent behavior based on the deviation level. Innovative ML will be instrumental in refining the cybersecurity system’s understanding of normal user behavior. For example, the algorithms can accommodate a loop mechanism where they learn from user feedback to continuously update verification processes and minimize false positives. Balancing security and user experience Balancing security and user experience As cybersecurity becomes an integral business imperative, user experience considerations take the spotlight. According to a recent survey, 85% of CISOs regard cybersecurity as a crucial goal, similar to revenue growth and market share. However, the deployment method must center around convenience and confidentiality. 85% of CISOs regard cybersecurity 85% of CISOs regard cybersecurity The complexity of collecting behavioral data from users depends on the type of indicator being integrated. Keystroke dynamics may require the user to simply type a few random sentences within a given timeframe. It gets more complicated when you ask users to walk around for a few minutes while the system captures their gait dynamics. How many people have the patience to go through such procedures? If a company must collect complex behavioral data, gamifying the process could be a solution. Making the procedure fun and rewarding provides the right mix of incentives to motivate users to comply with the request. provides the right mix of incentives provides the right mix of incentives Excessive friction in the authentication process can also be problematic. Imagine how frustrating it would be if a system required you to move your mouse repeatedly to verify your identity, but the process kept failing. Organizations can address this challenge by leveraging AI to adjust verification requirements dynamically — low-risk transactions may require minimal authentication, while high-risk activities trigger additional security steps. Integrate behavioral biometrics into AI-assisted cybersecurity. Integrate behavioral biometrics into AI-assisted cybersecurity. Integrating behavioral biometrics and AI represents the next step in the evolution of global cybersecurity measures and best practices. By introducing the ability to passively verify users based on their natural behaviors, organizations can stay ahead of sophisticated adversaries. Sources: Sources: https://expertinsights.com/insights/a-guide-to-behavioral-biometrics/ https://cybersecurity.asee.io/blog/what-is-behavioral-biometrics-authentication/ https://explodingtopics.com/blog/companies-using-ai https://www.logsign.com/blog/the-science-of-behavioral-biometrics-in-ueba/ https://hal.science/hal-03281104/document https://www.sciencedirect.com/science/article/abs/pii/S0952197623014021 https://www.uxdesigninstitute.com/blog/gamification-in-ux-design/ https://cyberseniors.org/uncategorized/how-hackers-and-scammers-use-ai-artificial-intelligence/ https://www.researchgate.net/publication/367302654_Artificial_Intelligence-Based_Behavioral_Biometrics https://expertinsights.com/insights/a-guide-to-behavioral-biometrics/ https://expertinsights.com/insights/a-guide-to-behavioral-biometrics/ https://expertinsights.com/insights/a-guide-to-behavioral-biometrics/ https://cybersecurity.asee.io/blog/what-is-behavioral-biometrics-authentication/ https://cybersecurity.asee.io/blog/what-is-behavioral-biometrics-authentication/ https://cybersecurity.asee.io/blog/what-is-behavioral-biometrics-authentication/ https://explodingtopics.com/blog/companies-using-ai https://explodingtopics.com/blog/companies-using-ai https://explodingtopics.com/blog/companies-using-ai https://www.logsign.com/blog/the-science-of-behavioral-biometrics-in-ueba/ https://www.logsign.com/blog/the-science-of-behavioral-biometrics-in-ueba/ https://www.logsign.com/blog/the-science-of-behavioral-biometrics-in-ueba/ https://hal.science/hal-03281104/document https://hal.science/hal-03281104/document https://hal.science/hal-03281104/document https://www.sciencedirect.com/science/article/abs/pii/S0952197623014021 https://www.sciencedirect.com/science/article/abs/pii/S0952197623014021 https://www.sciencedirect.com/science/article/abs/pii/S0952197623014021 https://www.uxdesigninstitute.com/blog/gamification-in-ux-design/ https://www.uxdesigninstitute.com/blog/gamification-in-ux-design/ https://www.uxdesigninstitute.com/blog/gamification-in-ux-design/ https://cyberseniors.org/uncategorized/how-hackers-and-scammers-use-ai-artificial-intelligence/ https://cyberseniors.org/uncategorized/how-hackers-and-scammers-use-ai-artificial-intelligence/ https://cyberseniors.org/uncategorized/how-hackers-and-scammers-use-ai-artificial-intelligence/ https://www.researchgate.net/publication/367302654_Artificial_Intelligence-Based_Behavioral_Biometrics https://www.researchgate.net/publication/367302654_Artificial_Intelligence-Based_Behavioral_Biometrics

How To Integrate Behavioral Biometrics Into AI Assistance for Security

Autonomys Network: ex-Jumio Executive Appointed CEO For New deAI Vision Ahead Of Mainnet Launch

অটোনমিস নেটওয়ার্ক: মেইননেট লঞ্চের আগে নতুন ডিএআই ভিশনের জন্য প্রাক্তন জুমিও এক্সিকিউটিভ সিইও নিযুক্ত হয়েছেন

Autonomys Network: ehemaliger Jumio-Manager vor Mainnet-Start zum CEO für neue deAI-Vision ernannt

Autonomys Network: ex ejecutivo de Jumio nombrado director ejecutivo para la nueva visión deAI antes del lanzamiento de Mainnet

Autonomys Network : un ancien cadre de Jumio est nommé PDG de la nouvelle vision deAI avant le lancement du réseau principal

ऑटोनोमिस नेटवर्क: पूर्व-जुमियो कार्यकारी को मेननेट लॉन्च से पहले नए डीएआई विजन के लिए सीईओ नियुक्त किया गया

Autonomys Network: メインネットの立ち上げに先立ち、元Jumio幹部が新たなdeAIビジョンのCEOに任命される

Autonomys Network: 전 Jumio 임원이 메인넷 출시를 앞두고 새로운 deAI 비전을 위해 CEO로 임명됨

Rede Autonomys: ex-executivo da Jumio nomeado CEO para nova visão deAI antes do lançamento da Mainnet

Сеть Autonomys: бывший исполнительный директор Jumio назначен генеральным директором новой концепции deAI в преддверии запуска основной сети

Autonomys Network: Eski Jumio Yöneticisi, Mainnet Lansmanı Öncesinde Yeni deAI Vizyonu İçin CEO Olarak Atandı

Autonomys Network: Cựu Giám đốc điều hành Jumio được bổ nhiệm làm Giám đốc điều hành cho Tầm nhìn deAI mới trước khi ra mắt Mainnet

Autonomys Network：前 Jumio 高管被任命为新 deAI 愿景的首席执行官，主网发布前夕

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