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FARFETCH exists for the love of fashion. We believe in empowering individuality. Our mission is to be the global technology platform for luxury fashion, connecting creators, curators and consumers.
FARFETCH is the leading global technology platform for the luxury fashion industry. Founded in 2007 by José Neves for the love of fashion, and launched in 2008, FARFETCH began as an e-commerce marketplace for luxury boutiques around the world. As of 31st December 2019, the FARFETCH Marketplace connects customers in over 190 countries with items from more than 50 countries and over 1,200 of the world’s best brands, boutiques and department stores, delivering a truly unique shopping experience and access to the most extensive selection of luxury on a single platform.
FARFETCH’s additional businesses include FARFETCH Platform Solutions, which services enterprise clients with e-commerce and technology capabilities; Browns and Stadium Goods, which offer luxury products to consumers; and New Guards, a platform for the development of global fashion brands. FARFETCH also invests in innovations such as its Store of the Future augmented retail solution, and develops key technologies, business solutions, and services for the luxury fashion industry.
Learn more about us:
www.aboutfarfetch.com
www.farfetchinvestors.com
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www.farfetchcareers.com

Farfetch

farfetch

The company is still well positioned to be the market leader in global luxury e-commerce, but new owner Coupang has its work cut out for it, writes Imran Amed.

A Letter to Farfetch’s New Owner | BoF

Coupang purchased Farfetch for $500 million USD last month.

Farfetch CEO José Neves Steps Down Following Coupang Acquisition

First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change Over the last five years, the dominant narrative surrounding data infrastructure has emphasized the importance of companies owning and leveraging their data as they acquire as much information about their users and customers as possible. Due to mounting privacy regulations, they need to gather the data themselves instead of relying on third-party data operators, such as ad networks or platforms like Google, Meta, and Amazon. Companies have fallen in line with this narrative and made the shift. But, in the battle for the best data, is first-party really better? Not by itself, but it could be with a little help from vectors, frameworks like RAG, and open-source foundation models like Llama 3. The Push for First-Party Data The argument for first-party data generally goes like this: companies need to become better stewards of data acquisition and management amid growing calls for data privacy. Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it– and they generally don’t like the answers to those questions. Access and Delete requests are rapidly increasing, and the data privacy landscape is changing quickly. Companies are finding it hard enough to figure out their own privacy management processes; they don’t want to have to worry about third parties' as well. But the shift to first-party is not all about privacy. There is also the notion that third-party data will lose value as we move towards the proverbial cookieless future. Companies can’t get the same granular details they once could so why should they devote budget to a service that delivers less than it used to? And then there is the perpetual concern that the big platforms and ad networks will make unexpected changes. For example, they could change their algorithms, restrict access to certain types of data, or alter advertising policies in a manner that could harm a business’ performance with little or no notice. Being dependent on another company’s practices leaves one vulnerable. Companies feel stuck because they’ve already invested so much time, money, and resources into their data strategy. In this light, the push to take back control of data seems essential. But is it practical? The First-Data Problems No One is Talking About Early results for companies taking a gamble on first-party data have not met expectations. We’re seeing case after case of consumer companies that made the shift suffering. Farfetch, Allbirds, and  SmileDirectClub are just a few examples. Would better, more strategic use of first-party data turn the tide on the rising customer acquisition costs? Still, the current reliance on first-party data– and the practices to extract that data– is a commonality across several companies that are experiencing tougher times today. It’s noticeable enough to make analysts, VCs, and marketers themselves ask if they were wrong to prioritize first-party data. The downsides of first-party data, as it is currently acquired and leveraged, are often considered to be underestimating the disappearance of device IDs, changing IP addresses, consumer adoption of fake emails, and ad blockers. While true, much more significant issues are at play. First, there is a massive talent gap. Big Tech and the platform companies snag the best. They have the most to offer, making it difficult for up-and-coming consumer companies to compete for the data scientists and ML talent needed to make sense of the information being collected and analyzed. Without truly exceptional people, companies have struggled. There is also a real tooling problem. The offerings available to companies are nowhere near equivalent to the tools Big Tech companies boast internally (which can be a factor in the talent gap). Tooling makes a difference, and most companies simply can’t compete right now. Finally, there is a volume challenge. Big Tech and the ad networks have mountains of data, as these companies pool and anonymize hundreds of billions of data points to make their models run effectively. By contrast, if a company has only its data to work with, there is just not enough for ML to work as promised. While these problems seem serious, is it time to give up on the potential– and the need for– first-party data? No way! The Power of Vectors The biggest issue hindering first-party data is how companies are trying to access it. Up to this point, businesses have taken an Old World approach. Models must be built from scratch to extract the value from data that businesses require. This takes time, money, and most of all talent; it depends on how good your ML engineers and data scientists are. However, as discussed above, there is not enough available talent to make this approach work better than leveraging third-party data. The lack of talent creates the bottleneck. However, this does not mean we need to give up on first-party data. We just need to change how we approach it. In the New World that is possible today, vectors and vector embeddings are the key. Vectors are generic mathematical objects that can represent data points' features or attributes while embedding models analyze patterns in data to generate these information-packed, meaningful representations learned from data; they capture the semantic relationships. Vector embeddings are the format that can encode everything you know about a user or a customer and make that information accessible to an analytics system, or leverageable to personalize the user experience, or even to catch fraud. There are so many possibilities. Vectors are poised to drive a sea change because they can power analytics in a fundamentally different way. Retrieval-augmented generation (RAG) is generating a lot of buzz right now for all it can enable, but vector embeddings are what make RAG useful. They are a central component of the framework that helps with context, responses, integration of retrieval and fine-tuning of models. Generating high-quality vectors and querying them correctly is a critical task that enables any RAG system to actually work. There are other frameworks, but RAG is particularly well-suited for a first-party data revolution. Sounds great. Let’s all figure out how to use vectors and vector embeddings. This is not the total answer. The issues like limited data sets and tooling remain. All is not yet wrapped up in a pretty bow, but I believe it soon will be. Because open-source, pre-trained foundation models like Meta’s Llama 2, which will give way to the more robust Llama 3 in July, can level the playing field. The issue of not having enough data volume compared to BigTech gets mitigated. By using an open source model pre-trained on large and diverse datasets, that model has a certain level of knowledge and understanding built in. Companies simply need to fine-tune Llama 2 (or Llama 3) on their specific domain or task with their data. This eases the bottleneck because in many cases, you no longer have to train a model from scratch. This may sound like an oversimplification, given that Llama helps companies deal with text, but most of the data companies run on is not text. The structured data companies run on need to be integrated into this process. For example, the user behavioural events which form a large percentage of first-party data typically, are not suitable to be processed by any LLM. This is changing so companies should be ready as new multimodal solutions emerge. Similarly, tooling is still lacking, but there is a lot of attention on the space, so great strides are being taken. It’s coming along! With the biggest issues fundamentally addressed, first-party data hype is back, baby! Companies don’t have to worry about third parties violating privacy, nor do they have to depend on Big Tech to help them know their customers. Expect to see first-party data explode this year as companies finally take full advantage– especially with Llama 3 at the ready. For all of its promise, perhaps the greatest potential for Llama 3 will be to actually solve the first-party data problem once and for all. First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change Over the last five years, the dominant narrative surrounding data infrastructure has emphasized the importance of companies owning and leveraging their data as they acquire as much information about their users and customers as possible. Due to mounting privacy regulations, they need to gather the data themselves instead of relying on third-party data operators, such as ad networks or platforms like Google, Meta , and Amazon . Companies have fallen in line with this narrative and made the shift. Google, Meta Amazon But, in the battle for the best data, is first-party really better? Not by itself, but it could be with a little help from vectors, frameworks like RAG, and open-source foundation models like Llama 3 . Llama 3 The Push for First-Party Data The argument for first-party data generally goes like this: companies need to become better stewards of data acquisition and management amid growing calls for data privacy . Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it– and they generally don’t like the answers to those questions. Access and Delete requests are rapidly increasing, and the data privacy landscape is changing quickly. Companies are finding it hard enough to figure out their own privacy management processes; they don’t want to have to worry about third parties' as well. first-party data data privacy Access Access Delete Delete But the shift to first-party is not all about privacy. There is also the notion that third-party data will lose value as we move towards the proverbial cookieless future. Companies can’t get the same granular details they once could so why should they devote budget to a service that delivers less than it used to? And then there is the perpetual concern that the big platforms and ad networks will make unexpected changes. For example, they could change their algorithms, restrict access to certain types of data, or alter advertising policies in a manner that could harm a business’ performance with little or no notice. Being dependent on another company’s practices leaves one vulnerable. Companies feel stuck because they’ve already invested so much time, money, and resources into their data strategy. In this light, the push to take back control of data seems essential. But is it practical? The First-Data Problems No One is Talking About Early results for companies taking a gamble on first-party data have not met expectations. We’re seeing case after case of consumer companies that made the shift suffering. Farfetch , Allbirds , and SmileDirectClub are just a few examples. Would better, more strategic use of first-party data turn the tide on the rising customer acquisition costs? Farfetch Farfetch Allbirds Allbirds SmileDirectClub SmileDirectClub Still, the current reliance on first-party data– and the practices to extract that data– is a commonality across several companies that are experiencing tougher times today. It’s noticeable enough to make analysts, VCs, and marketers themselves ask if they were wrong to prioritize first-party data. The downsides of first-party data, as it is currently acquired and leveraged, are often considered to be underestimating the disappearance of device IDs, changing IP addresses, consumer adoption of fake emails, and ad blockers. While true, much more significant issues are at play. First, there is a massive talent gap. Big Tech and the platform companies snag the best. They have the most to offer, making it difficult for up-and-coming consumer companies to compete for the data scientists and ML talent needed to make sense of the information being collected and analyzed. Without truly exceptional people, companies have struggled. There is also a real tooling problem. The offerings available to companies are nowhere near equivalent to the tools Big Tech companies boast internally (which can be a factor in the talent gap). Tooling makes a difference, and most companies simply can’t compete right now. Finally, there is a volume challenge. Big Tech and the ad networks have mountains of data, as these companies pool and anonymize hundreds of billions of data points to make their models run effectively. By contrast, if a company has only its data to work with, there is just not enough for ML to work as promised. While these problems seem serious, is it time to give up on the potential– and the need for– first-party data? No way! The Power of Vectors The biggest issue hindering first-party data is how companies are trying to access it. Up to this point, businesses have taken an Old World approach. Models must be built from scratch to extract the value from data that businesses require. This takes time, money, and most of all talent; it depends on how good your ML engineers and data scientists are. However, as discussed above, there is not enough available talent to make this approach work better than leveraging third-party data. The lack of talent creates the bottleneck. However, this does not mean we need to give up on first-party data. We just need to change how we approach it. In the New World that is possible today, vectors and vector embeddings are the key. Vectors are generic mathematical objects that can represent data points' features or attributes while embedding models analyze patterns in data to generate these information-packed, meaningful representations learned from data; they capture the semantic relationships. Vector embeddings are the format that can encode everything you know about a user or a customer and make that information accessible to an analytics system, or leverageable to personalize the user experience, or even to catch fraud. There are so many possibilities. Vectors are poised to drive a sea change because they can power analytics in a fundamentally different way. Retrieval-augmented generation (RAG) is generating a lot of buzz right now for all it can enable, but vector embeddings are what make RAG useful. They are a central component of the framework that helps with context, responses, integration of retrieval and fine-tuning of models. Generating high-quality vectors and querying them correctly is a critical task that enables any RAG system to actually work . There are other frameworks, but RAG is particularly well-suited for a first-party data revolution. to actually work Sounds great. Let’s all figure out how to use vectors and vector embeddings. This is not the total answer. The issues like limited data sets and tooling remain. All is not yet wrapped up in a pretty bow, but I believe it soon will be. Because open-source, pre-trained foundation models like Meta’s Llama 2, which will give way to the more robust Llama 3 in July, can level the playing field. The issue of not having enough data volume compared to BigTech gets mitigated. By using an open source model pre-trained on large and diverse datasets, that model has a certain level of knowledge and understanding built in. Companies simply need to fine-tune Llama 2 (or Llama 3) on their specific domain or task with their data. This eases the bottleneck because in many cases, you no longer have to train a model from scratch. This may sound like an oversimplification, given that Llama helps companies deal with text, but most of the data companies run on is not text. The structured data companies run on need to be integrated into this process. For example, the user behavioural events which form a large percentage of first-party data typically, are not suitable to be processed by any LLM. This is changing so companies should be ready as new multimodal solutions emerge. Similarly, tooling is still lacking, but there is a lot of attention on the space, so great strides are being taken. It’s coming along! With the biggest issues fundamentally addressed, first-party data hype is back, baby! Companies don’t have to worry about third parties violating privacy, nor do they have to depend on Big Tech to help them know their customers. Expect to see first-party data explode this year as companies finally take full advantage– especially with Llama 3 at the ready. For all of its promise, perhaps the greatest potential for Llama 3 will be to actually solve the first-party data problem once and for all.

How Vectors, RAG and Llama 3 Are Changing First-Party Data

কিভাবে ভেক্টর, রাগ এবং লামা 3 প্রথম পক্ষের ডেটা পরিবর্তন করছে

Wie Vectors, Rag und Llama 3 First-Party-Daten verändern

Cómo Vectors, Rag y Llama 3 están cambiando los datos propios

Comment Vectors, Rag et Llama 3 modifient les données propriétaires

वेक्टर्स, रैग और लामा 3 किस तरह से फर्स्ट-पार्टी डेटा को बदल रहे हैं

Vectors、Rag、Llama 3 がファーストパーティ データをどのように変えているのか

Vector, Rag 및 Llama 3가 자사 데이터를 어떻게 변화시키는가

Como Vectors, Rag e Llama 3 estão mudando os dados primários

Как Vectors, Rag и Llama 3 меняют собственные данные

Vektörler, Rag ve Llama 3 Birinci Taraf Verilerini Nasıl Değiştiriyor?

Cách Vectors, Rag và Llama 3 thay đổi dữ liệu của bên thứ nhất

Vectors、Rag 和 Llama 3 如何改变第一方数据

The current decade will always remain etched in the annals of tech history for the unprecedented advent of artificial intelligence. It wouldn’t be farfetched to say that AI is making an indelible mark on almost every aspect of human life. Tech pundits and gurus are already predicting its far-reaching transformative effects and the occupations it will render obsolete. A potent question is whether the cybersecurity landscape is slated to incur ramifications from this onslaught. Is AI going to edge out humans, or is it likely to augment human roles? What do the industry trends tell us about it? The Ever-evolving World of Cyber Threats According to Forbes, the share impact of AI is likely to affect 15% of the global workforce. However, the Cybersecurity realm will remain immune to it because of a few key points. First is the ever-evolving nature of cyber threats. What was once considered a bane for cyber security specialists has become a blessing in disguise. AI models are adept at identifying threats and risks, but they depend on pattern feed, allowing them to pinpoint deviations. AI lacks the intuition and critical thinking to cope with anomalies falling outside the jurisdiction of the feed. In this particular scenario, AI would be incapable of formulating timely countermeasures, resulting in the compromise of organizational infrastructure. A human brain, on the other hand, is capable of rational thinking and contextual understanding, making it more adaptable, superior, and capable of interpreting previously unencountered threats and security lapses. It can also devise on-the-spot inventive measures to safeguard digital assets. After such an instance, the AI feed pattern will likely be updated with the latest information to improve and enhance its threat detection by a human engineer! Ethical and Legal Considerations Traversing ethical and legal considerations is still not the forte of artificial intelligence. When it comes to cybersecurity, these ethics and considerations become very convoluted with human behavior added to the mix. For instance, how can privacy violations of an individual’s data be curbed in a way that doesn’t infringe any rights? Regulation of surveillance and monitoring in a non-pervasive manner and, lastly, drawing a boundary between user responsibility and organizational security protocols. In broad-spectrum, AI can be taught to discern these factors but it can't substitute for human judgment and decisiveness to reach decisions that are majorly aligned with implemented standards and practices. Incident Response and Management Incident response and management capability of AI and ML algorithms have been a much-debated topic. Some proponents deem it indispensable, and some opponents are quick to point out various proverbial chinks in the armor. Though the autonomous response of cybersecurity-based AI systems operates on containment, remediation, and recovery, but again they rely on historical security and threat analysis data points. The delegation of tasks according to the criticality of threat level and undertaking adaptive strategies are better deployed when relying on the collective cognitive prowess of a human-led response team. Who is Protecting the Protector? Lastly, the AI system, which an organization might trust to shield itself, is prone to attacks. In the absence of human oversight and supervision, miscreants find inventive ways to manipulate the AI and achieve their nefarious objectives. This includes intentional and subtle data manipulation to hoodwink the AI. The technical moniker of this is “adversarial attacks”. Adversarial attacks dupe the AI into misclassifying or failing to detect a threat. Hackers might also keenly and zealously observe the AI model and record its behavior when given certain inputs. This allows them to deduce the model’s behavior, response time, and preventive measures and then develop ways to bypass it. Conclusion In conclusion, it is safe to assume that in the near future, AI will play a pivotal role in cybersecurity, but it still hasn’t attained the level of maturity, refinement, and dependability to circumvent the human element. Cybersecurity specialists will remain crucial for nurturing, training, and refining these systems for peak performance. The current decade will always remain etched in the annals of tech history for the unprecedented advent of artificial intelligence. It wouldn’t be farfetched to say that AI is making an indelible mark on almost every aspect of human life. Tech pundits and gurus are already predicting its far-reaching transformative effects and the occupations it will render obsolete. A potent question is whether the cybersecurity landscape is slated to incur ramifications from this onslaught. Is AI going to edge out humans, or is it likely to augment human roles? What do the industry trend s tell us about it? industry trend The Ever-evolving World of Cyber Threats According to Forbes, the share impact of AI is likely to affect 15% of the global workforce. However, the Cybersecurity realm will remain immune to it because of a few key points. First is the ever-evolving nature of cyber threats. What was once considered a bane for cyber security specialists has become a blessing in disguise. AI models are adept at identifying threats and risks, but they depend on pattern feed, allowing them to pinpoint deviations. Forbes , Forbes AI lacks the intuition and critical thinking to cope with anomalies falling outside the jurisdiction of the feed. In this particular scenario, AI would be incapable of formulating timely countermeasures, resulting in the compromise of organizational infrastructure. A human brain, on the other hand, is capable of rational thinking and contextual understanding, making it more adaptable, superior, and capable of interpreting previously unencountered threats and security lapses. It can also devise on-the-spot inventive measures to safeguard digital assets. After such an instance, the AI feed pattern will likely be updated with the latest information to improve and enhance its threat detection by a human engineer! Ethical and Legal Considerations Traversing ethical and legal considerations is still not the forte of artificial intelligence. When it comes to cybersecurity, these ethics and considerations become very convoluted with human behavior added to the mix. For instance, how can privacy violations of an individual’s data be curbed in a way that doesn’t infringe any rights? Regulation of surveillance and monitoring in a non-pervasive manner and, lastly, drawing a boundary between user responsibility and organizational security protocols. In broad-spectrum, AI can be taught to discern these factors but it can't substitute for human judgment and decisiveness to reach decisions that are majorly aligned with implemented standards and practices. Incident Response and Management Incident response and management capability of AI and ML algorithms have been a much-debated topic. Some proponents deem it indispensable, and some opponents are quick to point out various proverbial chinks in the armor. Though the autonomous response of cybersecurity-based AI systems operates on containment, remediation, and recovery, but again they rely on historical security and threat analysis data points. The delegation of tasks according to the criticality of threat level and undertaking adaptive strategies are better deployed when relying on the collective cognitive prowess of a human-led response team. cybersecurity-based AI systems cybersecurity-based AI systems Who is Protecting the Protector? Lastly, the AI system , which an organization might trust to shield itself, is prone to attacks. In the absence of human oversight and supervision, miscreants find inventive ways to manipulate the AI and achieve their nefarious objectives. This includes intentional and subtle data manipulation to hoodwink the AI. The technical moniker of this is “adversarial attacks”. the AI system the AI system Adversarial attacks dupe the AI into misclassifying or failing to detect a threat. Hackers might also keenly and zealously observe the AI model and record its behavior when given certain inputs. This allows them to deduce the model’s behavior, response time, and preventive measures and then develop ways to bypass it. Adversarial attacks Adversarial attacks Conclusion In conclusion, it is safe to assume that in the near future, AI will play a pivotal role in cybersecurity, but it still hasn’t attained the level of maturity, refinement, and dependability to circumvent the human element. Cybersecurity specialists will remain crucial for nurturing, training, and refining these systems for peak performance.

The Persistent Human Element in the Future of Cybersecurity

Understanding Core Web Vitals: The Vital Signs of Your Website

মূল ওয়েব ভাইটাল বোঝা: আপনার ওয়েবসাইটের গুরুত্বপূর্ণ লক্ষণ

Grundlegende Web-Vitalwerte verstehen: Die Vitalfunktionen Ihrer Website

Comprensión de los elementos básicos de la web: los signos vitales de su sitio web

Comprendre les éléments essentiels du Web : les signes vitaux de votre site Web

कोर वेब वाइटल्स को समझना: आपकी वेबसाइट के महत्वपूर्ण संकेत

Core Web Vitals を理解する: Web サイトの重要な兆候

핵심 웹 바이탈 이해: 웹사이트의 바이탈 사인

Compreendendo os principais sinais vitais da Web: os sinais vitais do seu site

Понимание основных веб-жизненных показателей: жизненно важные признаки вашего веб-сайта

Temel Web Verilerini Anlamak: Web Sitenizin Yaşamsal Belirtileri

Hiểu các chỉ số quan trọng của trang web: Các dấu hiệu quan trọng của trang web của bạn

了解核心网络生命：您网站的生命体征

Businesses across the genres craving for , has been all about consistent revamping and digital evolution chains. Traversing from mainstream processes to tech-efficient landscapes has been a mammoth move and of course an imperative one. However, now one can say that scratching off the surface of digital transformation for businesses has come full circle with AI in metaverse. Where personalization, massive data optimization and immersive roll outs are easier than ever. But again, this is only the businesses capitalize on futuristic catalysts like ‘Fueling Innovation & Step-Ups’ if and when AI and Metaverse. Yes, there are virtual shopping, interactive displays, and AI-powered virtual assistants for sales. Or even the AI-powered virtual meeting platforms have been super tech-savvy catch-ups. But I can now safely say that it was quite last season because synergizing on the symphony of Metaverse with AI is the next wave. So, for all the modern businesses to hover around the ultra-competitive times and level up comprehensively on all fronts, calling dibs on Metaverse with AI is the only way out. The duo is not just rolling out benefits and perks in abundance for the businesses. They are changing the entire trajectory of digital transformation. So, let’s unravel the prowess of this tech-fusion ahead! The Need of the Hour: AI and Metaverse . But wait, is it the only catch when times have changed dramatically, and businesses are on an outlook for innovative leverages? Certainly, no! Right? AI and Metaverse have moved past the Traditional Bandwidths! Transcending limitations, Automating tasks and zapping the gap between the real and virtual worlds still does shine and serve for the businesses I mean, the swaps from manual to automation and clutching in the meeting rooms to highly hyper-realistic platforms are game changers. But, again would it streamline operations, as we live in the datasphere. Clearly businesses across the globe are equipped with data flowing from all directions and they do rely on it extensively. Well, was that it? Was that the big final? Without a second thought, NO! Okay, automation was magical. But considering the present times and the competitive march towards the future, integrating AI and the metaverse is not just a looming luxury but a necessity. Now, it’s natural to think why and how. For that, let us get you navigating through the same. Considering the cluster of futuristic capabilities that AI hosts, it is understood that the initial drive for the businesses to integrate AI was to add a ‘Dollop of automation, smart and modern dynamics’. However, with the digital horizons taking up on a massive scale, AI delivers more than ever. Why AI? Let’s explore your businesses as a ‘ Robust Machine.’ And for that machine to run efficiently, AI is the ‘Core Mechanism.’ It means if you, as a business loses the grip on mechanics, the machinery stops working. To be precise, it falls out completely if you let go of AI in digital transformation. For Example- Now, at this point it is quite natural of you to raise the most obvious question; Sure, but what does metaverse bring to the table? Why Metaverse? When it comes to businesses and considering it as a robust machine (for example, same as above), artificial intelligence is clustered machinery that is in charge of the process, production, and operational efficiency. Basically, it is a on the other hand, is a ‘Finished Product, which is not your average batch of products but a personalized, futuristic, and immersive one more like, . In other words, AI in metaverse are like two peas in a pod. And you cannot stack and put chips on one and leave the other. They just synergize so well together that no tech giant would want to ignore their massive impact. back-end ecosystem. , Metaverse a front end ecosystem Having said that- it does not matter if you are into retail, corporate, fintech or even narrowed down to marketing, production or designing. This ultimately brings us down to the HOW. How is this the only seed for taking ‘The next digital transformation leap’? And, for the same we need to tap onto the tech-trail. TECH-DUO How Integrating Metaverse and AI in digital transformation is the only move ahead for businesses? To help you understand it better, let us give you a simple formula. Technically Speaking- AI x Metaverse  (Backend + Frontend) Catering Modern-Day Business Requirements = Intelligent Agents of Computer Science fetching, formatting, filtering, and powering up data. AI: An Immersive space powered by AI, panning out real-life-like aspects for the users. Metaverse: In a nutshell, it does not matter what stream or genre your business falls into. The digital horizon of the times, roots on two thriving technologies i.e Metaverse and Artificial Intelligence. Right from operational efficiency, to acing a competitive edge in the market to delivering next-gen experiences to the users. This dynamic duo is certainly a ‘ solution, and integrating it is the only step to soar high for the businesses. one-size-fits-all’ Besides that, I do have some claims to make for AI in Metaverse; AI and Metaverse are . beyond the basics You can count on it for the ‘Innovative Next-Gen Dive.’ Yes, you ‘ Save money while facelifting your operations.’ No, you ‘Do not have to undertake a trail of tasks.’ Don’t worry, with below listed genres I will affirm the above claims. So, tag along! Training Simulations Break-free the mainstream physical conference room training which aren’t just passive but highly time and cost intensive. Metaverse digital transformation is here with immersive safety training with AI expertise to fuel in the adaptive, tailored yet cost effective training! Fusing in realistic scenarios in a spatial space with life-like learning sessions is what one can say that offers. But, I am still adamant about our claims. These training programs are cost-savvy, highly skill-efficient, and industry-agnostic. You can have medical professionals or even construction personnel to train. Metaverse with AI is the step-up you need. And when I say you need it, I mean it! Metaverse x AI Earlier the training sessions involved manual physical explorations, in-person training and direct machine/equipment contact, which was time consuming but at the same time extremely risky and even resource intensive. And, above all that, it’s now SO LAST SEASON! Here, I will tell you what gap it bridges. You are panning out collaborative virtual training with an expert who takes on a spatial space with all the required equipment/machinery available. By hopping onto this futuristic fusion, you are not just offering safety training and shrinking down the incidental risks. But, you are offering a simulated controlled environment, connecting through experts and dodging barriers. On top of that, you are cutting machine maintenance and operational costs. At the same time, it offers personalized, active, and real-time learning. Consider this: Transform Sales and Marketing Strategies If you thought AI powered chatbots, automated marketing campaigns and collaborative virtual meetings are all that AI and metaverse has gotten, then let me tell you. You are not just highly mistaken, but you are undermining its potential to a great extent. I will equip you with how Metaverse x AI can do wonders and revolutionize your sales and marketing. From consumer & company interaction to consumer x product interaction virtually. Brace Yourself: I meant for the fact all I know that the world is shrinking and businesses are trying their level best to modernize and level up digitally. To help you understand, let’s approach your brain wires in a simpler way. Earlier businesses used to analyze consumer preferences and market trends manually and showcased the products/services in mainstream displays. Personalization was a farfetched pick for businesses because coping with inconsistent consumer trends in the masses was not only difficult but impossible. There was a gap between consumers and businesses offerings due to lack of data analyzation, preference consideration and lack of customer engagement. For Sales: Be it campaigns, personalization virtual launches and down to chatbots everything is streamlined and up to the trends. Businesses can take a backseat in this digital age considering their putting the chips on this dynamic duo. However, this might not do justice to the claims stated above, which is why I’ll just quickly run you through the evolved sales process. With AI and Metaverse in Sales: How does it transform your Sales and Marketing processes? Pools in the data comprehensively, yet segregates the chunks into ‘Preferences, Patterns, Purchase history’ by leveraging predictive analytics. Further, the same data is brought to use in personalizing the entire marketing strategy and virtual immersive 3D launches. With that, AI seeps this data into the Virtual assistants in real-time thereby rolling out a personalized output to the users according to their preferences, likings and choices. AI – The entire data processing across the platforms and servers is lastly brought to show on the business front. Now this can be in the form of modern virtual product launches, 3D virtual try-ons, 3D product demonstrations and all of this in a hyper-realistic space i.e Metaverse. Metaverse Digital Transformation: Imagine virtually trying on a dress or getting to access a virtual test drive for that car that’s recently launched. It is now possible from your own living room but with all the immersiveness of actuality. Plus, what adds a cherry to the cake is all of this will be (automatically and in real-time) tailored to the customer’s preferences via virtual reality development. Not just that, you as a business can get vast amounts of data optimized (in no-time), display products in 3D space and let the products interact with your potential segment of customers (in real-time). And, and, and hit that sales target while finessing the modern marketing and sales game. A win-win right there like never before! Work and Play: Perfectly Balanced Gear up to bail out from the monotonous workloads! It’s time to hunch down the hamster wheel! Taking a backseat and letting things run on auto-pilot has always been synonymous with AI. But, now by syncing on the symphony of metaverse with AI it is no less than living in the future without waiting for 2050. The avatar aura is real and how. You are serving your clients round the clock and bringing in business globally. But there is no employee burnout issue nagging you, nor are the productivity dips hitting your sales anymore. Picture this: If you are running an e-commerce site- by counting on this duo. The AI powered chatbots do not just respond to the user-queries in real time. But, your users will be equipped with a 3D product display and that too tailored to their specific needs. All this is via virtual reality development. For Example- Your Perks: Customer attrition rate, offer modern services while saving resource costs. How time consuming and labor intensive is to run through numerous resumes, shortlisting potential candidates and further dealing with the onboarding process. But, it can be swapped to a rapid, streamlined, cost and time-savvy one. Every company understands the basics, if your employees are happy, they tend to perform better, and your company stays in business lane. Hence, what if there were virtual assistants powered by AI guiding your interns to understand the basics and recognize their potential? I mean think how quickly you will be able to streamline your HR processes before and after onboarding of your employees. Now Picture This- Implementing Metaverse with AI is a hardcore necessity. Because AI algorithms will fill in and sift potential candidates matching your recruitment needs, Metaverse will reform your onboarding process by providing a virtually immersive and simulated workspace for the candidates. And even better with gamification! Do you know how? Well, you guessed it right! In short there is no other way around but implement, integrate and inch in the via the futuristic fusion of Metaverse and AI. digital transformation Conclusion So, if you’re still second guessing on integrating Metaverse with AI and taking on that digital facelift, you are running late. Because while this is a catalyst and not merely an upgrade. The integration changes everything and the entirety of the ecosystem surrounding us. It is certainly a star of its discipline and hitting the throttle for businesses across the genres. As Metaverse x AI is not a mere privilege, you can take a raincheck on but an iconic move that your business will be living on in the future. We at initially thought the next innovative leap is just chatbots and frameless screens. Only ‘few’ perceived the information, trusting its ability to lead the current digital transformation phase. Maybe, its not too late to be the ‘few’ and crave an innovative leap for your business too.

The Next Digital Leap: AI and Metaverse Integration for Businesses

306 Stories To Learn About Artificial Intelligence

361 Stories To Learn About Big Data

Not to sound pompous, but this has been an extraordinary year for the field of artificial intelligence (AI), in more ways than what many experts would have imagined. In a short span of time, artificial intelligence and generative AI platforms have become game changers for many businesses, across a broad range of industries. For years, business leaders have been trying to improve their efficiency and boost productivity, while simultaneously improving their bottom line and reducing costs - now artificial intelligence is doing exactly what they have been looking for all this time. A small business recorded that nearly half, or around 48 percent of small businesses and medium-sized enterprises have already started using AI tools in some capacity in the past year. AI adoption survey It’s safe to say that the adoption of more advanced artificial intelligence tools and generative AI applications has been fairly successful, especially for smaller, more agile companies looking to get their foot in the door, while keeping their balance sheets intact. A Few Wrongs Make for A Terrible Mistake While there were some monumental moments this year that most people will remember as the field of artificial technology continues to develop, this wasn’t without a few or several memorable blunders. At the start of the year, several well-known technology and publication companies found themselves in hot water after publishing countless AI-generated articles that contained factual errors and cases of plagiarism. Quietly removing a few articles from a company website or blog is a lot easier than having to make up more than value losses. In late February, Google launched its generative AI platform, Bard, however, not everything went according to plan, and a few mistakes led to billions being wiped off the big tech’s market value within hours as share prices tumbled. $100 billion in market A flood of deep fakes surfacing on the internet, political leaders using leading up to the presidential elections, and a flurry of lawsuits had many companies up in arms this year as they tried to tame a wild beast running rampant in the streets. AI-generated imagery as part of their campaigns Not to mention former OpenAI CEO and ChatGPT developer, Sam Altman being booted by the same company he helped bring onto the map. Altman would later land a job at Microsoft, however, in a sudden change of direction, a new board of directors at OpenAI had managed to , offering Altman the opportunity to reinstate his role as chief executive at the company. reach an agreement of principles While artificial intelligence has been enjoying its moment in the sun, quite literally, the year ahead only brings more opportunities and new challenges. However, for small and medium-sized companies, with less experience, knowledge, and legal resources compared to their more established peers in Silicon Valley, running into a wall of trouble when adopting new artificial tools and systems can be a bigger, and more costly mistake than simply removing unfounded content from the internet or firing an intern. Problems that Organizations Need to Avoid when Building an AI Strategy Bringing AI into your business can be an exciting opportunity to experiment with new possibilities. However, without the necessary guidance, there are bound to be some failures along the way. As a small business owner, or entrepreneur, you need to clearly understand the importance of having clear objectives, but also what the long-term plan outlook holds. Lacking an AI Strategy Across different levels of the business, having an operational strategy enables you to navigate any challenges, and overcome barriers that you encounter along the way. Your strategy is your blueprint, guiding you toward the main objective of your business, product, service, and staff. Knowing the potential of artificial intelligence in the business environment will enable you to solve complex problems, and build more insightful solutions based on fundamental data metrics. Without a clear guideline, or strategy that will help work as the blueprint, you’ll find it more challenging to understand how artificial intelligence can do any good for your business, and how it can be useful to create more meaningful results. Knowing the business value of artificial intelligence is an important part of building an AI strategy, and creating forward-looking objectives for the organization and team. Being Inexperienced By introducing new tools, business owners will often need to have previous experience with the applications or have an existing team of employees who can skillfully manage working with this technology. However, with artificial intelligence, new applications are constantly changing, meaning that the expertise needed to successfully apply these tools in your business will either need to be an agile individual or a team that has the necessary experience to work with such projects. Having people onboard that are already equipped with the knowledge and experience of working with artificial intelligence, enables you to adapt quicker, and tap into various new opportunities simultaneously. Without a skilled team, or individual that can help guide you through the intricacies of these systems, making AI part of your business will only become more complex to resolve customer and business problems. Implementing the Wrong Tools Right from the beginning it’s important to know that not every AI application will serve a purpose for your business. The value is to understand how these tools work, and what their most basic functions are in terms of solving complex issues and boosting overall business efficiency. Ongoing research by found that more than a third of business leaders said that understanding the business value of artificial intelligence is perhaps one of their main challenges. Deloitte Without a clear strategy, or having considered which applications would be best suited for your business, customers, and employees, you will most likely end up wasting valuable resources on systems that are either impractical or don’t serve the long-term progress of your business. Knowing exactly what your business’s pain points are, and how AI can help resolve these problems could help create a more clear pathway to understanding the available various tools, and how each of them can contribute to the overarching development of your business. Failing to Test and Optimize Applications Contrary to your expectations, some applications have limited capabilities. This is often the case for more experimental tools that are either still in their development phase or have lesser-known real-world applications. Choosing the right tools isn’t enough. As a business owner and leader, you will need to constantly test these applications, and further ensure that these tools can be optimized for improved functionality. Consider a medium-sized real estate company that provides renters and prospective buyers with assistance in . Without optimization, these tools will quickly become outdated, perhaps providing clients with insufficient information or data related to their search queries. finding their next property By conducting thorough testing, and further making the necessary adjustments, you will ensure that the tools you have incorporated are working to the advantage of your business, and not in the opposite direction. Having Insufficient or Outdated Data Data has become the name of the game, and without having clear and relevant data, you will find yourself falling behind competitors and losing customers. Nowadays, nearly every company is using data as part of their decision-making process. By utilizing data, companies can make more informed decisions, develop tailored products and services based on their customer’s needs, and further optimize their systems to find applicable solutions. Within the ecosystem of artificial intelligence, data is a valuable asset that enables systems to efficiently resolve complex problems, or deliver more effective solutions. Using data, and most importantly, updating the data throughout the process, will ensure that AI systems can monitor changing circumstances, and overcome more challenging problems. Unlike more antiquated systems, AI systems will need to be constantly updated, monitored, and optimized to ensure more effective delivery of results. Additionally, as a business owner, you will need to consider how data changes over time, and that the outcomes delivered during the implementation process will likely be different several weeks or months down the line. Remember that implementing quality data can be just as important to the overall success and outcomes of AI tools. While having access to vast amounts of information can help a business or team better understand specific pockets of the market, it becomes a lot less valuable if the data is either outdated or no longer serves a direct purpose over the long term. Ignoring the Importance of A Risk Management Strategy Being a business owner is more than being a thought leader, or helping the company scale. A closer look reveals that many business owners often lead their teams through various challenges, and develop strategies to mitigate risks that can affect their near and long-term goals. Incorporating AI systems with a proper risk management strategy or plan can lead to bigger problems, resulting in the delivery of insufficient solutions, and further creating more cracks in the process. Although there are many business leaders and organizations that have been incorporating artificial intelligence within their business model over recent years - the majority of them often don’t have an AI risk management strategy or responsible AI program in place. In fact, one research by MIT Sloan Management Review and Boston Consulting Group found that while 42 percent of organizations had developed an AI strategy, only 19 percent said that they had implemented a risk management or responsible AI program. report Ignoring the importance of AI risk management, or not having a plan in place for when things go south can derail forward-looking plans, and require additional resources to resolve. Having Budget and Resource Constraints Incorporating artificial intelligence into your business model requires a substantial investment. For starters, advanced technology has become more than the traditional plug-in-and-play applications we’ve become accustomed to during the later years of the dot-com era. Having artificial intelligent systems is a long-term, and often continuous investment in the technology, data metrics, and expertise to manage and monitor these applications. Encountering budget and resource constraints could further back-track your progress, and derail the potential long-term objectives you’ve established. Part of the planning and implementation process requires the necessary allocation of resources that will help to support these applications, but more importantly, the various add-ons that help power artificial systems. AI technology is constantly changing and evolving. This would mean that your business needs to have the necessary resources to support this change and adjust direction every so often to ensure that existing applications are relevant to your long-term strategy, but more importantly, can adjust to a changing market dynamic. Ignoring New Opportunities for Growth As a small business, you will most likely pilot one or several AI projects on a small scale to consider the long-term needs of each application. However, those efforts would require you to plan for new growth opportunities, and ensure that the technology you’re using can scale as your business expands. With each new project, you will need to consider scalability over both the near and long term. As you begin to experiment with newer, and more advanced systems, you will notice different opportunities where these tools can be applied. You might find that one pilot project helps to resolve various problems you’ve encountered, however, applying this on another business level could mean that you are building towards a more robust approach to implementing AI applications across various functions of your business. On top of this, you will need to plan for ways in which you can allow these systems to scale as your business experiences different growth periods. As demand on one side of your business begins to pick up, you will need to ensure that AI tools are scaling with this demand and providing more efficient delivery. Not Aligning Your Expectations Having farfetch'd expectations regarding the possibilities of artificial intelligence can lead to increased bottlenecks in your business, and further cause you to incorporate different tools or develop different strategies to overcome these problems. Having a more dynamic, and open-minded approach will enable you to notice new opportunities that might present themselves at any given moment. More than this, it will allow you to become more agile, and realize that AI applications will only solve the problems it has been programmed to resolve. As a way to encourage the potential of any AI project, avoid setting the bar too high, as this would only lead to exaggerated expectations. Ensure that each project is carefully considered beforehand, but more than this, that you encourage a level of expectation that allows you to fail and make the necessary adjustments based on the overall success. Overlooking the Importance of Ethics As much as artificial intelligence is helping improve business efficiency and resolve critical problems, there are multiple concerns regarding the potential privacy violations these tools can have on a business and its clients. Already we’re seeing major players such as Google's parent company, Alphabet, and ChatGPT developers, OpenAI being regarding the breach of personal data and information of users to train AI models. hit with lawsuits While the use of this data is crucial for the forward-looking development of these models, illegally collecting this data, without prior user consent can lead to increased legal complications and damage your company’s reputation. As a business owner, you should be encouraged to address privacy issues with a transparent approach and ensure users or customers that the collection of sensitive data will be used transparently, and concerning existing privacy regulations. Keep in mind that artificial intelligence is an ever-evolving ecosystem that requires lawmakers to establish new ground rules that will help regulate the collection of personal data and information. But more importantly, protects users against unlawful data harvesting to train new AI models. Final Take Artificial intelligence can provide your business with new opportunities to overcome challenges and develop more dynamic solutions within a changing marketplace. As the landscape evolves, business owners will need to be encouraged about the potential these tools can present them with, but more importantly, how these tools can complement their forward-looking objectives.

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Introduction: In the realm of modern retail, a point-of-sale (POS) system is the beating heart of any business. It manages sales, inventory, customer interactions, and more. Two prominent contenders in the POS arena are the Lightspeed R-Series POS and VendHQ ( ). Lightspeed X Series POS Each offers a unique set of features designed to enhance the retail experience. In this in-depth comparison, we’ll explore the strengths and nuances of both systems to help you make an informed decision for your business. Lightspeed R-Series POS: Elevating Retail Management The Lightspeed R Series POS is a testament to innovation and adaptability in the retail sector. Engineered with cutting-edge technology, this system offers a plethora of tools to streamline operations and optimize customer interactions. Seamless Inventory Management: The R-Series POS excels in inventory management, allowing you to monitor stock levels, set up alerts for low inventory, and even reorder products seamlessly. The system’s intelligent analytics provide insights into sales trends, enabling you to make informed restocking decisions. Customer-Centric Features: With the R-Series, you can build robust customer profiles that include purchase history, preferences, and contact information. This enables personalized interactions and targeted marketing campaigns, fostering customer loyalty and satisfaction. Versatile Payment Options: The R-Series supports multiple payment methods, including credit/debit cards, mobile wallets, and contactless payments. This flexibility enhances the shopping experience and caters to a wide range of customer preferences. VendHQ (Lightspeed X-Series) POS: Simplicity and Scalability VendHQ, part of the Lightspeed X Series, is a cloud-based POS solution that emphasizes user-friendliness and scalability. Designed for businesses of all sizes, it empowers retailers to manage their operations efficiently. Cloud-Based Convenience: VendHQ’s cloud-based architecture ensures that your data is accessible from anywhere with an internet connection. This feature is especially beneficial for multi-location businesses or those with an online presence. Intuitive Interface: The X-Series POS prides itself on its user-friendly interface, making it easy for staff to learn and use the system effectively. Minimal training is required, enabling a smooth onboarding process for new employees. Add-On Integrations: VendHQ offers an array of integrations with third-party applications such as accounting software, e-commerce platforms, and CRM systems. This ecosystem allows you to tailor the POS system to your specific business needs. You can also use third-party applications like SKUPlugs to integrate the POS systems with marketplaces. SKUPlugs provides integration for both platforms with major e-commerce platforms like Shopify, Woocommerce, Bigcommerce, Wix, Magento, Squarespace, Amazon, Walmart, and Farfetch etc. Conclusion: Choosing the Right POS for Your Business Selecting between the and VendHQ (Lightspeed X-Series) POS hinges on your business requirements and priorities. The R-Series excels in robust inventory management and customer engagement, making it ideal for businesses seeking a comprehensive solution. Lightspeed R-Series POS On the other hand, VendHQ’s cloud-based simplicity and scalability cater to businesses aiming for a streamlined yet adaptable POS system. Both systems reflect Lightspeed’s commitment to innovation and customer satisfaction. Ultimately, your choice should align with your business goals, size, and operations. Whichever you choose, embracing the power of a Lightspeed POS system can undoubtedly elevate your retail management game, leading to enhanced customer experiences and operational efficiency. - Original Source Lightspeed R-Series Vs X-Series Comparison

Lightspeed R-Series POS Vs. VendHQ (Lightspeed X-Series) POS: What to Know About Each

POS Lightspeed Serie R vs. POS VendHQ (Lightspeed X-Series): una comparación completa

Lightspeed R-Series POS vs. VendHQ (Lightspeed X-Series) POS : une comparaison complète

लाइटस्पीड आर-सीरीज़ पीओएस बनाम। वेंडएचक्यू (लाइटस्पीड एक्स-सीरीज़) पीओएस: एक व्यापक तुलना

Lightspeed R シリーズ POS との比較VendHQ (Lightspeed X シリーズ) POS: 包括的な比較

POS Lightspeed Série R vs. VendHQ (Lightspeed X-Series) POS: uma comparação abrangente

Lightspeed R-Series POS Vs. VendHQ (Lightspeed X-Series) POS: So sánh toàn diện

Lightspeed R 系列 POS 对比VendHQ（Lightspeed X 系列）POS：综合比较

The Big Four by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . DISAPPEARANCE OF A SCIENTIST V. DISAPPEARANCE OF A SCIENTIST Personally, I don't think that, even when a jury had acquitted Robert Grant, alias Biggs, of the murder of Jonathan Whalley, Inspector Meadows was entirely convinced of his innocence. The case which he had built up against Grant—the man's record, the jade which he had stolen, the boots which fitted the footprints so exactly—was to his matter-of-fact mind too complete to be easily upset; but Poirot, compelled much against his inclination to give evidence, convinced the jury. Two witnesses were produced who had seen a butcher's cart drive up to the bungalow on that Monday morning, and the local butcher testified that his cart only called there on Wednesdays and Fridays. A woman was actually found who, when questioned, remembered seeing the butcher's man leaving the bungalow, but she could furnish no useful description of him. The only impression he seemed to have left on her mind was that he was clean-shaven, of medium height, and looked exactly like a butcher's man. At this description Poirot shrugged his shoulders philosophically. "It is as I tell you, Hastings," he said to me, after the trial. "He is an artist, this one. He disguises himself not with the false beard and the blue spectacles. He alters his features, yes; but that is the least part. For the time being he is the man he would be. He lives in his part." Certainly I was compelled to admit that the man who had visited us from Hanwell had fitted in exactly with my idea of what an Asylum attendant should look like. I should never for a moment have dreamt of doubting that he was genuine. It was all a little discouraging, and our experience on Dartmoor did not seem to have helped us at all. I said as much to Poirot, but he would not admit that we had gained nothing. "We progress," he said; "we progress. At every contact with this man we learn a little of his mind and his methods. Of us and our plans he knows nothing." "And there, Poirot," I protested, "he and I seem to be in the same boat. You don't seem to me to have any plans, you seem to sit and wait for him to do something." Poirot smiled. "Mon ami, you do not change. Always the same Hastings, who would be up and at their throats. Perhaps," he added, as a knock sounded on the door, "you have here your chance; it may be our friend who enters." And he laughed at my disappointment when Inspector Japp and another man entered the room. "Good-evening, moosior," said the Inspector. "Allow me to introduce Captain Kent of the United States Secret Service." Captain Kent was a tall, lean American, with a singularly impassive face which looked as though it had been carved out of wood. "Pleased to meet you, gentlemen," he murmured, as he shook hands jerkily. Poirot threw an extra log on the fire, and brought forward more easy-chairs. I brought out glasses and the whisky and soda. The captain took a deep draught, and expressed appreciation. "Legislation in your country is still sound," he observed. "And now to business," said Japp. "Moosior Poirot here made a certain request to me. He was interested in some concern that went by the name of the Big Four, and he asked me to let him know at any time if I came across a mention of it in my official line of business. I didn't take much stock in the matter, but I remembered what he said, and when the captain here came over with rather a curious story, I said at once, 'We'll go round to Moosior Poirot's.'" Poirot looked across at Captain Kent, and the American took up the tale. "You may remember reading, M. Poirot, that a number of torpedo boats and destroyers were sunk by being dashed upon the rocks off the American coast. It was just after the Japanese earthquake, and the explanation given was that the disaster was the result of a tidal wave. Now, a short time ago, a round-up was made of certain crooks and gunmen, and with them were captured some papers which put an entirely new face upon the matter. They appeared to refer to some organisation called the 'Big Four,' and gave an incomplete description of some powerful wireless installation—a concentration of wireless energy far beyond anything so far attempted, and capable of focusing a beam of great intensity upon some given spot. The claims made for this invention seemed manifestly absurd, but I turned them in to headquarters for what they were worth, and one of our highbrow professors got busy on them. Now it appears that one of your British scientists read a paper upon the subject before the British Association. His colleagues didn't think great shakes of it, by all accounts, thought it farfetched and fanciful, but your scientist stuck to his guns, and declared that he himself was on the eve of success in his experiments." "Eh, bien?" demanded Poirot, with interest. "It was suggested that I should come over here and get an interview with this gentleman. Quite a young fellow, he is, Halliday by name. He is the leading authority on the subject, and I was to get from him whether the thing suggested was anyway possible." "And was it?" I asked eagerly. "That's just what I don't know. I haven't seen Mr. Halliday—and I'm not likely to, by all accounts." "The truth of the matter is," said Japp, shortly, "Halliday's disappeared." "When?" "Two months ago." "Was his disappearance reported?" "Of course it was. His wife came to us in a great state. We did what we could, but I knew all along it would be no good." "Why not?" "Never is—when a man disappears that way." Japp winked. "What way?" "Paris." "So Halliday disappeared in Paris?" "Yes. Went over there on scientific work—so he said. Of course, he'd have to say something like that. But you know what it means when a man disappears over there. Either it's Apache work, and that's the end of it—or else it's voluntary disappearance—and that's a great deal the commoner of the two, I can tell you. Gay Paree and all that, you know. Sick of home life. Halliday and his wife had had a tiff before he started, which all helps to make it a pretty clear case." "I wonder," said Poirot thoughtfully. The American was looking at him curiously. "Say, mister," he drawled, "what's this Big Four idea?" "The Big Four," said Poirot, "is an international organisation which has at its head a Chinaman. He is known as Number One. Number Two is an American. Number Three is a Frenchwoman. Number Four, the 'Destroyer,' is an Englishman." "A Frenchwoman, eh?" The American whistled. "And Halliday disappeared in France. Maybe there's something in this. What's her name?" "I don't know. I know nothing about her." "But it's a mighty big proposition, eh?" suggested the other. Poirot nodded, as he arranged the glasses in a neat row on the tray. His love of order was as great as ever. "What was the idea in sinking those boats? Are the Big Four a German stunt?" "The Big Four are for themselves—and for themselves only, M. le Capitaine. Their aim is world domination." The American burst out laughing, but broke off at the sight of Poirot's serious face. "You laugh, monsieur," said Poirot, shaking a finger at him. "You reflect not—you use not the little gray cells of the brain. Who are these men who send a portion of your navy to destruction simply as a trial of their power? For that was all it was, Monsieur, a test of this new force of magnetical attraction which they hold." "Go on with you, moosior," said Japp good-humouredly. "I've read of super criminals many a time, but I've never come across them. Well, you've heard Captain Kent's story. Anything further I can do for you?" "Yes, my good friend. You can give me the address of Mrs. Halliday—and also a few words of introduction to her if you will be so kind." Thus it was that the following day saw us bound for Chetwynd Lodge, near the village of Chobham in Surrey. Mrs. Halliday received us at once, a tall, fair woman, nervous and eager in manner. With her was her little girl, a beautiful child of five. Poirot explained the purpose of our visit. "Oh! Monsieur Poirot, I am so glad, so thankful. I have heard of you, of course. You will not be like these Scotland Yard people, who will not listen or try to understand. And the French Police are just as bad—worse, I think. They are all convinced that my husband has gone off with some other woman. But he wasn't like that! All he thought of in life was his work. Half our quarrels came from that. He cared for it more than he did for me." "Englishmen, they are like that," said Poirot soothingly. "And if it is not work, it is the games, the sport. All those things they take au grand sérieux. Now, madame, recount to me exactly, in detail, and as methodically as you can, the exact circumstances of your husband's disappearance." "My husband went to Paris on Thursday, the 20th of July. He was to meet and visit various people there connected with his work, amongst them Madame Olivier." Poirot nodded at the mention of the famous French woman chemist, who had eclipsed even Madame Curie in the brilliance of her achievements. She had been decorated by the French Government, and was one of the most prominent personalities of the day. "He arrived there in the evening and went at once to the Hotel Castiglione in the Rue de Castiglione. On the following morning, he had an appointment with Professor Bourgoneau, which he kept. His manner was normal and pleasant. The two men had a most interesting conversation, and it was arranged that he should witness some experiments in the professor's laboratory on the following day. He lunched alone at the Café Royal, went for a walk in the Bois, and then visited Madame Olivier at her house at Passy. There, also, his manner was perfectly normal. He left about six. Where he dined is not known, probably alone at some restaurant. He returned to the hotel about eleven o'clock and went straight up to his room, after inquiring if any letters had come for him. On the following morning, he walked out of the hotel, and has not been seen again." "At what time did he leave the hotel? At the hour when he would normally leave it to keep his appointment at Professor Bourgoneau's laboratory?" "We do not know. He was not remarked leaving the hotel. But no petit déjeuner was served to him, which seems to indicate that he went out early." "Or he might, in fact, have gone out again after he came in the night before?" "I do not think so. His bed had been slept in, and the night porter would have remembered any one going out at that hour." "A very just observation, madame. We may take it, then, that he left early on the following morning—and that is reassuring from one point of view. He is not likely to have fallen a victim to any Apache assault at that hour. His baggage, now, was it all left behind?" Mrs. Halliday seemed rather reluctant to answer, but at last she said:—- "No—he must have taken one small suit-case with him." "H'm," said Poirot thoughtfully, "I wonder where he was that evening. If we knew that, we should know a great deal. Whom did he meet?—there lies the mystery. Madame, myself I do not of necessity accept the view of the police; with them is it always 'Cherchez la femme.' Yet it is clear that something occurred that night to alter your husband's plans. You say he asked for letters on returning to the hotel. Did he receive any?" "One only, and that must have been the one I wrote him on the day he left England." Poirot remained sunk in thought for a full minute, then he rose briskly to his feet. "Well, madame, the solution of the mystery lies in Paris, and to find it I myself journey to Paris on the instant." "It is all a long time ago, monsieur." "Yes, yes. Nevertheless, it is there that we must seek." He turned to leave the room, but paused with his hand on the door. "Tell me, madame, do you ever remember your husband mentioning the phrase, 'The Big Four'?" "The Big Four," she repeated thoughtfully. "No, I can't say I do." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Agatha Christie (2023). The Big Four. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/70114/pg70114-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

DISAPPEARANCE OF A SCIENTIST

The Big Four by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . WE HEAR MORE ABOUT LI CHANG YEN III. WE HEAR MORE ABOUT LI CHANG YEN For a day or two after our visit from the fake Asylum attendant I was in some hopes that he might return, and I refused to leave the flat even for a moment. As far as I could see, he had no reason to suspect that we had penetrated his disguise. He might, I thought, return and try to remove the body, but Poirot scoffed at my reasoning. "Mon ami," he said, "if you wish you may wait in to put salt on the little bird's tail, but for me I do not waste my time so." "Well then, Poirot," I argued, "why did he run the risk of coming at all. If he intended to return later for the body, I can see some point in his visit. He would at least be removing the evidence against himself; as it is, he does not seem to have gained anything." Poirot shrugged his most Gallic shrug. "But you do not see with the eyes of Number Four, Hastings," he said. "You talk of evidence, but what evidence have we against him? True, we have a body, but we have no proof even that the man was murdered—prussic acid, when inhaled, leaves no trace. Again, we can find no one who saw any one enter the flat during our absence, and we have found out nothing about the movements of our late friend, Mayerling.... "No, Hastings, Number Four has left no trace, and he knows it. His visit we may call a reconnaissance. Perhaps he wanted to make quite sure that Mayerling was dead, but more likely, I think, he came to see Hercule Poirot, and to have speech with the adversary whom alone he must fear." Poirot's reasoning appeared to me typically egotistical, but I forbore to argue. "And what about the inquest?" I asked. "I suppose you will explain things clearly there, and let the police have a full description of Number Four." "And to what end? Can we produce anything to impress a coroner's jury of your solid Britishers? Is our description of Number Four of any value? No; we shall allow them to call it 'Accidental Death,' and may be, although I have not much hope, our clever murderer will pat himself on the back that he deceived Hercule Poirot in the first round." Poirot was right as usual. We saw no more of the man from the asylum, and the inquest, at which I gave evidence, but which Poirot did not even attend, aroused no public interest. As, in view of his intended trip to South America, Poirot had wound up his affairs before my arrival, he had at this time no cases on hand, but although he spent most of his time in the flat I could get little out of him. He remained buried in an arm-chair, and discouraged my attempts at conversation. And then one morning, about a week after the murder, he asked me if I would care to accompany him on a visit he wished to make. I was pleased, for I felt he was making a mistake in trying to work things out so entirely on his own, and I wished to discuss the case with him. But I found he was not communicative. Even when I asked where we were going, he would not answer. Poirot loves being mysterious. He will never part with a piece of information until the last possible moment. In this instance, having taken successively a 'bus and two trains, and arrived in the neighbourhood of one of London's most depressing southern suburbs, he consented at last to explain matters. "We go, Hastings, to see the one man in England who knows most of the underground life of China." "Indeed! Who is he?" "A man you have never heard of—a Mr. John Ingles. To all intents and purposes, he is a retired Civil Servant of mediocre intellect, with a house full of Chinese curios with which he bores his friends and acquaintances. Nevertheless, I am assured by those who should know that the only man capable of giving me the information I seek is this same John Ingles." A few moments more saw us ascending the steps of The Laurels, as Mr. Ingles's residence was called. Personally, I did not notice a laurel bush of any kind, so deduced that it had been named according to the usual obscure nomenclature of the suburbs. We were admitted by an impassive-faced Chinese servant and ushered into the presence of his master. Mr. Ingles was a squarely-built man, somewhat yellow of countenance, with deep-set eyes that were oddly reflective in character. He rose to greet us, setting aside an open letter which he had held in his hand. He referred to it after his greeting. "Sit down, won't you? Halsey tells me that you want some information and that I may be useful to you in the matter." "That is so, monsieur. I ask of you if you have any knowledge of a man named Li Chang Yen?" "That's rum—very rum indeed. How did you come to hear about the man?" "You know him, then?" "I've met him once. And I know something of him—not quite as much as I should like to. But it surprises me that any one else in England should even have heard of him. He's a great man in his way—mandarin class and all that, you know—but that's not the crux of the matter. There's good reason to suppose that he's the man behind it all." "Behind what?" "Everything. The world-wide unrest, the labour troubles that beset every nation, and the revolutions that break out in some. There are people, not scaremongers, who know what they are talking about, and they say that there is a force behind the scenes which aims at nothing less than the disintegration of civilisation. In Russia, you know, there were many signs that Lenin and Trotsky were mere puppets whose every action was dictated by another's brain. I have no definite proof that would count with you, but I am quite convinced that this brain was Li Chang Yen's." "Oh, come," I protested, "isn't that a bit farfetched? How would a Chinaman cut any ice in Russia?" Poirot frowned at me irritably. "For you, Hastings," he said, "everything is farfetched that comes not from your own imagination; for me, I agree with this gentleman. But continue, I pray, monsieur." "What exactly he hopes to get out of it all I cannot pretend to say for certain," went on Mr. Ingles; "but I assume his disease is one that has attacked great brains from the time of Akbar and Alexander to Napoleon—a lust for power and personal supremacy. Up to modern times armed force was necessary for conquest, but in this century of unrest a man like Li Chang Yen can use other means. I have evidence that he has unlimited money behind him for bribery and propaganda, and there are signs that he controls some scientific force more powerful than the world has dreamed of." Poirot was following Mr. Ingles's words with the closest attention. "And in China?" he asked. "He moves there too?" The other nodded in emphatic assent. "There," he said, "although I can produce no proof that would count in a court of law, I speak from my own knowledge. I know personally every man who counts for anything in China to-day, and this I can tell you: the men who loom most largely in the public eye are men of little or no personality. They are marionettes who dance to the wires pulled by a master hand, and that hand is Li Chang Yen's. His is the controlling brain of the East to-day. We don't understand the East—we never shall; but Li Chang Yen is its moving spirit. Not that he comes out into the limelight—oh, not at all; he never moves from his palace in Pekin. But he pulls strings—that's it, pulls strings—and things happen far away." "And is there no one to oppose him?" asked Poirot. Mr. Ingles leant forward in his chair. "Four men have tried in the last four years," he said slowly; "men of character, and honesty, and brain power. Any one of them might in time have interfered with his plans." He paused. "Well?" I queried. "Well, they are dead. One wrote an article, and mentioned Li Chang Yen's name in connection with the riots in Pekin, and within two days he was stabbed in the street. His murderer was never caught. The offences of the other two were similar. In a speech or an article, or in conversation, each linked Li Chang Yen's name with rioting or revolution, and within a week of his indiscretion each was dead. One was poisoned; one died of cholera, an isolated case—not part of an epidemic; and one was found dead in his bed. The cause of the last death was never determined, but I was told by a doctor who saw the corpse that it was burnt and shrivelled as though a wave of electrical energy of incredible power had passed through it." "And Li Chang Yen?" inquired Poirot. "Naturally nothing is traced to him, but there are signs, eh?" Mr. Ingles shrugged. "Oh, signs—yes, certainly. And once I found a man who would talk, a brilliant young Chinese chemist who was a protégé of Li Chang Yen's. He came to me one day, this chemist, and I could see that he was on the verge of a nervous breakdown. He hinted to me of experiments on which he'd been engaged in Li Chang Yen's palace under the mandarin's direction—experiments on coolies in which the most revolting disregard for human life and suffering had been shown. His nerve had completely broken, and he was in the most pitiable state of terror. I put him to bed in a top room of my own house, intending to question him the next day—and that, of course, was stupid of me." "How did they get him?" demanded Poirot. "That I shall never know. I woke that night to find my house in flames, and was lucky to escape with my life. Investigation showed that a fire of amazing intensity had broken out on the top floor, and the remains of my young chemist friend were charred to a cinder." I could see from the earnestness with which he had been speaking that Mr. Ingles was a man mounted on his hobby horse, and evidently he, too, realised that he had been carried away, for he laughed apologetically. "But, of course," he said, "I have no proofs, and you, like the others, will merely tell me that I have a bee in my bonnet." "On the contrary," said Poirot quietly, "we have every reason to believe your story. We ourselves are more than a little interested in Li Chang Yen." "Very odd your knowing about him. Didn't fancy a soul in England had ever heard of him. I'd rather like to know how you did come to hear of him—if it's not indiscreet." "Not in the least, monsieur. A man took refuge in my rooms. He was suffering badly from shock, but he managed to tell us enough to interest us in this Li Chang Yen. He described four people—the Big Four—an organisation hitherto undreamed of. Number One is Li Chang Yen, Number Two is an unknown American, Number Three an equally unknown Frenchwoman, Number Four may be called the executive of the organisation—the destroyer. My informant died. Tell me, monsieur, is that phrase known to you at all? The Big Four." "Not in connection with Li Chang Yen. No, I can't say it is. But I've heard it, or read it, just lately—and in some unusual connection too. Ah, I've got it." He rose and went across to an inlaid lacquer cabinet—an exquisite thing, as even I could see. He returned with a letter in his hand. "Here you are. Note from an old sea-faring man I ran against once in Shanghai. Hoary old reprobate—maudlin with drink by now, I should say. I took this to be the ravings of alcoholism." He read it aloud:— "Dear Sir,—You may not remember me, but you did me a good turn once in Shanghai. Do me another now. I must have money to get out of the country. I'm well hid here, I hope, but any day they may get me. The Big Four, I mean. It's life or death. I've plenty of money, but I daren't get at it, for fear of putting them wise. Send me a couple of hundred in notes. I'll repay it faithful—I swear to that.—Your servant, sir, "Jonathan Whalley." "Dated from Granite Bungalow, Hoppaton, Dartmoor. I'm afraid I regarded it as rather a crude method of relieving me of a couple of hundred which I can ill spare. If it's any use to you—" He held it out. "Je vous remercie, monsieur. I start for Hoppaton à l'heure même." "Dear me, this is very interesting. Supposing I come along too? Any objection?" "I should be charmed to have your company, but we must start at once. We shall not reach Dartmoor until close on nightfall, as it is." John Ingles did not delay us more than a couple of minutes, and soon we were in the train moving out of Paddington bound for the West Country. Hoppaton was a small village clustering in a hollow right on the fringe of the moorland. It was reached by a nine-mile drive from Moretonhamstead. It was about eight o'clock when we arrived; but as the month was July, the daylight was still abundant. We drove into the narrow street of the village and then stopped to ask our way of an old rustic. "Granite Bungalow," said the old man reflectively, "it be Granite Bungalow you do want? Eh?" We assured him that this was what we did want. The old man pointed to a small gray cottage at the end of the street. "There be t'Bungalow. Do yee want to see t'Inspector?" "What Inspector?" asked Poirot sharply; "what do you mean?" "Haven't yee heard about t'murder, then? A shocking business t'was seemingly. Pools of blood, they do say." "Mon Dieu!" murmured Poirot. "This Inspector of yours, I must see him at once." Five minutes later we were closeted with Inspector Meadows. The Inspector was inclined to be stiff at first, but at the magic name of Inspector Japp of Scotland Yard, he unbent. "Yes, sir; murdered this morning. A shocking business. They 'phoned to Moreton, and I came out at once. Looked a mysterious thing to begin with. The old man—he was about seventy, you know, and fond of his glass, from all I hear—was lying on the floor of the living-room. There was a bruise on his head and his throat was cut from ear to ear. Blood all over the place, as you can understand. The woman who cooks for him, Betsy Andrews, she told us that her master had several little Chinese jade figures, that he'd told her were very valuable, and these had disappeared. That, of course, looked like assault and robbery; but there were all sorts of difficulties in the way of that solution. The old fellow had two people in the house; Betsy Andrews, who is a Hoppaton woman, and a rough kind of man-servant, Robert Grant. Grant had gone to the farm to fetch the milk, which he does every day, and Betsy had stepped out to have a chat with a neighbour. She was only away twenty minutes—between ten and half-past—and the crime must have been done then. Grant returned to the house first. He went in by the back door, which was open—no one locks up doors round here—not in broad daylight, at all events—put the milk in the larder, and went into his own room to read the paper and have a smoke. Had no idea anything unusual had occurred—at least, that's what he says. Then Betsy comes in, goes into the living-room, sees what's happened, and lets out a screech to wake the dead. That's all fair and square. Some one got in whilst those two were out, and did the poor old man in. But it struck me at once that he must be a pretty cool customer. He'd have to come right up the village street, or creep through some one's back yard. Granite Bungalow has got houses all round it, as you can see. How was it that no one had seen him?" The Inspector paused with a flourish. "Aha, I perceive your point," said Poirot. "To continue?" "Well, sir, fishy, I said to myself—fishy. And I began to look about me. Those jade figures, now. Would a common tramp ever suspect that they were valuable? Anyway, it was madness to try such a thing in broad daylight. Suppose the old man had yelled for help?" "I suppose, Inspector," said Mr. Ingles, "that the bruise on the head was inflicted before death?" "Quite right, sir. First knocked him silly, the murderer did, and then cut his throat. That's clear enough. But how the dickens did he come or go? They notice strangers quick enough in a little place like this. It came to me all at once—nobody did come. I took a good look round. It had rained the night before, and there were footprints clear enough going in and out of the kitchen. In the living-room there were two sets of footprints only (Betsy Andrews' stopped at the door)—-Mr. Whalley's (he was wearing carpet slippers) and another man's. The other man had stepped in the blood-stains, and I traced his bloody footprints—I beg your pardon, sir." "Not at all," said Mr. Ingles, with a faint smile; "the adjective is perfectly understood." "I traced them to the kitchen—but not beyond. Point Number One. On the lintel of Robert Grant's door was a faint smear—a smear of blood. That's point Number Two. Point Number Three was when I got hold of Grant's boots—which he had taken off—and fitted them to the marks. That settled it. It was an inside job. I warned Grant and took him into custody; and what do you think I found packed away in his portmanteau? The little jade figures and a ticket-of-leave. Robert Grant was also Abraham Biggs, convicted for felony and housebreaking five years ago." The Inspector paused triumphantly. "What do you think of that, gentlemen?" "I think," said Poirot, "that it appears a very clear case—of a surprising clearness, in fact. This Biggs, or Grant, he must be a man very foolish and uneducated, eh?" "Oh, he is that—a rough, common sort of fellow. No idea of what a footprint may mean." "Clearly he reads not the detective fiction! Well, Inspector, I congratulate you. We may look at the scene of the crime. Yes?" "I'll take you there myself this minute. I'd like you to see those footprints." "I, too, should like to see them. Yes, yes, very interesting, very ingenious." We set out forthwith. Mr. Ingles and the Inspector forged ahead. I drew Poirot back a little so as to be able to speak to him out of the Inspector's hearing. "What do you really think, Poirot. Is there more in this than meets the eye?" "That is just the question, mon ami. Whalley says plainly enough in his letter that the Big Four are on his track, and we know, you and I, that the Big Four is no bogey for the children. Yet everything seems to say that this man Grant committed the crime. Why did he do so? For the sake of the little jade figures? Or is he an agent of the Big Four? I confess that this last seems more likely. However valuable the jade, a man of that class was not likely to realise the fact—at any rate, not to the point of committing murder for them. (That, par exemple, ought to have struck the Inspector.) He could have stolen the jade and made off with it instead of committing a brutal and quite purposeless murder. Ah, yes; I fear our Devonshire friend has not used his little gray cells. He has measured footprints, and has omitted to reflect and arrange his ideas with the necessary order and method." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Agatha Christie (2023). The Big Four. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/70114/pg70114-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

WE HEAR MORE ABOUT LI CHANG YEN

In this article, we examine the potential disruption of the radio host/live-stream industry through the use of artificial intelligence technology. By analyzing the average radio host salary, the number of spots per hour, the total words spoken per hour, and other factors such as music licensing fees, we demonstrate that it is possible to create a fully automated radio station using AI technology at a fraction of the cost of hiring human talent. We showcase a live demonstration of one such solution currently active under Avalon Star Streams brand, where after running songs from a creative commons music stream, the AI generates new radio content during breaks between those songs, even allowing the random selection of new songs if desired. Finally, we provide details on the tech stack utilized including NodeJS docker image which allows us to control ffmpeg streams and manage playlists efficiently. Running Example At the time of this writing, you can find the example live-streaming at and . Twitch.TV YouTube If for any reason the demo is not live streaming feel free to checkout this YouTube video for an example. https://youtu.be/Jnb4bMJc7x8?embedable=true the YouTube video show cases 2 auto-generated scripts with the AI Voice Actor Antoni Starr. The first is a call for donations, and the second is a random ad play with announcing the next song. NOTE: Cost analysis The key driver behind the potential disruption of the radio host industry lies in the significant reduction in labor costs. Utilizing current pricing models, the yearly cost of AI voice services required to run a full radio station amounts only to approximately $4100 compared to a national average salary of radio hosts estimated around $42k [0]. With an average radio spot duration of 16 minutes and an average speaking rate of 140 words per minute [1][2], each radio spot consists of approximately 2,240 words. AI-powered content generation, such as ChatGPT, comes at a cost of around $0.002 per 750 words. Thus, the total cost of using GPT for one hour of content amounts to approximately $0.006. With radio hosts spending around 45.5 hours per month on air [1], the yearly cost of GPT usage is estimated to be approximately $55. Furthermore, integrating Eleven Labs’ AI voice technology, priced at $330 per month for 40 hours of usage [3], incurs an annual cost of around $4,000. Considering the combined expenses of GPT and Eleven Labs, the total cost of implementing AI for a radio host is approximately $4,100 per year. This represents a significant cost reduction compared to traditional production methods and opens up new possibilities for radio/live-stream hosts with limited budgets. Further considerations While some might argue that AI generated content lacks emotional depth and personal touch provided by human talent, recent advancements in natural language processing have shown otherwise. With deep learning algorithms, AI systems can now analyze vast troves of linguistic data and learn nuances in context, tone and cadence of speech. When trained properly, these systems are able to mimic human-like qualities while still maintaining accuracy and efficiency. In fact, many industries ranging from customer service to journalism have already seen initial success with implementing chat-bots and machine-generated content due to economic advantages, even in sectors thought immune to technological takeover. Ultimately, it seems reasonable to assume a similar future for broadcasting markets like radio hosting. Examples Under our Avalon Star Stream brand, we set up a proof of concept showcasing the efficiency of AI assisted broadcasting. Leveraging open-source tools like ffmpeg and integrated within our custom NodeJS application framework managed through Docker, we were able to achieve a functioning automated live-stream setup complete with real-time generation capabilities for its intermission radio jockey. The system, under default settings, will play 3 songs before attempting a song-break. During the song-break our model analyzes prompts received online during the previous three musical sets and produces its own original written material targeting the donators thanking them, before reading an ad for an imaginary product and continuing. Our model is told take the persona of a Radio Host living within the by the name of “Antoni Starr”. Fallout 4 Universe Due to budget constraints, Antoni employs a cost-saving strategy. While generating content, there is a 10% chance, limited to once per hour, that his system dynamically pulls and generates a new song-break. This method adds an element of surprise and uniqueness to the show while optimizing production costs for the purpose of this tech demo. All other ad-reads will come from a previously generated grab-bag created during testing. Additionally, due to the channel being so new, we are unable to turn on the subscribers/memberships to be used during announcements. Tech Stack While I have not decided to release my code for this yet, I have decided to talk about the tech stack. As seen from the above image, the tool leveraging various technologies (FFmpeg, WebDAV, ChatGPT, EleventLabs, MongoDB) and the application combines them into a platform for live-stream generation. WebDAV + MongoDB This piece of the tech-stack is here to help record generated content and act as a file-store. The WebxDAV aspect allows us to remotely store the music files and download them at instantiation for the stream. ChatGPT + Eleven Labs These are the workhorses of the generative content. When time to generate a new ad-break we leverage ChatGPT API with our custom prompt to get the next script. Our prompt will be pre-seeded with name/information from stream donators and a random fake product to ad-read. FFmpeg The workhorse of streaming. FFmpeg is responsible for all audio/visual you see on the stream. from the static image overlay, to the encoded video playing on the TV and the audio you hear. FFmpeg is the magic behind it all. HTML/CSS/JavaScript Not seen in the diagram above, there is a management interface to tweak params of the running stream. This allows for the admin to force ad-breaks, tweak ad-break rate algorithm and more. Additionally, as mentioned previously, the entire platform is running off of NodeJS Conclusion We examined the possibility of replacing radio hosts with artificial intelligence and concluded that, given certain conditions, it may indeed be possible to do so. Our findings suggest that AI-powered radio stations would have clear financial advantages over their human counterparts and be capable of producing high-quality content equal to or surpassing that of human DJs. Further consideration should be done to ensure that listeners remain engaged and attuned to the program’s offerings amidst such developments. Overall, while the idea of a completely automated radio station or live-stream, with a dynamic voiced personality, may initially seem farfetched, the reality is that emerging technologies are quickly making the notion feasible and practical. Thus, business leaders must recognize the changing landscape and adapt accordingly before they risk being left behind in an ever evolving marketplace. Live-stream Links and Twitch.TV Youtube If you want to see more of tool itself please don’t hesitate to reach out.

The Radio Host and Live-Stream Industry: Poised for GPT Disruption

রেডিও হোস্ট এবং লাইভ-স্ট্রিম শিল্প: GPT ব্যাঘাতের জন্য প্রস্তুত

Die Radiomoderator- und Live-Stream-Branche: Bereit für GPT-Störungen

La industria de los presentadores de radio y la transmisión en vivo: preparados para la interrupción de GPT

L'industrie de l'hôte radio et de la diffusion en direct : prête pour la perturbation des GPT

रेडियो होस्ट और लाइव-स्ट्रीम उद्योग: GPT व्यवधान के लिए तैयार

ラジオホストとライブストリーム業界: GPT 破壊の準備が整っている

라디오 진행자와 라이브 스트리밍 산업: GPT 혼란에 대비

O apresentador de rádio e a indústria de transmissão ao vivo: preparados para a interrupção do GPT

Radyo Sunucusu ve Canlı Yayın Sektörü: GPT Kesintisine Hazır

Ngành công nghiệp máy chủ radio và phát trực tiếp: Đã sẵn sàng cho sự gián đoạn của GPT

电台主持人和直播行业：为 GPT 颠覆做好准备

The “Completeness” Issue One of the most common issues in Quality Assurance (QA) for web scraping, yet disarming in its triviality, is ensuring the scraper collects all items from the target website. It’s a problem of continuously calibrating a tool that measures a constantly changing object. Why Does It Happen? From the easiest to detect to the most challenging (which does not mean easy to solve..), we have the following causes of incomplete data collection: the scraper gets blocked by anti-bot systems the scraper gets lost in A/B testing versions of the website the scraper is limited by the paging limits of the website/API the scraper is overlooking portions of the website (sometimes created after the scraper was designed) As a result, we have a partial data collection. Early Failure Detection Most web scraping use cases have Service Level Agreements (SLAs) that can result in penalty clauses. Quality Assurance aims to detect the potential issue as early as possible - . before SLAs are violated To do so, we need to increase the Failure Detection Rate (FDR) and reduce the False Alarm Rate (FAR). With a cherry on top: . keeping costs low How To Detect Failures Time series analysis We can monitor item count over time and trigger an alert when this drops. It’s a good starting point, but while effective with sudden changes (i.e., a drop by 50%), it is less functional when variations are incremental, generating either too many false alarms (FAR) or failing to detect errors. This happens because: Websites change quickly, especially when large We have no history in data to understand trends or seasonalities, which would allow applying more sophisticated time-series algorithms. The most critical limitation of this method is that it does not spot missing items if they have never been captured by the scraper. Example A fashion e-commerce website might have a “sales” category of the website that only pops up during official sales periods. If you build your scraper when the section is not there, you might never realize you are missing the sales items. Manual Inspection (Ground Truth) The manual inspection gives the highest confidence in results, as discussed . It provides a so-called Ground Truth, and you can benchmark the item count you collected against the item count performed manually. in this post Limitations: Hardly feasible for large websites (you can reliably tell how many items are on website, but not so reliably on ). Allbirds Farfetch Hardly scalable: It may work for a few websites, and it is done rarely, but things go uphill quickly when you need multiple large websites with a high frequency (read the Data Boutique approach on this in the article on ). Ground Truth Testing This would keep a good False Alarm Rate (FAR) but not achieve a reasonable Failure Detection Rate (FDR), as frequency would be too low. Independent Benchmarking An intelligent way to solve this is to benchmark your result, in terms of item count, against an independent collection. For this approach to properly work, the benchmark data has to be: Independent: to reduce the chance of being affected by the same coding biases Cost-effective: , web scraping is costly enough. Ça va sans dire An independent data collection is (almost) uncorrelated to your own data collection: it’s correlated because they look at the same object, so a failure of the observed object would indeed cause a loss in both data collections, but on the other hand, they’re results of independent processes, written by am maintained by different teams, with different techniques. Using a highly reliable data source strongly increases the trustworthiness of results. Let’s assume your current Failure Detection Rate (FDR) is 90%, meaning your system can automatically detect 90% of the times a scraper collects only partially from the website. Or, in other terms, your dataset, when published, contains 90% of the times a complete collection. If we assume that the benchmark data is a) as capable of detecting errors as the production data b) independent Using external data for QA would bring the Failure Detection Rate to 99% ( ). probability of the union of two events Monitor the total item count on your data collection Benchmark it with the total item count from the same website on Data Boutique When your count is lower than the benchmark, you have your failure detection. Why Data Boutique is a smart fit Since ’s datasets embed manual inspections in their QA process, using Data Boutique’s data as a benchmark is , and a way to improve the Quality Assurance process (QA) even when you do web-scraping internally because it is very likely that datasets published on Data Boutique exceed those levels of FDR. Data Boutique scalable cost-efficient, reliable The two data structures do not have to be the same: You are only comparing item counts and do not need the same structure, which makes it very easy to implement. Only the granularity has to be comparable. You can select the frequency for your QA that can be lower than the frequency of your acquisition (if you acquire items daily, you can have only weekly benchmarks, which would still go a very long way in improving data quality tests. Since Data Boutique’s data is Fractionable (as explained ), the cost of buying this data can be very low if compared to all other quality measures. in this post In other words, even if the data structure of Data Boutique is not a perfect match for your use case, using it for Quality Testing is a very efficient approach. Join the Project Data Boutique is a community for sustainable, ethical, high-quality web data exchanges. You can and add your request if a website is not listed. Saving datasets to your interest list will allow sellers to correctly size the demand for datasets and onboard the platform. browse the current catalog More on this project can be found on . our Discord channels Also published on Data Boutique

Improve Early Failure Detection (EFD) in Web Scraping With Benchmark Data

Mejore la detección temprana de fallas (EFD) en Web Scraping con datos de referencia

Améliorez la détection précoce des défaillances (EFD) dans le Web Scraping avec des données de référence

बेंचमार्क डेटा के साथ वेब स्क्रैपिंग में अर्ली फेल्योर डिटेक्शन (ईएफडी) में सुधार करें

ベンチマーク データを使用して Web スクレイピングの早期障害検出 (EFD) を改善する

Melhore a detecção precoce de falhas (EFD) em Web Scraping com dados de referência

Cải thiện khả năng phát hiện lỗi sớm (EFD) trong quét web bằng dữ liệu điểm chuẩn

使用基准数据改进 Web 抓取中的早期故障检测 (EFD)

Data (bad) quality is an issue with all kinds of data. Quality Assurance (QA) represents a significant portion of the effort of data projects. No data source is immune. But Web Data is more exposed to it: (at least a portion of it) and immediately spot inconsistencies, undermining users’ trust very rapidly. Users can verify the original website with no effort We call this the exposure. Ground Truth Test What Are Ground Truth Tests (GTT) According to Wikipedia Ground truth is information that is known to be real or true, provided by direct observation and measurement (i.e. empirical evidence) In web data, Ground Truth is the information the user can see visually on the website. . How do we know it really contains all listings? How do we know we are buying a extraction? Let’s assume we are buying a dataset with all Airbnb listings in Europe available to book on a given day complete We can’t manually count all Airbnb listings in Europe (and be sure we didn’t make mistakes ourselves). All we can do is analyze a small sample: We can go to the website, make a search, and see there are 506 available listings in Bordeaux, France, for May 6th through May 7th (as researched on April 23rd). If the dataset also shows 506 listings for Bordeaux, we trust the data and buy it. Users’ GTT Will Likely Fail The issue with this approach is that the test will likely fail. In fact, there are several fallacies in this process: Airbnb listings also include nearby locations (the expected number is wrong) The day of the test is different from the day the data was collected (the expected number is wrong) If ever the data provider did a test, it was likely on a different sample. All potential issues referred to the test case never came up (the dataset is also wrong, but the seller was unaware of it). The problem is relevant: in this scenario? How do we ever trust any dataset A Structured Approach to GTT Ground Truth Tests (GTT) are not just the only option users have: They are also easy to do and plain to understand: All the . If we structure a process to handle this, we will unlock web data to grow further. building elements of trust At DataBoutique, we reinforced domain-quality controls and time-series consistency controls, with a GTT process that follows these principles: (our own version of it) S.M.A.R.T. Metrics: , GTT cases are defined unambiguously: A set of filters that must be applied on the website, such as “number of Balenciaga products on Farfetch.com in Italy”, “number of job listings for Software Engineer in Zurich on Indeed.com”; Specific , as they result in an exact number, like product or item counts. This number is captured through a data-entry form online, stored, and used as a benchmark vs. the comparable metric of the web-collected dataset; Measurable , any user, regardless of their technical expertise, must be able to perform the test. No coding or data science skills are required. If the website does not present an explicit count of items, the manual counting must be a reasonable number; Achievable , as tests must be a relevant representation of the website, cover different cases, and allow fair (but achievable) coverage; Relevant , given the volatility of websites, the metrics have a time-based validity; when they expire, they must be refreshed. Time-bound : Users on the platform agree that data validation will happen through these tests. This way, the sellers can diagnose their data upstream, and the buyers can shorten their adoption process once they accept (and trust) the tests already in place. Shared and Agreed Upon : All DataBoutique accredited users can update and peer-review the GTT entries. A consensus-like mechanism prevents the process from misuse and economically rewards collaboration. This reward-based system allows good coverage of test cases and a scalable mechanism to cover thousands of websites. Collaborative The implementation workflow is quite linear: An initial GTT case is submitted upon dataset onboarding. A simple data-entry mask is present on each website data structure, and any accredited user can run the test and enter the expected values. GTT cases are updated in two moments: Upon expiry and each time a dataset fails a test (since website counts can vary rapidly). When a GTT is updated, and the dataset still fails, data submission is refused. The final customer receives only data that passed the tests. When a dataset has no valid GTT cases, it cannot be published. The consensus mechanism also puts a limit (i.e. max 70%) to the number of GTT cases submitted by the seller of that specific dataset. The seller has a strong incentive to update the GTT case fast (the sooner it’s updated, the sooner the data passes the test). The community of users, whose incentive is based on total platform revenue, has a more extended timeframe incentive to continuously update and add tests. The benefits of GTT in Data Boutique Increased number of tests performed on data Increased quality of onboarded data, as sellers had more tools to self-diagnose their extractions. Reduction of disputes upon misleading expectations from the end user (our goal:) Increased trust in buyers, which translates into more purchases. Also published . here

Web Data Can Have Quality Issues — Here's How Ground Truth Testing Can Help

Many still think of augmented reality as a fun toy. Often, the first thing that comes to mind when thinking about AR is Pokemon Go. But this perception is changing. Already, 76% of people expect to use AR as an everyday tool. By 2025 4.3B people will use AR frequently, compared to 2.2B people in 2022. Augmented reality is becoming the new norm for shopping. Companies around the world adopt AR and VR, so the market is booming. . It will grow up to $252B by 2028 Let’s dive into AR to see how companies use it today. Why Is AR Getting Popular? There are a few reasons for that. First, AR makes a great product description. It’s much more immersive than traditional photos and videos. With an AR app, you can fit a sofa into your apartment before buying it. It’s convenient. Second, it’s hip. Like it or not, but we live in a world where companies are fighting for your attention. AR is fresh; many industries don’t have AR solutions yet. Right now AR is a way to stand out. Third, the trendy Metaverse relies on AR. The growth of one will cause the other to grow too. Fourth, it lowers costs. An AR app can often replace a visit to a specialist. For example, some tools will help you with minor home repairs. Finally, it makes education more efficient. Businesses can use the technology to train their employees. For example, cooks in restaurants can learn recipes with AR glasses. The program would guide the cook to the correct ingredient and give instructions how to use it. All of that increases brand loyalty. That’s why companies use AR more and more. What AR Solutions Are Companies Building Now? Apps That Place Furniture Into Living Rooms It’s tricky to buy big items like furniture or a TV. You have to make sure that they will fit into the room. It’s a terrible feeling to buy a sofa only to realize that it doesn’t look very good inside. Once you bought a large item, it’s also not so simple to move around the apartment. So it's best to plan the layout ahead, and AR will help. That's why many furniture retailers have apps that make it easier to place items around before purchasing. IKEA was the first company to bring AR to the furniture market. If you have a phone, you can see what the sofa will look like in your apartment. This makes customers more confident about shopping. It increases conversions and lowers the return rates. Trying On Clothes & Accessories Another good application for AR is fashion. One example would be Farfetch's mobile store with integrated AR. Now, customers can try on sneakers at home. In their case, adding AR . increased unique product viewers by 580% Another example is that lets you try on glasses. Warby Parker’s app Buying glasses online is harder than buying clothes. It’s difficult to know in advance if the frames will fit. With a shirt you know your size, but with glasses it’s another story. Finding a pair with the right shape and width is a challenge. The AR app solves this issue. With the app, you can try on frames virtually. Customers feel safer buying online, and Warby Parker makes its business more cost efficient. It’s a win-win. Makeup Try On Buying makeup online used to be tricky. You could never know if the color was right. So, many women used to buy makeup in brick-and-mortar stores. This way they could try on lipstick and eye shadows. AR solves this issue as well. Companies like L’Oréal release makeup try-on apps. This increases customer confidence dramatically. In the case of L’Oréal, . AR technology tripled conversion rates User Manuals That Are Simple & Clear Let's face it, most people don't like reading user manuals. In most cases, they are just not simple to go through. With AR, user manuals can be much easier to follow. This makes customers more loyal to the brand. An example would be Hyundai's manual. It makes simple car maintenance available to everyone, and people love the app. Take a look at the promo video and the enthusiastic comments it gets: Hyundai's wild augmented reality owner's manual — CES 2016 I Get It, AR Is Getting Everywhere. So What? AR is getting better and more popular every year. As the gets more popular, so will AR. After all, it is the technology that the Metaverse relies on. Metaverse But it is still a new technology. There’s a lot of potential to it and many industries still don’t have AR solutions. Think about your business field. Can AR help you grow? Do your competitors already use it? Right now there’s a window of opportunity to stand out among your competitors. It might be the time to consider AR for your business.

AR Is Changing Online Shopping: What Does It Mean For Businesses?

Many internet celebrities tried to ride the crypto and NFT trend. When an rolled out, it was the opportunity for many Youtubers, social media influencers, and digital celebrities to cash in and cash in big. But it isn’t just exclusive to digital influencers - like Paris Hilton, Lindsay Lohan, and Bella Hadid, have also jumped on the bandwagon. Millions of dollars have been made through digital assets such as NFTs, and Logan Paul’s buy-in in 2021 should have increased his net worth, but it all backfired. His Azuki collection, acquired for $623,000, is only valued at $10 today. NFT marketplace traditional celebrities Logan Paul’s losses aren’t as great as they seem Media has a way of sensationalizing news because content is king, especially in the current digital age. Logan Paul is an avid investor and wouldn’t have sat idly by when his assets began depreciating. His diverse portfolio protects him from the worst of it, but his own creativity and initiative stopped his investment from disintegrating into nothing. During the downward trend in value over his Azuki collection, Logan Paul that he created a whole new set of NFTs called Originals 99 which was used to . So while Logan really did lose out on his initial investment, the big picture showed that he didn’t lose much money thanks to his own initiatives to salvage and make the most out of his predicament. announced offset his losses Is the cryptocurrency bubble bursting? The fall of the cryptocurrency market has led to many crashes. It is worth noting that when Logan Paul bought the items, the NFT transactions were at their peak, with over $500 million worth of transactions per day. At the time of writing, transactions have dipped considerably, with daily transactions struggling to exceed $10 million. NFTs weren’t the only thing that Logan Paul invested in. His portfolio was bursting with bitcoin and alternative coins. Having hit their peak last year at 2021, Logan Paul isn’t the only one reeling from his losses. But that doesn’t mean that the downward spiral will not pick up in the future. If we take a look at cryptocurrency trends, there are moments where it goes up and down, much like any other asset. However, it is also true that crypto and digital assets are much more volatile and while stocks on the market may dip or rise in increments, NFTs and crypto may go from a value of $10,000 to $10 in just a matter of days. Keeping this in mind, there is no bubble to burst. In order for a bubble to exist, its price movements have to be “ to its underlying value,” according to Coindesk, an authority on the topic of cryptocurrency and digital assets. unrelated Are NFTs and crypto here to stay? Digital assets might seem like , but they’re being taken seriously by many corporations and industries, which calls one to ponder: Are they really ? a novelty here to stay There will always be naysayers and cynics, but those that do not take risks will never see great returns. That’s the fundamental basis of investing and those that will get on the digital asset bandwagon are people who might ultimately turn a profit on it. As long as there are investors willing to take risks on NFTs and crypto, it could continue to flourish. NFTs have a huge untapped potential in business utility. According to CoinTelegraph, “There is pervasive fraud in supply chains with countless incidents of double financing, where malicious actors use copies of the same paperwork to illegally obtain money from multiple sources. With blockchain, this will disappear overnight as each and every shipment can be represented by only one single NFT that documents everything delivered — and, indeed, tracks it every step of the way.” This makes NFTs more than just digital fine art collections, a definition which has been adopted by the masses. It can indeed also be a viable investment opportunity especially since it represents a higher level of security. Due to their nature, NFTs make forgery much harder. Blockchain has spearheaded the path for revolutionary systems in the medical, financial, and education sectors. Creating secure databases is no small feat and bitcoin was able to supplement the technology needed. Even if cryptocurrency were to disappear over the next few years (though it is highly unlikely), blockchain would probably continue to be in use. What will be Logan Paul’s next move? While there’s no predicting what Logan Paul might do next, it wouldn’t be too farfetched to imagine a new NFT collection on the horizon. We should expect to see more daring acquisitions in the near future because it has all paid off one way or another, especially in the hands of an entrepreneurial spirit like Logan Paul, who has survived setbacks in the past and continued making comebacks following his mistakes.

The Truth Behind the Sensationalized Fall of Logan Paul’s NFT Collection in 2022

La verdad detrás de la sensacional caída de la colección NFT de Logan Paul en 2022

La vérité derrière la chute sensationnelle de la collection NFT de Logan Paul en 2022

2022 में लोगान पॉल के एनएफटी संग्रह के सनसनीखेज पतन के पीछे का सच

2022年のローガン・ポールのNFTコレクションのセンセーショナルな崩壊の背後にある真実

A verdade por trás da queda sensacionalista da coleção NFT de Logan Paul em 2022

Sự thật đằng sau sự sụp đổ gây xúc động của Bộ sưu tập NFT của Logan Paul vào năm 2022

2022 年 Logan Paul 的 NFT 收藏轰动倒下背后的真相

In a prior HackerNoon story, I reported on one of the most elaborate type of crypto scams out there. Here's one scammer who disguised his scheme so cleverly that he fooled Twitter into not only letting him promote his scam for months, but also actively censored the investigator who called him out. The scammer in question went by the handle @nuri0x on Twitter and claimed to be a former 1inch employee who has now founded the project MEVbots. The name of course suggests we have another instance of the at hand that has been circulating for the past few months. frontrunning bot scam The scam itself is exactly the same. The Twitter channel of MEVbots has a Vimeo video pinned (since YouTube is too quick in deleting these videos by now), which tricks viewers to deploy and fund a smart contract that sends the funds directly to the scammer's wallet. What makes this case so scandalous is that nuri0x has apparently paid Twitter to promote his scam. This was screenshotted by me: For those not versed in the German language, Twitter suggested me to follow the MEVbots channel and told me that this was a sponsored display. Of course, this makes the frontrunning bot scam a lot more effective. After all there are real frontrunning bots out there (selling for hundreds or thousands of dollars and making their owners fortunes), one might assume that Twitter carefully vets ad sponsors and filters out scammers. Now even if this was a real frontrunning bot, allowing them to buy paid displays would be a major scandal on Twitter's behalf. While frontrunning in crypto is technically not illegal, these bots steal from honest DEX traders. But letting a confirmed scammer do this is something different entirely. What further adds to the seeming trustworthiness of MEVbots is that its promoter nuri0x ran an ordinary account that perfectly looked like an honest, non-automated Web3 channel. MEVbots constantly tweets about successful sandwich attacks that were supposedly carried out by users of the frontrunning bot, alongside the on-chain transaction as "proof". In reality, these tweets were copied from the EigenPhi MEV Alert bot, which scans blockchains for sandwich attacks. Also, him pretending to be a former 1inch employee makes him seem trustworthy. In reality, his account was either stolen from some other Web3 personality, or botted up to over 50,000 followers (lots of Web3 channels, especially NFT promoters do that these days). 1inch has since confirmed that someone with the handle nuri0x never worked there: Yeah, they’re running a full scam on twitter and lying about previous employment. [We] reported both twitter handles hoping they’ll ban. Their complaint at least seemed to have been somewhat successful at last. The account nuri0x has been now, but the MEVbots account is still active after an estimated two month of running its scam. Furthermore, the scam became public knowledge after the famous "on-chain sleuth" @zachxbt reported on this in July. banned https://twitter.com/zachxbt/status/1549568222348836867 And what was Twitter's response? The starting post of Zachxbt's thread was partially hidden from view for some reason. His post still remained visible in his timeline, but was "unavailable" in quote tweets and did not appear in the Twitter search. Until lately. It's not farfetched to assume that Twitter shadowbanned (or rather shadow-deleted) Zachxbt's post, to protect a paying customer. This likely made a dent in the post's reach. It is also not farfetched to assume that Twitter's algorithms took further measures to limit the reach. Compared to other threads Zachxbt posted around the same time, the one in question has a noticeably lower amount of RTs, likes, and comments. Overall, I think that this was yet another burp by Twitter's shady algorithms, but one that did considerable damage. Zachxbt mentioned two victims that were scammed for a total of 44 ETH. With over month down the line, I estimate that nuri0x came away with well over 100,000 USD and his scam is still ongoing at the time of writing. Also, letting a scam run for almost six weeks after it became public and actively surpressing the dissemination of information warning about the scam, whilst taking money from the scammer for paid advertising, proves severe negligence on behalf of Twitter.

MEVgate: How Twitter Collaborated With a Known Crypto Scammer

MEVgate: cómo Twitter colaboró con un criptoestafador conocido

MEVgate : comment Twitter a collaboré avec un arnaqueur crypto connu

MEVgate: ट्विटर ने एक ज्ञात क्रिप्टो स्कैमर के साथ कैसे सहयोग किया

MEVgate: Twitter が既知の仮想通貨詐欺師とどのように協力したか

MEVgate: como o Twitter colaborou com um conhecido golpista criptográfico

MEVgate: Cách Twitter hợp tác với kẻ lừa đảo tiền điện tử đã biết

MEVgate：Twitter 如何与已知的加密诈骗者合作

Introduction This post will teach us to scrape Google Images results with Node JS using multiple methods. Web Scraping Google Images. Requirements Install Libraries Before we begin, install these libraries so we can move forward and prepare our scraper. npm i unirest
npm i cheerio To extract our HTML data, we will use Unirest JS, and for parsing the HTML data, we will use Cheerio JS. Target: Process Method-1 We have set up all the things to prepare our scraper. Now, let us discuss our first method to scrape Google Images. First, we will make a GET request on our target URL with the help of Unirest to extract the raw HTML data. let header = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.157 Safari/537.36"
};
return unirest
.get("https://www.google.com/search?q=nike&oq=nike&hl=en&tbm=isch&asearch=ichunk&async=_id:rg_s,_pms:s,_fmt:pc&sourceid=chrome&ie=UTF-8")
.headers(header)
.then((response) => {
let $ = cheerio.load(response.body); Step-by-step explanation: In the first step, we made a GET request to our target URL. In the second step, we passed the headers required with our target URL. Then we stored the returned response in the Cheerio instance. But one User Agent might not be enough as Google can block your request. So, we will make an array of User Agents and rotate it on every request. const selectRandom = () => {
    const userAgents =  ["Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.157 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36",
    ]
    var randomNumber = Math.floor(Math.random() * userAgents.length);
    return userAgents[randomNumber];
    }
    let user_agent = selectRandom();
    let header = {
    "User-Agent": `${user_agent}`
    } Copy the below target URL. Paste it into your browser, which will download a text file. Open that text file in your code editor and convert it into an HTML file. https://www.google.com/search?q=nike&oq=nike&hl=en&tbm=isch&asearch=ichunk&async=_id:rg_s,_pms:s,_fmt:pc&sourceid=chrome&ie=UTF-8 Additional parameters which can be used with this URL: - Term By Search parameter. Read more about this parameter in this article. tbs - Used to filter image results. chips - Used for pagination. = 0 will return the first page of results, = 1 will return the second page of results and so on. ijn ijn ijn Scroll the HTML file till the end of the style tag you will see the HTML tags of the respective image results. Now, we will parse the required things we want in our response and search for the title tag from the above image. You will find inside an anchor tag. Just below the title, we have the tag for our source as . .mVDMnf .FnqxG let images_results= [];
    $("div.rg_bx").each((i, el) => {
     images_results.push({    
     title: $(el).find(".iKjWAf .mVDMnf").text(),
     source: $(el).find(".iKjWAf .FnqxG").text()
    });
  }); After the end of the anchor tag, you will find the tag with the class name which contains a JSON string. div rg_meta {"bce":"rgb(249,252,249)","cb":21,"cl":21,"clt":"n","cr":21,"ct":21,"id":"qYZE1rcH_OCntM","isu":"www.nike.com","itg":0,"oh":1088,"os":"15KB","ou":"https://c.static-nike.com/a/images/w_1920,c_limit/bzl2wmsfh7kgdkufrrjq/image.jpg","ow":1920,"pt":"Nike.
Just Do It.
Nike.com","rh":"www.nike.com","rid":"mgtROrdDu1XGJM","rmt":0,"rt":0,"ru":"https://www.nike.com/","st":"www.nike.com","th":169,"tu":"https://encrypted-tbn0.gstatic.com/images?q\\u003dtbn:ANd9GcQQAtNCsBlvuD_5pu9bKrTr-Sv5mMwD1-hZE9MS4Px4GKk05naP\\u0026s","tw":298} We will parse it and extract the link and the URL of the original image from it. let images_results= [];
    $("div.rg_bx").each((i, el) => {
        let json_string = $(el).find(".rg_meta").text();
        images_results.push({
        title: $(el).find(".iKjWAf .mVDMnf").text(),
        source: $(el).find(".iKjWAf .FnqxG").text(),
        link: JSON.parse(json_string).ru,
        original: JSON.parse(json_string).ou,
    });     
  }); And at last, we will find the thumbnail URL. If you look at the HTML, there is an image tag under the first anchor tag, which contains the thumbnail URL. Now, our parser looks like this: let images_results= [];
    $("div.rg_bx").each((i, el) => {
        let json_string = $(el).find(".rg_meta").text();
        images_results.push({
        title: $(el).find(".iKjWAf .mVDMnf").text(),
        source: $(el).find(".iKjWAf .FnqxG").text(),
        link: JSON.parse(json_string).ru,
        original: JSON.parse(json_string).ou,
        thumbnail: $(el).find(".rg_l img").attr("src")? $(el).find(".rg_l img").attr("src") : $(el).find(".rg_l img").attr("data-src")
    });    
  }) Results: [
{
title: 'Shoes, Clothing & Accessories. Nike ...',
source: 'www.nike.com',
link: 'https://www.nike.com/in/men',
original: 'https://c.static-nike.com/a/images/w_1920,c_limit/mdbgldn6yg1gg88jomci/image.jpg',
thumbnail: 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTCsZWS0YPC1NFXd4g_Ucn4jkz8VYxL4VbLvWfKa5QI3PKRuHc&s'
},
{
title: 'Nike. Just Do It. Nike.com',
source: 'www.nike.com',
link: 'https://www.nike.com/',
original: 'https://static.nike.com/a/images/f_jpg,q_auto:eco/61b4738b-e1e1-4786-8f6c-26aa0008e80b/swoosh-logo-black.png',
thumbnail: 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRbbeIzjUozRCMzN8gaujUFBJlIFHheriDFvKhSCMD84JL8KeuX&s'
},
.... Here is the full code: const unirest = require("unirest");
    const cheerio = require("cheerio");
    
    const getImagesData = () => {
        const selectRandom = () => {
        const userAgents = [
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36",
            "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36",
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.157 Safari/537.36",
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36",
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36",
        ];
        var randomNumber = Math.floor(Math.random() * userAgents.length);
        return userAgents[randomNumber];
        };
        let user_agent = selectRandom();
        let header = {
        "User-Agent": `${user_agent}`,
        };
        return unirest
        .get(
            "https://www.google.com/search?q=nike&oq=nike&hl=en&tbm=isch&asearch=ichunk&async=_id:rg_s,_pms:s,_fmt:pc&sourceid=chrome&ie=UTF-8"
        )
        .headers(header)
        .then((response) => {
            let $ = cheerio.load(response.body);
    
            let images_results = [];
            $("div.rg_bx").each((i, el) => {
            let json_string = $(el).find(".rg_meta").text();
            images_results.push({
                title: $(el).find(".iKjWAf .mVDMnf").text(),
                source: $(el).find(".iKjWAf .FnqxG").text(),
                link: JSON.parse(json_string).ru,
                original: JSON.parse(json_string).ou,
                thumbnail: $(el).find(".rg_l img").attr("src") ? $(el).find(".rg_l img").attr("src") : $(el).find(".rg_l img").attr("data-src"),
            });
            });
    
            console.log(images_results);
        });
    };
    
    getImagesData(); Method - 2 In this method, we will use a simple GET request to fetch the first page results of Google Images. So, let us find the tags for the image results. https://www.google.com/search?q=Badminton&gl=us&tbm=isch First, we will find the tag for the title. Look at the above image. You will find the tag for the title as under the div with the class name . h3 MSM1fd const images_results = [];

    $(".MSM1fd").each((i,el) => {
        images_results.push({
        title: $(el).find("h3").text(),
        })
    }) Then we will find the tag for the source. If you look at the image, you will find the source of the image under the second anchor tag with the class name as inside the div with the class name . Also, this anchor tag contains our link. So, our parser would look like this: VFACy MSM1fd const images_results = [];

    $(".MSM1fd").each((i,el) => {
        images_results.push({
        image: $(el).find("img").attr("src") ? $(el).find("img").attr("src") : $(el).find("img").attr("data-src"),
        title: $(el).find("h3").text(),
        source: $(el).find("a.VFACy .fxgdke").text(),
        link: $(el).find("a.VFACy").attr("href")
        })
    }) The tag is the only image tag inside the , so it is not important to look for its class name as particular. img div Results {
    image: 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSjxyuvqYQfybxq9F2XgME-ya6xb81WUyw3Dpa-YA40-Fy7fx0IlOhXIrK17kNON-r6vNs&usqp=CAU',
    title: 'Nike for Men - Shop New Arrivals - FARFETCH',
    source: 'farfetch.com',
    link: 'https://www.farfetch.com/in/shopping/men/nike/items.aspx'
    },
    {
    image: 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSfCJOrZr0zFxogpQjNT_6kBQ3rmxSPqvCHPpTWLmpOltZinUpptGM-290ssFMCIzFnD1M&usqp=CAU',
    title: "Women's Clothing. Nike IN",
    source: 'nike.com',
    link: 'https://www.nike.com/in/w/womens-clothing-5e1x6z6ymx6'
    },
    .... Note: You will also find some images with base64 URLs. This method is fast, but we can't use pagination in this method, while in the first, we can use it. Another method you can work with is the Puppeteer Infinite Scrolling Method, which can solve the problem of pagination. But it is a very time-consuming method. Using Google Image API Using this API, you don’t have to worry about creating and maintaining the scraper, and also you can scale the number of requests easily without getting blocked. Example const axios = require('axios');

   axios.get('https://api.serpdog.io/images?api_key=APIKEY&q=football&gl=us')
  .then(response => {
    console.log(response.data);
  })
  .catch(error => {
    console.log(error);
  }); You can get your API Key by registering on this . link Results "image_results": [
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_Tu78LWxIu_M_sN_kMfj2guqIbu2VcSLyI84CQGbuFRIyTCVR&s",
        "title": "Football - Wikipedia",
        "source": "en.wikipedia.org",
        "link": "https://en.wikipedia.org/wiki/Football",
        "original": "https://upload.wikimedia.org/wikipedia/commons/b/b9/Football_iu_1996.jpg",
        "rank": 1
      },
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTxvsz_pjLnFyCnYyCxxY5rSHQCHjNJyYGFZqhQUtTm0XOzOWw&s",
        "title": "Soft toy, American football/brown - IKEA",
        "source": "www.ikea.com · In stock",
        "link": "https://www.ikea.com/us/en/p/oenskad-soft-toy-american-football-brown-90506769/",
        "original": "https://www.ikea.com/us/en/images/products/oenskad-soft-toy-american-football-brown__0982285_pe815602_s5.jpg",
        "rank": 2
      },
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTNJYuYBLrUxLrXkbnP18Y6DEgKf_H4HYGCzecsGRAoFtkiGEM&s",
        "title": "NFL postpones three games due to Covid ...",
        "source": "www.cnbc.com",
        "link": "https://www.cnbc.com/2021/12/17/nfl-will-postpone-some-games-over-covid-surge-source-says.html",
        "original": "https://image.cnbcfm.com/api/v1/image/106991253-1639786378304-GettyImages-1185558312r.jpg?v=1639786403",
        "rank": 3
      },
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTg4WJ88A83JqXwGXsWtiB5qSoHrU_RukbrfXdkWggEKMsJ5Ro1&s",
        "title": "USFL schedule Week 2: What football ...",
        "source": "www.sportingnews.com",
        "link": "https://www.sportingnews.com/us/nfl/news/usfl-schedule-week-2-football-tv-channels-times-scores/oadvrtsc5vn9l4knu8hvnpo0",
        "original": "https://library.sportingnews.com/2022-04/usfl-football-042122-getty-ftr.jpg",
        "rank": 4
      },
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTsOPQ2-nzsTrdRK1HHXIqB-x96yuiPg7pwfJsm8mToVlw5-UaM&s",
        "title": "Why is football called 'football'?",
        "source": "www.newsnationnow.com",
        "link": "https://www.newsnationnow.com/us-news/hold-why-is-football-called-football/",
        "original": "https://www.newsnationnow.com/wp-content/uploads/sites/108/2022/02/FootballGettyImages-78457130.jpg?w=1280",
        "rank": 5
      },
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS0WimlY0Nykjy8k5An6k4wViDvyVZzo_0K1MSZNZcwDFGsugNW&s",
        "title": "The Duke NFL Football | Wilson Sporting ...",
        "source": "www.wilson.com · In stock",
        "link": "https://www.wilson.com/en-gb/product/the-duke-nfl-football-wf10011",
        "original": "https://www.wilson.com/en-gb/media/catalog/product/b/c/bc340309-c2a3-441d-ac36-a26187fd94f0_yceho2py9sgzklxk.png",
        "rank": 6
      },
      {
        "image": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ6ZuMtA_I4WZZKXPhiqMpIHsdKJX-SVkcN2yo1KAQ9SQuxdCI&s",
        "title": "National Football League",
        "source": "www.nfl.com",
        "link": "https://www.nfl.com/",
        "original": "https://static.www.nfl.com/image/private/t_editorial_landscape_mobile/f_auto/league/iaesayubxpbxmbxfwm3b.jpg",
        "rank": 7
      }, Conclusion In this tutorial, we learned to scrape Google Search Images Results. If you have any questions,  feel free to ask me in the comments. Follow me on . Thanks for reading! Twitter Additional Resources Scrape Google Search Organic Results with Node JS Scrape Google Maps Reviews Scrape Google News Results

Scrape Google Images with Node JS

The era of traditional money is long gone. After the arrival of bank cards, and now the option of contactless payment, we find ourselves with a new alternative option to make payments: our hands. In this slogging thread, our tech community discussed how implementing a microchip can be an innovative option instead of our bank cards. We are already used to pulling out our contactless cards, so how is this new choice different? This Slogging thread by Sara Pinto, Mónica Freitas and Jack Boreham occurred in slogging's official #technology channel, and has been edited for readability. Sara Pinto Apr 11, 2022, 4:20 PM Are we closer to not needing physical money or even bank cards at all? https://www.bbc.com/news/business-61008730 Sara Pinto Apr 11, 2022, 4:25 PM "Patrick Paumen causes a stir whenever he pays for something in a shop or restaurant." "This is because the 37-year-old doesn't need to use a bank card or his mobile phone to pay. Instead, he simply places his left hand near the contactless card reader, and the payment goes through." "He is able to pay using his hand because back in 2019 he had a contactless payment microchip injected under his skin." "The procedure hurts as much as when someone pinches your skin," says Mr Paumen. Sara Pinto Apr 11, 2022, 4:26 PM "A microchip but it is only during the past decade that the technology has been available commercially." "And when it comes to implantable payment chips, British-Polish firm, Walletmor, says that last year it became the first company to offer them for sale." "The implant can be used to pay for a drink on the beach in Rio, a coffee in New York, a haircut in Paris - or at your local grocery store," says founder and chief executive Wojtek Paprota. "It can be used wherever contactless payments are accepted." was first implanted into a human back in 1998, Sara Pinto Apr 11, 2022, 4:32 PM This company's chip "weights less than a gram and is little bigger than a grain of rice", and it's completely safe since it has regulatory approval. So, my question to this community is: would you adopt this technology? Sara Pinto Apr 11, 2022, 4:37 PM You can find in this news that a survey from last year questioned more than 4 000 people from the UK and Europe, and 51% said they would consider this. Sara Pinto Apr 11, 2022, 4:37 PM What about you? Mónica Freitas Jack Boreham Abeer Limarc Ambalina Mónica Freitas Apr 13, 2022, 2:38 PM Sara Pinto, on one hand, no more wallets! Which makes it convenient. But on another hand, actually having a chip on me seems too much into the sci-fi-ish. Would you? Sara Pinto Apr 14, 2022, 10:52 AM Mónica Freitas, it sounds so futuristic that I even thought they were joking haha. I don't know if I would try. It's convenient, I like the idea of not having to bring cards around. But is it absolutely necessary? It's good that you don't need a wallet, but bringing out your card has little to no work haha. I feel like this is a bit extravagant Mónica Freitas Apr 14, 2022, 1:31 PM Sara Pinto, I think more and more we're following down the path of convenience and speed. We want everything to come easy and fast to us (that's probably why we no longer have the concentration levels to watch a 10min video for example; TikTok made us prefer the 30sec videos). So having a chip with your payment details is just another way of making life easier though only on not important aspects, IMO. 💚 1 Sara Pinto Apr 15, 2022, 10:29 AM Mónica Freitas, I couldn't agree more. We're getting used to having everything quick and with no issue. Maybe because of what every social media tells us, as you said. There's a product/app to facilitate every task we have to do. About the chip, we can't deny it is helpful, but definitely not crucial or revolutionary Mónica Freitas Apr 18, 2022, 11:25 AM Sara Pinto oh absolutely! There are some many more areas that require technological advances - health for instance. It's a great invention but, as you said, not crucial Sara Pinto Apr 19, 2022, 7:14 PM Mónica Freitas, I hope they can apply this kind of technology in a more useful manner. For example, a chip that has all your medical information. I don't know if it's farfetched, but that would be really useful in emergencies Mónica Freitas Apr 20, 2022, 1:48 PM Sara Pinto that'd be a great use! Even for allergies and health conditions information. 🔥 1 Jack Boreham Apr 20, 2022, 5:42 PM Personally I cannot wait until physical money is gone. Who needs it! Not me Jack Boreham Apr 20, 2022, 5:43 PM I would love to be a human bank card. At least when I become rich I can say I actually look like a million bucks 😂 😂 1 Sara Pinto Apr 25, 2022, 4:06 PM Jack Boreham, maybe I'm still a bit old-fashioned and can't wrap my head around this, but I love the enthusiasm! Sara Pinto Apr 25, 2022, 4:08 PM I have to admit it would look pretty could to just swipe your hand, and it's done! It's impressive how a chip inside of us can have this effect. I can't wait to see what they will do with this type of technology

Are We Close to Abolishing Physical Money and Bank Cards?

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