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The blockchain landscape is evolving, and IoTeX is advancing this progress with its latest upgrade, IoTeX 2.0. As someone deeply involved with DePIN (Decentralized Physical Infrastructure Networks), I was interested to read the IoTeX 2.0 white paper. This new version introduces several key staking opportunities designed to enhance rewards and contribute to network security. These features not only offer potential financial benefits but also allow participants to play an integral role in the future of decentralized infrastructure. Understanding DePIN and Its Importance Decentralized Physical Infrastructure Networks (DePIN) represent a transformative approach to building, operating, and managing physical infrastructure. Unlike traditional models, which are often controlled by large corporations and governments, DePINs leverage blockchain technology to decentralize ownership and operation. This allows individuals to contribute to and benefit from the infrastructure networks, whether for telecom, energy, or other utilities. Why DePIN Matters: DePIN democratizes access to essential services, fosters innovation, and promotes transparency. Traditional infrastructure models can be monopolistic, leading to high costs and limited choices for consumers. DePINs address these issues by enabling individuals to participate in and gain equity from the networks they help build. This promotes competition, innovation, and a more equitable distribution of the benefits and profits of infrastructure development. By decentralizing these networks, DePINs enhance security, trust, and efficiency, making essential services more accessible and affordable. The Role of IoTeX in DePIN IoTeX 2.0 is designed to support the DePIN ecosystem by providing the infrastructure and tools necessary for decentralized physical networks. Its modular architecture supports various DePIN projects, enabling them to build on a secure and scalable platform. By leveraging IoTeX’s blockchain technology, these projects can operate transparently and efficiently, ensuring that the benefits of decentralization are fully realized. The Modular Security Pool (MSP) One of the standout features of IoTeX 2.0 is the introduction of the Modular Security Pool (MSP). This innovative mechanism allows $IOTX holders to re-stake their tokens, providing security for DePIN Infrastructure Modules (DIMs). By doing so, you not only earn base rewards but also receive extra incentives from DIM projects. This dual reward system boosts your earnings while supporting the security of the IoTeX Layer 1 blockchain. How MSP Works The MSP creates a unified trust layer for DePIN infrastructure by collecting staking security from various established L1/L2s and lending this security to new DIMs. By re-staking $IOTX, holders effectively lease security to these DIMs, enhancing their reliability and trustworthiness. In return, DIM builders incentivize stakers with additional rewards, creating a dynamic economy within the IoTeX network. Enhanced Security and Restaking Rewards The MSP enhances IoTeX’s security by incentivizing DIM builders to allocate restaked $IOTX tokens. This creates a new economy within the IoTeX ecosystem, enhancing both security and reward potential for participants. By participating in the MSP, users contribute to the overall security of the network while enjoying increased staking rewards. Impact on Network Security The goals of the MSP design are to ensure that IoTeX's security model is scalable and flexible. DIM builders are motivated to attract more stakers by offering competitive incentives, strengthening the security of individual DIMs and contributing to the overall robustness of the IoTeX blockchain. This development is something that IoTeX is hoping will make it a more attractive platform for developers and users. Inflationary Staking Rewards To maintain competitive staking returns, IoTeX 2.0 introduces inflationary rewards, a strategy employed by top blockchains like Ethereum and Solana. This ensures continuous incentives for staking, encouraging more users to participate and secure the network. Comparison with Other Blockchains IoTeX's inflationary rewards model is designed to provide consistent and attractive returns for stakers, again, similar to established blockchains such as Ethereum and Solana. The goal of this approach is not only to incentivize current stakers by ensuring staking rewards remain competitive, but also attract new participants, thereby increasing the overall security and stability of the network. Governance and Community Engagement Of course, staking on IoTeX 2.0 as with other chains involves is more than just earning rewards. By staking tokens, participants gain voting power, allowing them to influence key decisions within the ecosystem. This governance model empowers the community, making each staker a vital part of the network’s future. Role of Governance Governance in IoTeX 2.0 is similar to other chains in that it involves active engagement in decision-making processes that shape the future of the network. Stakers can propose and vote on important initiatives, such as protocol upgrades, DIM integrations, and community projects. This decentralized approach ensures that the network evolves in a manner that aligns with the interests of its users, fostering a sense of ownership and responsibility among the community. Deflationary Burning IoTeX 2.0 also incorporates new deflationary mechanisms. Deflationary burning is a mechanism to balance inflation in crypto networks by reducing the token supply based on network usage. For instance, Ethereum's EIP-1559 burns gas fees to maintain a stable ETH supply. Similarly, the IoTeX Network has implemented the Burn-Drop program since 2020, burning approximately 4% of the total IOTX supply. With IoTeX 2.0, the Burn-Drop program will be discontinued and new sources of deflationary burn deflationary burning will be integrated at the protocol level. This includes burning gas fees, burning IOTX for new on-chain identities, and periodic burns based on the adoption of modular products like W3bstream. These measures aim to counterbalance staking rewards and drive net deflationary supply as network adoption grows. Economic Impact It is clear from the white paper that the burn-to-certify mechanism is a crucial aspect of IoTeX's tokenomics. By reducing the total supply of $IOTX through burning, the network can counterbalance inflationary pressures from staking rewards. The hope is that this will ensure that the value of $IOTX is maintained over time, providing long-term stability and confidence for investors. Additionally, this mechanism is intended to support broader adoption by making IoTeX more attractive for new projects and applications requiring secure and verifiable credentials. Potential Challenges and Considerations While IoTeX 2.0 seems to offer numerous benefits, it is important to consider potential challenges. The success of the MSP and other mechanisms depends on widespread adoption and active participation from the community. There is also the inherent risk of volatility in token value, which can affect the attractiveness of staking rewards. Moreover, the competitive landscape of blockchain technology means IoTeX must continuously innovate to maintain its position and appeal. Moreover, the blockchain market is rapidly evolving, with new technologies and protocols emerging regularly. IoTeX must keep pace with these trends to remain competitive. Monitoring the adoption rates and user engagement levels will be critical in assessing the long-term viability of IoTeX 2.0. Additionally, regulatory developments in various jurisdictions could impact the operation and growth of blockchain networks, including IoTeX. As with any blockchain project, user experience and technical challenges can impact adoption. Ensuring that the staking process is user-friendly and that the network can handle increased transaction volumes without compromising performance will be key to IoTeX's success. Addressing any technical issues promptly and effectively will also be crucial in maintaining user trust and engagement. Conclusion The new staking opportunities presented by IoTeX 2.0 offer promising benefits. By participating in the MSP, benefiting from inflationary rewards, and engaging in governance, users can maximize their earnings and contribute to the growth and security of the IoTeX network. However, potential participants should also be aware of the associated risks and stay informed about ongoing developments. Monitoring market trends, regulatory changes, and user adoption will be critical in evaluating the long-term success of IoTeX 2.0. For more detailed information, it is recommended to read the IoTeX 2.0 white paper. The blockchain landscape is evolving, and IoTeX is advancing this progress with its latest upgrade, IoTeX 2.0. As someone deeply involved with DePIN (Decentralized Physical Infrastructure Networks), I was interested to read the IoTeX 2.0 white paper. This new version introduces several key staking opportunities designed to enhance rewards and contribute to network security. These features not only offer potential financial benefits but also allow participants to play an integral role in the future of decentralized infrastructure. Understanding DePIN and Its Importance Understanding DePIN and Its Importance Decentralized Physical Infrastructure Networks (DePIN) represent a transformative approach to building, operating, and managing physical infrastructure. Unlike traditional models, which are often controlled by large corporations and governments, DePINs leverage blockchain technology to decentralize ownership and operation. This allows individuals to contribute to and benefit from the infrastructure networks, whether for telecom, energy, or other utilities. Why DePIN Matters: Why DePIN Matters: DePIN democratizes access to essential services, fosters innovation, and promotes transparency. Traditional infrastructure models can be monopolistic, leading to high costs and limited choices for consumers. DePINs address these issues by enabling individuals to participate in and gain equity from the networks they help build. This promotes competition, innovation, and a more equitable distribution of the benefits and profits of infrastructure development. By decentralizing these networks, DePINs enhance security, trust, and efficiency, making essential services more accessible and affordable. The Role of IoTeX in DePIN The Role of IoTeX in DePIN IoTeX 2.0 is designed to support the DePIN ecosystem by providing the infrastructure and tools necessary for decentralized physical networks. Its modular architecture supports various DePIN projects, enabling them to build on a secure and scalable platform. By leveraging IoTeX’s blockchain technology, these projects can operate transparently and efficiently, ensuring that the benefits of decentralization are fully realized. The Modular Security Pool (MSP) The Modular Security Pool (MSP) One of the standout features of IoTeX 2.0 is the introduction of the Modular Security Pool (MSP). This innovative mechanism allows $IOTX holders to re-stake their tokens, providing security for DePIN Infrastructure Modules (DIMs). By doing so, you not only earn base rewards but also receive extra incentives from DIM projects. This dual reward system boosts your earnings while supporting the security of the IoTeX Layer 1 blockchain. How MSP Works How MSP Works The MSP creates a unified trust layer for DePIN infrastructure by collecting staking security from various established L1/L2s and lending this security to new DIMs. By re-staking $IOTX, holders effectively lease security to these DIMs, enhancing their reliability and trustworthiness. In return, DIM builders incentivize stakers with additional rewards, creating a dynamic economy within the IoTeX network. Enhanced Security and Restaking Rewards Enhanced Security and Restaking Rewards The MSP enhances IoTeX’s security by incentivizing DIM builders to allocate restaked $IOTX tokens. This creates a new economy within the IoTeX ecosystem, enhancing both security and reward potential for participants. By participating in the MSP, users contribute to the overall security of the network while enjoying increased staking rewards. Impact on Network Security Impact on Network Security The goals of the MSP design are to ensure that IoTeX's security model is scalable and flexible. DIM builders are motivated to attract more stakers by offering competitive incentives, strengthening the security of individual DIMs and contributing to the overall robustness of the IoTeX blockchain. This development is something that IoTeX is hoping will make it a more attractive platform for developers and users. Inflationary Staking Rewards Inflationary Staking Rewards To maintain competitive staking returns, IoTeX 2.0 introduces inflationary rewards, a strategy employed by top blockchains like Ethereum and Solana. This ensures continuous incentives for staking, encouraging more users to participate and secure the network. Comparison with Other Blockchains Comparison with Other Blockchains IoTeX's inflationary rewards model is designed to provide consistent and attractive returns for stakers, again, similar to established blockchains such as Ethereum and Solana. The goal of this approach is not only to incentivize current stakers by ensuring staking rewards remain competitive, but also attract new participants, thereby increasing the overall security and stability of the network. Governance and Community Engagement Governance and Community Engagement Of course, staking on IoTeX 2.0 as with other chains involves is more than just earning rewards. By staking tokens, participants gain voting power, allowing them to influence key decisions within the ecosystem. This governance model empowers the community, making each staker a vital part of the network’s future. Role of Governance Role of Governance Governance in IoTeX 2.0 is similar to other chains in that it involves active engagement in decision-making processes that shape the future of the network. Stakers can propose and vote on important initiatives, such as protocol upgrades, DIM integrations, and community projects. This decentralized approach ensures that the network evolves in a manner that aligns with the interests of its users, fostering a sense of ownership and responsibility among the community. Deflationary Burning Deflationary Burning IoTeX 2.0 also incorporates new deflationary mechanisms. Deflationary burning is a mechanism to balance inflation in crypto networks by reducing the token supply based on network usage. For instance, Ethereum's EIP-1559 burns gas fees to maintain a stable ETH supply. Similarly, the IoTeX Network has implemented the Burn-Drop program since 2020, burning approximately 4% of the total IOTX supply. With IoTeX 2.0, the Burn-Drop program will be discontinued and new sources of deflationary burn deflationary burning will be integrated at the protocol level. This includes burning gas fees, burning IOTX for new on-chain identities, and periodic burns based on the adoption of modular products like W3bstream. These measures aim to counterbalance staking rewards and drive net deflationary supply as network adoption grows. Economic Impact Economic Impact It is clear from the white paper that the burn-to-certify mechanism is a crucial aspect of IoTeX's tokenomics. By reducing the total supply of $IOTX through burning, the network can counterbalance inflationary pressures from staking rewards. The hope is that this will ensure that the value of $IOTX is maintained over time, providing long-term stability and confidence for investors. Additionally, this mechanism is intended to support broader adoption by making IoTeX more attractive for new projects and applications requiring secure and verifiable credentials. Potential Challenges and Considerations Potential Challenges and Considerations While IoTeX 2.0 seems to offer numerous benefits, it is important to consider potential challenges. The success of the MSP and other mechanisms depends on widespread adoption and active participation from the community. There is also the inherent risk of volatility in token value, which can affect the attractiveness of staking rewards. Moreover, the competitive landscape of blockchain technology means IoTeX must continuously innovate to maintain its position and appeal. Moreover, the blockchain market is rapidly evolving, with new technologies and protocols emerging regularly. IoTeX must keep pace with these trends to remain competitive. Monitoring the adoption rates and user engagement levels will be critical in assessing the long-term viability of IoTeX 2.0. Additionally, regulatory developments in various jurisdictions could impact the operation and growth of blockchain networks, including IoTeX. As with any blockchain project, user experience and technical challenges can impact adoption. Ensuring that the staking process is user-friendly and that the network can handle increased transaction volumes without compromising performance will be key to IoTeX's success. Addressing any technical issues promptly and effectively will also be crucial in maintaining user trust and engagement. Conclusion Conclusion The new staking opportunities presented by IoTeX 2.0 offer promising benefits. By participating in the MSP, benefiting from inflationary rewards, and engaging in governance, users can maximize their earnings and contribute to the growth and security of the IoTeX network. However, potential participants should also be aware of the associated risks and stay informed about ongoing developments. Monitoring market trends, regulatory changes, and user adoption will be critical in evaluating the long-term success of IoTeX 2.0. For more detailed information, it is recommended to read the IoTeX 2.0 white paper . IoTeX 2.0 white paper

IoTeX 2.0: Enhancing Decentralized Infrastructure Through Staking and Governance

Building Your AI Radiologist: A Fun Guide to Creating a Pneumonia Detector with VGG16

আপনার এআই রেডিওলজিস্ট তৈরি করা: VGG16 দিয়ে নিউমোনিয়া ডিটেক্টর তৈরির জন্য একটি মজার গাইড

Aufbau Ihres KI-Radiologen: Eine unterhaltsame Anleitung zum Erstellen eines Lungenentzündungsdetektors mit VGG16

Construyendo su radiólogo con IA: una guía divertida para crear un detector de neumonía con VGG16

Construire votre radiologue IA : un guide amusant pour créer un détecteur de pneumonie avec VGG16

अपना AI रेडियोलॉजिस्ट तैयार करें: VGG16 के साथ न्यूमोनिया डिटेक्टर बनाने के लिए एक मजेदार गाइड

AI 放射線科医の育成: VGG16 を使った肺炎検出器の作成ガイド

AI 방사선 전문의 구축: VGG16을 사용하여 폐렴 감지기 생성을 위한 재미있는 가이드

Construindo seu radiologista de IA: um guia divertido para criar um detector de pneumonia com VGG16

Создание вашего искусственного радиолога: забавное руководство по созданию детектора пневмонии с помощью VGG16

Yapay Zeka Radyoloğunuzu Oluşturma: VGG16 ile Pnömoni Dedektörü Oluşturmak İçin Eğlenceli Bir Kılavuz

Xây dựng bác sĩ X quang AI của bạn: Hướng dẫn thú vị để tạo máy phát hiện bệnh viêm phổi với VGG16

打造你的 AI 放射科医生：使用 VGG16 创建肺炎检测器的有趣指南

HDR or SDR? A Study of Scaled and Compressed Videos: Details of Subjective Study

So it was a fine evening; I was at my work desk with my coffee, which had just been delivered by a Dasher. I was endlessly scrolling through my X account, taking it slowly sip by sip, enjoying the aroma of the seeds. And that’s when I stumbled upon this viral meme. It made me question my reality like Westworld bot. As a person in a creative profession (at least when I do copywriting), I was just staring into the abyss of my overflowing sink. The dishes have piled up high like the despair in my soul, a veritable Mount Everest of ceramic, metal, and leftover spaghetti. The glint of the silverware was mocking my ambitions, the greasy pots, and pans were whispering my impending doom. And that’s when I realized, like Madris Lidaka said, the creative battle with AI was raging on, and I might be losing. Fast. The Problem: The Never-Ending Cycle Let’s get real. Adulting is hard. Like…real hard. But, Adulting with a creative soul? Damn near impossible. Particularly the relentless Sisyphean task of washing dishes. Just when you think you've caught up, the sink laughs in your face and fills up again with one evil laugh. It’s a never-ending cycle, a black hole of mundane tasks that sucks away your precious creative energy. The Hero's Journey (Or Not) Unlike Frodo from LOTR, who had the singular, epic quest of destroying the One Ring, we creative souls face a far more insidious foe. These greasy plates and stained coffee mugs might as well be indestructible horcruxes, anchoring us to a life of drudgery. Each dish scrubbed feels like another step away from our next masterpiece. The hero’s journey? More like a tragic tale of defeat. Because it’s a CYCLE! Stats Time! Did you know that, according to the Bureau of Labor Statistics, the average American spends approximately 1 hour and 27 minutes per day on household chores, with a significant chunk devoted to dishwashing? That’s over 9 hours a week, 468 hours a year. Imagine the novels, songs, and artworks lost to the abyss of dirty dishes. Even if we get into “super-procrastination, “ I still think we can get more work done. The Dark Side of the Dishes Channel your inner Jedi. Feel the Force flow through you. Now, imagine it being choked by a pile of dirty spoons. The lightsaber of your creativity flickers and dims as you scrub away at stubborn stains. The Dark Side isn’t some far-off galactic evil; it’s right here, in your kitchen sink. So You Think You Can Be Creative? Wash Those Dishes First... (Or Maybe Not) Ernest Hemingway didn’t pen “The Old Man and the Sea” while elbow-deep in dishwater. Jane Austen didn’t craft “Pride and Prejudice” between cycles of washing and drying. Of Course, I know they didn’t live during the digital age. But even if they did, what are the chances that we’d have gotten their masterpieces? Yet here we are, torn from our keyboards and sketchpads, reduced to the banal existence of Home Ec students. The muse of creativity isn’t a fan of dish soap, it seems. No, I am not going to attach photos of my room and trigger your OCD. No, I didn’t mean it LITERALLY! We need a hero, and we need it now. Yeah, I know about AI-based dishwashers. They are the knights in shining armor. These machines are the brave knights ready to slay the dragon of dirty dishes. No more pruney fingers, no more lost hours. Just gleaming plates and the sweet, sweet taste of creative freedom. But I wasn’t just talking about dishwashers, had I? My point is to look at the bigger picture. It’s a universal struggle, a relatable cry for help from creative people everywhere. Imagine a world where AI handles the mundane. You wake up, pour yourself a cup of coffee, and head straight to your writing desk. No dishes to distract you, no chores to drain your creativity. In this utopia, you’re free to craft your next novel, compose your next symphony, or paint your next masterpiece. It sounds like something right out of the old cartoon show Richie Rich. Damn, they got everything predicted. The AI tech in your house can be the unsung hero, working tirelessly behind the scenes. However, our creative juice will be overflowing and we could have more masterpieces. So What is the Solution? The Moral of the Story is to Embrace AI, Unleash Creativity AI isn’t here to replace you; it’s here to collaborate. Think of AI technology as a tool to empower your creativity, freeing you from the clutches of dirty dishes. There is a thin line where the intersection of technology and creativity lies. It should be a partnership, a symbiotic relationship where both humans and machines thrive. On the other hand, even if we get Generative AI to do the work for us, I believe that assessing the output would be challenging as creativity is often person-specific or even situation-specific. Here is a research paper from the Journal of Business and Psychology that fully addresses this ambiguity. So, the next time you find yourself drowning in a sea of dirty dishes and feeling the weight of the mundane threatening to crush your creative spirit, remember: there's hope. This isn't just about conquering the sink; it's about a future where technology empowers our creativity. It's about reclaiming those precious stolen hours and letting your creative spirit truly soar. So, fire up your imagination, unleash your artistic talents, and let AI handle the mundane. The world awaits your next masterpiece, one at a time. So it was a fine evening; I was at my work desk with my coffee, which had just been delivered by a Dasher. I was endlessly scrolling through my X account , taking it slowly sip by sip, enjoying the aroma of the seeds. X account And that’s when I stumbled upon this viral meme . It made me question my reality like Westworld bot. viral meme As a person in a creative profession (at least when I do copywriting), I was just staring into the abyss of my overflowing sink. The dishes have piled up high like the despair in my soul, a veritable Mount Everest of ceramic, metal, and leftover spaghetti. The glint of the silverware was mocking my ambitions, the greasy pots, and pans were whispering my impending doom. And that’s when I realized, like Madris Lidaka said, the creative battle with AI was raging on, and I might be losing. Fast. Madris Lidaka The Problem: The Never-Ending Cycle Let’s get real. Adulting is hard. Like…real hard. But, Adulting with a creative soul? Damn near impossible. Particularly the relentless Sisyphean task of washing dishes. Just when you think you've caught up, the sink laughs in your face and fills up again with one evil laugh. It’s a never-ending cycle, a black hole of mundane tasks that sucks away your precious creative energy. The Hero's Journey (Or Not) Unlike Frodo from LOTR, who had the singular, epic quest of destroying the One Ring, we creative souls face a far more insidious foe. These greasy plates and stained coffee mugs might as well be indestructible horcruxes, anchoring us to a life of drudgery. Each dish scrubbed feels like another step away from our next masterpiece. The hero’s journey? More like a tragic tale of defeat. Because it’s a CYCLE! Stats Time! Did you know that, according to the Bureau of Labor Statistics , the average American spends approximately 1 hour and 27 minutes per day on household chores, with a significant chunk devoted to dishwashing? That’s over 9 hours a week, 468 hours a year. Imagine the novels, songs, and artworks lost to the abyss of dirty dishes. Even if we get into “super-procrastination, “ I still think we can get more work done. Bureau of Labor Statistics The Dark Side of the Dishes Channel your inner Jedi. Feel the Force flow through you. Now, imagine it being choked by a pile of dirty spoons. The lightsaber of your creativity flickers and dims as you scrub away at stubborn stains. The Dark Side isn’t some far-off galactic evil; it’s right here, in your kitchen sink. So You Think You Can Be Creative? Wash Those Dishes First... (Or Maybe Not) Ernest Hemingway didn’t pen “The Old Man and the Sea” while elbow-deep in dishwater. Jane Austen didn’t craft “Pride and Prejudice” between cycles of washing and drying. Of Course, I know they didn’t live during the digital age. But even if they did, what are the chances that we’d have gotten their masterpieces? “The Old Man and the Sea” “The Old Man and the Sea” “Pride and Prejudice” “Pride and Prejudice” Yet here we are, torn from our keyboards and sketchpads, reduced to the banal existence of Home Ec students. The muse of creativity isn’t a fan of dish soap, it seems. No, I am not going to attach photos of my room and trigger your OCD. No, I didn’t mean it LITERALLY! No, I didn’t mean it LITERALLY! We need a hero, and we need it now. Yeah, I know about AI-based dishwashers. They are the knights in shining armor. These machines are the brave knights ready to slay the dragon of dirty dishes. No more pruney fingers, no more lost hours. Just gleaming plates and the sweet, sweet taste of creative freedom. But I wasn’t just talking about dishwashers, had I? But I wasn’t just talking about dishwashers, had I? My point is to look at the bigger picture. It’s a universal struggle, a relatable cry for help from creative people everywhere. Imagine a world where AI handles the mundane. You wake up, pour yourself a cup of coffee, and head straight to your writing desk. No dishes to distract you, no chores to drain your creativity. In this utopia, you’re free to craft your next novel, compose your next symphony, or paint your next masterpiece. It sounds like something right out of the old cartoon show Richie Rich. Damn, they got everything predicted. The AI tech in your house can be the unsung hero, working tirelessly behind the scenes. However, our creative juice will be overflowing and we could have more masterpieces. So What is the Solution? So What is the Solution? The Moral of the Story is to Embrace AI, Unleash Creativity AI isn’t here to replace you; it’s here to collaborate. Think of AI technology as a tool to empower your creativity, freeing you from the clutches of dirty dishes. There is a thin line where the intersection of technology and creativity lies. It should be a partnership, a symbiotic relationship where both humans and machines thrive. intersection of technology and creativity On the other hand, even if we get Generative AI to do the work for us, I believe that assessing the output would be challenging as creativity is often person-specific or even situation-specific. Here is a research paper from the Journal of Business and Psycholog y that fully addresses this ambiguity. Journal of Business and Psycholog So, the next time you find yourself drowning in a sea of dirty dishes and feeling the weight of the mundane threatening to crush your creative spirit, remember: there's hope. This isn't just about conquering the sink; it's about a future where technology empowers our creativity. It's about reclaiming those precious stolen hours and letting your creative spirit truly soar. So, fire up your imagination, unleash your artistic talents, and let AI handle the mundane. The world awaits your next masterpiece, one at a time.

AI, Please Wash My Dishes, Let Me Write: A Desperate Plea for Creative Freedom

এআই, দয়া করে আমার থালা-বাসন ধুয়ে ফেলুন, আমাকে লিখতে দিন: সৃজনশীল স্বাধীনতার জন্য একটি মরিয়া আবেদন

KI, bitte wasche mein Geschirr, lass mich schreiben: Ein verzweifelter Appell für kreative Freiheit

AI, por favor lava mis platos, déjame escribir: una súplica desesperada por la libertad creativa

AI, s'il vous plaît, faites ma vaisselle, laissez-moi écrire : un plaidoyer désespéré pour la liberté de création

एआई, कृपया मेरे बर्तन धोएँ, मुझे लिखने दें: रचनात्मक स्वतंत्रता के लिए एक हताश अपील

AIよ、皿を洗って、私に文章を書かせてください: 創造の自由を求める切実な嘆願

AI, 설거지를 해주세요, 글을 쓰겠습니다: 창작의 자유를 위한 간절한 호소

AI, por favor, lave minha louça, deixe-me escrever: um apelo desesperado por liberdade criativa

ИИ, пожалуйста, помойте мою посуду, позвольте мне написать: отчаянный призыв к творческой свободе

AI, Lütfen Bulaşıklarımı Yıka, Bırak Yazayım: Yaratıcı Özgürlük İçin Umutsuz Bir Dilekçe

AI, Hãy rửa bát cho tôi, để tôi viết: Lời cầu xin tuyệt vọng cho sự tự do sáng tạo

人工智能，请帮我洗碗，让我写作：对创作自由的迫切请求

The FOSDEM conference on February 3rd, 2024, featured a presentation on Manticore Vector Search by Peter Zaitsev, co-founder of Manticore, and Sergey Nikolaev, CEO of Manticore. This event showcased the latest about vector search in databases. For a closer look, a recording of Zaitsev’s talk is available above. Below, you can see a more detailed summary of the topic in the form of an article. https://youtu.be/sHwUgxztcqs?si=uqOUnQoaopRodgbL&embedable=true Introduction In the past two to three years, the database landscape has seen several key changes: A new category of ‘vector databases’ has emerged, featuring open-source platforms such as Milvus in 2019, Vespa in 2020, Weaviate in 2021, and Qdrant in 2022, as well as cloud solutions like Pinecone, introduced in 2019. These databases are dedicated to vector search, focusing on the use of various machine learning models. However, they may lack traditional database capabilities such as transactions, analytics, data replication, and so on. Elasticsearch added vector search capabilities in 2019. Then, from 2022 to 2023, established databases, including Redis, OpenSearch, Cassandra, ClickHouse, Oracle, MongoDB, and Manticore Search, in addition to cloud services from Azure, Amazon AWS, and Cloudflare, started offering vector search functionality. Other well-known databases, like MariaDB, are in the process of integrating vector search capabilities*. For PostgreSQL users, there is the ‘pgvector’ extension, which has implemented this functionality since 2021. While MySQL has not announced plans for native vector search functionality, proprietary extensions from providers such as PlanetScale and AlibabaCloud are available. Vector space and vector similarity Let’s discuss why so many databases recently enabled vector search functionality and what it is at all. Let’s begin with a tangible example. Consider two colors: red, with an RGB code of (255, 0, 0), and orange, with an RGB code of (255, 200, 152). To compare them, let’s plot them on a three-dimensional graph, where each point represents a different color, and the axes correspond to the red, green, and blue components of the colors. We then draw vectors from the origin of the graph to the points representing our colors. Now, we have two vectors: one representing red and the other - orange. If we want to find the similarity between these two colors, one approach could be to simply measure the angle between the vectors. This angle can vary from 0 to 90 degrees, or if we normalize it by taking the cosine, it will vary from 0 to 1. However, this method does not consider the magnitude of the vectors, which means that the cosine will yield the same value for colors A, A1, and A2 even though they represent different shades. To address this issue, we can use the cosine similarity formula, which accounts for vector lengths - the vector’s dot product divided by the product of their magnitudes. This concept is the essence of vector search. It’s straightforward to visualize with colors, but now imagine that, instead of three color axes, we have a space with hundreds or thousands of dimensions, where each axis represents a specific feature of an object. While we can’t easily depict this on a slide or fully visualize it, mathematically, it is feasible, and the principle remains the same: you have vectors in a multi-dimensional space, and you compute the similarity between them. There are a few other formulas to find vector similarity: for example, the dot product similarity and the Euclidean distance, but as the OpenAI API docs say, the difference between them typically doesn’t matter much. Vector features: sparse vectors So, an object may have various features. The color with Red, Green, and Blue components is the simplest example. In real life, it’s typically more complex. In text search, for instance, we may represent documents as high-dimensional vectors. This leads us to the concept of “Bag of Words.” This model transforms text into vectors where each dimension corresponds to a unique word, and the value may be a binary indicator of the word’s presence, a count of occurrences, or a word weight based on its frequency and inversed document frequency (called TF-IDF), which reflects how important a word is to a document in a collection. This is so called sparse vector since most values are zero as most documents don’t have many words. When talking about full-text search in libraries and search engines like , , and , sparse vectors help make searches faster. Basically, you can make a special kind of index that ignores documents without the search term. So you don’t have to check every single document against your search each time. The sparse vectors are also easily understandable. They can be, in a sense, reverse-engineered. Each dimension corresponds to a specific, clear feature, so we can trace back from our vector representation to the original text. This concept has already existed for about 50 years. Lucene Elasticsearch Manticore Search Vector features: dense vectors Traditional text search methods like , which have been around for decades, produce sparse word vectors that depend on term frequency. The main issue? They typically overlook the context in which words are used. For instance, the term “apple” could be associated with both fruit and the technology company without distinction, potentially ranking them similarly in a search. TF-IDF But consider this analogy: In a vector space, which two objects would have closer proximity: a cat and a dog or a cat and a car? Traditional methods that generate sparse vectors — like the one shown at the top of the image below - may struggle to provide a meaningful answer. Sparse vectors are often high-dimensional, with most values being zero, representing the absence of most words in a given document or context. Then came the revolution of deep learning, introducing . These are dense vector representations, as illustrated by the lower part of the image. In contrast to sparse vectors, which may have tens of thousands of dimensions, dense vectors are lower-dimensional (such as 784 dimensions in the image) but packed with continuous values that capture nuanced semantic relationships. This means the same word can have different vector representations depending on its context, and different words can have similar vectors if they share contexts. Technologies like and use these dense vectors to capture complex language features, including semantic relationships, distinguishing synonyms from antonyms, and comprehending irony and slang — tasks that were quite challenging for earlier methods. contextual embeddings BERT GPT Moreover, deep learning extends beyond text, enabling the processing of complex data like images, audio, and video. These can also be transformed into dense vector representations for tasks such as classification, recognition, and generation. The rise of deep learning coincided with the explosion in data availability and computational power, allowing the training of sophisticated models that uncover deeper and subtler patterns in data. Embeddings The vectors such models provide are called “embeddings.” It’s important to understand that unlike the sparse vectors shown previously, where each element can represent a clear feature like a word present in the document, each element of an embedding also represents a specific feature, but in most cases, we don’t even know what the feature is. For example, and used the GloVe model to vectorize Wikipedia and then visualized the values for some words with different colors. We can see here that: Jay Alammar made an interesting experiment A consistent red line appears across various words, indicating similarity in one dimension, though the specific attribute it represents remains unidentified. Terms like “woman” and “girl” or “man” and “boy” display similarities across multiple dimensions, suggesting relatedness. Interestingly, “boy” and “girl” share similarities distinct from “woman” and “man,” hinting at an underlying theme of youth. Except for one instance involving the term “water,” all analyzed words pertain to people, with “water” serving to differentiate between conceptual categories. Distinct similarities between “king” and “queen,” apart from other terms, may suggest an abstract representation of royalty. So, dense vector embeddings, generated through deep learning, capture a vast amount of information in a compact form. Unlike sparse vectors, every dimension of a dense embedding is generally non-zero and carries some semantic significance. This richness comes with a cost - with dense embeddings, since each dimension is densely populated with values, we can’t simply skip documents that don’t contain a specific term. Instead, we face the computational intensity of comparing the query vector with every document vector in the dataset. It’s a brute-force approach that is naturally resource-intensive. However, specialized indexes tailored for dense vectors have been developed. These indexes, such as KD-trees, Ball trees, or more modern approaches like (Hierarchical Navigable Small World) graphs, are pretty smart, but they sometimes have to guess a bit to be fast. This guessing can mean they don’t always get the answer 100% right. The most popular index that databases adopt is HNSW, which stands for Hierarchical Navigable Small World. It’s used by the extension for Postgres, , , , , , , and . Its algorithm builds a multi-layered graph structure. Each layer is a graph where each node (representing a data point) is connected to its closest neighbors. HNSW pgvector Lucene Opensearch Redis SOLR Cassandra Manticore Search Elasticsearch The bottom layer contains all nodes (data points), and each successive upper layer contains a subset of the nodes from the layer below. The topmost layer has the fewest nodes. Searching starts from the upper layers and gradually moves down to the lower layers. This hierarchical approach makes the search process efficient. In a nutshell, HNSW, like any other index, just pregenerates some shortcuts that you then can use for faster query processing. There are other vector indexes, such as maintained by Spotify and others, each with its own set of pros and cons in terms of performance, resource consumption, and accuracy loss. Annoy Image: https://cdn.sanity.io/images/vr8gru94/production/d6e3a660654d9cb55f7ac137a736539e227296b6-1920x1080.png K-nearest neighbours Vector search is actually an umbrella term that includes various tasks such as clustering and classification and others. But commonly, the first feature a database adds for vector search is the ‘K-nearest neighbor search’ (KNN), or its close relative, the ‘Approximate nearest neighbor search’ (ANN). It is appealing because it enables databases to find documents that are most similar to a given document’s vector, which enhances databases with the powerful capabilities of a search engine, which they previously lacked. Traditional search engines like Lucene, Elasticsearch, SOLR, and Manticore Search handle various natural language processing tasks — such as morphology, synonyms, stopwords, and exceptions — all aimed at finding documents that match a given query. KNN accomplishes a similar goal but through different means - just comparing the vectors associated with documents in the table that are usually provided by an external machine-learning model. Let’s explore what a typical vector search looks like in databases, taking as our example. Manticore Search First, we create a table with a column titled : image_vector create table test ( title text, image_vector float_vector knn_type='hnsw' knn_dims='4' hnsw_similarity='l2' ); This vector is of a , which is important because databases that don’t support this data type must first add it, since dense vectors are usually stored in float arrays. At this time, you also usually configure the field by specifying the vector dimension size, vector index type, and its properties. For instance, we specify that we want to use the HNSW index, the vector will have a dimensionality of 5, and the similarity function will be , which is the Euclidean distance. float type l2 Then, we insert a few records into the table: insert into test values ( 1, 'yellow bag', (0.653448,0.192478,0.017971,0.339821) ), ( 2, 'white bag', (-0.148894,0.748278,0.091892,-0.095406) ); Each record has a title and a corresponding vector, which, in a real-world scenario, could be the output from a deep learning model that encodes some form of high-dimensional data, such as an image or sound, an embedding from text, or something else from the OpenAI API. This operation stores the data in the database and may trigger rebuilding or adjusting the index. Next, : we perform a vector search by utilizing the KNN function select id, knn_dist() from test where knn ( image_vector, 5, (0.286569,-0.031816,0.066684,0.032926) );

+------+------------+
| id   | knn_dist() |
+------+------------+
|    1 | 0.28146550 |
|    2 | 0.81527930 |
+------+------------+
2 rows in set (0.00 sec) Here, we’re querying the database to locate the vectors that are closest to our specified input vector. The numbers in parentheses define the particular vector we’re seeking the nearest neighbors for. This step is critical for any database that aims to implement vector search capabilities. At this step, the database can either utilize specific indexing methods, such as HNSW or perform a brute-force search by comparing the query vector against each vector in the table to find the closest matches. The results returned show the titles of the nearest vectors to our input vector and their respective distances from the query. Lower distance values indicate closer matches to the search query. Embedding computation Most databases and search engines so far rely on external embeddings. This means that when you insert a document, you must obtain its embedding from an external source beforehand and include it with the document’s other fields. The same applies when searching for similar documents: if the search is for a user query rather than an existing document, you need to use a machine learning model to calculate an embedding for it, which is then passed to the database. This process can lead to compatibility issues, the need to manage additional data processing layers, and potential inefficiencies in search performance. The operational complexity of this approach is also higher than necessary. Along with the database, you may have to keep another service running to generate embeddings. Some search engines like OpenSearch, Elasticsearch, and Typesense are now making things easier by automatically creating embeddings. They can even use tools from other companies, like OpenAI, to do this. I think we’ll see more of this soon. More databases will start to make embeddings on their own, which could really change how we search for and analyze data. This change means that databases will do more than just store data; they’ll actually understand it. By using machine learning and AI, these databases will be smarter and able to predict, adapt, and handle data in more advanced ways. Hybrid search approaches Some search engines have adopted a method known as hybrid search, which combines traditional keyword-based search with advanced neural network techniques. Hybrid search models excel in situations that require both exact keyword matches, as provided by traditional search techniques, and broader context recognition, as offered by vector search capabilities. This balanced approach can enhance the accuracy of search results. For example, measured by comparing it to the classical BM25 ranking and the ColBERT model individually. In their approach, they used the classical BM25 as the first-phase ranking model and calculated the hybrid score only for the top-ranking K documents according to the BM25 model. It was found that the hybrid search mode outperformed each of them in most tests. Vespa the accuracy of their hybrid search Another simpler method is Reciprocal Rank Fusion (RRF), a technique that combines rankings from different search algorithms. RRF calculates a score for each item based on its rank in each list, where higher rankings receive better scores. The score is determined by the formula 1 / (rank + k), where ‘rank’ is the item’s position in the list, and ‘k’ is a constant used to adjust the influence of lower rankings. By summing these modified reciprocal ranks from each source, RRF emphasizes consensus among different systems. This method blends the strengths of various algorithms, leading to more robust and comprehensive search results. Conclusion Vector search is more than just a concept or a niche functionality of search engines; it’s a practical tool transforming how we retrieve data. In recent years, the database field has seen major changes, with the rise of new vector-focused databases and established ones adding vector search features. This reflects a strong need for more advanced search functions, a need that vector search can meet. Advanced indexing methods, like HNSW, have made vector search faster. Looking ahead, we expect databases to do more than just support vector search; they’ll likely create embeddings themselves. This will make databases simpler to use and more powerful, turning them from basic storage spaces into smart systems that can understand and analyze data. In short, vector search is a significant shift in data management and retrieval, marking an exciting development in the field. Also published . here

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A Treatise on Painting by da Vinci Leonardo, is part of the HackerNoon Books Series. You can jump to any chapter in this book . LANDSCAPE. here MISCELLANEOUS OBSERVATIONS. LANDSCAPE. Chap. CCCXXIII.— Of Objects seen at a Distance. Any dark object will appear lighter when removed to some distance from the eye. It follows, by the contrary reason, that a dark object will appear still darker when brought nearer to the eye. Therefore the inferior parts of any object whatever, placed in thick air, will appear farther from the eye at the bottom than at the top; for that reason the lower parts of a mountain appear farther off than its top, which is in reality the farthest. Chap. CCCXXIV.— Of a Town seen through a thick Air. The eye which, looking downwards, sees a town immersed in very thick air, will perceive the top of the buildings darker, but more distinct than the bottom. The tops detach against a light ground, because they are seen against the low and thick air which is beyond them. This is a consequence of what has been explained in the preceding chapter. Chap. CCCXXV.— How to draw a Landscape. Contrive that the trees in your landscape be half in shadow and half in the light. It is better to represent them as when the sun is veiled with thin clouds, because in that case the trees receive a general light from the sky, and are darkest in those parts which are nearest to the earth. Chap. CCCXXVI.— Of the Green of the Country. Of the greens seen in the country, that of trees and other plants will appear darker than that of fields and meadows, though they may happen to be of the same quality. Chap. CCCXXVII.— What Greens will appear most of a blueish Cast. Those greens will appear to approach nearest to blue which are of the darkest shade when remote. This is proved by the seventh proposition, which says, that blue is composed of black and white seen at a great distance. Chap. CCCXXVIII.— The Colour of the Sea from different Aspects. When the sea is a little ruffled it has no sameness of colour; for whoever looks at it from the shore, will see it of a dark colour, in a greater degree as it approaches towards the horizon, and will perceive also certain lights moving slowly on the surface like a flock of sheep. Whoever looks at the sea from on board a ship, at a distance from the land, sees it blue. Near the shore it appears darkish, on account of the colour of the earth reflected by the water, as in a looking-glass; but at sea the azure of the air is reflected to the eye by the waves in the same manner. Chap. CCCXXIX.— Why the same Prospect appears larger at some Times than at others. Objects in the country appear sometimes larger and sometimes smaller than they actually are, from the circumstance of the air interposed between the eye and the horizon, happening to be either thicker or thinner than usual. Of two horizons equally distant from the eye, that which is seen through the thicker air will appear farther removed; and the other will seem nearer, being seen through a thinner air. Objects of unequal size, but equally distant, will appear equal if the air which is between them and the eye be of proportionable inequality of thickness, viz. if the thickest air be interposed between the eye and the smallest of the objects. This is proved by the perspective of colours, which is so deceitful that a mountain which would appear small by the compasses, will seem larger than a small hill near the eye; as a finger placed near the eye will cover a large mountain far off. Chap. CCCXXX.— Of Smoke. Smoke is more transparent, though darker towards the extremities of its waves than in the middle. It moves in a more oblique direction in proportion to the force of the wind which impels it. Different kinds of smoke vary in colour, as the causes that produce them are various. Smoke never produces determined shadows, and the extremities are lost as they recede from their primary cause. Objects behind it are less apparent in proportion to the thickness of the smoke. It is whiter nearer its origin, and bluer towards its termination. Fire appears darker, the more smoke there is interposed between it and the eye. Where smoke is farther distant, the objects are less confused by it. It encumbers and dims all the landscape like a fog. Smoke is seen to issue from different places, with flames at the origin, and the most dense part of it. The tops of mountains will be more seen than the lower parts, as in a fog. Chap. CCCXXXI.— In what Part Smoke is lightest. Smoke which is seen between the sun and the eye will be lighter and more transparent than any other in the landscape. The same is observed of dust, and of fog; while, if you place yourself between the sun and those objects, they will appear dark. Chap. CCCXXXII.— Of the Sun-beams passing through the Openings of Clouds. The sun-beams which penetrate the openings interposed between clouds of various density and form, illuminate all the places over which they pass, and tinge with their own colour all the dark places that are behind: which dark places are only seen in the intervals between the rays. Chap. CCCXXXIII.— Of the Beginning of Rain. When the rain begins to fall, it tarnishes and darkens the air, giving it a dull colour, but receives still on one side a faint light from the sun, and is shaded on the other side, as we observe in clouds; till at last it darkens also the earth, depriving it entirely of the light of the sun. Objects seen through the rain appear confused and of undetermined shape, but those which are near will be more distinct. It is observable, that on the side where the rain is shaded, objects will be more clearly distinguished than where it receives the light; because on the shady side they lose only their principal lights, whilst on the other they lose both their lights and shadows, the lights mixing with the light part of the rain, and the shadows are also considerably weakened by it. Chap. CCCXXXIV.— The Seasons are to be observed. In Autumn you will represent the objects according as it is more or less advanced. At the beginning of it the leaves of the oldest branches only begin to fade, more or less, however, according as the plant is situated in a fertile or barren country; and do not imitate those who represent trees of every kind (though at equal distance) with the same quality of green. Endeavour to vary the colour of meadows, stones, trunks of trees, and all other objects, as much as possible, for Nature abounds in variety . ad infinitum Chap. CCCXXXV.— The Difference of Climates to be observed. Near the sea-shore, and in southern parts, you will be careful not to represent the Winter season by the appearance of trees and fields, as you would do in places more inland, and in northern countries, except when these are covered with ever-greens, which shoot afresh all the year round. Chap. CCCXXXVI.— Of Dust. Dust becomes lighter the higher it rises, and appears darker the less it is raised, when it is seen between the eye and the sun. Chap. CCCXXXVII.— How to represent the Wind. In representing the effect of the wind, besides the bending of trees, and leaves twisting the wrong side upwards, you will also express the small dust whirling upwards till it mixes in a confused manner with the air. Chap. CCCXXXVIII.— Of a Wilderness. Those trees and shrubs which are by their nature more loaded with small branches, ought to be touched smartly in the shadows, but those which have larger foliage, will cause broader shadows. Chap. CCCXXXIX.— Of the Horizon seen in the Water. By the sixth proposition, the horizon will be seen in the water as in a looking-glass, on that side which is opposite the eye. And if the painter has to represent a spot covered with water, let him remember that the colour of it cannot be either lighter or darker than that of the neighbouring objects. Chap. CCCXL.— Of the Shadow of Bridges on the Surface of the Water. The shadows of bridges can never be seen on the surface of the water, unless it should have lost its transparent and reflecting quality, and become troubled and muddy; because clear water being polished and smooth on its surface, the image of the bridge is formed in it as in a looking-glass, and reflected in all the points situated between the eye and the bridge at equal angles; and even the air is seen under the arches. These circumstances cannot happen when the water is muddy, because it does not reflect the objects any longer, but receives the shadow of the bridge in the same manner as a dusty road would receive it. Chap. CCCXLI.— How a Painter ought to put in Practice the Perspective of Colours. To put in practice that perspective which teaches the alteration, the lessening, and even the entire loss of the very essence of colours, you must take some points in the country at the distance of about sixty-five yards from each other; as trees, men, or some other remarkable objects. In regard to the first tree, you will take a glass, and having fixed that well, and also your eye, draw upon it, with the greatest accuracy, the tree you see through it; then put it a little on one side, and compare it closely with the natural one, and colour it, so that in shape and colour it may resemble the original, and that by shutting one eye they may both appear painted, and at the same distance. The same rule may be applied to the second and third tree at the distance you have fixed. These studies will be very useful if managed with judgment, where they may be wanted in the offscape of a picture. I have observed that the second tree is less by four fifths than the first, at the distance of thirteen yards. Chap. CCCXLII.— Various Precepts in Painting. The superficies of any opake body participates of the colour of the transparent medium interposed between the eye and such body, in a greater or less degree, in proportion to the density of such medium and the space it occupies. The outlines of opake bodies will be less apparent in proportion as those bodies are farther distant from the eye. That part of the opake body will be the most shaded, or lightest, which is nearest to the body that shades it, or gives it light. The surface of any opake body participates more or less of the colour of that body which gives it light, in proportion as the latter is more or less remote, or more or less strong. Objects seen between lights and shadows will appear to have greater relievo than those which are placed wholly in the light, or wholly in shadow. When you give strength and precision to objects seen at a great distance, they will appear as if they were very near. Endeavour that your imitation be such as to give a just idea of distances. If the object in nature appear confused in the outlines, let the same be observed in your picture. The outlines of distant objects appear undetermined and confused, for two reasons: the first is, that they come to the eye by so small an angle, and are therefore so much diminished, that they strike the sight no more than small objects do, which though near can hardly be distinguished, such as the nails of the fingers, insects, and other similar things: the second is, that between the eye and the distant objects there is so much air interposed, that it becomes thick; and, like a veil, tinges the shadows with its own whiteness, and turns them from a dark colour to another between black and white, such as azure. Although, by reason of the great distance, the appearance of many things is lost, yet those things which receive the light from the sun will be more discernible, while the rest remain enveloped in confused shadows. And because the air is thicker near the ground, the things which are lower will appear confused; and . vice versâ When the sun tinges the clouds on the horizon with red, those objects which, on account of their distance, appear blueish, will participate of that redness, and will produce a mixture between the azure and red, which renders the prospect lively and pleasant; all the opake bodies which receive that light will appear distinct, and of a reddish colour, and the air, being transparent, will be impregnated with it, and appear of the colour of lilies. The air which is between the earth and the sun when it rises or sets, will always dim the objects it surrounds, more than the air any where else, because it is whiter. It is not necessary to mark strongly the outlines of any object which is placed upon another. It ought to detach of itself. If the outline or extremity of a white and curved surface terminate upon another white body, it will have a shade at that extremity, darker than any part of the light; but if against a dark object, such outline, or extremity, will be lighter than any part of the light. Those objects which are most different in colour, will appear the most detached from each other. Those parts of objects which first disappear in the distance, are extremities similar in colour, and ending one upon the other, as the extremities of an oak tree upon another oak similar to it. The next to disappear at a greater distance are, objects of mixed colours, when they terminate one upon the other, as trees, ploughed fields, walls, heaps of rubbish, or of stones. The last extremities of bodies that vanish are those which, being light, terminate upon a dark ground; or being dark, upon a light ground. Of objects situated above the eye, at equal heights, the farthest removed from the eye will appear the lowest; and if situated below the eye, the nearest to it will appear the lowest. The parallel lines situated sidewise will concur to one point. Those objects which are near a river, or a lake, in the distant part of a landscape, are less apparent and distinct than those that are remote from them. Of bodies of equal density, those that are nearest to the eye will appear thinnest, and the most remote thickest. A large eye-ball will see objects larger than a small one. The experiment may be made by looking at any of the celestial bodies, through a pin-hole, which being capable of admitting but a portion of its light, it seems to diminish and lose of its size in the same proportion as the pin-hole is smaller than the usual apparent size of the object. A thick air interposed between the eye and any object, will render the outlines of such object undetermined and confused, and make it appear of a larger size than it is in reality; because the linear perspective does not diminish the angle which conveys the object to the eye. The aerial perspective carries it farther off, so that the one removes it from the eye, while the other preserves its magnitude. When the sun is in the West the vapours of the earth fall down again and thicken the air, so that objects not enlightened by the sun remain dark and confused, but those which receive its light will be tinged yellow and red, according to the sun’s appearance on the horizon. Again, those that receive its light are very distinct, particularly public buildings and houses in towns and villages, because their shadows are dark, and it seems as if those parts which are plainly seen were coming out of confused and undetermined foundations, because at that time every thing is of one and the same colour, except what is enlightened by the sun. Any object receiving the light from the sun, receives also the general light; so that two kinds of shadows are produced: the darkest of the two is that which happens to have its central line directed towards the centre of the sun. The central lines of the primitive and secondary lights are the same as the central lines of the primitive and secondary shadows. The setting sun is a beautiful and magnificent object when it tinges with its colour all the great buildings of towns, villages, and the top of high trees in the country. All below is confused and almost lost in a tender and general mass; for, being only enlightened by the air, the difference between the shadows and the lights is small, and for that reason it is not much detached. But those that are high are touched by the rays of the sun, and, as was said before, are tinged with its colour; the painter therefore ought to take the same colour with which he has painted the sun, and employ it in all those parts of his work which receive its light. It also happens very often, that a cloud will appear dark without receiving any shadow from a separate cloud, according to the situation of the eye; because it will see only the shady part of the one, while it sees both the enlightened and shady parts of the other. Of two objects at equal height, that which is the farthest off will appear the lowest. Observe the first cloud in the cut, though it is lower than the second, it appears as if it were higher. This is demonstrated by the section of the pyramidical rays of the low cloud at M A, and the second (which is higher) at N M, below M A. This happens also when, on account of the rays of the setting or rising sun, a dark cloud appears higher than another which is light. Chap. CCCXLIII.— The Brilliancy of a Landscape. The vivacity and brightness of colours in a landscape will never bear any comparison with a landscape in nature when illumined by the sun, unless the picture be placed so as to receive the same light from the sun itself. 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. da Vinci Leonardo (2014). A Treatise on Painting. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/46915/pg46915-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

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