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October has been the best month for crypto for all of 2023, with price jumping from $26.5K to well beyond $37K. Bitcoin’s This renewed optimism, driven by the expectations of a spot Bitcoin ETF approval in the U.S., has injected fresh positivity into the market sentiment and the overall crypto market. The net change in supply of leading stablecoin positive territory over the past 90 days; this is the first time since Terra’s collapse in May 2022. entered Spot trading volumes on centralized exchanges have broken a four-month streak of declining volumes and marked the highest level since March 2023. Crypto ETF Updates Bitcoin Spot ETFs are the talk of the crypto town hall and are what is driving the upward momentum currently for the industry. Below are a few of the things that caught my eye over the last week: The GBTC discount is currently at its lowest level since July 2021, marking a significant drop compared to December 2022 when it hit highs of 48%. Grayscale’s Bitcoin Trust Discount is down to 13.38%: Last week, BlackRock doubled down on its crypto ETF applications, registering the iShares Ethereum Trust as a Delaware statutory trust. ETH price surged past $2,000 on this news. Blackrock Files for a Spot ETH ETF: The XRP token surged over 12% on a false report that Blackrock had also filed a “BlackRock iShares XRP Trust”. Clearly, there are some mischievous characters out to take advantage of the ETF hype. XRP Surges Briefly After a Fake BlackRock Filing: While crypto natives have their hopes high for the positive effect a Bitcoin ETF approval will bring, JPMorgan recently published a report arguing that the rally looks 'overdone.' JP Morgan Skeptical on Bullish ETF Momentum: The next decision day on the approval of a spot ETF is happening today, however, I find it highly unlikely that there will be any approvals before the 10th of January 2024, at the earliest, when ARK’s ETF will face its final deadline. Currently, my thesis on the market is that we will continue to tread higher in anticipation of positive ETF news, which will do for a nice continuation of the upside into December. What happens thereafter is anyone’s guess - A by Nasdaq last year revealed that 72% of financial advisers would likely allocate some capital to crypto if the authorities were to approve an ETF. survey Noteworthy Mentions Binance Blockchain Week in Istanbul Last week, the Bitcoin Global Macro team had the opportunity to attend the Binance Blockchain Week in Istanbul. What particularly caught my attention is the crypto adoption levels in Turkey; over 52% of Turkish adults invest in crypto, and several stores accept BTC payments. Industry Shakers JPMorgan rolls out programmable payments via JPM Coin JPMorgan has initiated the implementation of programmable payments, which involve the automatic execution of payments, through its proprietary blockchain-based payment system known as JPM Coin. The testing phase for programmable payments began in 2021, with Siemens AG being a key partner in this pioneering effort. Over the next few weeks, two additional companies, FedEx and Cargill, are anticipated to leverage this feature. Australia Updates Its CGT Guidance to Include Wrapped Tokens and DeFi Australia's tax authority has reaffirmed its position that capital gains tax on crypto products applies to wrapped tokens and token interactions with decentralized lending protocols, as outlined in updated guidance. The recent update explicitly encompasses wrapped tokens, various "DeFi lending" and "borrowing arrangements," or any instance of transferring a crypto asset to an address not under your control. Also published here. Enjoyed these insights? There's more where that came from. Follow me on for daily tidbits and updates. Twitter/X

Are Crypto Markets Positioned for the Next Uptick? 

ক্রিপ্টো মার্কেট কি পরবর্তী আপটিকের জন্য অবস্থান করছে?

Sind die Kryptomärkte für den nächsten Aufschwung gerüstet?

¿Están los criptomercados posicionados para el próximo repunte?

Les marchés de la cryptographie sont-ils positionnés pour la prochaine hausse ?

क्या क्रिप्टो बाजार अगली तेजी के लिए तैयार हैं?

仮想通貨市場は次の上昇に向けて準備ができているのか?

암호화폐 시장은 다음 상승세에 대비하고 있습니까?

Os criptomercados estão posicionados para o próximo aumento?

Готовы ли крипторынки к следующему подъему?

Kripto Piyasaları Bir Sonraki Yükselişe Hazır mı?

Thị trường tiền điện tử có được định vị cho đợt tăng giá tiếp theo không?

加密货币市场是否已做好下一次上涨的准备？

PayPal’s user base could generate demand for over 15% of Bitcoin’s current market cap over the next 12 months. By now, most of us have heard that PayPal has begun to enable buying and selling of cryptocurrencies in recent weeks in the U.S. and will roll it out to select international markets in the first half of 2021.  Initially it includes four crypto staples: Bitcoin (BTC), Bitcoin Cash (BCH), Ethereum (ETH) and Litecoin (LTC). Over 361 million active PayPal users will be able to use their cryptocurrency balances to pay for goods and services with 26 million merchants worldwide! It cannot be overstated that this is a major leap in crypto's utility. as PayPal users, especially millennials, begin to buy up these coins.  And we suspect that other digital payment card competitors to PayPal and Square (Cash App) are not far behind in their own cryptocurrency initiatives. For investors, this means that these four cryptocurrencies will enjoy big demand tailwinds in the next 6-12 months Since PayPal’s announcement on Oct 21, Bitcoin’s price has gone up 50% from $12K to $18K.  Sure, there have been other catalysts (institutional and corporate allocations to Bitcoin), but we believe that PayPal is the main reason behind the recent gains.  And it is just the beginning. General public is eager to dip their toe in the digital assets, but for most, it remains out of reach due to its complexity and security concerns.  PayPal takes all that complexity and uncertainty away. We expect the demand for the four select cryptocurrencies could drive their prices substantially higher over the next 6-12 months. As of Nov 2, only 10% of PayPal users in the U.S. had access to the crypto services, and as of Nov 12, it was rolled out to all U.S. users.  Citing big demand, PayPal raised the maximum amount a user could buy from $10K per week to $20K per week. How much BTC could PayPal’s user base buy? And the buying has clearly begun.  PayPal’s volumes are going through itBit exchange (owned by Paxos, US-based and licensed).  Their volume has tripled in recent weeks from an average of $5.6M a day to $15.7M average, even reaching $31M on Nov 17! Square enabled Bitcoin buying starting in 2018, which was the “crypto winter” post the ICO bubble in late 2017 / early 2018.  Activity really began to pick up in 2019 and accelerate in 2020. To gauge future BTC demand from PayPal user base, we can do some quick math using Square (Cash App) as a guide. Square has about 30M users on its Cash App, which enables Bitcoin buying.  In 3Q20 (ending September 30), Cash App users bought $1.63B worth of Bitcoin or $54 per user on average, and that was up 87% from prior quarter. If we assume that PayPal’s user base buys the same amount of $54/user, that means that 361M users could buy $19.7B worth of Bitcoin.  Is that meaningful?  You bet it is. Bitcoin’s current market cap is $326.8B. PayPal’s users could generate demand for 6.0% of Bitcoin’s market cap! That would move the price, and we are seeing that already. But let us look ahead a little.  Square Cash App users’ spend on Bitcoin was up 87% in 3Q20 from prior quarter and it is not unreasonable to expect high double digit growth in 4Q20 and throughout 2021 to about $122 average per user in 4Q21. With these growth assumptions in average spend per user, Importantly, PayPal users are not traders, they are investors, aka HODLers.   Hence, a sizeable portion of Bitcoins could be removed from the market, creating even greater scarcity. we can easily foresee PayPal users generating demand for 15% worth of Bitcoin’s market cap at the current price. And what happens when all the other PayPal competitors match this move?  And when neobanks like Monzo, Atom Bank, Tide, Chime, Simple, N26, Monese join PayPal’s move?  And if we layer on demand from institutional investors and corporations that are gradually allocating just a few percent of their assets into digital assets…. Bitcoin will moon.

PayPal's Bitcoin Play Will To Create A Sharp Uptick in Crypto-Adoption

The rise of electronic health records and the growing use of data analytics in healthcare have made data governance a key focus area for improving healthcare services to be safer, more efficient, and better tailored to individual patient needs. The stakes are high, as a single breach in healthcare data can jeopardize patient confidentiality and weaken trust in the entire healthcare system. Astra Security reports that 51% of healthcare organizations have seen an uptick in data breaches since 2019, with hospitals facing increasing amounts of breaches over the last 12 years. Data governance is important not just for security but for ensuring data quality, integrity, and accessibility – all critical for informed decision-making in healthcare. The HIPAA (Health Insurance Portability and Accountability Act) Journal revealed that a whopping 133 million records were exposed or mishandled in 2023 alone, underscoring the need for proper data governance. Healthcare data leaders like Nithin Narayan Koranchirath are rising to the challenge by championing proper data governance strategies in the healthcare industry. Nithin’s belief is that more awareness of the essential role data governance plays in healthcare will lead to increased adoption by the industry. Managing Data in Healthcare Data governance is not a box to tick and then be forgotten about; it is a strategic necessity in the healthcare industry. With the increasing amount of Electronic Health Records (EHRs) and medical research data, healthcare institutions must guarantee that data remains precise, accessible, and secure. With inconsistencies in data across different EHR systems, there are more administrative tasks and potential risks to patient well-being than ever before. The challenges for healthcare providers in handling data are maintaining privacy and security standards, complying with regulations, managing data sources effectively, and ensuring seamless communication between different systems. Establishing a data governance program can enhance the delivery of healthcare services, foster innovation and research, improve decision-making in healthcare, and ensure compliance with laws like HIPAA and GDPR (General Data Protection Regulation). An Authority on Healthcare Data Management Nithin Narayan Koranchirath, a professional in healthcare data management, stresses the significance of data governance. He has years of expertise overseeing various healthcare data systems, and emphasizes that "efficient data management goes beyond compliance, it aims at enhancing outcomes and streamlining healthcare services." Data governance might appear as a subject typically reserved exclusively for IT experts, but its impact extends to everyone involved in the healthcare network. At its core, data governance entails managing data from the collection phase to its end use in patient care or research. Nithin’s pivotal moment came when leading the Data and Analytics platform at his current organization, one of the United States’s largest healthcare providers, where he witnessed the transformative impact of data-driven insights. "This role allowed me to witness firsthand the power of data-driven insights in revolutionizing data analytics delivery and administration," Nithin reflects, solidifying his commitment to analytics and data-driven decision-making. Grasping proper data governance practices empowers healthcare professionals with the knowledge to make informed decisions. This fundamental understanding reduces the chances of mistakes that could jeopardize safety. For patients, it offers reassurance that their personal data is handled with care and confidentiality. Additionally, implementing data governance measures such as utilizing Enterprise Master Patient Indexes (EMPI) can significantly enhance healthcare quality by providing an overview of patient records. Implementing a Data Governance Framework Tailored for Healthcare Healthcare professionals frequently encounter obstacles with data, such as difficulty accessing and safeguarding data, disorganized and isolated data sets, limited resource availability, and insufficient staff collaboration and training. Overcoming these challenges involves defining roles for adopting security measures, selecting a data management solution, allocating resources for data experts, and educating staff on effective data management practices. When assessing options for data governance solutions, healthcare institutions should take into account several factors, including integration with existing systems, compliance with requirements, scalability, and ease of use. Platforms like Big Data, AWS, and Snowflake offer data governance capabilities for overseeing data governance processes while effectively addressing security concerns and regulatory demands. Nithin’s work in implementing a comprehensive data governance framework at his organization via Big Data, AWS, and Snowflake underscores the practical benefits of such knowledge. "The implementation of robust data governance practices enabled us to achieve greater compliance with regulatory requirements and mitigate risks associated with data management," he explains. For healthcare professionals, this translates to better decision-making capabilities, and for patients, it means the assurance of data privacy and security. AWS and Snowflake are cloud-based data management solutions that align seamlessly with the needs of the healthcare industry. It offers functionalities like data discovery, traceability, and search capabilities to help organizations streamline their approach to data governance while simultaneously enhancing security and compliance measures efficiently. At the end of the day, leaders such as C-suite executives and IT managers ultimately bear responsibility for ensuring data governance practices are followed in an organization. However, every individual working within the healthcare sector plays a role in its success. Effective leadership should ensure that all staff members know the importance of data governance, while data experts should have a grasp on how data impacts staff operations. Future Outlook for Data Governance in Healthcare In an era defined by numerous technological advancements in healthcare, the importance of data governance is only set to grow. As we look towards the future, data governance in healthcare is poised to become a cornerstone of the United States healthcare system, playing a pivotal role in shaping economic outcomes. The U.S. healthcare sector, one of the largest contributors to the national economy, faces rising costs, inefficiencies, and demands for improved patient outcomes and privacy. Nithin’s hard work in solving these issues will have lasting positive effects not only on the healthcare industry but on the tech sector and the U.S. economy at large. Additionally, with the standardization of data practices, interoperability across different healthcare systems can be improved. This improvement facilitates better coordination of care, especially for chronic disease management, which is a significant expenditure for the U.S. economy. Data governance addresses these challenges by enhancing the quality, accessibility, and security of healthcare data. The path toward establishing effective data governance within healthcare is challenging but critical. As Nithin Narayan Koranchirath says himself, "Data governance is the backbone of a trustworthy healthcare system." By ensuring the integrity, accessibility, and security of healthcare data, the U.S. can improve its healthcare outcomes, reduce costs, and foster innovation, thereby enhancing its economic stability and growth. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author The rise of electronic health records and the growing use of data analytics in healthcare have made data governance a key focus area for improving healthcare services to be safer, more efficient, and better tailored to individual patient needs. The stakes are high, as a single breach in healthcare data can jeopardize patient confidentiality and weaken trust in the entire healthcare system. Astra Security reports that 51% of healthcare organizations have seen an uptick in data breaches since 2019, with hospitals facing increasing amounts of breaches over the last 12 years. Data governance is important not just for security but for ensuring data quality, integrity, and accessibility – all critical for informed decision-making in healthcare. The HIPAA (Health Insurance Portability and Accountability Act) Journal revealed that a whopping 133 million records were exposed or mishandled in 2023 alone, underscoring the need for proper data governance. 51% of healthcare organizations 51% of healthcare organizations 133 million records 133 million records Healthcare data leaders like Nithin Narayan Koranchirath are rising to the challenge by championing proper data governance strategies in the healthcare industry. Nithin’s belief is that more awareness of the essential role data governance plays in healthcare will lead to increased adoption by the industry. Managing Data in Healthcare Managing Data in Healthcare Data governance is not a box to tick and then be forgotten about; it is a strategic necessity in the healthcare industry. With the increasing amount of Electronic Health Records (EHRs) and medical research data, healthcare institutions must guarantee that data remains precise, accessible, and secure. With inconsistencies in data across different EHR systems, there are more administrative tasks and potential risks to patient well-being than ever before. The challenges for healthcare providers in handling data are maintaining privacy and security standards, complying with regulations, managing data sources effectively, and ensuring seamless communication between different systems. Establishing a data governance program can enhance the delivery of healthcare services, foster innovation and research, improve decision-making in healthcare, and ensure compliance with laws like HIPAA and GDPR (General Data Protection Regulation). An Authority on Healthcare Data Management An Authority on Healthcare Data Management Nithin Narayan Koranchirath, a professional in healthcare data management, stresses the significance of data governance. He has years of expertise overseeing various healthcare data systems, and emphasizes that "efficient data management goes beyond compliance, it aims at enhancing outcomes and streamlining healthcare services." Data governance might appear as a subject typically reserved exclusively for IT experts, but its impact extends to everyone involved in the healthcare network. At its core, data governance entails managing data from the collection phase to its end use in patient care or research. Nithin’s pivotal moment came when leading the Data and Analytics platform at his current organization, one of the United States’s largest healthcare providers, where he witnessed the transformative impact of data-driven insights. "This role allowed me to witness firsthand the power of data-driven insights in revolutionizing data analytics delivery and administration," Nithin reflects, solidifying his commitment to analytics and data-driven decision-making. Grasping proper data governance practices empowers healthcare professionals with the knowledge to make informed decisions. This fundamental understanding reduces the chances of mistakes that could jeopardize safety. For patients, it offers reassurance that their personal data is handled with care and confidentiality. Additionally, implementing data governance measures such as utilizing Enterprise Master Patient Indexes (EMPI) can significantly enhance healthcare quality by providing an overview of patient records. Implementing a Data Governance Framework Tailored for Healthcare Implementing a Data Governance Framework Tailored for Healthcare Healthcare professionals frequently encounter obstacles with data, such as difficulty accessing and safeguarding data, disorganized and isolated data sets, limited resource availability, and insufficient staff collaboration and training. Overcoming these challenges involves defining roles for adopting security measures, selecting a data management solution, allocating resources for data experts, and educating staff on effective data management practices. When assessing options for data governance solutions, healthcare institutions should take into account several factors, including integration with existing systems, compliance with requirements, scalability, and ease of use. Platforms like Big Data, AWS, and Snowflake offer data governance capabilities for overseeing data governance processes while effectively addressing security concerns and regulatory demands. offer data governance capabilities offer data governance capabilities Nithin’s work in implementing a comprehensive data governance framework at his organization via Big Data, AWS, and Snowflake underscores the practical benefits of such knowledge. "The implementation of robust data governance practices enabled us to achieve greater compliance with regulatory requirements and mitigate risks associated with data management," he explains. For healthcare professionals, this translates to better decision-making capabilities, and for patients, it means the assurance of data privacy and security. AWS and Snowflake are cloud-based data management solutions that align seamlessly with the needs of the healthcare industry. It offers functionalities like data discovery, traceability, and search capabilities to help organizations streamline their approach to data governance while simultaneously enhancing security and compliance measures efficiently. At the end of the day, leaders such as C-suite executives and IT managers ultimately bear responsibility for ensuring data governance practices are followed in an organization. However, every individual working within the healthcare sector plays a role in its success. Effective leadership should ensure that all staff members know the importance of data governance, while data experts should have a grasp on how data impacts staff operations. Future Outlook for Data Governance in Healthcare Future Outlook for Data Governance in Healthcare In an era defined by numerous technological advancements in healthcare, the importance of data governance is only set to grow. As we look towards the future, data governance in healthcare is poised to become a cornerstone of the United States healthcare system, playing a pivotal role in shaping economic outcomes. The U.S. healthcare sector, one of the largest contributors to the national economy, faces rising costs, inefficiencies, and demands for improved patient outcomes and privacy. Nithin’s hard work in solving these issues will have lasting positive effects not only on the healthcare industry but on the tech sector and the U.S. economy at large. Additionally, with the standardization of data practices, interoperability across different healthcare systems can be improved. This improvement facilitates better coordination of care, especially for chronic disease management, which is a significant expenditure for the U.S. economy. Data governance addresses these challenges by enhancing the quality, accessibility, and security of healthcare data. The path toward establishing effective data governance within healthcare is challenging but critical. As Nithin Narayan Koranchirath says himself, "Data governance is the backbone of a trustworthy healthcare system." By ensuring the integrity, accessibility, and security of healthcare data, the U.S. can improve its healthcare outcomes, reduce costs, and foster innovation, thereby enhancing its economic stability and growth. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Why Data Governance in Healthcare Matters in 2024 With Nithin Narayan Koranchirath

কেন নিথিন নারায়ণ কোরানচিরথের সাথে 2024 সালে স্বাস্থ্যসেবা বিষয়ে ডেটা গভর্নেন্স

Warum Data Governance im Gesundheitswesen im Jahr 2024 wichtig ist – mit Nithin Narayan Koranchirath

Por qué es importante la gobernanza de datos en la atención sanitaria en 2024 con Nithin Narayan Koranchirath

Pourquoi la gouvernance des données dans le secteur de la santé est importante en 2024 avec Nithin Narayan Koranchirath

2024 में स्वास्थ्य सेवा में डेटा गवर्नेंस क्यों मायने रखता है, नितिन नारायण कोरानचिराथ के साथ

2024 年に医療におけるデータ ガバナンスが重要な理由 (ニティン ナラヤン コランチラート氏)

Nithin Narayan Koranchirath와 함께 2024년에 의료 분야의 데이터 거버넌스가 중요한 이유

Por que a governança de dados na área da saúde é importante em 2024 com Nithin Narayan Koranchirath

Почему управление данными в здравоохранении имеет значение в 2024 году с Нитин Нараян Коранчират

Nithin Narayan Koranchirath ile 2024'te Sağlık Hizmetlerinde Veri Yönetişimi Neden Önemli?

Tại sao quản trị dữ liệu trong lĩnh vực chăm sóc sức khỏe lại quan trọng vào năm 2024 với Nithin Narayan Koranchirath

Nithin Narayan Koranchirath 谈 2024 年医疗保健数据治理为何如此重要

Calling All Writers: Learn What The Crypto-API Writing Contest Is All About From The CoinGecko Team

সমস্ত লেখকদের কল করা হচ্ছে: CoinGecko টিম থেকে ক্রিপ্টো-এপিআই লেখার প্রতিযোগিতাটি কী তা জানুন

Aufruf an alle Autoren: Erfahren Sie vom CoinGecko-Team, worum es beim Crypto-API-Schreibwettbewerb geht

Llamando a todos los escritores: aprenda de qué se trata el concurso de redacción Crypto-API del equipo CoinGecko

Appel à tous les écrivains : découvrez en quoi consiste le concours d'écriture Crypto-API auprès de l'équipe CoinGecko

सभी लेखकों को बुलावा: कॉइनगेको टीम से जानें क्रिप्टो-एपीआई लेखन प्रतियोगिता क्या है

ライターの皆さんへ: CoinGecko チームから暗号 API ライティング コンテストについて学びましょう

모든 작가에게 전화하기: CoinGecko 팀으로부터 Crypto-API 글쓰기 콘테스트의 모든 내용을 알아보세요

Ligando para todos os escritores: saiba do que se trata o concurso de redação de Crypto-API da equipe CoinGecko

Обращение ко всем писателям: узнайте, что такое конкурс авторов Crypto-API, от команды CoinGecko

Tüm Yazarlara Çağrı: CoinGecko Ekibinden Kripto API Yazma Yarışmasının Neyle İlgili Olduğunu Öğrenin

Kêu gọi tất cả người viết: Tìm hiểu nội dung cuộc thi viết về tiền điện tử-API từ nhóm CoinGecko

召集所有作家：从 CoinGecko 团队了解 Crypto-API 写作比赛的全部内容

Many organizations that we’ve spoken to are in the exploration phase of using for AI-powered personalization, recommendations, semantic search and anomaly detection. The recent and astronomical improvements in accuracy and accessibility of large language models (LLMs) including BERT and OpenAI have made companies rethink how to build relevant search and analytics experiences. vector search In this blog, we capture engineering stories from 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications. We hope these stories will be helpful to engineering teams who are thinking through the full lifecycle of vector search all the way from generating embeddings to production deployments. What is vector search? Vector search is a method for efficiently finding and retrieving similar items from a large dataset based on representations of the data in a high-dimensional space. In this context, items can be anything, such as documents, images, or sounds, and are represented as vector embeddings. The similarity between items is computed using distance metrics, such as or , which quantify the closeness of two vector embeddings. cosine similarity Euclidean distance The vector search process usually involves: : Where relevant features are extracted from the raw data to create vector representations using models such as , or Generating embeddings word2vec BERT Universal Sentence Encoder : The vector embeddings are organized into a data structure that enables efficient search using algorithms such as or Indexing FAISS HNSW : Where the most similar items to a given query vector are retrieved based on a chosen distance metric like cosine similarity or Euclidean distance Vector search To better visualize vector search, we can imagine a 3D space where each axis corresponds to a feature. The time and the position of a point in the space is determined by the values of these features. In this space, similar items are located closer together and dissimilar items are farther apart. GITHUB JULIE-MILLS ^
  |          x Item 1
  |         /
  |        /
  |       /x Item 2
  |      /
  |     /
  |    /x Item 3
  |   /
  |  /
  | /
  +-------------------> Given a query, we can then find the most similar items in the dataset. The query is represented as a vector embedding in the same space as the item embeddings, and the distance between the query embedding and each item embedding is computed. The item embeddings with the shortest distance to the query embedding are considered the most similar. Query item:         x
                    |
  ^                 |
  |          x Item 1|
  |         /       |
  |        /        |
  |       /x Item 2 |
  |      /          |
  |     /           |
  |    /x Item 3    |
  |   /             |
  |  /              |
  | /               |
  +-------------------> This is obviously a simplified visualization as vector search operates in high-dimensional spaces. In the next sections, we’ll summarize 5 engineering blogs on vector search and highlight key implementation considerations. The full engineering blogs can be found below: by Jinfeng Zhuang at Pinterest PinText: A Multitask Text Embedding System in Pinterest by Alexandre Tamborrino at Spotify Introducing Natural Language Search for Podcast Episodes by Senthilkumar Gopal, Shubhangi Tandon, Christopher Miller, Deepika Srinivasan, Rui Kong, Selcuk Kopru and Srinivas Bhagavathula at eBay How eBay’s New Search Feature Was Inspired By Window Shopping by Mihajlo Grbovic at AirBnb Listing Embeddings in Search Ranking by Mitchell Koch, Aamir Manasawala, Raghav Ramesh at Doordash Personalized Store Feed with Vector Embeddings Pinterest: Interest search and discovery Pinterest uses for image search and discovery across multiple areas of its platform, including recommended content on the home feed, related pins and search using a multitask learning model. vector search A multi-task model is trained to perform multiple tasks simultaneously, often sharing underlying representations or features, which can improve generalization and efficiency across related tasks. In the case of Pinterest, the team trained and used the same model to drive recommended content on the homefeed, related pins and search. Pinterest trains the model by pairing a users search query (q) with the content they clicked on or pins they saved (p). Here is how Pinterest created the (q,p) pairs for each task: : Word embeddings are derived from the selected subject (q) and the pin clicked on or saved by the user (p). Related Pins : Word embeddings are created from the search query text (q) and the pin clicked on or saved by the user (p). Search : Word embeddings are generated based on the interest of the user (q) and the pin clicked on or saved by the user (p). Homefeed To obtain an overall entity embedding, Pinterest averages the associated word embeddings for related pins, search and the homefeed. Pinterest created and evaluated its own supervised Pintext-MTL (multi-task learning) against unsupervised learning models including GloVe, word2vec as well as a single-task learning model, PinText-SR on precision. PinText-MTL had higher precision than the other embedding models, meaning that it had a higher proportion of true positive predictions among all positive predictions. Pinterest also found that multi-task learning models had a higher recall, or a higher proportion of relevant instances correctly identified by the model, making them a better fit for search and discovery. To put this all together in production, Pinterest has a multitask model trained on streaming data from the homefeed, search and related pins. Once that model is trained, vector embeddings are created in a large batch job using either Kubernetes+Docker or a map-reduce system. The platform builds a search index of vector embeddings and runs a K-nearest neighbors (KNN) search to find the most relevant content for users. Results are cached to meet the performance requirements of the Pinterest platform. Spotify: Podcast search Spotify combines keyword and to retrieve relevant podcast episode results for users. As an example, the team highlighted the limitations of keyword search for the query “electric cars climate impact”, a query which yielded 0 results even though relevant podcast episodes exist in the Spotify library. To improve recall, the Spotify team used Approximate Nearest Neighbor (ANN) for fast, relevant podcast search. semantic search The team generates vector embeddings using the as it is multilingual, supporting a global library of podcasts, and produces high-quality vector embeddings. Other models were also evaluated including , a model trained on a big corpus of text data, but found that BERT was better suited for word embeddings than sentence embeddings and was pre-trained only in English. Universal Sentence Encoder CMLM model BERT Spotify builds the vector embeddings with the query text being the input embedding and a concatenation of textual metadata fields including title and description for the podcast episode embeddings. To determine the similarity, Spotify measured the cosine distance between the query and episode embeddings. To train the base Universal Sentence Encoder CMLM model, Spotify used positive pairs of successful podcast searches and episodes. They incorporated in-batch negatives, a technique highlighted in papers including and , to generate random negative pairings. Testing was also conducted using synthetic queries and manually written queries. Dense Passage Retrieval for Open-Domain Question Answering (DPR) Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook To incorporate vector search into serving podcast recommendations in production, Spotify used the following steps and technologies: : Spotify indexes the episode vectors offline in batch using , a search engine with native support for ANN. One of the reasons that Vespa was chosen is that it can also incorporate metadata filtering post-search on features like episode popularity. Index episode vectors Vespa : Spotify uses to generate a query vector. Vertex AI was chosen for its support for GPU inference, which is more cost effective when using large transformer models to generate embeddings, and for its query cache. After the query vector embedding is generated, it is used to retrieve the top 30 podcast episodes from Vespa. Online inference Google Cloud Vertex AI Semantic search contributes to the identification of pertinent podcast episodes, yet it is unable to fully supplant keyword search. This is due to the fact that semantic search falls short of exact term matching when users search an exact episode or podcast name. Spotify employs a hybrid search approach, merging semantic search in Vespa with keyword search in , followed by a conclusive re-ranking stage to establish the episodes displayed to users. Elasticsearch eBay: Image search Traditionally, search engines have displayed results by aligning the search query text with textual descriptions of items or documents. This method relies extensively on language to infer preferences and is not as effective in capturing elements of style or aesthetics. eBay introduces to help users find relevant, similar items that meet the style they’re looking for. image search eBay uses a multi-modal model which is designed to process and integrate data from multiple modalities or input types, such as text, images, audio, or video, to make predictions or perform tasks. eBay incorporates both text and images into its model, producing image embeddings utilizing a Convolutional Neural Network (CNN) model, specifically , and title embeddings using a text-based model such as . Every listing is represented by a vector embedding that combines both the image and title embeddings. Resnet-50 BERT Once the multi-modal model is trained using a large dataset of image-title listing pairs and recently sold listings, it is time to put it into production in the site search experience. Due to the large number of listings at eBay, the data is loaded in batches to HDFS, eBay’s data warehouse. eBay uses Apache Spark to retrieve and store the image and relevant fields required for further processing of listings, including generating listing embeddings. The listing embeddings are published to a columnar store such as HBase which is good at aggregating large-scale data. From HBase, the listing embedding is indexed and served in Cassini, a search engine created at eBay. The pipeline is managed using Apache Airflow, which is capable of scaling even when there is a high quantity and complexity of tasks. It also provides support for Spark, Hadoop, and Python, making it convenient for the machine learning team to adopt and utilize. Visual search allows users to find similar styles and preferences in the categories of furniture and home decor, where style and aesthetics are key to purchase decisions. In the future, eBay plans to expand visual search across all categories and also help users discover related items so they can establish the same look and feel across their home. AirBnb: Real-time personalized listings Search and similar listings features drive 99% of bookings on the AirBnb site. AirBnb built a to improve similar listing recommendations and provide real-time personalization in search rankings. listing embedding technique AirBnb realized early on that they could expand the application of embeddings beyond just word representations, encompassing user behaviors including clicks and bookings as well. To train the embedding models, AirBnb incorporated over 4.5M active listings and 800 million search sessions to determine the similarity based on what listings a user clicks and skips in a session. Listings that were clicked by the same user in a session are pushed closer together; listings that were skipped by the user are pushed further away. The team settled on the dimensionality of a listing embedding of d=32 given the tradeoff between offline performance and memory needed for online serving. https://youtu.be/aWjsUEX7B1I?si=GREROVrWxWqtqL-m&embedable=true AirBnb found that certain listings characteristics do not require learning, as they can be directly obtained from metadata, such as price. However, attributes like architecture, style, and ambiance are considerably more challenging to derive from metadata. Before moving to production, AirBnb validated their model by testing how well the model recommended listings that a user actually booked. The team also ran an A/B test comparing the existing listings algorithm against the vector embedding-based algorithm. They found that the algorithm with vector embeddings resulted in a 21% uptick in CTR and 4.9% increase in users discovering a listing that they booked. The team also realized that vector embeddings could be used as part of the model for real-time personalization in search. For each user, they collected and maintained in real time, using Kafka, a short-term history of user clicks and skips in the last two weeks. For every search conducted by the user, they ran two similarity searches: based on the geographic markets that were recently searched and then the similarity between the candidate listings and the ones the user has clicked/skipped Embeddings were evaluated in offline and online experiments and became part of the real-time personalization features. Doordash: Personalized store feeds Doordash has a wide variety of stores that users can choose to order from and being able to surface the most relevant stores using personalized preferences improves search and discovery. Doordash wanted to apply to its store feed algorithms using vector embeddings. This would enable Doordash to uncover similarities between stores that were not well-documented including if a store has sweet items, is considered trendy or features vegetarian options. latent information Doordash used a derivative of word2vec, an embedding model used in natural language processing, called store2vec that it adapted based on existing data. The team treated each store as a word and formed sentences using the list of stores viewed during a single user session, with a maximum limit of 5 stores per sentence. To create user vector embeddings, Doordash summed the vectors of the stores from which users placed orders in the past 6 months or up to 100 orders. As an example, Doordash used vector search to find similar restaurants for a user based on their recent purchases at popular, trendy joints 4505 Burgers and New Nagano Sushi in San Francisco. Doordash generated a list of similar restaurants measuring the cosine distance from the user embedding to store embeddings in the area. You can see that the stores that were closest in cosine distance include Kezar Pub and Wooden Charcoal Korean Village BBQ. Doordash incorporated store2vec distance feature as one of the features in its larger recommendation and personalization model. With vector search, Doordash was able to see a 5% increase in click-through-rate. The team is also experimenting with new models like , model optimizations and incorporating real-time onsite activity data from users. seq2seq Key considerations for vector search Pinterest, Spotify, eBay, Airbnb and Doordash create better search and discovery experiences with vector search. Many of these teams started out using text search and found limitations with fuzzy search or searches of specific styles or aesthetics. In these scenarios, adding vector search to the experience made it easier to find relevant, and often personalized, podcasts, pillows, rentals, pins and eateries. There are a few decisions that these companies made that are worth calling out when implementing vector search: : Many started out using an off-the-shelf model and then trained it on their own data. They also recognized that language models like word2vec could be used by swapping words and their descriptions with items and similar items that were recently clicked. Teams like AirBnb found that using derivatives of language models, rather than image models, could still work well for capturing visual similarities and differences. Embedding models : Many of these companies opted to train their models on past purchase and click through data, making use of existing large-scale datasets. Training : While many companies adopted ANN search, we saw that Pinterest was able to combine metadata filtering with KNN search for efficiency at scale. Indexing : Vector search rarely replaces text search. Many times, like in Spotify’s example, a final ranking algorithm is used to determine whether vector search or text search generated the most relevant result. Hybrid search : We’re seeing many teams use batch-based systems to create the vector embeddings, given that these embeddings are rarely updated. They employ a different system, frequently Elasticsearch, to compute the query vector embedding live and incorporate real-time metadata in their search. Productionizing Rockset, a real-time search and analytics database, recently added support for . Give vector search on Rockset a try for real-time personalization, recommendations, anomaly detection and more by starting a with $300 in credits today. vector search free trial

A Look Into 5 Use Cases for Vector Search from Major Tech Companies

প্রধান প্রযুক্তি কোম্পানি থেকে ভেক্টর অনুসন্ধানের জন্য 5টি ব্যবহারের ক্ষেত্রে একটি নজর৷

Ein Blick auf 5 Anwendungsfälle für die Vektorsuche bei großen Technologieunternehmen

Una mirada a cinco casos de uso de búsqueda de vectores de las principales empresas tecnológicas

Un aperçu de 5 cas d'utilisation de la recherche de vecteurs par de grandes entreprises technologiques

प्रमुख टेक कंपनियों द्वारा वेक्टर सर्च के लिए 5 उपयोग मामलों पर एक नज़र

大手テクノロジー企業によるベクトル検索の 5 つのユースケースを検証

주요 기술 회사의 벡터 검색에 대한 5가지 사용 사례 살펴보기

Uma análise de 5 casos de uso para pesquisa vetorial de grandes empresas de tecnologia

Взгляд на 5 вариантов использования векторного поиска от крупных технологических компаний

Büyük Teknoloji Şirketlerinden Vektör Aramaya Yönelik 5 Kullanım Durumuna Bir Bakış

Xem xét 5 trường hợp sử dụng tìm kiếm vectơ từ các công ty công nghệ lớn

了解主要科技公司矢量搜索的 5 个用例

The Growing Impact of Electric Vehicles in India's Sustainable Future

Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal, it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment. Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. 70% of the toxic waste 70% of the toxic waste While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. computer purchases surged by 34% computer purchases surged by 34% The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. 100 times more gold 100 times more gold How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. bought a $40 hard drive bought a $40 hard drive What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. cost over $4.45 million cost over $4.45 million How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. roughly 800 laptops worth of e-waste roughly 800 laptops worth of e-waste Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. up to 40% of its e-waste up to 40% of its e-waste While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal , it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. international law making it illegal international law making it illegal What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment.

What Is E-Waste Hacking?

ই-ওয়েস্ট হ্যাকিং কি?

Was ist Elektroschrott-Hacking?

¿Qué es el pirateo de residuos electrónicos?

Qu’est-ce que le piratage des déchets électroniques ?

ई-कचरा हैकिंग क्या है?

電子廃棄物ハッキングとは何ですか?

전자 폐기물 해킹이란 무엇입니까?

O que é hackeamento de lixo eletrônico?

Что такое взлом электронных отходов?

E-Atık Hacking Nedir?

Hack rác thải điện tử là gì?

什么是电子垃圾黑客？

The cryptocurrency landscape continues to evolve with contrasting trends across various tokens. Ethereum (ETH) is grappling with a dip in trading volume despite recent price gains, while Shiba Inu (SHIB) celebrates a significant rise, and Furrever Token (FURR) captivates the market with a successful presale, illustrating the varied trajectories and potential within the crypto sector. **Ethereum's (ETH) Stability in Question as Trading Volume Dips Despite Surpassing $3,000** Despite recently crossing the $3,000 threshold and moving past the 100 EMA on the ETH/USD chart, Ethereum faces uncertain prospects due to a significant drop in trading volume. This decline in volume is a concerning indicator that casts doubt on the sustainability of the recent price surge. Ethereum is currently struggling to break through the 26 EMA, a critical short-term moving average that has become a formidable barrier. If Ethereum can successfully navigate past this resistance, it could aim for higher levels, potentially reaching around $3,236. Conversely, failure to surpass this point could result in a fallback to lower support levels, with a possible dip to around $2,890. The future trajectory of Ethereum heavily depends on its trading volume. Increased volume is essential for confirming market trends and demonstrating buyer commitment. Presently, the low trading volume suggests a lack of robust market support for the price rise. Without a significant uptick in trading volume and market liquidity, Ethereum’s ability to sustain its position above $3,000 could be jeopardized, threatening any further price advancements. Shiba Inu's (SHIB) Remarkable Journey: From Memecoin to a Prominent Crypto Asset Shiba Inu, once known primarily as a memecoin, has recently taken significant strides that signal its evolving role in the cryptocurrency market. The project announced a successful fundraising effort, securing $12 million from several high-profile institutional investors. This funding is designated to develop Treat, a privacy layer aimed at boosting user confidentiality and security within the Shiba Inu ecosystem. The initiative to enhance privacy through Treat involves implementing Fully Homomorphic Encryption (FHE), a cutting-edge technology provided by Zama.AI. This encryption allows for computations on encrypted data without exposing the data itself, paving the way for potential transformations in sectors like encrypted lending platforms, tokenized asset exchanges, and encrypted machine-learning models. Shiba Inu's growth trajectory over the past year has been impressive, marked by a 164% increase in its value. This growth is accompanied by the launch of .shib domains and the expansion of Shibarium, its layer-2 scaling solution, which now serves over 1 million users. Recent on-chain data highlights extensive whale activity within the Shiba Inu network, with more than 3.19 trillion SHIB, valued at approximately $82.49 million, moved in just 24 hours. This includes significant transactions involving large amounts of SHIB moving to and from exchanges, indicating a dynamic and active trading environment. Despite some concerns about potential sell-offs by large holders, Shiba Inu's market value has continued to climb, showing a 6.65% increase in the last 12 hours and a 16% rise over the past week. The increasing number of Shiba Inu holders, now exceeding 1.4 million, reflects growing trust and interest in its potential as a stable and valuable digital asset. Moreover, the SHIB team has proactively established support clusters at strategic price points to mitigate potential volatility. These clusters serve as psychological markers where significant buying could potentially re-ignite, supporting the coin's price during market dips. Shiba Inu's evolution from a playful memecoin to a serious contender in the crypto space is underscored by its recent developments, robust community support, and promising technological advancements, positioning it as a potential millionaire maker in the evolving digital asset landscape. Furrever Token (FURR): A Charming New Player in the Crypto Arena Furrever Token (FURR) has burst onto the crypto scene, quickly distinguishing itself from established memecoins like Dogecoin (DOGE) and Shiba Inu (SHIB) with its endearing, kitty-themed appeal. This token not only offers a heartwarming experience centered around the universal charm of adorable cats but also aims to bring a fresh wave of cuteness to the cryptocurrency space. Furrever Token is strategically positioned to foster project growth and sustainability with its token distribution: 65% of its tokens were made available during the presale, and a significant 10% is locked for the team, underlining the project's long-term commitment. This approach has already attracted considerable investment, with the project raising over $1,000,000, reflecting strong investor confidence in its unique vision. The project emphasizes security and compliance, with thorough audits by Securi Lab enhancing its credibility in the market. Moreover, Furrever Token has cultivated a warm and engaging community atmosphere, boasting nearly 4,000 active members on Telegram. This sense of community makes Furrever Token more than just a cryptocurrency; it's a movement towards a more delightful and engaging crypto experience. As the presale phase concludes and Furrever Token prepares for its PancakeSwap debut, anticipation is building within the crypto community about the impact this enchanting token will have. With potential returns of up to 15X and a current price of $0.000648, Furrever Token stands ready to capture the hearts and wallets of crypto enthusiasts and investors, offering a cheerful alternative to more traditional memecoins. For further information or any assistance regarding Furrever Token, reach out only through the official channel at support@furrevertoken.com to avoid potential scams. Discover the Most Exclusive Presale Opportunity of 2024 Now:Furrever Token Official Website  |   Visit Furrever Token Presale Join Official Telegram Group       |   Follow Official X Account This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the program here. The cryptocurrency landscape continues to evolve with contrasting trends across various tokens. Ethereum (ETH) is grappling with a dip in trading volume despite recent price gains, while Shiba Inu (SHIB) celebrates a significant rise, and Furrever Token (FURR) captivates the market with a successful presale, illustrating the varied trajectories and potential within the crypto sector. Furrever Token (FURR) Furrever Token (FURR) **Ethereum's (ETH) Stability in Question as Trading Volume Dips Despite Surpassing $3,000 ** Despite recently crossing the $3,000 threshold and moving past the 100 EMA on the ETH/USD chart, Ethereum faces uncertain prospects due to a significant drop in trading volume. This decline in volume is a concerning indicator that casts doubt on the sustainability of the recent price surge. Ethereum is currently struggling to break through the 26 EMA, a critical short-term moving average that has become a formidable barrier. If Ethereum can successfully navigate past this resistance, it could aim for higher levels, potentially reaching around $3,236. Conversely, failure to surpass this point could result in a fallback to lower support levels, with a possible dip to around $2,890. The future trajectory of Ethereum heavily depends on its trading volume. Increased volume is essential for confirming market trends and demonstrating buyer commitment. Presently, the low trading volume suggests a lack of robust market support for the price rise. Without a significant uptick in trading volume and market liquidity, Ethereum’s ability to sustain its position above $3,000 could be jeopardized, threatening any further price advancements. Shiba Inu's (SHIB) Remarkable Journey: From Memecoin to a Prominent Crypto Asset Shiba Inu, once known primarily as a memecoin, has recently taken significant strides that signal its evolving role in the cryptocurrency market. The project announced a successful fundraising effort, securing $12 million from several high-profile institutional investors. This funding is designated to develop Treat, a privacy layer aimed at boosting user confidentiality and security within the Shiba Inu ecosystem. The initiative to enhance privacy through Treat involves implementing Fully Homomorphic Encryption (FHE), a cutting-edge technology provided by Zama.AI. This encryption allows for computations on encrypted data without exposing the data itself, paving the way for potential transformations in sectors like encrypted lending platforms, tokenized asset exchanges, and encrypted machine-learning models. Shiba Inu's growth trajectory over the past year has been impressive, marked by a 164% increase in its value. This growth is accompanied by the launch of .shib domains and the expansion of Shibarium, its layer-2 scaling solution, which now serves over 1 million users. Recent on-chain data highlights extensive whale activity within the Shiba Inu network, with more than 3.19 trillion SHIB, valued at approximately $82.49 million, moved in just 24 hours. This includes significant transactions involving large amounts of SHIB moving to and from exchanges, indicating a dynamic and active trading environment. Despite some concerns about potential sell-offs by large holders, Shiba Inu's market value has continued to climb, showing a 6.65% increase in the last 12 hours and a 16% rise over the past week. The increasing number of Shiba Inu holders, now exceeding 1.4 million, reflects growing trust and interest in its potential as a stable and valuable digital asset. Moreover, the SHIB team has proactively established support clusters at strategic price points to mitigate potential volatility. These clusters serve as psychological markers where significant buying could potentially re-ignite, supporting the coin's price during market dips. Shiba Inu's evolution from a playful memecoin to a serious contender in the crypto space is underscored by its recent developments, robust community support, and promising technological advancements, positioning it as a potential millionaire maker in the evolving digital asset landscape. Furrever Token (FURR): A Charming New Player in the Crypto Arena Furrever Token (FURR) has burst onto the crypto scene, quickly distinguishing itself from established memecoins like Dogecoin (DOGE) and Shiba Inu (SHIB) with its endearing, kitty-themed appeal. This token not only offers a heartwarming experience centered around the universal charm of adorable cats but also aims to bring a fresh wave of cuteness to the cryptocurrency space. Furrever Token (FURR) Furrever Token (FURR) Furrever Token is strategically positioned to foster project growth and sustainability with its token distribution: 65% of its tokens were made available during the presale, and a significant 10% is locked for the team, underlining the project's long-term commitment. This approach has already attracted considerable investment, with the project raising over $1,000,000, reflecting strong investor confidence in its unique vision. The project emphasizes security and compliance, with thorough audits by Securi Lab enhancing its credibility in the market. Moreover, Furrever Token has cultivated a warm and engaging community atmosphere, boasting nearly 4,000 active members on Telegram. This sense of community makes Furrever Token more than just a cryptocurrency; it's a movement towards a more delightful and engaging crypto experience. As the presale phase concludes and Furrever Token prepares for its PancakeSwap debut, anticipation is building within the crypto community about the impact this enchanting token will have. With potential returns of up to 15X and a current price of $0.000648, Furrever Token stands ready to capture the hearts and wallets of crypto enthusiasts and investors, offering a cheerful alternative to more traditional memecoins. For further information or any assistance regarding Furrever Token, reach out only through the official channel at support@furrevertoken.com to avoid potential scams. support@furrevertoken.com support@furrevertoken.com Discover the Most Exclusive Presale Opportunity of 2024 Now: Furrever Token Official Website | Visit Furrever Token Presale Join Official Telegram Group | Follow Official X Account Furrever Token Official Website Furrever Token Official Website Visit Furrever Token Presale Visit Furrever Token Presale Join Official Telegram Group Join Official Telegram Group Follow Official X Account Follow Official X Account This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the program here. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the program here. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the program here . here

Ethereum Wavers with a Volume Drop, Shiba Inu Soars by 164%, & Furrever Token Over $1M in Presales

একটি ভলিউম ড্রপ সহ ইথেরিয়াম ওয়েভার্স, শিবা ইনু 164% বৃদ্ধি পেয়েছে, এবং ফুরেভার টোকেন $1M-এর উপরে

Ethereum schwankt mit Volumenrückgang, Shiba Inu steigt um 164 % und Furrever-Token über 1 Mio. USD im Vorverkauf

Ethereum vacila con una caída de volumen, Shiba Inu se dispara un 164% y el token Furrever supera el millón de dólares en preventas

Ethereum vacille avec une baisse de volume, Shiba Inu monte en flèche de 164 % et le jeton Furrever dépasse 1 million de dollars en préventes

इथेरियम में वॉल्यूम में गिरावट, शिबा इनु में 164% की उछाल, और फुर्रेवर टोकन की प्रीसेलिंग $1 मिलियन से अधिक

イーサリアムは取引量減少で動揺、柴犬は164%急騰、Furreverトークンはプレセールで100万ドル超

거래량 감소로 이더리움이 흔들리고, 시바견이 164% 급등하고, Furrever 토큰이 사전 판매에서 100만 달러 이상을 기록했습니다.

Ethereum oscila com queda de volume, Shiba Inu dispara 164% e Furrever Token ultrapassa US$ 1 milhão em pré-vendas

Ethereum колеблется с падением объема, Shiba Inu взлетает на 164%, а токены Furrever на предварительных продажах превышают 1 миллион долларов

Ethereum Wavers Hacim Düşüşüyle, Shiba Inu %164 Yükseldi ve Furrever Token Ön Satışlarda 1 Milyon Doları Aştı

Ethereum dao động với khối lượng giảm, Shiba Inu tăng vọt 164% và mã thông báo Furrever bán trước hơn 1 triệu đô la

以太坊交易量下跌，柴犬价格飙升 164%，Furrever 代币预售额超过 100 万美元

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Since n.d.

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