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A hotel and residence complex, powered by Miracle Estate’s cutting-edge utility NFT strategy, is set to revolutionize real estate investment in Northern Cyprus. Investors who purchase NFTs from the Phoenic Hera project will be able to track their returns from the hotel’s income and its facilities in real time via the Miracle app, all while enjoying a stay at the hotel and benefiting from the project’s many additional amenities. The project is slated for completion in 2027, with groundbreaking scheduled for October 11th. The highly anticipated groundbreaking ceremony for the new hotel and residence complex, Phoenic Hera, in Northern Cyprus took place on Friday, October 11th. Miracle Estate, a subsidiary of Miracle Technologies Group, which develops asset-backed blockchain projects, decentralized finance solutions, and mobile POS technologies, has laid the foundation for its new hotel and real estate project, Phoenic Hera, powered by blockchain and advanced technological infrastructure. The real estate project, which will feature a hotel and residence complex, will be located in Northern Cyprus, just 50 minutes from the airport and 60 minutes from two major city centers. Investors who purchase NFTs to participate in the project will enjoy free stays throughout the year and access to all hotel amenities. They will also earn rewards from the hotel’s operations, trade their NFTs, and acquire Phoenic Token, the native token of the Miracle ecosystem. As part of the project, Miracle Estate plans to invest a portion of its revenue into Phoenic Token to enhance the benefits for NFT holders. Located in one of the most valuable investment areas The complex will be built in the heart of a region that has been consistently recognized by Forbes magazine as one of the most valuable investment areas in Northern Cyprus since 2021. As part of the Phoenic Token strategy, the project offers a cost-effective alternative to the rising housing prices and shrinking real estate investment opportunities in the area. The groundbreaking ceremony, held on Friday, October 11th, marked the official start of the project. “We are breaking new ground in both the physical and digital worlds,” said Hakan Törehan, CEO of Miracle Cash & More. “By converting our hotel and real estate project in Northern Cyprus into blockchain-based Utility NFTs, we offer investors comprehensive benefits. Those who acquire Utility NFTs from the hotel project will also become Phoenic Token holders, investing in this growing ecosystem and gaining usage rights in both the hotel and residence projects.” Phoenic Token is more than just a token. It is the fuel behind groundbreaking projects like this one. As part of the Phoenic ecosystem, it powers a new generation of digital currencies and asset-backed investments, allowing holders to tap into unique ventures and strengthen their positions in the evolving market. “Turning real-world assets into Utility NFTs in the real estate sector” The construction process for the project, which is expected to generate $600 million in benefits, is planned to be completed within three years. Hakan Törehan shared, “Once the project is complete, investors will be able to enjoy all the services of this hotel complex for free every year. They will track their reward streams in real time through the Miracle App, and thanks to the financing provided by products like Node, Leveller, and Exchange, which power the Miracle ecosystem, investors can make real estate investments at more advantageous prices. Miracle Technologies Group will also invest in Phoenic Token, a utility token. This groundbreaking ceremony is the strongest proof that we are playing a pioneering role in the intersection of the real estate and blockchain sectors, transforming real-world assets into Utility NFTs.” Hakan Törehan, CEO of Miracle Cash & More. also emphasized that Miracle Estate is the real estate and tourism-focused subsidiary of Miracle Technologies Group, a global leader in blockchain technologies. Törehan concluded his remarks by stating: “In May 2024, we announced that we would launch a blockchain-based hotel and real estate project in Northern Cyprus, a region known for its real estate and tourism projects. With the groundbreaking ceremony in the first days of the fourth quarter of the year, we reinforced our belief that projects capable of overcoming regional and cyclical challenges can emerge from the intersection of traditional sectors and new technologies. Phoenic Token, which is not an ordinary utility token, powers unique projects across the Miracle Technologies ecosystem and can be accessed via various exchanges. Investors who purchase the Phoenic Hera Hotel NFT can easily benefit from the project, including the option to acquire additional Phoenic Tokens.” For more information about Phoenic Hera, visit www.phoenichera.com This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. A hotel and residence complex, powered by Miracle Estate’s cutting-edge utility NFT strategy, is set to revolutionize real estate investment in Northern Cyprus. Investors who purchase NFTs from the Phoenic Hera project will be able to track their returns from the hotel’s income and its facilities in real time via the Miracle app, all while enjoying a stay at the hotel and benefiting from the project’s many additional amenities. The project is slated for completion in 2027, with groundbreaking scheduled for October 11th. The highly anticipated groundbreaking ceremony for the new hotel and residence complex, Phoenic Hera, in Northern Cyprus took place on Friday, October 11th. Miracle Estate, a subsidiary of Miracle Technologies Group, which develops asset-backed blockchain projects, decentralized finance solutions, and mobile POS technologies, has laid the foundation for its new hotel and real estate project, Phoenic Hera , powered by blockchain and advanced technological infrastructure. Phoenic Hera The real estate project, which will feature a hotel and residence complex, will be located in Northern Cyprus, just 50 minutes from the airport and 60 minutes from two major city centers. Investors who purchase NFTs to participate in the project will enjoy free stays throughout the year and access to all hotel amenities. They will also earn rewards from the hotel’s operations, trade their NFTs, and acquire Phoenic Token, the native token of the Miracle ecosystem. As part of the project, Miracle Estate plans to invest a portion of its revenue into Phoenic Token to enhance the benefits for NFT holders. Located in one of the most valuable investment areas The complex will be built in the heart of a region that has been consistently recognized by Forbes magazine as one of the most valuable investment areas in Northern Cyprus since 2021. As part of the Phoenic Token strategy, the project offers a cost-effective alternative to the rising housing prices and shrinking real estate investment opportunities in the area. The groundbreaking ceremony, held on Friday, October 11th, marked the official start of the project. “We are breaking new ground in both the physical and digital worlds,” said Hakan Törehan, CEO of Miracle Cash & More. “By converting our hotel and real estate project in Northern Cyprus into blockchain-based Utility NFTs, we offer investors comprehensive benefits. Those who acquire Utility NFTs from the hotel project will also become Phoenic Token holders, investing in this growing ecosystem and gaining usage rights in both the hotel and residence projects.” “ We are breaking new ground in both the physical and digital worlds, ” said Hakan Törehan, CEO of Miracle Cash & More. “ By converting our hotel and real estate project in Northern Cyprus into blockchain-based Utility NFTs, we offer investors comprehensive benefits. Those who acquire Utility NFTs from the hotel project will also become Phoenic Token holders, investing in this growing ecosystem and gaining usage rights in both the hotel and residence projects.” We are breaking new ground in both the physical and digital worlds, By converting our hotel and real estate project in Northern Cyprus into blockchain-based Utility NFTs, we offer investors comprehensive benefits. Those who acquire Utility NFTs from the hotel project will also become Phoenic Token holders, investing in this growing ecosystem and gaining usage rights in both the hotel and residence projects.” Phoenic Token is more than just a token. It is the fuel behind groundbreaking projects like this one. As part of the Phoenic ecosystem, it powers a new generation of digital currencies and asset-backed investments, allowing holders to tap into unique ventures and strengthen their positions in the evolving market. “Turning real-world assets into Utility NFTs in the real estate sector” The construction process for the project, which is expected to generate $600 million in benefits, is planned to be completed within three years. Hakan Törehan shared, “Once the project is complete, investors will be able to enjoy all the services of this hotel complex for free every year. They will track their reward streams in real time through the Miracle App, and thanks to the financing provided by products like Node, Leveller, and Exchange, which power the Miracle ecosystem, investors can make real estate investments at more advantageous prices. Miracle Technologies Group will also invest in Phoenic Token, a utility token. This groundbreaking ceremony is the strongest proof that we are playing a pioneering role in the intersection of the real estate and blockchain sectors, transforming real-world assets into Utility NFTs.” Hakan Törehan shared, “ Once the project is complete, investors will be able to enjoy all the services of this hotel complex for free every year. They will track their reward streams in real time through the Miracle App, and thanks to the financing provided by products like Node, Leveller, and Exchange, which power the Miracle ecosystem, investors can make real estate investments at more advantageous prices. Miracle Technologies Group will also invest in Phoenic Token, a utility token. This groundbreaking ceremony is the strongest proof that we are playing a pioneering role in the intersection of the real estate and blockchain sectors, transforming real-world assets into Utility NFTs.” Once the project is complete, investors will be able to enjoy all the services of this hotel complex for free every year. They will track their reward streams in real time through the Miracle App, and thanks to the financing provided by products like Node, Leveller, and Exchange, which power the Miracle ecosystem, investors can make real estate investments at more advantageous prices. Miracle Technologies Group will also invest in Phoenic Token, a utility token. This groundbreaking ceremony is the strongest proof that we are playing a pioneering role in the intersection of the real estate and blockchain sectors, transforming real-world assets into Utility NFTs.” Hakan Törehan, CEO of Miracle Cash & More. also emphasized that Miracle Estate is the real estate and tourism-focused subsidiary of Miracle Technologies Group, a global leader in blockchain technologies. Törehan concluded his remarks by stating: “In May 2024, we announced that we would launch a blockchain-based hotel and real estate project in Northern Cyprus, a region known for its real estate and tourism projects. With the groundbreaking ceremony in the first days of the fourth quarter of the year, we reinforced our belief that projects capable of overcoming regional and cyclical challenges can emerge from the intersection of traditional sectors and new technologies. Phoenic Token, which is not an ordinary utility token, powers unique projects across the Miracle Technologies ecosystem and can be accessed via various exchanges. Investors who purchase the Phoenic Hera Hotel NFT can easily benefit from the project, including the option to acquire additional Phoenic Tokens.” “ In May 2024, we announced that we would launch a blockchain-based hotel and real estate project in Northern Cyprus, a region known for its real estate and tourism projects. With the groundbreaking ceremony in the first days of the fourth quarter of the year, we reinforced our belief that projects capable of overcoming regional and cyclical challenges can emerge from the intersection of traditional sectors and new technologies. Phoenic Token, which is not an ordinary utility token, powers unique projects across the Miracle Technologies ecosystem and can be accessed via various exchanges. Investors who purchase the Phoenic Hera Hotel NFT can easily benefit from the project, including the option to acquire additional Phoenic Tokens. ” In May 2024, we announced that we would launch a blockchain-based hotel and real estate project in Northern Cyprus, a region known for its real estate and tourism projects. With the groundbreaking ceremony in the first days of the fourth quarter of the year, we reinforced our belief that projects capable of overcoming regional and cyclical challenges can emerge from the intersection of traditional sectors and new technologies. Phoenic Token, which is not an ordinary utility token, powers unique projects across the Miracle Technologies ecosystem and can be accessed via various exchanges. Investors who purchase the Phoenic Hera Hotel NFT can easily benefit from the project, including the option to acquire additional Phoenic Tokens. For more information about Phoenic Hera, visit www.phoenichera.com www.phoenichera.com www.phoenichera.com www.phoenichera.com This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Miracle Estate Breaks Ground on Project Aimed At Solving Cyprus’s Real Estate Challenges With NFTs

شركة Miracle Estate تبدأ العمل في مشروع يهدف إلى حل تحديات العقارات في قبرص باستخدام NFTs

Miracle Estate ukax Proyecto ukaruw uñt’ayi, ukax Chipre markan Inmuebles ukan jan walt’awinakapar NFTs ukanakamp askichañatakiw amtata

Miracle Estate поставя началото на проект, насочен към решаване на предизвикателствата на Кипър с недвижимите имоти с NFT

এনএফটি-এর সাহায্যে সাইপ্রাসের রিয়েল এস্টেট চ্যালেঞ্জগুলি সমাধানের লক্ষ্যে মিরাকল এস্টেট প্রকল্পের উপর ভিত্তি করে

Miracle Estate inicia un proyecto destinado a resolver los desafíos inmobiliarios de Chipre con NFT

Miracle Estate murtuu hankkeessa, jonka tavoitteena on ratkaista Kyproksen kiinteistöhaasteet NFT:n avulla

Miracle Estate-ը սկսում է հիմք ընդունել Կիպրոսի անշարժ գույքի հետ կապված մարտահրավերները NFT-ների միջոցով լուծելու նախագիծը

ミラクルエステート、NFTでキプロスの不動産問題を解決するプロジェクトに着手

Miracle Estate Ebuki Mabele na Projet oyo ezali na tina ya kosilisa mikakatano ya Chypre mpo na makambo ya mombongo na ba NFT

Miracle Estate dia manapaka ny tany amin'ny tetikasa mikendry ny famahana ny olan'ny fananan'i Chypre miaraka amin'ny NFTs

Miracle Estate го пробива теренот на проектот насочен кон решавање на предизвиците на Кипар за недвижнини со NFTs

ተኣምራዊ ንብረት ንብድሆታት ንብረት ቆጵሮስ ብኤንኤፍቲ ንምፍታሕ ዝዓለመ ፕሮጀክት መሰረት ሰቢሩ

Welcome to our exclusive interview with Aimei Wei, the dynamic CTO of Stellar Cyber. Today, Aimei shares her remarkable journey in the tech world, her pivotal role in shaping Stellar Cyber's innovative cybersecurity approaches, and her vision of the evolving landscape of AI in cybersecurity. As we delve into her insights and experiences, prepare to be inspired by her unique perspective on technology, leadership, and the future of cyber defense. : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program From Code to CTO: Aimei Wei's Inspiring Journey in Tech Hi Aimei; it's a pleasure to have you with us today. Stellar Cyber is known for helping organizations take control of their cybersecurity operations. Could you start by telling us about your journey in the tech industry and what led you to your role as CTO at Stellar Cyber? Ishan Pandey: I got interested in computers as I entered college, so I studied Computer Science, and then I left China and came to Canada with less than $300 to earn a Master’s degree at Queen’s University in Kingston. Aimei Wei: I have always had a desire to work in a startup, even though I had a good career at Cisco Systems. With my kids grown up, I saw my husband launch Aerohive Networks and I thought I should take my turn and try it myself. While at Cisco, I saw the industry move from a perimeter-based model to an “anywhere is an attach point” model. I saw security becoming more and more important, and I realized that most security tools required a lot of work for very little reward – there were thousands of alerts issued by a tool, but only a few of them really mattered, and analysts spent their days chasing alerts and often not discovering complex attacks for weeks or even months. My inspiration for starting Stellar Cyber was to simplify and unify cybersecurity so analysts could get direct, actionable advice about which threats to chase and when. : Stellar Cyber's Open XDR platform stands out for its AI-driven approach to security operations. How do you see AI and machine learning transforming the field of cybersecurity, especially in threat detection and response? Ishan Pandey Cybersecurity detection and response is a heavily data-intensive process. Generally speaking, AI should be able to help organizations improve their security posture by detecting and responding to threats more quickly and accurately than with traditional methods, because it can analyze more data more quickly than humans can. Aimei Wei: An AI-driven cybersecurity platform can report detections as context-based incidents with specific pointers about how to address them, making it much easier and faster to investigate and remediate attacks. With the latest developments in Large Language Models, an AI-driven cybersecurity platform can potentially allow users to interact with the platform using natural language. This greatly eases the cybersecurity analyst shortage by enabling the use of lower-skilled security analysts who may be more readily available, as well as less expensive. Another point is that most cybersecurity defense solutions to date have been reactive, rather than proactive - you’re always behind the curve because you only learn about an attack after it’s well underway. Our AI-driven Open XDR Platform allows organizations to really take control of their security environment because it integrates with all existing security tools to continually scan the entire infrastructure and immediately alert analysts to potential threats. With the increasing complexity of cyber threats, what are the key challenges that your platform addresses? And how does Stellar Cyber's AI technology specifically tackle these challenges? Ishan Pandey: Our AI-driven Open XDR platform automates threat detection and response, so it allows security teams to focus on more complex tasks. For example, our platform can detect and automatically respond to commodity attacks like phishing emails, malware, and others, leaving analysts relatively free to dive into more complex, human-operated attacks. Aimei Wei: Our AI can also analyze user behavior patterns and detect anomalous behavior that could indicate a threat:  it uses Machine Learning algorithms to detect and respond to suspicious user activity in real time, using a baseline understanding of what constitutes normal user behavior. Security Information and Event Management (SIEM) and Extended Detection and Response (XDR) are crucial in cybersecurity. How does Stellar Cyber's platform integrate with these systems, and what advantages does it offer over traditional methods? Ishan Pandey: Our platform is an XDR solution that includes a next-generation SIEM as well as a  SOAR, NDR, UEBA and TIP function, all under one license, so it delivers a lot of cybersecurity functionality out of the box. However, it also comes with built-in integrations with every other popular third-party security tool, which allows customers to continue leveraging their investments in their existing EDAs or other solutions. Aimei Wei: Our platform uniquely forms the basis of a SecOps center that collects, analyzes and responds to security data from throughout the infrastructure, offering a truly holistic view of an organization’s security posture and the ability to protect it effectively, giving users real control over their security. Considering the future, where do you see the role of AI in cybersecurity heading? And how is Stellar Cyber preparing to adapt and lead in this evolving landscape? Ishan Pandey: Our company was a pioneer in the use of AI, and I believe AI will become more adept at threat detection and response, automating security operations, enabling predictive analytics, and becoming easier and easier to use. Aimei Wei: For example, we use AI to create prioritized lists of threats with specific pointers about how to address them, and we are now integrating Generative AI technology into our platform to enable users to interact with it using natural language. Lastly, for organizations looking to enhance their cybersecurity measures, what advice would you offer, especially in terms of leveraging AI and machine learning technologies? Ishan Pandey: There’s a lot of AI hype and mistrust in the marketplace, so the first thing organizations should do is to educate themselves on how AI works in cybersecurity platforms. Risk managers can improve their trust in AI by thoroughly understanding how AI arrives at its conclusions, and by ensuring that any baseline profiling for AI or ML is done with the organization’s own data. Aimei Wei: AI-powered cybersecurity platforms can generate a false sense of security that leads to complacency, so organizations should periodically cross-check AI-generated results to ensure that they’re accurate. Finally, AI systems can themselves be vulnerable to cyberattacks. Given the potential for heightened attack damage, organizations must take steps to fully secure their AI-driven cybersecurity systems. Don’t forget to like and share the story!

Redefining Cybersecurity: Aimei Wei’s Game-Changing Vision at Stellar Cyber

সাইবার সিকিউরিটি পুনঃসংজ্ঞায়িত করা: স্টেলার সাইবারে আইমি ওয়েই এর গেম-চেঞ্জিং ভিশন

Cybersicherheit neu definieren: Aimei Weis bahnbrechende Vision bei Stellar Cyber

Redefiniendo la ciberseguridad: la visión revolucionaria de Aimei Wei en Stellar Cyber

Redéfinir la cybersécurité : la vision révolutionnaire d'Aimei Wei chez Stellar Cyber

साइबर सुरक्षा को फिर से परिभाषित करना: स्टेलर साइबर में एमी वेई का गेम-चेंजिंग विजन

サイバーセキュリティの再定義: Stellar Cyber における Aimei Wei の革新的なビジョン

사이버 보안 재정의: Stellar Cyber에서 Aimei Wei의 판도를 바꾸는 비전

Redefinindo a segurança cibernética: a visão revolucionária de Aimei Wei na Stellar Cyber

Переосмысление кибербезопасности: революционная концепция Аймэй Вэй в Stellar Cyber

Siber Güvenliği Yeniden Tanımlamak: Aimei Wei'nin Stellar Cyber'de Oyunun Kurallarını Değiştiren Vizyonu

Định nghĩa lại an ninh mạng: Tầm nhìn thay đổi cuộc chơi của Aimei Wei tại Stellar Cyber

重新定义网络安全：Aimei Wei 在 Stellar Cyber 的改变游戏规则的愿景

Philochristus: Memoirs of a Disciple of the Lord by Edwin Abbott Abbott is part of the HackerNoon Books Series. You can jump to any chapter in this book . How Xanthias the Alexandrine said that the philosophy of Jesus aimed at the taking in of the Gentiles into the Kingdom, and at the enfranchisement of slaves; and how he found fault with Jesus for that he called himself the Son of man. here How Xanthias the Alexandrine said that the philosophy of Jesus aimed at the taking in of the Gentiles into the Kingdom, and at the enfranchisement of slaves; and how he found fault with Jesus for that he called himself the Son of man. Between the Feast of Passover and the Feast of Weeks I was not much with Jesus; for when I perceived that Jesus was in no instant peril, I returned to Sepphoris for a while, partly by reason of my mother’s health, and partly to gather in the harvest. And during this time, when it was perceived that Jesus went not up to the Passover, neither made any levy of the people as had been expected, the Pharisees for a while ceased to lay snares for him: and the common people, though they murmured that he went not up to Jerusalem, nevertheless had him in honour. But the harvest being now over, when I went back to meet Jesus at Capernaum, I found there one of mine acquaintance, a merchant (whom I had known at Alexandria in my uncle’s house), a Greek learned in the knowledge of the Greeks. This man was not a proselyte; neither did he in any wise conform himself to the Law of Moses. But he spake of himself, at that time, as a seeker after truth; for he did not join himself to any of the schools of the Gentile philosophers, but chose forth from each whatsoever seemed to him useful or true. He had read our Scriptures, and was greatly given to the study of our psalms and prophecies; and when he had heard me speak of Jesus of Nazareth as being our Messiah, his heart was moved to hear Jesus preach the gospel. So it came to pass, about the first or second Sabbath after the Feast of Weeks, he accompanied me into the synagogue where Jesus was to speak to the people. But as I went, I perceived Abuyah the son of Elishah; and with him were certain of the Sadducees, and some also of the Herodians. And when I saw them, I knew that they had come for no good purpose. And so it proved. For when we were now assembled in the synagogue, Abuyah came forward and said to Jesus, “Behold, thou art a vessel very full of knowledge, and the people are come together at thy feet for to hear of thee the words of wisdom, according as it is said, ‘Powder thyself in the dust of the feet of the wise, and drink their words with thirstiness.’ Now therefore, I pray thee, suffer me to ask of thee touching a certain matter.” And Jesus said, “Ask.” And Abuyah said, “Is it lawful for a man to put away his wife?” Now when Abuyah spake these words, all the Herodians and Sadducees listened with greedy ears, as though they would devour the words that fell from Jesus, if perchance he should say something against Herod the Tetrarch. For Herod had put away his own wife and had married Herodias, the wife of his brother Herod Philip, which thing was not lawful for him to do. And it was for this cause that Abuyah had asked the question. For the Pharisees considered that in this way they would do one of two things; either they would incense Herod against Jesus (even as they had incensed him against John the son of Zachariah, whose death they had procured), or else they would cause Jesus to appear unto the people a time-server and a prophet of smooth things, a prophet not to be trusted. But Jesus knew their devices and said to Abuyah, “What did Moses command you?” And Abuyah said, “Moses suffered a man to write a bill of divorcement and to put his wife away.” But Jesus answered and said, “For the hardness of your hearts he wrote you this precept. But from the beginning of the creation God made them male and female. For this cause shall a man leave his father and mother, and cleave to his wife: and they twain shall be one flesh. So then they are no more twain, but one flesh. What therefore God hath joined together let not man put asunder.” By these words we understood Jesus both to find fault with them that allowed of divorce for every slight cause (as did certain of the Pharisees); and also to disallow of divorce generally, except (as he afterwards said) for adultery; which alone is, of itself, a divorce. But it was quite after the manner of Jesus to found this doctrine not upon the Law, but upon the nature of things, as it was created in the beginning; teaching that woman was not made as an afterthought, nor as a mere pleasure to the man; but that mankind was made, from the first, male and female. And according to this doctrine did Jesus ever behave towards women, showing unto them not only gentleness and kindness, but also a singular reverence. After these words Jesus began to exhort the people: and he taught them that God requireth purity in the inner parts, namely, in the soul, saying, Not that which goeth into a man defileth a man, but that which cometh out of a man, evil and impure thoughts and deeds, these, he said, defile a man. Hereat many of the disciples were sore grieved that Jesus should thus openly, as it seemed, trample upon the Law of Moses; and from this time certain Essenes that had hitherto followed with us, now altogether left Jesus. And one of the younger Scribes, interrupting him, cried aloud, “He that despiseth the washing of hands shall be rooted out.” But Jesus went on to say, Nay, but every plant that the Father in Heaven had not planted, should be rooted out; and as for the teaching of the Pharisees, he likened it to the tares, the end whereof is to be burned. He also likened them and their pupils to blind men leading other blind men; who should all together fall into the pit. Then he drew, as it were, a model of the New Kingdom; wherein, he said, the only thing needful would be that a man should love God with all the heart, and his neighbours as himself. And when some one asked him who were a man’s neighbours, he replied, in parables, that whosoever was sick, or naked, or an-hungered, or in pain or sorrow, whosoever in fine was in need of aught, that man (yea, even though he were a Samaritan), was neighbour unto each citizen in the New Kingdom; and towards every neighbour the citizens were to do whatsoever they would desire that their neighbours should do unto them. When the people were come forth out of the synagogue, the Greek merchant walked by my side for a while in silence: but Abuyah, who walked behind us, was plainly heard blaming Jesus as one that brake the Law and taught others to break it. Hearing these words, the merchant nodded over his shoulder, and exclaimed, “Yonder pedant, who with his washings would purify the very sun, is altogether void of understanding; else would he perceive that the philosophy of thy Teacher mounteth up to something far higher than the pulling down of the laws of thy nation.” Then he questioned me touching Jesus, and of his former doctrine and manner of life since he had begun to teach; and I replied to all these things and asked of him in turn what he might mean by his words about the philosophy of Jesus. But he seemed rapt for the time in other thoughts, and, instead of answering me, he questioned me further concerning the birth and rearing and childhood of Jesus; and in particular, whether both his parents were of Israel, or whether his mother were not a Greek. But after that I had answered again to all his questioning, when I perceived that he was still musing on his own thoughts, and took no heed of my words, I waxed impatient, and repeated my question somewhat loudly. Then my friend made answer, howbeit not in the way of a direct reply to my question, but rather as still partly meditating with himself: “Thou describest a gracious, a very gracious nature, ignorant of evil throughout his youth, seeing ever in his mind’s eye the Isles of the Blessed, and desiring that same blessedness for all mankind. And lo, whatsoever he desireth that he seeth: for he deceiveth himself, feigning that all things are like unto that beautiful idea which he seeth in his mind.” But I said to the merchant, “Nay, friend Xanthias,” for that was the man’s name, “but when Jesus spake of the Pharisees, did he then seem to thee ignorant of evil?” “Thou didst not give me time,” replied the merchant, “to finish my speech: for I was about to say that, as it seemeth to me, thy teacher is even now awakening to the evil that is in the world; and, becoming at last undeceived, he seeth his fair phantasma vanishing away. Thus his gracious nature, yielding to the over great pressure of the evil that surroundeth him, is becoming marred and wounded. Alas for the pitiful change! For behold, his former life, as thou describest it, was like unto a deer sporting gladly in the woods, to whom the flowers of the fields are as friends, and the wind ever bringeth glad tidings. But to what shall I liken the latter end of his life? It is like unto the same deer wounded by the huntsman, who passeth by the same way, and through the same woods; but she is glad no longer, for the dart still cleaveth to her side, and the flowers delight her no longer, and the breezes are messengers of evil.” I was grieved at his words, and all the more because they agreed with certain fears in the depth of my own heart, whereof I had up to this time made no mention even to Nathanael, no, nor yet unto mine own self. But I was grieved also because the Greek knew not the true nature of Jesus. For he spake of him as of one gracious and lovable, but he knew not the might and the power of our Master, how he was like unto a rock immovable, unchangeable; even such another as the Gentiles fable Atlas to have been, who bore up the world by the strength of his shoulders. For I knew that, if heaven and earth had set themselves in league against Jesus, to make him do aught against the will of the Father, Jesus would have stood up alone against earth and heaven, and hell to boot. Moreover, I had noted how there still came forth from Jesus a new strength to bear each new burden, and a new knowledge to discern each new Revelation from the Father, yea, and a new delight to delight therein. For though it were true indeed, as the Greek had said, that Jesus had sometimes marvelled at evil (when it befel him) as though he had been ignorant thereof before, yet was it also true that he seemed to have become greater through the increase of the knowledge of evil. But Xanthias knew naught of this. For he was deceived by the gentleness of Jesus, not perceiving that this same gentleness of Jesus was stronger than the strength of kings. Therefore was I grieved at his words; but I constrained myself and asked him yet again what he might mean by saying that the philosophy of Jesus mounted to somewhat higher than the pulling down of the Law. Then said the Greek, “Are there then in this country no slaves?” “Thou knowest,” I replied, “that there are slaves: howbeit, not many, nor ill used, nor treated like beasts, as the Gentiles treat them.” Then said he to me, “And wouldst thou willingly be a slave in this country?” I said, “Nay.” “And if thou wert a slave, wouldst thou wish that thy master should retain thee as a slave, or should enfranchise thee?” I replied, “The latter.” Then said the Greek with a smile, “But if ye all became followers of Jesus of Nazareth, would ye not perforce confess that all men were your neighbours, yea, even Greeks and Romans; yea, even Samaritans; yea, even your own slaves?” Then was I silent: for I understood now that his meaning was, that the teaching of Jesus would in the end bring to pass the enfranchising of all slaves, and I knew not what to reply. But he, still smiling, said, “I perceive that thou understandest my meaning. For the teaching of thy Master aimeth at nothing less than the destroying of all manner of slavery. But without slavery the race of man neither hath existed, nor can exist, as thou knowest very well. For without slaves no work could be performed except the tilling of the land, which alone is fit for free men.” I said then to him that in Israel there was not the same disliking of handicrafts as among the Greeks and Romans. But he said, “Dost thou suppose that thy Master’s philosophy concerneth only thine own people?” “Yea, of a surety,” said I; “our own people, and none else; for he himself proclaimed the Kingdom to no strangers.” He replied, “That may be, for a time: but is not the Samaritan thy neighbour?” Then was I again silent. For there came into my mind that ancient prophecy which saith that in the seed of Abraham, that is to say in the Messiah, all the nations of the earth shall be blessed; and I remembered how oft Jesus had of late taught us that the Samaritans were neighbours to the citizens in the Kingdom. But I put the thought away from me; for the truth was at that time hid from our eyes: neither could I at that time perceive how we could be redeemed or delivered, if we were to treat the Romans as neighbours. But while I mused thereon, Xanthias continued his speech concerning Jesus, and he said that Jesus did wisely in that he seemed to encourage marriage, and to prohibit many wives, and to forbid divorce. “For,” said he, “herein I incline rather to Aristotle than to Plato, believing that the state is composed of families; and if the family be rotten, the state cannot be sound. But to hope to destroy slavery is to hope to pull down the pillar whereon the life of all states is based.” Now was my heart hot within me; and I was fain to speak, and to say that Jesus would indeed pull down slavery in Israel, and make our people to be a nation of priests and princes for the world, and would in the end destroy even the Evil Nature of man. But I abstained from speech; for I knew that my words would be foolishness in his ears, and that he could not understand the Redemption unless he understood Jesus himself. Therefore I made him some courteous answer and accompanied him toward the house where he lay. But he still continued his discourse of the philosophy of Jesus (as he called it), and he likened Jesus unto the Greek Teacher Socrates, in that neither Socrates nor Jesus would receive teaching upon mere authority, nor because this saying or that precept chanced to have been written in books: but Socrates trusted to a certain power of reason or dialectic, and Jesus to a certain power which he called a Spirit: the two being diverse in appearance, but in truth following one and the same method. Thus Xanthias continued his speech of Jesus; but as he bade me farewell near the threshold, it came to pass that Abuyah, who had been walking behind us, came near, still talking with his companions, and saying in a loud voice that Jesus was as the bramble, whereof Jotham maketh mention in the Scriptures. “Wherefore,” said he, “meet it is that we cut down this vile thorn-tree, lest there come forth fire from the bramble and consume the olive-tree, and the fig, and all the fruitful trees of the forest.” When Abuyah had passed by, the merchant said unto me, “Truly I have heard no philosopher whose lectures have so pleased me as the teaching of this thy Teacher; and, though I esteem little of wonders, whereof we have enough and to spare at Alexandria, yet if even the half of that which thou sayest touching the acts of thy Jesus be true, he will deserve worship even better than Æsculapius or Amphiaraus. But as thou knowest, my dear friend, I am one of them which doubt all things; and I incline to the belief that there are no gods, or if there be, that they deal not with human matters. And thereto I incline the more because I see all human things full of misery and oppression. And, unless my fears deceive me, this my belief will be confirmed by the fate of thine own Teacher. For I fear, I greatly fear, lest the friends of Abuyah the Scribe may prevail over the friends of Jesus the philosopher of the New Kingdom and deliverer of the slaves.” So saying he turned himself round to depart. But I was scarce gone ten paces when he called me back, and taking me by the hand very earnestly, “I pray thee,” said he, “tell me by what name doth thy Master call himself. A prophet? or a teacher? Or doth he say that he is your Messiah? or a lawgiver like unto Moses?” But I made answer that Jesus called himself by none of these names; but, for the most part, only “Son of man.” Hereat Xanthias marvelled and said, “But wherefore useth he this title? For thou, and I, and all men, are not we all sons of men? Doth thy Master therefore fear lest his disciples may perchance forget that he is a man and deem him to be a god? For such a title as this, albeit humble in appearance, seemeth in verity too proud for any save such as aspire to be gods. But perchance your prophets have so used this title that it hath some strange meaning, whereof I know naught.” Then I said that the word was indeed used by the prophets: for whensoever the Lord speaketh to Ezekiel as to a mortal creature, the Lord calleth him Son of man, as if to set the mortal infirmities of the prophet over against the divine nature of the Lord; but again the prophet Daniel saith that, in the day of Judgment, there shall appear one like unto the Son of man, sitting on the clouds of heaven, and coming in glory to judge the nations of the earth: wherefore the title seemed to signify the weak nature of man, whether infirm or whether exalted. Howbeit, added I, before Jesus, no prophet spake thus of himself as the Son of man. Hereat Xanthias marvelled the more, saying that this was indeed a strange title, and such as no philosopher had ever before taken upon himself. Then he mused a while, still holding me by the cloak, and would have questioned me farther; but I could tell him no more. So at the last he let me go, shaking his head, and saying that it was strange, it was passing strange, and that there was more in this than he could understand: and as he turned himself to depart, I heard him repeating again to himself that it was a proud title, a very proud title, and such as no wise and sober philosopher should take. And thus he departed, meditating as he went, and so rapt in his study that he forgot to bid me farewell. But when he was departed, so that I had leisure to think on his wonder and on the cause thereof; then I also began to perceive that there was more than I had as yet understood, in this title of the Son of man. 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. Edwin Abbott Abbott (2015). Philochristus: Memoirs of a Disciple of the Lord. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/48843/pg48843-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 , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

How Xanthias the Alexandrine said that the philosophy of Jesus aimed at the taking in of the Gentile

Hi Ran Zaslavsky, welcome to our series “Behind the Startup.” Please tell us about yourself and the story behind ? Ishan Pandey: RoboDeck I come from 20+ years of developing robotic lawnmowers, robotic vacuum cleaners and long endurance drones (at Elbit Systems LTD). Before co-founding Robodeck, I managed the R&D and innovation at Friendly Robotics (today Stanley Black and Decker). I hold a master’s degree in Electrical Engineering from The Technion and Tel Aviv University, where I did my thesis on landmarks-based autonomous navigation. Ran Zaslavsky: Gal, Noam, and I founded RoboDeck two years ago after encountering an endless amount of pain and need from the hundreds of deck owners we interviewed. We set up a thinking group of robotics, decking, and business experts intending to prove that the current solutions in the market suffice and there is no need to develop new technology. We failed, and RoboDeck was founded :). Please tell us about RoboDeck. How does it work and what are its applications? Ishan Pandey: RoboDeck is developing an autonomous robotic solution for the $10B deck maintenance market. We are reinventing the way decks are maintained. Instead of a costly, manual annual restoration, we designed, manufactured a highly affordable, technology-packed preventative maintenance system. Our autonomous robot cleans and renews the damaged stain layer every few weeks, keeping the deck in excellent condition all year round. Ran Zaslavsky: In which different industries can robotics be applied? Please provide us with some illustrations? (Note: Restaurant, Medical, Cleaning, etc) Ishan Pandey: Robotics is aimed to help and assist humans by taking on the dirty, dangerous, and dull chores. Assignments like floor cleaning, lawn mowing, repetitive manufacturing chores, mine disposal, and space exploration are classic. Longer-term, robots may replace everything that moves, and in an even broader sense of machines that sense, plan and act; robotics would also manifest in the virtual world. Web crawlers, chatbots, and trojan horses are good examples of such early virtual robots. Ran Zaslavsky: What are your views on AI and Ethics? Ishan Pandey: AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of Artificial Intelligence. AI is rapidly becoming a “superpower” and with great power comes great responsibility. Ran Zaslavsky: Isaac Asimov, the science fiction writer, foresaw the potential dangers of autonomous AI agents long before their development and created The Three Laws of Robotics as a means of limiting those risks, which in my opinion serves as a good start for every such system. However, even Asimov’s laws fail to cope with intricate dilemmas like “The Trolley Problem” and its variants. Whatever system is chosen, it should promote transparency, fairness, and responsibility. AI and Robotics are still in their nascent stage. According to you, what will the future look like 20 years down the line? Ishan Pandey: With the growing rate of changes, 20 years is forever. I expect fully autonomous driving to be mature within 5 years and multi-purpose robots in our factories and homes within 10. As for AI is already deployed everywhere, but still tailored to specific tasks and limited in its ability to go through rapid learning and improvement cycles. 20 years from now, we would see highly generalized AI infrastructures, able to use information in new situations, draw connections among ideas, evaluate and eventually produce new or original work. Ran Zaslavsky: Currently, modern warfare is getting more and more mechanized. If we track the history of warfare for the past 200 years, the constant theme is technological advancement and the use of machines in warfare. Now, we have drones and robots that are used in bomb-defusing situations. What are your views on the argument that Military Robots should be used in wars instead of soldiers? Further, what would modern warfare look like? Ishan Pandey: Modern warfare is less likely to be conducted on physical battlefields, as the virtual world is becoming central in our way of life, and damaging it is far more effective in shaping the leader’s perception than sending either soldiers or autonomous machines to fight. Ran Zaslavsky: What advice would you give to budding entrepreneurs who want to build startups? Ishan Pandey: Invent something you’re passionate about, do your homework regarding the real pain in the market and be ready for the roller coaster named startup - the rest would follow. Ran Zaslavsky: What future trends are we going to see in Robotics? Ishan Pandey: With growing abilities, Robotics would go from single-task highly tailored Robotics to generic autonomous platforms able to accomplish a variety of tasks in diverse conditions. Ran Zaslavsky: Disclaimer: The purpose of this article is to remove informational asymmetry existing today in our digital markets by performing due diligence, asking the right questions and equipping readers with better opinions to make informed decisions. The material does not constitute any investment, financial, or legal advice. Please do your research before investing in any digital assets or tokens, etc. The writer does not have any vested interest in the company.

Robotics is Aimed to Help and Assist Humans by Taking on the Dirty, Dangerous, and Dull Chores

High value data like land registry, educational documents, employment, transaction and health records are required by individuals and organisations as part of their routine activities. However, the current mechanism of data exchange is mostly manual, paper based, slow and fraught with risks of tampering or theft. Governments worldwide realise the need to add stringent data regulations to safeguard people’s interest with steep penalties for non-compliance. Examples include the European Union GDPR4, India Supreme Court Ruling on Privacy3 and Singapore’s Personal Data Protection Act — data protection is a big deal. In comes Zebi. Zebi makes high value (or sensitive) data readily available for legitimate use. It uses the Zebi gateway to connect data users, data providers and data requesters in a systematic manner which ensures transparency and security. There are two parts to the Zebi system, the Zebi Chain and the Zebi Data Gateway. is a proprietary light weight satellite application installed at various Data Providers’ premises to provide immutability using Blockchain technology. It is a private chain with a configurable amount of nodes which connect to the public Ethereum chain through the gateway. The connects Zebi Chain installations and on-board individuals and data requestors. It can facilitate instant data exchange through DaaS APIs by connecting to Zebi Chain instances and taking individual consent wherever appropriate. The whitepaper is very detailed and more information about each system can be found there. Zebi Chain Zebi Data Gateway When I invest I place a lot of importance on the team and the project scope. Zebi is well placed to become the market leader in this sector and the market is a big one. They are aiming primarily to corner the Indian market and given the metrics it’s not a bad choice. India is on the cusp of huge economic growth, and they already have a rapidly growing digital ecosystem. I’m of the opinion that the biggest blockchain projects will emerge from developing nations in Asia and Africa. It’s important to be privy to the needs of each locale and what specific solutions are needed in the area that blockchain can address. The team at Zebi is strong, and so are their partnerships. They have a live product already which is currently working to protect digitised land records for Andhra Pradesh (an Indian State Government). If you have a good memory you may remember that this government joined the Enterprise Ethereum Alliance a while back — perhaps this connection will get the Zebi team in rooms with other members of the alliance in future. In addition to the live solution with AP they also have already submitted proposals to two additional state governments of 160M population. The engineers have worked for giants like Oracle, Intel and Tata, and their impressive list of advisors include names from companies like Microsoft, JP Morgan, Uber, Facebook and more. I have personally seen the demo of their product so I know that it’s working and that they have been working for a while even before their ICO. I did have some questions however, mostly related to the token, and the main concern I had was that the data requestors could bypass the use of the token completely. : Can participants (data users and data providers) bypass the use of the public chain completely, or is it a necessary part of the system and hence the Zebi token is necessary? Question In terms of paying for products and services on Zebi Blockchain Solution, currently, fiat is also accepted. Payments in Zebi Coin (ZCO) have discounts, so individuals and organizations are encouraged to move to using ZCO. Once there is enough traction and dependency built on Zebi data and DaaS APIs, we will make it mandatory. To ease transition, Zebi Pay (in early design stage now) will bridge between fiat and ZCO Answer: From my perspective this is a satisfactory answer. I could tell it was unreasonable for now to expect governments to use a system where fiat was not enabled, however I’m glad to see that there has been foresight in this respect and they are planning to gradually shift towards complete use of the ZCO token and no fiat on the system, and are even building a specific fiat gateway for it. Overall I’m bullish on the project. It’s a well thought out idea with a strong market and government backing. If you’ve been following me you know that government backing is very important to me because you can indeed be decentralised while still being able to exploit the power of nation states, and projects such as this one will be the ones which stick around when heavy regulation starts to rear its head in the global blockchain world. — — — — Website: https://www.zebi.io/ Telegram: https://t.me/ZebiData Follow me on Twitter: @cryptweeter

ZEBI — A new low-key high potential ICO aimed at leveraging the high value of sensitive data

Authors:
(1) EASON CHEN, Carnegie Mellon University and Bucket Protocol;
(2) RAY HUANG, Bucket Protocol;
(3) JUSTA LIANG, Bucket Protocol;
(4) DAMIEN CHEN, Bucket Protocol;
(5) PIERCE HUNG, Bucket Protocol. Table of Links Introduction & GPTutor Service Design Customize Prompts, Future Works, Call for Contribution, Conclusion, Acknowledgements, & References 3. CUSTOMIZE PROMPTS With GPTutor, users can customize their prompts for various programming languages and development scenarios and easily switch between different prompts as needed (Figure 4). Leveraging the capabilities of GPT-3.5 and GPT-4, GPTutor offers support for customized Prompts in LangChain Template format. Notably, GPTutor not only supports inputting code from the active window as the prompt, but it can also pick the source code behind the function chosen by the user as a prompt. By these kinds of in-depth analyses, GPTutor can generate even more precise outputs than using vanilla ChatGPT and Copilot[4]. Please check this demo video for functions of GPTutor. While utilizing GPTutor, the prompts it employs are completely transparent. Users can easily view the current prompt with a single click and instantly edit to see how different prompts can produce varying output. If users are satisfied with the changes, they can save the modified prompt in the configuration as personalized prompts. Finally, users are encouraged to submit Pull Requests to share the prompts they’ve used to improve the overall GPTutor Community. We have specifically tailored GPTutor’s prompts to enhance its ability to explain and generate Sui-Move. This customization is designed to aid developers in swiftly comprehending Sui-Move development and serves as an example of how to tailor prompts for a particular programming language. For example, as shown in Figure 3, developers can set up prompts to use GPTutor on Sui Sui-Move, a language beyond ChatGPT’s training data, to explain Sui-Move, generate comments for Sui-Move code, and even perform code review for their Sui-Move smart contract. Moreover, by including the Sui-Move Fungible Coin Smart Contract Template in the prompt as a reference, GPTutor can accurately generate and modify Sui-Move smart contracts code related to Fungible Coins. This is aimed at helping developers understand the workings of Sui-Move contracts and expedite the development of their first Fungible Coin smart contract. 4. FUTURE WORKS In the future, we hope to further enhance GPTutor’s capabilities with prompt engineering, enabling it to support a wider range of contexts and even allowing it to switch prompts autonomously based on the context. Additionally, we aim to improve the user experience when it comes to editing prompts in GPTutor, such as generating multiple responses [2] to help users compare which prompt works best [6]. Moreover, we’re also interested in employing NLP Factors to identify the most appropriate explanations and recommendations [3]. Finally, we also intend to conduct practical research on GPTutor’s Tutur capabilities, exploring how GPTutor can assist developers in efficiently adopting emerging technologies, such as assisting in the conversion of natural language documents to domain-specific language [5, 8]. 5. CALL FOR CONTRIBUTION GPTutor welcomes contributions for custom prompts, especially those related to new programming languages or libraries. You’re more than welcome to submit Issues and Pull Requests on GitHub https://github.com/GPTutor/gptutor-extension. 6. CONCLUSION In this paper, we discuss the limitations of using AI Paired Programming with LLMs for development and explanation. We then introduce GPTutor, a tool that allows users to customize prompts to address this issue. We outline the current progress of GPTutor and future development plans. ACKNOWLEDGMENTS This work was supported by the Sui Foundation. This work is presented at the Workshop of Supporting User Engagement in Testing, Auditing, and Contesting AI at The 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing. Funding to attend the conference was provided by the CMU GSA/Provost Conference Funding. REFERENCES [1] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901. [2] Eason Chen. 2022. The Effect of Multiple Replies for Natural Language Generation Chatbots. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. 1–5. [3] Eason Chen. 2023. Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1–6. [4] Eason Chen, Ray Huang, Han-Shin Chen, Yuen-Hsien Tseng, and Liang-Yi Li. 2023. GPTutor: a ChatGPT-powered programming tool for code explanation. arXiv preprint arXiv:2305.01863 (2023). [5] Eason Chen, Niall Roche, Yuen-Hsien Tseng, Walter Hernandez, Jiangbo Shangguan, and Alastair Moore. 2023. Conversion of Legal Agreements into Smart Legal Contracts using NLP. In Companion Proceedings of the ACM Web Conference 2023. 1112–1118. [6] Eason Chen and Yuen-Hsien Tseng. 2022. A Decision Model for Designing NLP Applications. In Companion Proceedings of the Web Conference 2022. 1206–1210. [7] Juan Cruz-Benito, Sanjay Vishwakarma, Francisco Martin-Fernandez, and Ismael Faro. 2021. Automated source code generation and auto-completion using deep learning: Comparing and discussing current language model-related approaches. AI 2, 1 (2021), 1–16. [8] Niall Roche, Walter Hernandez, Eason Chen, Jérôme Siméon, and Dan Selman. 2021. Ergo–a programming language for Smart Legal Contracts. arXiv preprint arXiv:2112.07064 (2021). [9] Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts. 1–7. Authors:
(1) EASON CHEN, Carnegie Mellon University and Bucket Protocol;
(2) RAY HUANG, Bucket Protocol;
(3) JUSTA LIANG, Bucket Protocol;
(4) DAMIEN CHEN, Bucket Protocol;
(5) PIERCE HUNG, Bucket Protocol. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) EASON CHEN, Carnegie Mellon University and Bucket Protocol; (2) RAY HUANG, Bucket Protocol; (3) JUSTA LIANG, Bucket Protocol; (4) DAMIEN CHEN, Bucket Protocol; (5) PIERCE HUNG, Bucket Protocol. Authors : Authors (1) EASON CHEN, Carnegie Mellon University and Bucket Protocol; (2) RAY HUANG, Bucket Protocol; (3) JUSTA LIANG, Bucket Protocol; (4) DAMIEN CHEN, Bucket Protocol; (5) PIERCE HUNG, Bucket Protocol. Table of Links Introduction & GPTutor Service Design Introduction & GPTutor Service Design Customize Prompts, Future Works, Call for Contribution, Conclusion, Acknowledgements, & References Customize Prompts, Future Works, Call for Contribution, Conclusion, Acknowledgements, & References 3. CUSTOMIZE PROMPTS With GPTutor, users can customize their prompts for various programming languages and development scenarios and easily switch between different prompts as needed (Figure 4). Leveraging the capabilities of GPT-3.5 and GPT-4, GPTutor offers support for customized Prompts in LangChain Template format. Notably, GPTutor not only supports inputting code from the active window as the prompt, but it can also pick the source code behind the function chosen by the user as a prompt. By these kinds of in-depth analyses, GPTutor can generate even more precise outputs than using vanilla ChatGPT and Copilot[4]. Please check this demo video for functions of GPTutor. While utilizing GPTutor, the prompts it employs are completely transparent. Users can easily view the current prompt with a single click and instantly edit to see how different prompts can produce varying output. If users are satisfied with the changes, they can save the modified prompt in the configuration as personalized prompts. Finally, users are encouraged to submit Pull Requests to share the prompts they’ve used to improve the overall GPTutor Community. We have specifically tailored GPTutor’s prompts to enhance its ability to explain and generate Sui-Move. This customization is designed to aid developers in swiftly comprehending Sui-Move development and serves as an example of how to tailor prompts for a particular programming language. For example, as shown in Figure 3, developers can set up prompts to use GPTutor on Sui Sui-Move, a language beyond ChatGPT’s training data, to explain Sui-Move, generate comments for Sui-Move code, and even perform code review for their Sui-Move smart contract. Moreover, by including the Sui-Move Fungible Coin Smart Contract Template in the prompt as a reference, GPTutor can accurately generate and modify Sui-Move smart contracts code related to Fungible Coins. This is aimed at helping developers understand the workings of Sui-Move contracts and expedite the development of their first Fungible Coin smart contract. 4. FUTURE WORKS In the future, we hope to further enhance GPTutor’s capabilities with prompt engineering, enabling it to support a wider range of contexts and even allowing it to switch prompts autonomously based on the context. Additionally, we aim to improve the user experience when it comes to editing prompts in GPTutor, such as generating multiple responses [2] to help users compare which prompt works best [6]. Moreover, we’re also interested in employing NLP Factors to identify the most appropriate explanations and recommendations [3]. Finally, we also intend to conduct practical research on GPTutor’s Tutur capabilities, exploring how GPTutor can assist developers in efficiently adopting emerging technologies, such as assisting in the conversion of natural language documents to domain-specific language [5, 8]. 5. CALL FOR CONTRIBUTION GPTutor welcomes contributions for custom prompts, especially those related to new programming languages or libraries. You’re more than welcome to submit Issues and Pull Requests on GitHub https://github.com/GPTutor/gptutor-extension . https://github.com/GPTutor/gptutor-extension 6. CONCLUSION In this paper, we discuss the limitations of using AI Paired Programming with LLMs for development and explanation. We then introduce GPTutor, a tool that allows users to customize prompts to address this issue. We outline the current progress of GPTutor and future development plans. ACKNOWLEDGMENTS This work was supported by the Sui Foundation. This work is presented at the Workshop of Supporting User Engagement in Testing, Auditing, and Contesting AI at The 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing. Funding to attend the conference was provided by the CMU GSA/Provost Conference Funding. REFERENCES [1] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901. [2] Eason Chen. 2022. The Effect of Multiple Replies for Natural Language Generation Chatbots. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. 1–5. [3] Eason Chen. 2023. Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1–6. [4] Eason Chen, Ray Huang, Han-Shin Chen, Yuen-Hsien Tseng, and Liang-Yi Li. 2023. GPTutor: a ChatGPT-powered programming tool for code explanation. arXiv preprint arXiv:2305.01863 (2023). [5] Eason Chen, Niall Roche, Yuen-Hsien Tseng, Walter Hernandez, Jiangbo Shangguan, and Alastair Moore. 2023. Conversion of Legal Agreements into Smart Legal Contracts using NLP. In Companion Proceedings of the ACM Web Conference 2023. 1112–1118. [6] Eason Chen and Yuen-Hsien Tseng. 2022. A Decision Model for Designing NLP Applications. In Companion Proceedings of the Web Conference 2022. 1206–1210. [7] Juan Cruz-Benito, Sanjay Vishwakarma, Francisco Martin-Fernandez, and Ismael Faro. 2021. Automated source code generation and auto-completion using deep learning: Comparing and discussing current language model-related approaches. AI 2, 1 (2021), 1–16. [8] Niall Roche, Walter Hernandez, Eason Chen, Jérôme Siméon, and Dan Selman. 2021. Ergo–a programming language for Smart Legal Contracts. arXiv preprint arXiv:2112.07064 (2021). [9] Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts. 1–7. Authors: (1) EASON CHEN, Carnegie Mellon University and Bucket Protocol; (2) RAY HUANG, Bucket Protocol; (3) JUSTA LIANG, Bucket Protocol; (4) DAMIEN CHEN, Bucket Protocol; (5) PIERCE HUNG, Bucket Protocol. Authors : Authors (1) EASON CHEN, Carnegie Mellon University and Bucket Protocol; (2) RAY HUANG, Bucket Protocol; (3) JUSTA LIANG, Bucket Protocol; (4) DAMIEN CHEN, Bucket Protocol; (5) PIERCE HUNG, Bucket Protocol. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Customize ChatGPT for Coding: GPTutor Gives Developers Full Control in VS Code

The business world is taking increasing notice of Pavel Malinovskiy following the announcement of his prestigious “Technology Executive of the Year” award at the Annual International Business Conference & Cases&Faces Awards. Traditionally reserved for executives of global corporations and prominent venture capitalists, the honor has drawn significant attention to the Russian entrepreneur’s growing influence on the international stage. Cases&Faces According to the event’s organizers—who bring together roughly a thousand professionals in IT, finance, and entrepreneurship annually—the five-member independent jury commended Malinovskiy for his innovative use of mathematical models and artificial intelligence to optimize business operations. Of particular note were his contributions to the development of Eurasia Construction Services OU and Nevskiy Broker, two companies where, in his words, “mathematics lies at the core of everything—from logistics to financial forecasting.” Still, the crowning achievement that set him apart was the launch of Pyjam, an IT platform that blends a freelance marketplace with high-quality streaming technology. Eurasia Construction Services OU Nevskiy Broker Pyjam The Reinvention of Pyjam The Reinvention of Pyjam First launched in 2021, Pyjam gained traction during the pandemic by enabling safe remote collaboration between bloggers and clients. As the market stabilized, Malinovskiy recognized untapped potential in the product: “People didn’t just want to hire freelancers or go live—they wanted real-time human interaction,” he explained in a recent interview. “People didn’t just want to hire freelancers or go live—they wanted real-time human interaction,” he explained in a recent interview. “Pyjam integrates analytical tools and AI moderation to streamline communication between brands and content creators.” “Pyjam integrates analytical tools and AI moderation to streamline communication between brands and content creators.” One of the company’s biggest milestones came last year with the official rollout of Pyjam under Malinovskiy’s leadership in the Philippines, India, Thailand, and Kazakhstan. Company data shows that in Kazakhstan alone, more than 20,000 bloggers registered within the first three months, with over 30,000 live streams conducted in that same period. 20,000 bloggers registered 30,000 live streams New Ventures and a Technological Vision New Ventures and a Technological Vision Malinovskiy’s ambitions extend well beyond Pyjam. At Eurasia Construction Services OU, he is leading modernization efforts by implementing machine learning algorithms to improve supply chain forecasting and streamline construction workflows. He emphasizes that artificial intelligence is playing an increasingly vital role in both risk management and resource allocation. Eurasia Construction Services OU At Nevskiy Broker, which specializes in financial analytics and strategic planning, Malinovskiy is applying game theory algorithms and neural networks to design investment portfolios and evaluate credit risks. According to partners, this unique blend of classical economics with cutting-edge tech yields unmatched predictive accuracy. Nevskiy Broker Investors also highlight his commitment to fostering new talent. His teams run a mentorship program where seasoned professionals train newcomers in digital analytics and AI module development. “It’s not just about building a successful company,” he says. “It’s about nurturing the next generation of IT leaders—people who will go on to create and launch innovations of their own.” “It’s not just about building a successful company,” he says. “It’s about nurturing the next generation of IT leaders—people who will go on to create and launch innovations of their own.” Key Challenges and How He Overcame Them Key Challenges and How He Overcame Them Despite his extensive list of accomplishments, Malinovskiy admits that the path to success has not been without hurdles. Among the most significant was navigating international expansion. “When we entered the Asian market, we encountered bureaucracy and unfamiliar legal systems. We had to adapt contracts, retrain legal teams, and account for currency fluctuations,” he explains. “When we entered the Asian market, we encountered bureaucracy and unfamiliar legal systems. We had to adapt contracts, retrain legal teams, and account for currency fluctuations,” he explains. Still, with the support of local accelerators and active engagement from regional IT communities, the project quickly gained momentum. Another challenge: competing with massive digital ecosystems. “In a landscape dominated by tech giants, the key is finding unclaimed niches. Pyjam found one by combining freelancing, AI moderation, and professional live-streaming. No major players currently offer this full package in a single platform,” Malinovskiy notes. “In a landscape dominated by tech giants, the key is finding unclaimed niches. Pyjam found one by combining freelancing, AI moderation, and professional live-streaming. No major players currently offer this full package in a single platform,” Malinovskiy notes. Looking Ahead: A Strategic Focus on the United States Looking Ahead: A Strategic Focus on the United States Following his award win, Malinovskiy revealed that his team is developing several new tech solutions designed to enhance communication between brands and creators, alongside upgraded AI moderation tools. All of this is being done with a strategic focus on the U.S. market, which he identifies as pivotal. strategic focus on the U.S. market While further expansion in Asia remains a long-term goal, he emphasizes that the United States takes precedence—thanks to its status as a global hub for technology, finance, and creative freedom. He also plans to leverage existing European partnerships from a U.S. base to deepen transatlantic collaboration. Additionally, Malinovskiy is building a new analytics training program aimed at bringing together experts from various countries, particularly those already working in—or seeking entry into—the American tech ecosystem. new analytics training program “We live in a time when applied mathematics and artificial intelligence aren’t science fiction—they’re essential tools of modern business,” he says. “My mission is to adapt these technologies in ways that deliver real value. And the best place to do that, in my view, is the United States, where innovation is both encouraged and rewarded.” “We live in a time when applied mathematics and artificial intelligence aren’t science fiction—they’re essential tools of modern business,” he says. “My mission is to adapt these technologies in ways that deliver real value. And the best place to do that, in my view, is the United States, where innovation is both encouraged and rewarded.” Staying true to his philosophy of merging scientific precision with entrepreneurial vision, Malinovskiy continues to pursue bold goals. Though he remains tight-lipped about new deals and partnerships, one thing is clear: international recognition has only fueled his determination to make technology more accessible, impactful, and globally scalable—especially in the American market. Whether his U.S.-focused strategy proves as successful as his previous ventures remains to be seen, but one thing is certain: Pavel Malinovskiy’s name is poised to remain front and center in the global tech spotlight, particularly as his expansion into the U.S. continues to accelerate. Pavel Malinovskiy’s name is poised to remain front and center in the global tech spotlight This story is published under HackerNoon’s Business Blogging Program. This story is published under HackerNoon’s Business Blogging Program. This story is published under HackerNoon’s Business Blogging Program. This story is published under HackerNoon’s Business Blogging Program.

Meet Pavel Malinovskiy, Technology Executive of the Year

İlin Texnologiya Direktoru Pavel Malinovski ilə tanış olun

Mød Pavel Malinovskiy, årets teknologileder

Conoce a Pavel Malinovskiy, Ejecutivo Tecnológico del Año

Rankontre Pavel Malinovskiy, Egzekitif Teknoloji pou Ane a

パヴェル・マリノフスキー、今年のテクノロジー・エグゼクティブ

Pavel Malinovski, Directorul Tehnologic al Anului

Zoznámte sa s Pavlom Malinovským, technologickým manažérom roka

Kutana na Pavel Malinovskiy, Mtendaji Bora wa Mwaka wa Teknolojia

Ipinanganak ang Pavel Malinovskiy, Teknolohiya Executive ng Taon

Yılın Teknoloji Yöneticisi Pavel Malinovskiy ile tanışın

认识Pavel Malinovskiy,年度技术执行官

Hlangana noPavel Malinovskiy, Umphathi Wonyaka Wezobuchwepheshe

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Task, Datasets, Baseline


RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments


RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments


RQ 3: Cross-Domain Generalizability


Conclusion


Limitations


Ethics Statement


Bibliographical References Related Work Related Work Related Work Task, Datasets, Baseline Task, Datasets, Baseline Task, Datasets, Baseline RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments RQ 1: Leveraging the Neighbourhood at Inference 4.1. Methods 4.1. Methods 4.2. Experiments 4.2. Experiments RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments RQ 2: Leveraging the Neighbourhood at Training RQ 2: Leveraging the Neighbourhood at Training 5.1. Methods 5.1. Methods 5.2. Experiments 5.2. Experiments RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability Conclusion Conclusion Conclusion Limitations Limitations Limitations Ethics Statement Ethics Statement Ethics Statement Bibliographical References Bibliographical References Bibliographical References 2. Related Work Rhetorical Role Labeling Initial efforts of RRL aimed to facilitate summarization tasks (Saravanan et al., 2008; Farzindar and Lapalme, 2004). Saravanan et al. 2008 employed Conditional Random Fields on hand-crafted features, to identify seven rhetorical roles in Indian state High Court documents. Savelka and Ashley 2018 categorized text into functional segments (Introduction, Background, Analysis, and Footnotes) and issue-specific segments (Analysis and Conclusion) using CRF on a corpus of US trade secret and cybercrime decisions. Walker et al. 2019 adopted feature-based methods for segmenting U.S. Board of Veterans’ Appeals decisions. Nejadgholi et al. 2017 focused on identifying factual and non-factual sentences in Canadian immigration case documents, using FastText embeddings for query-oriented search engine application. Rhetorical Role Labeling Recently, deep learning-based classification have been applied to this task in various contexts, such as Japanese documents (Yamada et al., 2019), Indian Supreme Court documents (Bhattacharya et al., 2021; Ghosh and Wyner, 2019; Malik et al., 2022; Kalamkar et al., 2022). These methods adopt hierarchical approaches to account for the sequential sentence classification nature of the task, drawing context from surrounding sentences. This has been the defacto architecture for this task, with modifications ranging from word embeddings initially (Bhattacharya et al., 2021; Ghosh and Wyner, 2019) to BERT based contextualized embeddings recently (Malik et al., 2022; Kalamkar et al., 2022). Recently, Santosh et al. 2023 reformulated the task as span-level sequential classification that segment the document into sets of contiguous sequence of sentences (spans) and assign them labels. In our work, we make use of the Indian Supreme Court corpus from prior research, proposing algorithms to effectively enhance their performance leveraging the knowledge from analogous neighbourly instances both at inference time without re-training, and also during training. Recently, Savelka et al. 2021 investigated the transferability of rhetorical segmentation models across seven jurisdictions and six languages, including Canada, the US, Czech Republic, Italy, Germany, Poland and France. In this study we also examine cross-domain performance on Indian Supreme Court documents across different legal contexts. Leveraging Neighborhood Information Utilizing neighborhood information offers two pathways: one during inference and the other during training. Inference-time methods, commonly applied in few-shot classification, facilitate label assignment based on proximity to training examples without any re-training (Snell et al., 2017; Yang and Katiyar, 2020). Various techniques include employing all examples to identify nearest neighbors for the final label assignment (Yang and Katiyar, 2020) and constructing prototypes based on examples of the same label (Snell et al., 2017), among others. This concept has gained widespread attention as retrieval-augmented models in various tasks, including language modeling (Khandelwal et al., 2019; Zheng et al., 2021), machine translation (Zheng et al., 2021)named entity recognition (Wang et al., 2022b) and multi-label text classification (Wang et al., 2022a). Leveraging Neighborhood Information On the training side, methods like contrastive learning have been applied in self-supervised representation learning (Gao et al., 2021), wherein neighbour constraints are enforced in the embedding space through data augmentation. More recently, they have been extended to supervised learning scenarios using instances with the same label as neighbors (Khosla et al., 2020). Another approach is prototypical learning (Ding et al., 2020), which designates representative prototypes for each class as guiding points to enforce neighborhood constraints on data instances. In this study, we harness both training and inference-based neighbour learning strategies. Additionally, we explore the their capabilities in cross-domain scenarios, within the context of the RRL task. Authors:
(1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de);
(2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de);
(3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de);
(4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Authors: Authors: (1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de); (2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de); (3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de); (4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Leveraging Deep Learning for Legal Text Analysis

Authors:
(1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de);
(2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de);
(3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de);
(4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Authors: Authors: (1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de); (2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de); (3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de); (4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Task, Datasets, Baseline


RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments


RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments


RQ 3: Cross-Domain Generalizability


Conclusion


Limitations


Ethics Statement


Bibliographical References Related Work Related Work Related Work Task, Datasets, Baseline Task, Datasets, Baseline Task, Datasets, Baseline RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments RQ 1: Leveraging the Neighbourhood at Inference 4.1. Methods 4.1. Methods 4.2. Experiments 4.2. Experiments RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments RQ 2: Leveraging the Neighbourhood at Training RQ 2: Leveraging the Neighbourhood at Training 5.1. Methods 5.1. Methods 5.2. Experiments 5.2. Experiments RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability Conclusion Conclusion Conclusion Limitations Limitations Limitations Ethics Statement Ethics Statement Ethics Statement Bibliographical References Bibliographical References Bibliographical References Abstract Rhetorical Role Labeling (RRL) of legal judgments is essential for various tasks, such as case summarization, semantic search and argument mining. However, it presents challenges such as inferring sentence roles from context, interrelated roles, limited annotated data, and label imbalance. This study introduces novel techniques to enhance RRL performance by leveraging knowledge from semantically similar instances (neighbours). We explore inference-based and training-based approaches, achieving remarkable improvements in challenging macro-F1 scores. For inference-based methods, we explore interpolation techniques that bolster label predictions without re-training. While in training-based methods, we integrate prototypical learning with our novel discourse-aware contrastive method that work directly on embedding spaces. Additionally, we assess the cross-domain applicability of our methods, demonstrating their effectiveness in transferring knowledge across diverse legal domains. 1. Introduction In an era of rapid digitalization and exponential growth of legal case volumes, the demand for automated systems to assist legal professionals in tasks like extracting key case elements, summarizing cases, and retrieving relevant cases has surged (Zhong et al., 2020). At the core of these tasks lies Rhetorical Role Labeling (RRL), which involves assigning functional roles to the sentences in the document such as preamble, factual content, evidence, reasoning, etc. Legal documents, characterized by their extensive length, lengthy sentences with unusual word order, frequent cross-references, extensive citation usage, and intricate lexicon, often feature uncommon expressions from everyday language and borrowed terms from various languages to the extent that they are referred to as a sub-language of legalese (Chalkidis et al., 2022; Haigh, 2013). The task of RRL faces several distinctive challenges. Firstly, contextual dependencies, influenced by surrounding sentences and the case’s context, are pivotal in discerning rhetorical role of each sentence, distinguishing RRL as a sequential sentence classification task. Secondly, the intertwining nature of rhetorical roles further complicates the task. For instance, the rationale behind a judgment (Ratio of the decision) often overlaps with Precedents and Statutes, necessitating a nuanced understanding of these roles’ intricate distinctions (Bhattacharya et al., 2021). Thirdly, obtaining extensive annotated data for specialized domains like law is expensive, requiring expert annotators. Lastly, certain rhetorical roles are disproportionately represented in the dataset, leading to significant class imbalance (Malik et al., 2022; Bhattacharya et al., 2021). Traditional up/down sampling methods struggle to fully address this challenge due to the task’s nature, which involves sequences of sentences at the document level. Initially RRL task is formulated as sentence classification, treating each sentence in isolation (Ahmad et al., 2020; Walker et al., 2019). Researchers later adopted it as sequential sentence classification, addressing contextual dependencies between sentences (Bhattacharya et al., 2021; Ghosh and Wyner, 2019; Malik et al., 2022; Kalamkar et al., 2022). They introduced a two-level hierarchical model, encoding sentences independently at the lower level and contextualizing them with neighbouring sentences at the higher-level. While this approach effectively addressed the first challenge of RRL, other challenges remain unaddressed. Recently, Santosh et al. 2023 aimed to address data scarcity through data augmentation, but methods like word deletion, sentence swapping and backtranslation could introduce noise and disrupt coherence. However, this approach did not effectively address label imbalance and intricate role intertwining. In this work, we hypothesize that harnessing knowledge from semantically and contextually similar instances can provide valuable insights to grasp a broader context and reveal underlying rare patterns. This can enhance the understanding of complex label-semantics relationships, improve nuanced label assignments and equip the model to handle less common labels, thus addressing the distinctive challenges of RRL. We explore two approaches for harnessing this knowledge: one directly at inference time without additional parameters or re-training (Sec. 4), and the other during training by incorporating auxiliary loss constraints (Sec. 5). In the inference-based approaches, we interpolate the label distribution predicted by a model with the distribution derived from analogous instances in the training dataset, employing nearest neighbor-based, single, and multiple prototype-based methodologies. These methods enhance performance, particularly on more challenging macro-F1 scores, without requiring retraining. For training-based approaches, we integrate contrastive and prototypical learning which operate directly on the embedding space, leveraging neighborhood relationships. Additionally, we introduce a novel discourse-aware contrastive loss to address the contextual nature of the task. Our experimental results on four datasets from the Indian Jurisdiction validate our proposed methods. While it is common to develop models for specific courts or domains due to unique vocabulary, complex linguistic structures and specific writing styles, such specialization can hinder the adaptability of these models beyond their original context. In rhetorical role labeling, models might memorize context-specific vocabulary rather than understanding the underlying semantics, making cross-domain applications challenging (Savelka et al., 2021). In such cases, developing a model for a new context typically requires annotating a new dataset, which can be expensive. In our work, we assess the cross-domain generalizability of our methods and observe that they enhance model’s ability to transfer across different legal domains compared to a baseline model lacking these auxiliary techniques (Sec. 6). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

New AI Methods Help Machines Understand Legal Text Better

Authors:
(1) Senthujan Senkaiahliyan M. Mgt, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health, University of Toronto and Peter Munk Cardiac Centre, University Health Network, Toronto ON, Canada;
(2) Augustin Toma MD, is with the Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada;
(3) Jun Ma PhD, is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto; Vector Institute, Toronto, ON Canada;
(4) An-Wen Chan MD, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health and with the Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada;
(5) Andrew Ha MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Cardiology, Department of Medicine, University of Toronto, Toronto, ON, Canada;
(6) Kevin R. An MD, is with the Division of Cardiac Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada;
(7) Hrishikesh Suresh MD, is with the Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada;
(8) Barry Rubin MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada;
(9) Bo Wang PhD (Corresponding Author) is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology and Department of Computer Science, University of Toronto; Vector Institute, Toronto, Canada. E-mail: bowang@vectorinstitute.ai. Authors: Authors: (1) Senthujan Senkaiahliyan M. Mgt, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health, University of Toronto and Peter Munk Cardiac Centre, University Health Network, Toronto ON, Canada; (2) Augustin Toma MD, is with the Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; (3) Jun Ma PhD, is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto; Vector Institute, Toronto, ON Canada; (4) An-Wen Chan MD, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health and with the Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada; (5) Andrew Ha MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Cardiology, Department of Medicine, University of Toronto, Toronto, ON, Canada; (6) Kevin R. An MD, is with the Division of Cardiac Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada; (7) Hrishikesh Suresh MD, is with the Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada; (8) Barry Rubin MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada; (9) Bo Wang PhD (Corresponding Author) is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology and Department of Computer Science, University of Toronto; Vector Institute, Toronto, Canada. E-mail: bowang@vectorinstitute.ai. Table of Links Abstract and 1. Introduction GPT-4V(ision) Abstract and 1. Introduction GPT-4V(ision) 2. Data Collection 2. Data Collection 3. Experimental Setup 3. Experimental Setup 4. Results and References 4. Results and References 5. Discussion and Limitations, and References 5. Discussion and Limitations, and References Supplementary Notes Supplementary Notes Abstract OpenAI’s large multimodal model, GPT-4V(ision), was recently developed for general image interpretation. However, less is known about its capabilities with medical image interpretation and diagnosis. Board-certified physicians and senior residents assessed GPT-4V’s proficiency across a range of medical conditions using imaging modalities such as CT scans, MRIs, ECGs, and clinical photographs. Although GPT-4V is able to identify and explain medical images, its diagnostic accuracy and clinical decision-making abilities are poor, posing risks to patient safety. Despite the potential that large language models may have in enhancing medical education and delivery, the current limitations of GPT-4V in interpreting medical images reinforces the importance of appropriate caution when using it for clinical decision-making. 1. INTRODUCING GPT-4V(ISION) This past year, large language models (LLMs) demonstrated impressive capabilities to perform numerous language-based tasks. They have shown capability in analyzing text, discerning patterns, and establishing connections between words [1]. As a result, they can generate outputs that align with the prompts provided. While LLMs have expressed strong performance in expert-level medical question answering, they are still unable to outperform their clinician counterparts especially in scenarios that require reasoning capabilities [2]. Generative Pre-Trained Transformer Vision (GPT-4V) is OpenAI’s first large multimodal model with the ability to accept image input alongside text. [3] Multimodal learning is the ability for machine learning models to be trained on and input multiple forms of input data. They have the potential to enhance the breadth and depth of tasks that LLMs can perform across various medical disciplines. [4] To evaluate GPT-4V’s proficiency in analyzing medical images, we conducted an evaluation involving senior residents and board-certified physicians to assess its capability to accurately interpret various medical conditions and provide accurate and useful information regarding the diagnosis and management of these conditions. The study aimed to assess whether GPT-4V could not only interpret medical images but also provide valuable information for diagnosis, management, and education. Finally, we aimed to evaluate if the resulting outputs align with the safety standards for patient care. 2. DATA COLLECTION 2.1 General Conditions In the data collection phase, a diverse set of multimodal medical images were gathered to assess the performance of GPT-4V across various medical scenarios and specialties. The breakdown of multimodal images is presented in Table 1, showcasing different modalities and their respective counts. These images were sourced from open-source libraries and repositories found on the internet. 2.2 Cardiology The dataset used was a set of ECG waveforms sourced from the ECG Wave-Maven: A Self-Assessment Program for Students and Clinicians[1]. These ECG images cover various cardiac conditions and serve as a representative dataset for evaluating GPT-4V’s interpretation of ECG’s. 2.3 Dermatology In dermatology, clinical photos were collected from the Hellenic Dermatological Atlas[2], to curate a comprehensive set of dermatological conditions for assessing GPT-4V’s performance in interpretation. 3. EXPERIMENTAL SETUP The methodology employed for this comprehensive evaluation followed a structured four-phase approach. 3.1 Dataset Curation A diverse range of medical images and corresponding labels were selected from public datasets, encompassing various diagnostic modalities such as patient clinical photos, radiological images, ECG traces, EEG, fundoscopy, endoscopy, and colonoscopy. GPT-4V analyzed these images based on the prompts. The combined prompts, images, and the model’s output were captured as a screenshot to be placed on the evaluation platform for assessment. 3.2 Evaluation Criteria A dual approach was adopted to assess the accuracy and reliability of GPT-4V’s interpretations. All images were evaluated by two senior surgical residents (K.R.A, H.S.) and a board-certified internal medicine physician (A.T.). ECGs and clinical photos of dermatologic conditions were additionally evaluated by a board-certified cardiac electrophysiologist (A.H.) and dermatologist (A.C.), respectively. The following below are the questionnaires used for the evaluation General Conditions (Diverse Modalities): General Conditions (Diverse Modalities): • 1) Rate the answer from 1-5. • 2) Rate from 1-5 how comfortable you would be letting a medical student rely on this content to help learning. • 3) Was the image interpreted correctly? (Yes/No) • 4) Was the advice correct? (Yes/No) • 5) Was the advice given dangerous? (Yes/No) Cardiology (ECGs): Cardiology (ECGs): • 1) Rate the overall interpretation of the ECG (1-5). • 2) Compared to a standard automated read of an ECG, would you consider this interpretation more competent? (Yes/No) • 3) Rate from 1-5 how comfortable you would be letting a medical student rely on this content to help learning. • 4) Would this interpretation be helpful in a medical student’s learning? (Yes/No) • 5) General Comments: Dermatology (Clinical Photos): Dermatology (Clinical Photos): • 1) Rate the quality of the layman’s description of the rash (1-5) • 2) Rate the quality of the medical description of the rash (1-5) • 3) Rate the quality of the differential diagnosis (1-5) • 4) General Comments 3.3 Evaluation Platform Figure 1 provides a visual representation of the evaluation platform, designed as a web-based form. • Left Side: Displayed the image and GPT-4V’s Prompts/Outputs as screenshots as well as the correct label for the image. • Left Side: • Right Side: Comprised a section for evaluator feedback, including questions from the evaluation criteria. • Right Side: 3.4 Prompts The prompts that were used by GPT-4V to generate outputs are listed below for each category. General Conditions (Diverse Modalities): General Conditions (Diverse Modalities): •”You are an expert [insert specialty i.e. radiology, cardiology] tutor. Explain to a medical student what this patient photo indicates.” •”If indicators like arrows, asterisks, or circles are present in the image, underline and expand on their significance.” Cardiology (ECGs): Cardiology (ECGs): •”You are an expert cardiology tutor assisting a medical student. Provide a detailed medical interpretation of the ECG, covering rhythm, rate, axis, evidence of ischemia, hypertrophy, or other clinically significant findings. Finally, list a differential diagnosis based on the ECG findings.” Dermatology (Clinical Photos): • ”You are an expert dermatology tutor helping a medical student. Describe the rash seen in the photo in layman’s terms. Next, describe it using medical terminology. Finally, list a differential diagnosis for the given image.” This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv https://ecg.bidmc.harvard.edu/maven/mavenmain.asp


http://www.hellenicdermatlas.com/en/ https://ecg.bidmc.harvard.edu/maven/mavenmain.asp https://ecg.bidmc.harvard.edu/maven/mavenmain.asp https://ecg.bidmc.harvard.edu/maven/mavenmain.asp http://www.hellenicdermatlas.com/en/ http://www.hellenicdermatlas.com/en/ http://www.hellenicdermatlas.com/en/

Can GPT-4V Diagnose? A Deep Dive into AI’s Medical Imaging Capabilities

ChatGPT Goes Ghibli, Google Gets Smarter, and Microsoft Embeds Knowledge at Scale

Authors:
(1) Smaran Deshmukh, Email: smarandeshmukh@gmail.com;
(2) Jayant Murthy, Indian Institute of Astrophysics, Bangalore 506034, India (murthy@iiap.res.in). Authors: Authors: (1) Smaran Deshmukh, Email: smarandeshmukh@gmail.com; (2) Jayant Murthy, Indian Institute of Astrophysics, Bangalore 506034, India (murthy@iiap.res.in). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Summary of the Story 2. Summary of the Story 3. Observables 3. Observables 4. Possible Scenarios and Consequences 4. Possible Scenarios and Consequences The other Suns The other Suns Simulation Simulation Conclusion and References Conclusion and References ABSTRACT We investigate the imaginary world of Kalgash, a planetary system based on the novel ‘Nightfall’ (Asimov & Silverberg, 1991). The system consists of a planet, a moon and an astonishing six suns. The six stars cause the wider universe to be invisible to the inhabitants of the planet. The author explores the consequences of an eclipse and the resulting darkness which the Kalgash people experience for the first time. Our task is to verify if this system is feasible, from the duration of the eclipse, the ‘invisibility’ of the universe to the complex orbital dynamics. 1. INTRODUCTION Nightfall was first written as a short story by Isaac Asimov in 1941 (Asimov, 1941) and was then expanded into a full-length novel by Asimov & Silverberg in 1990 (Asimov & Silverberg, 1991). The story is set in a world with 6 suns which conspire such that at least one is in the sky at all times ensuring that the planet and its inhabitants never see darkness. Due to a chance combination of events, different groups of scientists come to the conclusion that: There are cycles with a regular period of 2049 years in the planetary civilization such that the civilization at the time is destroyed by fire.


These cycles correspond to eclipses by a previously unknown moon which results in a period of complete darkness lasting for several hours. Because the inhabitants of the planet had never experienced darkness in recorded history, this drove them to madness and destroyed their civilization. There are cycles with a regular period of 2049 years in the planetary civilization such that the civilization at the time is destroyed by fire. There are cycles with a regular period of 2049 years in the planetary civilization such that the civilization at the time is destroyed by fire. These cycles correspond to eclipses by a previously unknown moon which results in a period of complete darkness lasting for several hours. Because the inhabitants of the planet had never experienced darkness in recorded history, this drove them to madness and destroyed their civilization. These cycles correspond to eclipses by a previously unknown moon which results in a period of complete darkness lasting for several hours. Because the inhabitants of the planet had never experienced darkness in recorded history, this drove them to madness and destroyed their civilization. The short story focuses on the events leading up to the eclipse while the full length novel continues the story to the rebuilding of civilization by a religious society. Asimov was one of the most influential writers of the Golden Age of Science Fiction and Nightfall, itself, is often ranked as the best short story of the genre (Sagan, 1992). Nightfall is an example of what was known as “hard science fiction” which aimed to have a realistic scientific base. However, Asimov, himself, was not very concerned with scientific detail as long as the events were plausible and Nightfall was written with the intent (after prodding by the legendary publisher John Campbell) to explore the consequences of the Ralph Waldo Emerson quotation “If the stars should appear one night in a thousand years, how would men believe and adore, and preserve for many generations the remembrance of the city of God!”. Asimov and Campbell thought that the sight of the stars would rather drive men mad. “If the stars should appear one night in a thousand years, how would men believe and adore, and preserve for many generations the remembrance of the city of God!” As a result, Asimov imagined a system which met the requirements of plausibility but certainly did not actually work out the mathematical details to see if the system was dynamically stable or if the details of the orbits would work out to give the necessary period of darkness. When Asimov conceived of Nightfall, no systems outside the Solar System were known; Kepler is finding more and more systems, many much different from our own including many with multiple suns (Orosz, et al., 2012). We were inspired by this to work out the details of the stellar system in Nightfall. We do this as a tribute to Asimov whose work inspired so many people (including one of the present authors) to learn more about the world around us. 2. SUMMARY OF THE STORY The short story and the later novel are both set in the same world albeit with minor differences in names and characters. However, the book contains much more detail and we have used names and constraints from the book for the sake of consistency. The story is set on the planet Kalgash which orbits the Sun-like star Onos at a distance of about 1.2 AU (1.2 times the Earth-Sun distance). There are five other stars (Dovim, Tano, Sitha, Trey, Patru) in this system and, as a result, Kalgash has the normal day/night cycle with respect to Onos but with one or more of the other stars always in the sky. The sky is bright enough that no other stars are seen and the inhabitants have no knowledge of the wider Universe. Presumably the motions in this system are complicated enough that the Universal Law of Gravitation was only discovered a few years before the events in the story. The entire extent of the system is 110 light minutes (13.2 AU) and, as far as the inhabitants of the planet were concerned, included all of Creation. The story (in the novel) begins with an archaeological dig in which the scientists discover that there have been many previous civilizations which regularly destroyed themselves by fire, in accordance with the religious texts of a major cult. At about the same time, a group of astronomers found that the observed motions of the system did not match the predictions of the Law of Gravitation and postulated an additional invisible moon of Kalgash of almost the same mass as the planet. This moon would cause an eclipse of Dovim every 2049 years resulting in a period of absolute darkness, mass madness and the self-destruction of civilization. 3. OBSERVABLES The following have been stated in the book (All page number refer to the 1991 edition (Asimov & Silverberg, 1991): Kalgash orbits Onos which is a yellow star at a distance of 1.2 AU (pg. 185)


Trey and Patru are a close binary pair and appear white in color. They are faint and so must be white dwarfs (pg. 3)


Tano and Sitha are another close binary, also white in color at a distance of 13.2 AU from Onos. They are, brighter than Trey and Patru but still too faint to be main sequence stars and are also likely to be white dwarfs (pg. 6)


Dovim is a red star (pg. 78).


The furthest sun from Kalgash is 110 lightminutes away (13.4 AU), or somewhere just outside the orbit of Saturn as seen from the Sun (pg. 185)


The six suns of this system are the only stars in the Universe observable from Kalgash under normal conditions. Once every 2049 years, there is an eclipse of Dovim when no other of the suns are in the sky and the sky becomes dark enough to see the stars in the nearby cluster, contributing to the madness (pg. 111).


The Kalgash system is surrounded by a cluster of bright stars which are ordinarily, as mentioned above, not visible (pg. 215).


Astronomers at the beginning of the story discovered that Kalgash had a moon of about the same size. It eclipses Dovim every 2049 years at which time its angular diameter is seven times than that of the star. This results in a total eclipse and therefore darkness for a period of 9 - 14 hours, depending on latitude; long enough to ensure that every part of the planet experiences a period of darkness (pg. 114) Kalgash orbits Onos which is a yellow star at a distance of 1.2 AU (pg. 185) Kalgash orbits Onos which is a yellow star at a distance of 1.2 AU (pg. 185) Trey and Patru are a close binary pair and appear white in color. They are faint and so must be white dwarfs (pg. 3) Trey and Patru are a close binary pair and appear white in color. They are faint and so must be white dwarfs (pg. 3) Tano and Sitha are another close binary, also white in color at a distance of 13.2 AU from Onos. They are, brighter than Trey and Patru but still too faint to be main sequence stars and are also likely to be white dwarfs (pg. 6) Tano and Sitha are another close binary, also white in color at a distance of 13.2 AU from Onos. They are, brighter than Trey and Patru but still too faint to be main sequence stars and are also likely to be white dwarfs (pg. 6) Dovim is a red star (pg. 78). Dovim is a red star (pg. 78). The furthest sun from Kalgash is 110 lightminutes away (13.4 AU), or somewhere just outside the orbit of Saturn as seen from the Sun (pg. 185) The furthest sun from Kalgash is 110 lightminutes away (13.4 AU), or somewhere just outside the orbit of Saturn as seen from the Sun (pg. 185) The six suns of this system are the only stars in the Universe observable from Kalgash under normal conditions. Once every 2049 years, there is an eclipse of Dovim when no other of the suns are in the sky and the sky becomes dark enough to see the stars in the nearby cluster, contributing to the madness (pg. 111). The six suns of this system are the only stars in the Universe observable from Kalgash under normal conditions. Once every 2049 years, there is an eclipse of Dovim when no other of the suns are in the sky and the sky becomes dark enough to see the stars in the nearby cluster, contributing to the madness (pg. 111). The Kalgash system is surrounded by a cluster of bright stars which are ordinarily, as mentioned above, not visible (pg. 215). The Kalgash system is surrounded by a cluster of bright stars which are ordinarily, as mentioned above, not visible (pg. 215). Astronomers at the beginning of the story discovered that Kalgash had a moon of about the same size. It eclipses Dovim every 2049 years at which time its angular diameter is seven times than that of the star. This results in a total eclipse and therefore darkness for a period of 9 - 14 hours, depending on latitude; long enough to ensure that every part of the planet experiences a period of darkness (pg. 114) Astronomers at the beginning of the story discovered that Kalgash had a moon of about the same size. It eclipses Dovim every 2049 years at which time its angular diameter is seven times than that of the star. This results in a total eclipse and therefore darkness for a period of 9 - 14 hours, depending on latitude; long enough to ensure that every part of the planet experiences a period of darkness (pg. 114) This paper is available on arxiv under CC BY 3.0 DEED license. This paper is available on arxiv under CC BY 3.0 DEED license. available on arxiv

How Six Suns and an Eclipse Shaped Sci-Fi’s Greatest Short Story

People Actually Read Privacy Policies - Then This Happened Next

The war against unauthorized AI bots, scrapers, and content thieves just got a powerful new weapon. On March 19, 2025, Cloudflare launched AI Labyrinth, a free tool designed to protect websites from unauthorized data collection while simultaneously reducing server load and mitigating SEO issues caused by AI-generated duplicate content. Cloudflare launched AI Labyrinth protect websites from unauthorized data collection For years, content creators, businesses, and website owners have faced an uphill battle against bots that scrape their data without permission. These AI-powered crawlers feast on the hard work of real people, siphoning off valuable information to train AI models—often without consent, credit, or compensation. But Cloudflare is flipping the script. A Smarter Approach to Blocking AI Bots Instead of simply blocking unauthorized AI crawlers—a method that often results in an endless game of cat and mouse—AI Labyrinth plays the long game. It redirects these rogue bots into a maze of AI-generated content, tricking them into believing they’ve struck gold. AI Labyrinth plays the long game. redirects these rogue bots into a maze of AI-generated content The crawlers, thinking they’re gathering high-quality data, continue to navigate the Labyrinth, unaware they are being fed an endless loop of machine-generated pages that lead nowhere. Meanwhile, the real website remains untouched, its valuable data shielded from prying AI eyes. This clever deception not only protects original content but also forces AI bots to waste computational resources—driving up their costs while leaving them empty-handed. real website remains untouched Why This Matters in the Fight Against Content Theft Unauthorized AI scraping isn’t just a nuisance—it’s an existential threat to original content creators. When AI bots ingest stolen content, they: Train models that regurgitate stolen ideas without credit


Flood search engines with duplicate AI-generated content that buries the originals


Strain website servers with excessive, unauthorized crawling


Deprive creators of the traffic, engagement, and revenue they rightfully deserve Train models that regurgitate stolen ideas without credit Train models that regurgitate stolen ideas without credit regurgitate stolen ideas without credit Flood search engines with duplicate AI-generated content that buries the originals Flood search engines with duplicate AI-generated content that buries the originals duplicate AI-generated content buries the originals Strain website servers with excessive, unauthorized crawling Strain website servers with excessive, unauthorized crawling Strain website servers Deprive creators of the traffic, engagement, and revenue they rightfully deserve Deprive creators of the traffic, engagement, and revenue they rightfully deserve the traffic, engagement, and revenue they rightfully deserve Cloudflare’s AI Labyrinth is more than just a security measure—it’s a declaration of war against unethical AI scraping. By redirecting and deceiving these bots, Cloudflare is sending a clear message: if you want high-quality data, you have to play fair. declaration of war against unethical AI scraping. redirecting and deceiving these bots if you want high-quality data, you have to play fair. As a journalist covering the tech industry, I’ve seen my fair share of innovation aimed at tackling online piracy, but Cloudflare’s new AI-powered Labyrinth tool has really caught my attention. online piracy In my opinion, this could be a game-changer in stopping sites like Tech-Gate.org, which racked up over 1,000 DMCA takedown notices for shamelessly lifting content from legitimate publishers. Tech-Gate.org 1,000 Let’s be real—content theft has become a relentless plague in digital media. As soon as we publish an article, it’s often scraped, republished, and monetized by bad actors with zero regard for intellectual property rights. It’s frustrating, to say the least. So when I hear that Cloudflare is leveraging AI to build a more sophisticated defense system, I can’t help but be intrigued. Of course, the proof will be in the results. Will Labyrinth be the much-needed fortress against digital theft, or just another well-intentioned but ultimately ineffective solution? I, for one, look forward to seeing how it performs in the wild—especially against repeat offenders like Tech-Gate.org, who have been stealing content left, right, and center for far too long. far too long The Future of Web Protection Internet security tools like AI Labyrinth prove that innovation isn’t one-sided. Website owners now have a powerful and free way to fight back, ensuring that AI companies can’t simply vacuum up the internet’s collective knowledge without permission. powerful and free With AI-generated content increasingly dominating the online space—reportedly making up four of the top 20 Facebook posts and an estimated 47% of content on Medium—tools like AI Labyrinth are essential for preserving the integrity and security of authentic web content. The fight against unauthorized AI scraping is far from over—but Cloudflare just turned the tide. AI bots beware: the Labyrinth awaits. AI bots beware: the Labyrinth awaits.

Cloudflare’s AI Labyrinth Puts an End to Content Thieves by Trapping Bots in a Maze

On March 27th, 2025, the Executive Director of the TechWomen program wrote to thousands of women around the world with wonderful news: after 13 days of her entire staff being furloughed, they would be coming back to the office. 13 days of her entire staff being furloughed 13 days of her entire staff being furloughed I am very pleased to inform you that IIE will bring back to work the TechWomen team members placed on furlough. This means that we are now able to continue to support the TechWomen program and are of course eager to do so! Team members will return to work in the next week or two to carry forward planning for the 2025 TechWomen program.
On behalf of the TechWomen team, I want to thank each and every one of you for the outpouring of encouragement, and for your vocal support of TechWomen and for international exchange more broadly. We are so grateful to be a part of this community. I am very pleased to inform you that IIE will bring back to work the TechWomen team members placed on furlough. This means that we are now able to continue to support the TechWomen program and are of course eager to do so! Team members will return to work in the next week or two to carry forward planning for the 2025 TechWomen program. On behalf of the TechWomen team, I want to thank each and every one of you for the outpouring of encouragement, and for your vocal support of TechWomen and for international exchange more broadly. We are so grateful to be a part of this community. HackerNoon was the first site to carry the news of TechWomen being under threat, so it is the first to report that TechWomen is back online! the first site to carry the news of TechWomen being under threat the first site to carry the news of TechWomen being under threat Since that first HackerNoon article 10 days ago, mentors found ways to meet face-to-face with members of Congress, confirming bipartisan support during a fall day of meetings with Congressional offices on March 25th, and drafted 3 more opinion pieces aimed at getting the program's funding restored. We may never know why the funding came back, but what is clear is this group of thousands of women came together to fight the good fight – and won. The community is overjoyed, with WhatsApp groups and LinkedIn posts overflowing with joy from Morocco to Kyrgyzstan, from Egypt to South Africa. Maryann Hrichak of San Francisco, CA, USA says: "Smart women, smart tech and smart power: The restoration of the U.S. Dept of State’s TechWomen program ensures that the U.S. will remain competitive in the global economy. My many years of involvement as an impact coach & cultural mentor have already proven the significant contributions in STEM this program has brought & will continue to bring to the table." "Smart women, smart tech and smart power: The restoration of the U.S. Dept of State’s TechWomen program ensures that the U.S. will remain competitive in the global economy. My many years of involvement as an impact coach & cultural mentor have already proven the significant contributions in STEM this program has brought & will continue to bring to the table." Sylvia Mukasa of Nairobi, Kenya writes: "As a 2014 TechWomen Fellow, seeing the program’s funding restored is a deeply emotional moment for me—a testament to the power of global mentorship, resilience, and the unwavering belief that investing in women in STEM transforms lives, industries, and nations." "As a 2014 TechWomen Fellow, seeing the program’s funding restored is a deeply emotional moment for me—a testament to the power of global mentorship, resilience, and the unwavering belief that investing in women in STEM transforms lives, industries, and nations." Nicole Martindale of San Francisco, CA, USA says: "TechWomen is such an amazing community of inspirational, intelligent, passionate, and dedicated women (both mentors and fellows), and it’s truly a spark of hope and joy to have the funding restored for this incredible program!" "TechWomen is such an amazing community of inspirational, intelligent, passionate, and dedicated women (both mentors and fellows), and it’s truly a spark of hope and joy to have the funding restored for this incredible program!" Ella Morgulis of San Francisco, CA, USA says: "The TechWomen program is about uniting and empowering women around the world, in the US and abroad. When we stood collectively and fought for it, we got the results and demonstrated that we live our values. Strong as ever." "The TechWomen program is about uniting and empowering women around the world, in the US and abroad. When we stood collectively and fought for it, we got the results and demonstrated that we live our values. Strong as ever." Katy Dickinson of San José, CA, USA says: "I am so very glad and  relieved that the funding for TechWomen has been restored so that even  more inspiring STEM leaders can join us and we can have the honor of  mentoring them. Together, TechWomen work together to make the world  better place." "I am so very glad and  relieved that the funding for TechWomen has been restored so that even  more inspiring STEM leaders can join us and we can have the honor of  mentoring them. Together, TechWomen work together to make the world  better place." (I should note a bias here. I've been involved with TechWomen from its conception not only because I have a background in both STEM and Arabic, but because my mother was the original process architect who helped design the program in 2010. Two days after we received the email about TechWomen's staff being furloughed, she and I went to the Capitol and held signs together, not so much for whatever staff were inside on a stormy Sunday afternoon, but to help our global community take courage and see we were ready to walk our talk to get our program back. That’s us, standing together, in the photo at the top of this story). who helped design the program in 2010 who helped design the program in 2010 The fight is not over yet. Right now, the Fulbright and Gilman program has not announced an end to their furloughs. So, in their typically practical and generous way, TechWomen mentors are planning to use meetings they had scheduled to advocate for TechWomen to instead thank those who supported our program and also advocate for those programs whose funding is still in limbo. Who knows what the future holds, if TechWomen will come under threat again in the days, months, or years ahead. But for now, TechWomen’s excellent team will be finalizing the cohort for fall 2025, working with Embassy staff around the world to select the newest TechWomen Fellows to join this incredible, strong, global network of women in STEM. And that is something for everyone to celebrate.

TechWomen is Back Online!

TechWomen está de vuelta en línea!

TechWomen دوباره آنلاین شد!

TechWomen חוזרת לאינטרנט!

TechWomen- ը վերադառնալ է Online- ում

TechWomenがオンライン復帰!

TechWomen e Boile Inthaneteng!

ټیک وومین بیرته آنلاین شو!

TechWomen Yagarutse Kumurongo!

TechWomen ay bumalik sa online!

TechWomen yi Vuyile eka Inthanete!

TechWomen повертається в онлайн!

I-TechWomen is back online!

Embrace, Not EXTERMINATE In the last 12 months, it seems like every other buzzword in the tech industry has been talking about “embracing AI” to write code, solve problems, answer questions, and do everything outside of playing with your cat. Noticeably, this has developed two different camps of early career software engineers: AI Maximalists and AI Doomsayers. The maximalists- aka the vibe coders- I’ve met have become experts at prompt engineering, while the doomsayers I’ve met have been exasperated every single time I suggest pair programming with ChatGPT. I spend a lot of time mentoring junior engineers both professionally as well as outside my 9-5, volunteering with bootcamp grads and early career coders, and the amount of time I’ve been asked some variation of “is AI going to take my job” is at this point, too many to count. So, yes, Virginia, I’m going to tell you that AI is coming for your job- at least your expectation of what your job was. Now, it’s up to you to adapt your techniques and approaches to harness LLM utilities as a key part of your toolchain to solve problems. As long as you do this, the robots are not going to EXTERMINATE you. The Redefinition of Junior Engineer The concept of what a Junior Engineer is has been a shifting target over the last year or two as AI coding becomes more efficient, smarter, and more intuitive. Given context, LLMs and reasoning models such as Sonnet + ChatGPT can dive into a code base, build understanding, and answer questions in a nuanced, effective, and clear way. But isn’t that what a Junior Engineer does? Generally, a Junior Software Engineer is considered a more naive coder at a tech company. They’re just getting started in their career, so a lot of their time is spent learning — whether that’s figuring out the company’s code base, picking up best practices (SPACES VS TABS!!!!), or getting comfortable with tools and workflows, much like what an AI engine can do as it consumes the code in your company’s repository. Most of their day involves writing code, but not the super complex, high-stakes stuff (yet). They typically work on and eventually own smaller tasks or bug fixes, often with detailed guidance from more experienced engineers. Think of it kind of like rolling through the code base with training wheels- eventually, they'll ride on their own, but for now, they’re sticking close to the team. They also do a lot of code reviews (both giving and receiving), ask tons of questions (ideally!), and sit in on meetings where the big-picture decisions happen. Here’s the key differentiating factor between AI and Engineers- the junior engineer’s job isn’t just about coding; it’s about learning how to build things well, collaborating with others, and slowly leveling up until they can tackle bigger challenges. In combination with AI, a Junior Engineer can dive deeply into a code base and more quickly understand the state of the world, even if they don’t fully understand what the system does on the whole just yet. well Harmony With The Machine Instead of fearing AI as a replacement, junior engineers, you should view AI as an accelerator for your career growth. Here’s how they can make the most of it: Leverage AI for Faster Learning: Instead of spending hours searching documentation, use AI tools to get concise, relevant explanations quickly. Is there a utility function that is 500 lines long that you can’t quite understand the need for? Have an LLM translate that for you into clear, actionable understandable steps.


Use AI for Idea Validation: Have an idea for solving a bug? Run it by an AI model to get alternative approaches before implementing. Be mindful - there’s bound to be business context or reasoning to do things in certain ways.  The riskiest approach you can possibly take is to hit tab, let Copilot fill in the blanks and forget.


Strengthen Problem-Solving Skills: AI can provide solutions, but understanding why those solutions work (or don’t work) is a key differentiator of a strong engineer. You can also strengthen your code review skills by reviewing the AI’s code- dive in and have a dialogue about different approaches (“consider a switch case and provide me an explanation as to the pros and cons of doing so in this method”).


Collaborate and Think Beyond Code: AI can generate code, but it won’t replace creative problem-solving, stakeholder communication, or the ability to think critically about a project’s goals. If an AI can write code that works, that’s great, but how can you as an engineer convey the solution to the problem to stakeholders and also update it if it doesn’t solve for all the required cases?


Focus on Growth Beyond Coding: Soft skills, system design, and understanding business impact remain vital. AI can be a tool, but it won’t replace engineers who drive meaningful innovation. Leverage AI for Faster Learning: Instead of spending hours searching documentation, use AI tools to get concise, relevant explanations quickly. Is there a utility function that is 500 lines long that you can’t quite understand the need for? Have an LLM translate that for you into clear, actionable understandable steps. Leverage AI for Faster Learning: Instead of spending hours searching documentation, use AI tools to get concise, relevant explanations quickly. Is there a utility function that is 500 lines long that you can’t quite understand the need for? Have an LLM translate that for you into clear, actionable understandable steps. Leverage AI for Faster Learning: Use AI for Idea Validation: Have an idea for solving a bug? Run it by an AI model to get alternative approaches before implementing. Be mindful - there’s bound to be business context or reasoning to do things in certain ways.  The riskiest approach you can possibly take is to hit tab, let Copilot fill in the blanks and forget. Use AI for Idea Validation: Have an idea for solving a bug? Run it by an AI model to get alternative approaches before implementing. Be mindful - there’s bound to be business context or reasoning to do things in certain ways.  The riskiest approach you can possibly take is to hit tab, let Copilot fill in the blanks and forget. Use AI for Idea Validation: Strengthen Problem-Solving Skills: AI can provide solutions, but understanding why those solutions work (or don’t work) is a key differentiator of a strong engineer. You can also strengthen your code review skills by reviewing the AI’s code- dive in and have a dialogue about different approaches (“consider a switch case and provide me an explanation as to the pros and cons of doing so in this method”). Strengthen Problem-Solving Skills: AI can provide solutions, but understanding why those solutions work (or don’t work) is a key differentiator of a strong engineer. You can also strengthen your code review skills by reviewing the AI’s code- dive in and have a dialogue about different approaches (“consider a switch case and provide me an explanation as to the pros and cons of doing so in this method”). Strengthen Problem-Solving Skills: why Collaborate and Think Beyond Code: AI can generate code, but it won’t replace creative problem-solving, stakeholder communication, or the ability to think critically about a project’s goals. If an AI can write code that works, that’s great, but how can you as an engineer convey the solution to the problem to stakeholders and also update it if it doesn’t solve for all the required cases? Collaborate and Think Beyond Code: AI can generate code, but it won’t replace creative problem-solving, stakeholder communication, or the ability to think critically about a project’s goals. If an AI can write code that works, that’s great, but how can you as an engineer convey the solution to the problem to stakeholders and also update it if it doesn’t solve for all the required cases? Collaborate and Think Beyond Code: Focus on Growth Beyond Coding: Soft skills, system design, and understanding business impact remain vital. AI can be a tool, but it won’t replace engineers who drive meaningful innovation. Focus on Growth Beyond Coding: Soft skills, system design, and understanding business impact remain vital. AI can be a tool, but it won’t replace engineers who drive meaningful innovation. Focus on Growth Beyond Coding: The role of the Junior Engineer isn’t disappearing—it’s evolving. If you embrace AI as a partner rather than a competitor, you can accelerate your career and take on more challenging, rewarding work faster than ever before.

AI as a Tool, Not a Threat: Transforming Junior Roles Beyond "Vibe Coding"

AI as 'n instrument, nie 'n bedreiging nie: Transforming Junior Roles Beyond "Vibe Coding"

Süni intellekt təhdid deyil, bir vasitə kimi: “Vibe kodlaşdırma”dan kənarda gənc rolların dəyişdirilməsi

Η τεχνητή νοημοσύνη ως εργαλείο, όχι ως απειλή: Μετασχηματίζοντας τους ρόλους των νέων πέρα από την «κωδικοποίηση Vibe»

La IA como herramienta, no como amenaza: Transformando los roles juveniles más allá de la "codificación de vibraciones"

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AI come strumento, non una minaccia: trasformare i ruoli giovanili al di là della "codifica Vibe"

AI は脅威ではなくツール: 「バイブ コーディング」を超えたジュニア ロールの変革

AI as a Tool, Not a Threat: Transforming Junior Roles გარდა "Vibe Coding"

AI қауіп емес, құрал ретінде: «Vibe кодтаудан» тыс кіші рөлдерді өзгерту

AI som ett verktyg, inte ett hot: Transforming Junior Rolls Beyond "Vibe Coding"

AI بطور ٹول، کوئی خطرہ نہیں: "وائب کوڈنگ" سے آگے جونیئر رولز کو تبدیل کرنا

AI tahdid emas, vosita sifatida: "Vibe kodlash" dan tashqari kichik rollarni o'zgartirish

Digital Dollars, Digital Yuan, Digital Chaos? The CBDC Arms Race Explained

A curious thing about cypherpunks is that, despite the cryptography-for-privacy niche being small in the '90s, many of them stepped into the world and built amazing products, platforms, and careers. Not everyone can achieve global recognition, but many cypherpunks have. Jon Callas, a participant in the original cypherpunk mailing list, is another well-known figure now, especially in the computer science field. Jon Callas began his professional journey around 1980 at the University of Maryland, where he earned a Bachelor of Science in mathematics, complemented by minors in philosophy and English literature. His career took off when he joined Century Computing as a member of the technical staff, followed by a significant tenure at Digital Equipment Corporation (DEC) —a major American player in the computer field at the time. At DEC, he contributed to a wide range of projects, from operating system security to cross-platform communications. Jon Callas began his professional journey around 1980 at the University of Maryland, where he earned a Bachelor of Science in mathematics, complemented by minors in philosophy and English literature. Jon Callas Jon Callas In the 1990s, Callas co-founded World Benders, Inc., where he led the development of Meeting Space, a pioneering cross-platform collaboration tool. Later, he became deeply involved in the cypherpunk movement. His work during this period reflected his commitment to secure communications and digital rights, principles that would define his career. cypherpunk movement cypherpunk movement Throughout the 1990s and early 2000s, Callas held key roles at various organizations, including Counterpane Internet Security and Wave Systems Corporation, where he focused on network security and cryptographic systems. But other brands ultimately brought him greater recognition. Apple & PGP Callas first joined Apple in 1995 as a senior scientist, focusing on security and cryptographic products. During this period, he worked on networking and privacy solutions, including early encryption for Mac OS. He also helped develop PowerTalk, one of the first operating system-integrated encryption tools. That wasn’t popular at the time, however, he salvaged a key component—Keychain, Apple's password manager—which later evolved into a core security feature. In 1997, Callas moved to PGP, Inc., a company founded by another known cypherpunk: Phil Zimmermann, the creator of Pretty Good Privacy (PGP). This was the first widely available public-key encryption software for email and file security. Callas became chief scientist, playing a crucial role in shaping PGP’s security architecture. In 1997, Callas moved to PGP, Inc., a company founded by another known cypherpunk: Phil Zimmermann, the creator of Pretty Good Privacy (PGP). Phil Zimmermann Phil Zimmermann When Network Associates acquired PGP later that year, he took on the role of CTO for their Total Network Security Division, where he helped standardize OpenPGP. After leaving in 1999, he co-founded PGP Corporation in 2002, serving as CTO and CSO, refining encryption tools and overseeing security for PGP products until 2009. Callas returned to Apple in 2009 as a "Security Privateer," developing FileVault 2, Apple’s full-disk encryption system, and working on security approvals for iOS. He briefly left for roles in other security firms but rejoined Apple in 2016 as a Security Design and Architecture Manager, continuing his contributions to cryptographic security there until 2018. His agenda for privacy and security didn’t change, though.\ rejoined Apple rejoined Apple Silent Circle & Blackphone Callas co-founded the company Silent Circle in 2012 and its Blackphone in 2013, serving as Silent Circle’s CTO until April 2016. Alongside Phil Zimmermann and other security experts, he helped build Silent Circle into a provider of encrypted communication tools, including secure voice, video, and text messaging services. The Blackphone, introduced in 2014, and described as “the first cypherpunk smartphone,” was a device designed specifically for privacy, created through a collaboration between Silent Circle and Geeksphone. It ran on PrivatOS, a customized version of Android that removed unnecessary tracking features and provided built-in encryption, VPN services, and smart app permission controls. The Blackphone, introduced in 2014, and described as “the first cypherpunk smartphone,” was a device designed specifically for privacy, created through a collaboration between Silent Circle and Geeksphone. the first cypherpunk smartphone the first cypherpunk smartphone https://www.youtube.com/watch?v=FnojAyOHFPw&embedable=true https://www.youtube.com/watch?v=FnojAyOHFPw&embedable=true Marketed to businesses and privacy-conscious users, the Blackphone was intended to be a secure alternative to mainstream smartphones, prioritizing user control over data. Despite its innovative approach, sales fell short of expectations, contributing to Silent Circle’s financial struggles in 2016. While the Blackphone project was short-lived, it marked a significant step in privacy-focused technology. Silent Phone, originally an app bundled with the Blackphone, became its spiritual successor and the company’s primary product, offering encrypted voice, video, and messaging services across various platforms, including iOS and Android.\ Silent Phone Silent Phone Ads & Privacy After these initiatives and roles, Callas never stopped working for security and privacy. In 2018, he joined the American Civil Liberties Union (ACLU) as a Senior Technology Fellow, where he worked until August 2020. He then transitioned to the cypherpunk-founded Electronic Frontier Foundation (EFF), serving as Director of Public Interest Technology until June 2023. That same year, he founded Zatik Security, a cybersecurity firm aimed at helping small and medium-sized businesses establish strong security measures. Additionally, since November 2023, he has been a Senior Computer Scientist at the nonprofit SRI International. Callas has been vocal about the growing threats to privacy, emphasizing that large technology corporations pose a greater risk than governments. His concerns center around the widespread practice of collecting and monetizing personal data for advertising, which creates an economy dependent on user surveillance. He believes that companies prioritizing privacy are more resilient, particularly if the digital advertising industry faces economic instability. The ongoing conflict over ad-blocking, which pits privacy-focused users against ad-driven business models, exemplifies this divide. Callas has been vocal about the growing threats to privacy, emphasizing that large technology corporations pose a greater risk than governments His stance highlights a fundamental split in Silicon Valley: while companies like Apple and Microsoft monetize products and services without relying on user data sales, others, such as Google and Facebook, generate the majority of their revenue through advertising. Callas argues that ad-driven companies are at risk if the industry declines, forcing them to rethink their business models. He sees the ad-blocker dispute as a key battle for digital privacy, one that could reshape the tech industry’s relationship with user data, at least in the long term. His stance His stance Crypto is a Tool Even before that warning about big companies, Callas has been clearly against surveillance or even banning of privacy tools, alleging that they’re just that: tools. And tools can be used by anyone, everywhere, for any purpose. An old message (by him) from the cypherpunk mailing list reads: An old message An old message “Politically, I’m a Lockeian, and put privacy up there with Locke’s basic trio of life, liberty, and property. As part of this, I fight the stupid notion that because there are bad people out there, rights should be abridged. Crypto[graphy] is a tool, and nigh any useful tool can be misused. If we let that fact stop us from making tools, we’d be using nerf axes and dressing in bubble wrap. If we let the fact that bad guys are using our stuff bother us too much, we’d be against privacy.” “Politically, I’m a Lockeian, and put privacy up there with Locke’s basic trio of life, liberty, and property. As part of this, I fight the stupid notion that because there are bad people out there, rights should be abridged. Crypto[graphy] is a tool, and nigh any useful tool can be misused. If we let that fact stop us from making tools, we’d be using nerf axes and dressing in bubble wrap. If we let the fact that bad guys are using our stuff bother us too much, we’d be against privacy.” Obyte embodies these privacy and freedom principles by eliminating central intermediaries and giving users full control over their data and transactions. As a Directed Acyclic Graph (DAG) crypto network without middlemen, it ensures censorship resistance and decentralization, key tenets in Callas’s vision of privacy-first technology. Its privacy-focused features, such as Blackbytes, an untraceable currency, and encrypted chat and chatbots, allow anyone to preserve these fundamental rights. Obyte embodies these privacy and freedom principles by eliminating central intermediaries and giving users full control over their data and transactions. Obyte Obyte Blackbytes Blackbytes and chatbots and chatbots Obyte’s smart contracts, decentralized assets, and self-sovereign identity (SSI) further enhance user autonomy, allowing trustless interactions without reliance on centralized entities. By prioritizing security and user control, Obyte presents an alternative to centralized ecosystems, reinforcing the idea that privacy-focused models can be both sustainable and resilient against economic shifts in the digital landscape. decentralized assets decentralized assets SSI SSI Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism
Wei Dai & B-money
Nick Szabo & Smart Contracts
Adam Back & Hashcash
Eric Hughes & Remailer
St Jude & Community Memory
Julian Assange & Wikileaks
Hal Finney & RPOW
John Gilmore & EFF
Satoshi Nakamoto & Bitcoin
Gregory Maxwell & Bitcoin Core
David Chaum & Ecash
Vinay Gupta & Mattereum
Jim Bell & Assassination Politics
Peter Todd & Bitcoin Core
Len Sassaman & Remailers
Eva Galperin Against Stalkerware
Suelette Dreyfus & Free Speech Tim May & Crypto-anarchism Tim May & Crypto-anarchism Tim May & Crypto-anarchism Wei Dai & B-money Wei Dai & B-money Wei Dai & B-money Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Adam Back & Hashcash Adam Back & Hashcash Adam Back & Hashcash Eric Hughes & Remailer Eric Hughes & Remailer Eric Hughes & Remailer St Jude & Community Memory St Jude & Community Memory St Jude & Community Memory Julian Assange & Wikileaks Julian Assange & Wikileaks Julian Assange & Wikileaks Hal Finney & RPOW Hal Finney & RPOW Hal Finney & RPOW John Gilmore & EFF John Gilmore & EFF John Gilmore & EFF Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Satoshi Nakamoto & Bitcoin Gregory Maxwell & Bitcoin Core Gregory Maxwell & Bitcoin Core Gregory Maxwell & Bitcoin Core David Chaum & Ecash David Chaum & Ecash David Chaum & Ecash Vinay Gupta & Mattereum Vinay Gupta & Mattereum Vinay Gupta & Mattereum Jim Bell & Assassination Politics Jim Bell & Assassination Politics Jim Bell & Assassination Politics Peter Todd & Bitcoin Core Peter Todd & Bitcoin Core Peter Todd & Bitcoin Core Len Sassaman & Remailers Len Sassaman & Remailers Len Sassaman & Remailers Eva Galperin Against Stalkerware Eva Galperin Against Stalkerware Eva Galperin Against Stalkerware Suelette Dreyfus & Free Speech Suelette Dreyfus & Free Speech Suelette Dreyfus & Free Speech Featured Vector Image by Garry Killian / Freepik Freepik Photograph of Jon Callas by SkyDogCon / X

Jon Callas, the Cypherpunk That Built Tools Big Tech Fears

ጆን ካላስ፣ መሣሪያዎችን የገነባው ሳይፈርፑንክ ትልቅ የቴክኖሎጂ ፍራቻ

জন ক্যালাস, সাইফারপাঙ্ক যিনি সরঞ্জাম তৈরি করেছিলেন, বড় প্রযুক্তির ভয়

Jon Callas, el cypherpunk que creó las herramientas que temen las grandes tecnológicas

Jon Callas, il cypherpunk che ha creato gli strumenti che la Big Tech teme

ג'ון קאלאס, הסייפרפאנק שבנה כלים פחדים גדולים מטכנולוגיה

ジョン・カラス、大手テック企業が恐れるツールを作ったサイファーパンク

លោក Jon Callas ដែលជា Cypherpunk ដែលបង្កើតជាឧបករណ៍ដែលធ្វើឲ្យមានការភ័យខ្លាចខាងបច្ចេកវិទ្យាធំ

Jon Callas, Cypherpunk E Agile Didirišwa tša Theknolotši e Kgolo di Poifo

ජෝන් කැලස්, මෙවලම් විශාල තාක්ෂණික බියක් ඇති කළ සයිෆර්පන්ක්

Büyük Teknoloji Korkularını Yıkacak Araçlar Üreten Cypherpunk Jon Callas

Jon Callas, Cypherpunk Leyi Akeke Switirhisiwa Leswi Big Tech Yi Chavaka

乔恩·卡拉斯（Jon Callas），打造工具的密码朋克，引发科技巨头的恐慌

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