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Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 4 Kandinsky Architecture In our work, we opted to deliver state-of-the-art text-to-image synthesis. In the initial stages of our research, we experimented with multilingual text encoders, such as mT5 (Xue et al., 2021), XLMR (Conneau et al., 2020), XLMR-CLIP[7], to facilitate robust multilingual text-to-image generation. However, we discovered that using the CLIP-image embeddings instead of standalone text encoders resulted in improved image quality. As a result, we adopted an image prior approach, utilizing diffusion and linear mappings between text and image embedding spaces of CLIP, while keeping additional conditioning with XLMR text embeddings. That is why Kandinsky uses two text encoders: CLIP-text with image prior mapping and XLMR. We have set these encoders to be frozen during the training phase. The significant factor that influenced our design choice was the efficiency of training latent diffusion models, as compared to pixel-level diffusion models (Rombach et al., 2022). This led us to focus our efforts on the latent diffusion architecture. Our model essentially comprises three stages: text encoding, embedding mapping (image prior), and latent diffusion. The construction of our model involves three primary steps: text encoding, embedding mapping (image prior), and latent diffusion. At the embedding mapping step, which we also refer to as the image prior, we use the transformer-encoder model. This model was trained from scratch with a diffusion process on text and image embeddings provided by the CLIP-ViT-L14 model. A noteworthy feature in our training process is the use of elementwise normalization of visual embeddings. This normalization is based on full-dataset statistics and leads to faster convergence of the diffusion process. We implemented inverse normalization to revert to the original CLIP-image embedding space in the inference stage. The image prior model is trained on text and image embeddings, provided by the CLIP models. We conducted a series of experiments and ablation studies on the specific architecture design of the image prior model (Table 3, Figure 6). The model with the best human evaluation score is based on a 1D-diffusion and standard transformer-encoder with the following parameters: num_layers=20, num_heads=32, and hidden_size=2048. The latent diffusion part employs a UNet model along with a custom pre-trained autoencoder. Our diffusion model uses a combination of multiple condition signals: CLIP-image embeddings, CLIPtext embeddings, and XLMR-CLIP text embeddings. CLIP-image and XLMR-CLIP embeddings are merged and utilized as an input to the latent diffusion process. Also, we conditioned the diffusion process on these embeddings by adding all of them to the time-embedding. Notably, we did not skip the quantization step of the autoencoder during diffusion inference as it leads to an increase in the diversity and the quality of generated images (cf. Figure 4). In total, our model comprises 3.3 B parameters (Table 2). We observed that the image decoding was our main bottleneck in terms of generated image quality; hence, we developed a Sber-MoVQGAN, our custom implementation of MoVQGAN (Zheng et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence[11]. The comparison of our autoencoder with competitors can be found in Table 4. This paper is available on arxiv under CC BY 4.0 DEED license. [7] https://github.com/FreddeFrallan/ Multilingual-CLIP [8] https://github.com/Stability-AI/ stablediffusion [9] https://github.com/ai-forever/ru-dalle [10] https://github.com/gligen/GLIGEN [11] https://github.com/ai-forever/MoVQGAN Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 4 Kandinsky Architecture In our work, we opted to deliver state-of-the-art text-to-image synthesis. In the initial stages of our research, we experimented with multilingual text encoders, such as mT5 (Xue et al., 2021), XLMR (Conneau et al., 2020), XLMR-CLIP[7], to facilitate robust multilingual text-to-image generation. However, we discovered that using the CLIP-image embeddings instead of standalone text encoders resulted in improved image quality. As a result, we adopted an image prior approach, utilizing diffusion and linear mappings between text and image embedding spaces of CLIP, while keeping additional conditioning with XLMR text embeddings. That is why Kandinsky uses two text encoders: CLIP-text with image prior mapping and XLMR. We have set these encoders to be frozen during the training phase. The significant factor that influenced our design choice was the efficiency of training latent diffusion models, as compared to pixel-level diffusion models (Rombach et al., 2022). This led us to focus our efforts on the latent diffusion architecture. Our model essentially comprises three stages: text encoding, embedding mapping (image prior), and latent diffusion. The construction of our model involves three primary steps: text encoding, embedding mapping (image prior), and latent diffusion. At the embedding mapping step, which we also refer to as the image prior, we use the transformer-encoder model. This model was trained from scratch with a diffusion process on text and image embeddings provided by the CLIP-ViT-L14 model. A noteworthy feature in our training process is the use of elementwise normalization of visual embeddings. This normalization is based on full-dataset statistics and leads to faster convergence of the diffusion process. We implemented inverse normalization to revert to the original CLIP-image embedding space in the inference stage. The image prior model is trained on text and image embeddings, provided by the CLIP models. We conducted a series of experiments and ablation studies on the specific architecture design of the image prior model (Table 3, Figure 6). The model with the best human evaluation score is based on a 1D-diffusion and standard transformer-encoder with the following parameters: num_layers=20, num_heads=32, and hidden_size=2048. The latent diffusion part employs a UNet model along with a custom pre-trained autoencoder. Our diffusion model uses a combination of multiple condition signals: CLIP-image embeddings, CLIPtext embeddings, and XLMR-CLIP text embeddings. CLIP-image and XLMR-CLIP embeddings are merged and utilized as an input to the latent diffusion process. Also, we conditioned the diffusion process on these embeddings by adding all of them to the time-embedding. Notably, we did not skip the quantization step of the autoencoder during diffusion inference as it leads to an increase in the diversity and the quality of generated images (cf. Figure 4). In total, our model comprises 3.3 B parameters (Table 2). We observed that the image decoding was our main bottleneck in terms of generated image quality; hence, we developed a Sber-MoVQGAN, our custom implementation of MoVQGAN (Zheng et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence[11]. The comparison of our autoencoder with competitors can be found in Table 4. 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 [7] https://github.com/FreddeFrallan/ Multilingual-CLIP [8] https://github.com/Stability-AI/ stablediffusion [9] https://github.com/ai-forever/ru-dalle [10] https://github.com/gligen/GLIGEN [11] https://github.com/ai-forever/MoVQGAN

Russian Scientists Say New AI Architecture Produces State-of-the-Art Text-to-Image Synthesis

After months of anticipation, Cryptex announced the launch of TCAP, The World’s First Total Cryptocurrency Market Cap Token. Real-time price exposure to the entire Cryptocurrency sector in a single solution. The entire market. Yours at the push of a button. TCAP is not a fixed basket of tokens. TCAP is not an ICO, nor a rebased elastic. TCAP is a new, 250% fully backed, fully collateralized asset that’s both audited and accurately representative of the entire cryptocurrency complex by total market capitalization. Our team has been hard at work on this concept for the past 14 months. Today, we’re proud to say our vision is now a reality. TCAP is currently live on Rinkeby testnet powered by decentralized Chainlink oracles that provide real-time total market cap crypto data aggregated from many of the most reputable crypto data providers in the world, bringing reliable, tamperproof data on-chain to our TCAP smart contract. How Does TCAP Work? As of this writing, the total crypto market cap currently resides at 650 billion dollars. Our smart contracts call the exact total market cap from Chainlink’s price reference contract, and then add a divisor to that number, just like the S&P 500 or any major financial index would. The price calculation for TCAP is as follows: = $650,000,000,000 c = 10,000,000,000 d = = $65.00 r c Where is the total cryptocurrency market cap, is the divisor and is the TCAP token price. c d r TCAPs are then minted upon being collateralized by an underlying asset, such as ETH, WBTC, or DAI. What the user receives in return is a newly minted asset represented by a token that moves in real-time to the penny of the underlying total market cap metric our smart contract is tracking. Each TCAP is algorithmically collateralized by a corresponding amount of ETH, WBTC, or DAI needed to appropriately back each token by 250%, thus creating price exposure to the entire cryptocurrency sector with the single push of a button. Understanding Cryptex Cryptex is laser-focused on building innovative, open-source financial products for the global Crypto community. Using Ethereum’s smart contract system, Cryptex is able to build decentralized financial instruments such as Total Market Cap Token and many more others soon to come. Pairing real-world data supplied on the Ethereum blockchain alongside collateralized backings for generating innovative new tokens, we’re introducing DeFi products that were previously not possible without a centralized custodian or manager. Now, Cryptex is providing the framework for decentralized financial products that offer exposure to more than just BTC, ETH, and other DeFi tokens. Why Chainlink Oracles? Chainlink has established itself as the industry standard decentralized oracle solution, becoming the most widely used and fastest-growing price oracles within the DeFi ecosystem, currently securing 4B+ in value. Chainlink’s Price Feed framework employs decentralization at both the oracles and data source levels to ensure no single point of failure in data delivery and sourcing. Chainlink’s decentralized oracles source price data from premium off-chain data aggregators and broadcast it on-chain using a network of independent, Sybil resistant node operators run by leading blockchain DevOps and security teams. Through this framework, TCAP smart contracts can obtain highly available, accurate, tamperproof market cap data to ensure fair pricing for TCAP. Preston Van Loon, Co-Founder and Blockchain Lead for Cryptex and Co-Founder of Prysmatic Labs says “TCAP is an exciting new asset that demands precise data on-chain. Chainlink enables Cryptex to aggregate multiple data points from external sources within the smart contract framework on Ethereum. Using a trust minimized oracle framework allows TCAP to minimize the trust of Cryptex and ultimately enables us to create better trustless financial products.” Joe Sticco, Co-Founder and CEO of Cryptex says “The beauty of working with Chainlink is they provide us with decentralized total crypto market cap data, which via the TCAP smart contract, we can then algorithmically match up on chain to cross-asset collateral for creating not only our initial TCAP token but a forthcoming lineup of fully decentralized data-backed financial tokens for the entire ecosystem.” Experience TCAP on Testnet Join over 500 people that have already committed to experiencing TCAP on testnet by following the steps in . this post Follow Cryptex You can keep up with Cryptex on , , and . You can access the technical documentation, whitepaper, and Quantstamp audit by visiting the . Twitter Reddit Discord Cryptex website Follow the Chainlink Network Chainlink is trusted to deliver definitive truth by hundreds of organizations to provide continuous, reliable data feeds. To learn more, visit and follow on Twitter. chain.link @chainlink | | | | | | | | | Website Twitter Discord Reddit YouTube Telegram Events GitHub Price Feeds DeFi Also published at https://medium.com/cryptexfinance/cryptex-selects-chainlink-to-power-its-flagship-token-tcap-71461e73579f

Chainlink is Set to Power Cryptex's Flagship Token, TCAP

What would a decentralized internet look like? 1. Introduction The internet is a household name around the world but very few people actually grasp the deep meaning encapsulated in that simple, single word. It’s a deep rooted abstraction. A far reaching symbolism. A great mystery that I will gently unravel in this evolving tale. Conceived in the 1960’s, the internet is one of the most transformative technological innovations in human history. It began as a research project by computer scientists and engineers to discover how the cumbersome, isolated, first generation computers could transfer information without direct physical access. However, it has since evolved into a global network connecting billions of people, devices, and systems. Nowadays, the internet is more than just a tool for communication; it has revolutionized how we work, socialize, learn, shop, and access information. The structure and functioning of the internet are underpinned by a variety of infrastructure, software applications and protocols e.g., transfer protocols, that ensure data moves seamlessly across networks. The physical infrastructure includes; Data servers were internet information and code are hosted, under water cables which interconnect these server sites globally, a network of masts and satellites for global wireless access and devices like phones and laptops which access and contribute to the network. Data servers were internet information and code are hosted, under water cables which interconnect these server sites globally, a network of masts and satellites for global wireless access and devices like phones and laptops which access and contribute to the network. The software packages include; Web browsers for accessing the global web and networking software for managing local area networks e.g.; WiFi, WLAN etc. Web browsers for accessing the global web and networking software for managing local area networks e.g.; WiFi, WLAN etc. Among the transfer protocols, the Hypertext Transfer Protocol (HTTP) plays a central role in enabling users to access the vast and interconnected world of the World Wide Web (WWW). The Internet and Transfer Protocols The Internet and Transfer Protocols At its core, the internet is a vast network of interconnected computers and devices, communicating through standardized protocols that allow for the exchange of information. These protocols ensure that data sent from one machine can be accurately understood and processed by another. Transfer protocols, are essential as they define the rules for how data is packaged, transmitted, and delivered between systems. There are many transfer protocols (e.g., File Transfer Protocol, Internet Protocol, Domain Name System etc.) that serve various purposes on the internet, but one of the most important is the Hypertext Transfer Protocol (HTTP) which is the foundation of data exchange on the World Wide Web, enabling the transfer of documents and media between servers and clients. HTTP governs how requests and responses are made between a web browser and a server, allowing users to load webpages, view images, and interact with online content. The World Wide Web The World Wide Web While the internet refers to the physical infrastructure of interconnected networks, the World Wide Web (WWW) is a system of accessing and sharing information over the internet. Tim Berners-Lee, a British computer scientist, invented the World Wide Web in 1989 as a way to enable scientists to share research and documents across the globe. The World Wide Web uses the HTTP protocol and relies on web pages, which are often written in Hypertext Markup Language (HTML) and linked together with hyperlinks. In its early days, the web was a static HTML space where users could view documents and simple content. However, it quickly grew into a dynamic and interactive space, with the development of technologies such as JavaScript, CSS, and advanced server-side scripting. Web 1.0: The Static Web Web 1.0: The Static Web Web 1.0, often referred to as the "static web," was the first generation of the World Wide Web. During this phase, websites were simple, static and primarily read-only, with minimal interaction from users. The websites consisted mostly of text, images, and basic navigation links and though users could browse information, there was little in the way of engagement or dynamic content. Web 2.0: The Interactive Web Web 2.0: The Interactive Web In the early 2000s, the internet underwent a major transformation with the advent of Web 2.0. This shift marked the move from a static, informational web to a more interactive, social, and user-driven environment. Web 2.0 is characterized by the rise of social media platforms, blogs, wikis, and user-generated content. Websites became more dynamic, featuring interactive elements such as videos, comments, and social sharing tools. The focus shifted from simply providing information to creating experiences, where users could actively participate, share, and collaborate. Web 2.0 also saw the growth of cloud computing, enabling users to access and store data online, further decentralizing the web. Web 1 and Web 2 constitute the current state of the net, a phase known as the centralized internet. Advantages of the centralized internet Advantages of the centralized internet Complete global access and connectivity
Access to financial infrastructure for internet banking
Totally free access to most websites
Monetized facilities for employment purposes
Centralized control of servers and websites for proper monitoring and security Complete global access and connectivity Access to financial infrastructure for internet banking Totally free access to most websites Monetized facilities for employment purposes Centralized control of servers and websites for proper monitoring and security Disadvantages of the centralized internet Disadvantages of the centralized internet The main disadvantage of the centralized internet is its centralized ownership and control. Most of the existing internet hardware and websites are owned and operated by private companies who have total control over the humongous information there. This information includes private information which can be used to track unsuspecting internet users. In fact, they has actually been leaks accusing top governments around the world of leveraging the internet to spy on people through their devices. This means that your device takes audio or video recordings without your permission or knowledge which raises privacy concerns. leaks accusing top governments This monopolistic and tyrannical tendencies of major internet companies and governments inspired software developers to come up with the idea of a more decentralized internet which currently being developed. This is known as web 3, a new iteration of the net where internet information will be stored on all the individual devices connected to the net, hence, it cannot be owned or manipulated by any single individual. This is the idea of decentralization in internet technology. Web 3.0: The Decentralized and Semantic Web Web 3.0: The Decentralized and Semantic Web Looking forward, the next phase of the internet is Web 3.0. Often referred to as the "decentralized web," Web 3.0 is characterized by the integration of blockchain technology, decentralized applications (dApps), and enhanced privacy features. Unlike Web 2.0, where data is often controlled by large corporations, Web 3.0 aims to give users more control over their data and digital identities. It also seeks to create a more intelligent web by leveraging artificial intelligence and machine learning to provide more personalized and relevant experiences. The idea of a decentralized internet started from the finance world where financial institutions leveraged on their authority to grant certain unfair favors resulting in the financial crises of 2008. This is better explained in my previous article on bitcoin. This financial crises prompted Idealistic tech savvy professionals like Satoshi Nakamoto to initiate the development of a decentralized global network for financial transactions. Nakamoto leveraged blockchain technology (an existing decentralized storage system using the connected devices as memory) to create the first global cryptocurrency i.e., bitcoin. my previous article The birth of the bitcoin proved the practicality of decentralized global networking spurring a worldwide team of developers to begin expanding the blockchain infrastructure into a new kind of internet known as web 3. With Web 3.0, the internet is becoming a more decentralized, secure, and intelligent space, pushing the boundaries of what is possible online. 2. The Internet of the Future Imagine this; In the near future, you find yourself living in a world shaped by automation and Web3 technology. The Earth is one global nation. Earth City, a sprawling artificial island in the center of the Atlantic Ocean, serves as the capital of this new world order. The infrastructure of the world is interconnected by underwater hyperloop systems and futuristic speed-train bridges that allow people to travel between continents in a matter of minutes. Borders are now obsolete, with only provinces existing for documentation and security purposes. Automated systems dominate every facet of life. Self-driving vehicles cruise the roads with almost no accidents, while autonomous spaceships shuttle goods and people throughout the solar system. The once uninhabitable planets are now thriving colonies. Advanced automated farming systems provide abundance to the population, and artificial intelligence controls resource distribution. Basic needs such as water, electricity, and education are universally free, ensuring that every person’s fundamental needs are met. In this age of automation, prosperity has become a reality for everyone, as technology fuels growth in sectors like art, entertainment, and exploration. But it’s the rise of Web3 that truly transforms society. In this new era, the Internet is decentralized, governed not by corporations or governments, but by the people through open-source protocols. This shift toward Web3 ushered in the age of Decentralized Finance (DeFi), a revolution that eliminates intermediaries like banks and financial institutions. Instead, all financial activities are managed through decentralized applications (DApps) operating on blockchain technology and AI. This new structure allows individuals to directly control their financial destiny, cutting out the centralized entities that once held power over the global economy. Decentralized Finance (DeFi) In the early days of Web3, ordinary people flourished. Cryptocurrencies and other DFAs (Digital Financial Assets) enabled individuals to invest, trade, and build wealth independently of traditional banks. The decentralized nature of Web3 ensured that no single entity could monopolize or manipulate the Cryptex market. The financial freedom that came with DeFi was exhilarating, and people used it to create wealth, fund creative projects, and build entirely new economies. DFAs (Digital Financial Assets) Cryptex DAOs (Decentralized Autonomous Organizations) emerged as the backbone of this new society, giving individuals the power to vote on policies and participate in global decision-making. But this dream was short-lived. Over time, centralized corporations began to infiltrate the Web3 space. The big financial institutions, once seen as relics of the old system, launched their own blockchain networks and DApps. They formed powerful alliances, effectively merging with Web3 to create a hybrid system. centralized corporations This merger, which became known as the “Big Merge,” saw the introduction of Digital Native Currencies (DiNaCs)—digital versions of national currencies, such as the digital dollar, euro, or yen. These DiNaCs, issued by government-backed corporations, quickly became the dominant form of exchange on the decentralized platform. “Big Merge,” Digital Native Currencies (DiNaCs) The once decentralized and egalitarian Web3 had come under threat of corporate control with centralized blockchain corporations, or “Big Chain”, attempting to dominate the system. They controlled the issuance and trade of DiNaCs and formed their own DAOs, collectively known as the DAOC (Decentralized Autonomous Organization of Corporations), which allowed them to manipulate policies on the Web3 network. “Big Chain” DAOC (Decentralized Autonomous Organization of Corporations) But there was a glimmer of hope. A mysterious entity known only as DNC—short for DeFi Not CeFi—emerged as the face of resistance against the Big Chain. DNC’s presence on GloDExSys (Global Decentralized Exchange System) was a revelation. They quickly climbed the ranks of the trading leaderboard, a feat never before achieved by individuals since the Big Merge. People were intrigued—how was DNC managing to challenge the dominance of the Big Chain? For a brief period, DNC seemed to be the rebellion Web3 had been waiting for, striking fear into the hearts of the corporate giants. DNC DeFi Not CeFi DNC’s mission is clear; “Take back our finternet!” “Take back our finternet!” Amidst this struggle, Web3 itself continued to evolve. Spacecoin, the first cryptocurrency with truly decentralized infrastructure marked the next stage of the revolution. Spacecoin’s infrastructure was designed to be immune to centralized control, and it became the one true global currency, accepted across all industries. Spacecoin Cryptocurrency trading, or Cryptex, is the new norm. People engaged in speculative trading across a range of industry-based cryptocurrencies like Techon (for technology), Agrocoin (for agriculture), and Muse (for music). These tokens allowed users to invest in niche sectors, creating a dynamic and ever-changing global economy. ryptocurrency trading Cryptex Techon (for technology), Agrocoin (for agriculture), and Muse (for music) As you watch these developments unfold from your star ship, a silent transcontinental jet from space ex, you reflect on the strange world you’re exploring. Earth City, with its towering structures and futuristic systems, stands as a testament to human achievement. You gaze upon  the billboard ahead, emblazoned with the web 3 pledge. Those immortal words read; “We all are free people. Free to live, free to love, free to prosper!” “We all are free people. Free to live, free to love, free to prosper!” But as you ponder the ideals of freedom and equality, the sound of your phone rings— waking you from your trance. It’s your boss calling, bringing you back to reality. With a sigh, you return to the harsh reality of your daily life, where the dream of a decentralized future feels more distant than ever. The world you dreamed of may never come to pass. But in the back of your mind, the resistance continues, and the dream of a truly free, decentralized world lingers on. Web 2 vs Web 3 Web 2 vs Web 3 Privacy: Due to its centralized nature, web 2 is very vulnerable to leaks, spying and sales of user data. This will be greatly minimized by web 3 since the data is not stored on any central server.
Accessibility: Web 2 already has global accessibility but several factors like government policies, hosting costs and network outages can limit connectivity. Web 3 aims to eliminate these other factors.
Censorship Resistance: Unlike web 2 which is operated by private bodies that can promote their own agenda through censorship, web 3 is openly managed and the general public can vote on what gets censored if any.
Monetization: Though web 2 is already monetized in some countries, web 3 will introduce more opportunities for monetization on a global scale.
Government Access: Though web 3 is more decentralized than web 2, as highlighted in the story above, it may still experience some government control if traditional financial instruments are made available on the blockchain. This will minimize government monopoly though and open up the financial market to true capitalistic competitiveness. Privacy: Due to its centralized nature, web 2 is very vulnerable to leaks, spying and sales of user data. This will be greatly minimized by web 3 since the data is not stored on any central server. Privacy Accessibility: Web 2 already has global accessibility but several factors like government policies, hosting costs and network outages can limit connectivity. Web 3 aims to eliminate these other factors. Accessibility Censorship Resistance: Unlike web 2 which is operated by private bodies that can promote their own agenda through censorship, web 3 is openly managed and the general public can vote on what gets censored if any. Censorship Resistance Monetization: Though web 2 is already monetized in some countries, web 3 will introduce more opportunities for monetization on a global scale. Monetization Government Access: Though web 3 is more decentralized than web 2, as highlighted in the story above, it may still experience some government control if traditional financial instruments are made available on the blockchain. This will minimize government monopoly though and open up the financial market to true capitalistic competitiveness. Government Access 3. Pros & Cons of a decentralized Internet The story above already highlights the great benefits of a decentralized internet. The people benefit the most of course. They get to fully control their financial destiny with very little possibility of another crash like the 2008 financial crises. They’re exposed to a much more robust portfolio of money making opportunities. Their data is secure from leaks and spying, plus autocratic regimes e.g., North Korea get little control over how their citizens can access the internet. Many benefits abound in web 3. The major downside of web 3 will be difficulty in regulating activities on the networks. Lots of voting may have to occur to implement policies and this my be quite slow. This regulation problem also constitutes the major obstacle in web 3 practicality. I call it the paradox of decentralized coordination as explained in this my article. How do you regulate a decentralized platform without centralized control? We’ll find out. Another problem is the numerous frauds and hacks already occurring on the network. this my article As highlighted in the story above, decentralization will likely remain a continuous struggle and future inventions may tackle these obstacles. 4. A Nod to Champions of Decentralization Space Coin remains a top web 3 project that I find quite promising and exciting. It is the worlds first web 3 project running on a network of decentralized satellite infrastructure. Space coin takes decentralization as far as the hardware level ensuring great difficulty for any giant corporation which may seek to dominate the web 3 space when fully operational. I believe it holds great promise. Space Coin 5. Conclusion The internet has come a long way since its inception, with each new phase introducing innovations that shape the way we connect, share, and interact. From the early days of Web 1.0 to the rise of Web 2.0 and the promising future of Web 3.0, the internet continues to evolve, driven by new technologies such as transfer protocols, HTTP, and blockchain. As the internet becomes increasingly integrated into our daily lives, understanding these foundational technologies and their evolution is essential for navigating our developing information age. In summary, web 3 is upon us presenting great opportunities for anyone around the world. To benefit from this revolution, here are the following suggestions. Consider investing in promising new crypto projects.
Learn Web 3 programming to get a high paying job.
Learn crypto trading. Consider investing in promising new crypto projects. Learn Web 3 programming to get a high paying job. Learn crypto trading.

E-volution: An Internet Odyssey

E-volución: una odisea de Internet

E-VOLUTION: AN INTERNET ODYSSEY

E-evolutie: een internet odyssey

E-wolucja: internetowa odyseja

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E-volution: Internet Odyssey

E-VOLUTION: A Internet Odyssey waxaa laga yaqaan 'E-VOLUTION'

E-Revolution: An Internet Odyssey படத்தின் காப்புரிமை

Електронна еволюція: Інтернет-одіссея

Lời bài hát: E-Volution: An Internet Odyssey

电子进化：互联网之旅

I-E-volution: I-Odyssey ye-Inthanethi

NEW YORK, United States, December 3rd, 2024/Chainwire/--Perennial announced the launch of Perennial Intents, a unique intents layer for perpetual futures, designed to unify DeFi’s fragmented liquidity landscape and deliver a centralized exchange trading experience on-chain. By sourcing liquidity from on-chain and off-chain venues, Perennial Intents is delivering deeper markets, better prices, and a unified trading experience designed to move DeFi forward. Tackling DeFi’s Liquidity Fragmentation “Perennial Intents arrive at a pivotal time for DeFi," said Kevin Britz, Founder of Perennial. "Despite its growth, only a fraction of crypto’s order flow happens on-chain, most of which is fragmented across hundreds of L1s and L2s. The rise of appchains and isolated AMMs has led to over 100 chains with $10M+ in TVL (DeFiLlama), each operating its own siloed financial ecosystem. This fragmented liquidity leads to worse trade execution, with higher costs, increased slippage, and limited leverage opportunities.” According to the team, Perennial Intents address these challenges by consolidating order flow into a unified liquidity layer. Instead of fragmenting liquidity into silos like appchains or AMM pools, intent-based trading integrates order flow across multiple venues, creating a more cohesive and efficient system. A Hybrid Model for the Future of DeFi Although intents are not new to DeFi, Perennial Intents introduce a layered model that combines intent-based off-chain order matching with on-chain AMM settlement. Perennial claims this model streamlines trading by pairing intent-based order matching with on-chain AMM settlement. The team claims this hybrid approach guarantees optimal price execution for traders while enabling solvers to dynamically manage liquidity without long-term collateral constraints—unlocking deeper markets and greater efficiency. One-Click Trading and the Perennial Petals Program. Alongside Perennial Intents, the launch includes two additional upgrades: one-click trading and the Perennial Petals points program. Traders can now enjoy seamless trading with a single collateral account, while the Petals program rewards users with points for their trading activity, with 2x points available during the initial launch period. The team at Arbitrum shared their excitement for the launch, highlighting the transformative potential of intent-based derivatives. "Perennial’s work with intent-based derivatives is transforming DeFi by aligning market interactions with users’ specific goals," said Peter Haymond, Senior Partnerships Manager at Offchain Labs. "This approach lets users define their desired financial outcomes, enabling more efficient and personalized trading on Arbitrum." About Perennial Perennial is a DeFi-native derivatives primitive designed to serve as the liquidity backbone for DeFi. Backed by leading investors, including Polychain, Variant, and Archetype, Perennial has facilitated over $2.8 billion in trading volume. Its growing ecosystem includes integrations with prominent trading interfaces like Kwenta, Siren, Rage Trade, and Cryptex Finance. For more information on Perennial Intents, users can visit their website or join the community on Discord. Contact Head of Marketing Lucas Terry Perennial lucas@perennial.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here NEW YORK, United States, December 3rd, 2024/Chainwire/--Perennial announced the launch of Perennial Intents, a unique intents layer for perpetual futures, designed to unify DeFi’s fragmented liquidity landscape and deliver a centralized exchange trading experience on-chain. By sourcing liquidity from on-chain and off-chain venues, Perennial Intents is delivering deeper markets, better prices, and a unified trading experience designed to move DeFi forward. Tackling DeFi’s Liquidity Fragmentation “Perennial Intents arrive at a pivotal time for DeFi," said Kevin Britz, Founder of Perennial. “Perennial Intents arrive at a pivotal time for DeFi," said Kevin Britz, Founder of Perennial. "Despite its growth, only a fraction of crypto’s order flow happens on-chain, most of which is fragmented across hundreds of L1s and L2s. The rise of appchains and isolated AMMs has led to over 100 chains with $10M+ in TVL (DeFiLlama), each operating its own siloed financial ecosystem. This fragmented liquidity leads to worse trade execution, with higher costs, increased slippage, and limited leverage opportunities.” "Despite its growth, only a fraction of crypto’s order flow happens on-chain, most of which is fragmented across hundreds of L1s and L2s. The rise of appchains and isolated AMMs has led to over 100 chains with $10M+ in TVL ( DeFiLlama ), each operating its own siloed financial ecosystem. This fragmented liquidity leads to worse trade execution, with higher costs, increased slippage, and limited leverage opportunities.” DeFiLlama DeFiLlama According to the team, Perennial Intents address these challenges by consolidating order flow into a unified liquidity layer. Instead of fragmenting liquidity into silos like appchains or AMM pools, intent-based trading integrates order flow across multiple venues, creating a more cohesive and efficient system. A Hybrid Model for the Future of DeFi Although intents are not new to DeFi, Perennial Intents introduce a layered model that combines intent-based off-chain order matching with on-chain AMM settlement. Perennial claims this model streamlines trading by pairing intent-based order matching with on-chain AMM settlement. The team claims this hybrid approach guarantees optimal price execution for traders while enabling solvers to dynamically manage liquidity without long-term collateral constraints—unlocking deeper markets and greater efficiency. One-Click Trading and the Perennial Petals Program. Alongside Perennial Intents, the launch includes two additional upgrades: one-click trading and the Perennial Petals points program. Traders can now enjoy seamless trading with a single collateral account, while the Petals program rewards users with points for their trading activity, with 2x points available during the initial launch period. The team at Arbitrum shared their excitement for the launch, highlighting the transformative potential of intent-based derivatives. "Perennial’s work with intent-based derivatives is transforming DeFi by aligning market interactions with users’ specific goals," said Peter Haymond, Senior Partnerships Manager at Offchain Labs. "Perennial’s work with intent-based derivatives is transforming DeFi by aligning market interactions with users’ specific goals," said Peter Haymond, Senior Partnerships Manager at Offchain Labs. "This approach lets users define their desired financial outcomes, enabling more efficient and personalized trading on Arbitrum." "This approach lets users define their desired financial outcomes, enabling more efficient and personalized trading on Arbitrum." About Perennial Perennial is a DeFi-native derivatives primitive designed to serve as the liquidity backbone for DeFi. Backed by leading investors, including Polychain, Variant, and Archetype, Perennial has facilitated over $2.8 billion in trading volume. Perennial Perennial Its growing ecosystem includes integrations with prominent trading interfaces like Kwenta , Siren , Rage Trade , and Cryptex Finance . Kwenta Kwenta Siren Siren Rage Trade Rage Trade Cryptex Finance Cryptex Finance For more information on Perennial Intents, users can visit their website or join the community on Discord . website website Discord Discord Contact Head of Marketing Lucas Terry Perennial lucas@perennial.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Perennial Unveils a Novel Intent Layer For Perpetuals - Solving DeFi’s Fragmented Liquidity Problem

Perennial presenta una nueva capa de intención para los perpetuos: solución al problema de la liquidez fragmentada de DeFi

Perennial devwale yon nouvo kouch entansyon pou moun pèmanan - rezoud pwoblèm likidite fragman DeFi a

Perennial-ը բացում է նոր նպատակային շերտ հավերժականների համար՝ լուծելով DeFi-ի մասնատված իրացվելիության խնդիրը

Perennial svela un nuovo livello di intenti per i perpetui - Risolvere il problema di liquidità frammentata della DeFi

Perennial が Perpetuals 向けの新しいインテント レイヤーを発表 - DeFi の断片化された流動性問題を解決

Perennial이 Perpetuals를 위한 새로운 Intent Layer를 공개 - DeFi의 분산된 유동성 문제 해결

Perennial ujawnia nową warstwę intencji dla kontraktów wieczystych – rozwiązuje problem rozdrobnionej płynności DeFi

Perennial revela uma nova camada de intenção para perpétuos - resolvendo o problema de liquidez fragmentada do DeFi

Perennial Perpetualespaq huk musuq Intención capata riqsichin - DeFi’s Fragmentado Liquidez Sasachakuyninta allichay

Sannadlaha Jooga ah ayaa daaha ka qaadaya lakab ujeedo cusub oo waarta - Xallinta dhibaatada dareeraha ee jajaban ee DeFi

Perennial представляє новий рівень намірів для безстрокових інструментів - вирішення проблеми фрагментованої ліквідності DeFi

Perennial tiết lộ một lớp ý định mới cho Perpetuals - Giải quyết vấn đề thanh khoản phân mảnh của DeFi

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References V. EXPERIMENTAL RESULTS A. Comparison of CUP against Baselines • LLM-Rec also performs poorly. Solely relying on the LLM’s latent knowledge about books is not sufficient when coping with long-tail items that appear sparsely in the LLM’s training data. Popularity and position bias [31] further aggravate this adverse effect. Thus, to address RQ1 (see Section I-B), merely and fully resorting to LLMs alone is not a viable solution. We also ran extended CUP configurations where precomputed CF vectors were incorporated (see Section III-B). These achieved only very minor gains in some of the settings, and there was even a detrimental effect on some cases. We omit these hybrid methods here. We will revisit them in Subsection V-C for sensitivity studies on data density. Another observation is that the weighted training almost always improves performance. Therefore, the following subsections focus on results with weighted training samples. B. Comparison of CUP Configurations • On the GR-1K-rich data, the overall performance is higher than for AM across all configurations. This can be attributed to the fact that GR has longer reviews, and these texts tend to be more informative than the ones in the AM data (which sometimes refer to packaging, shipment and other non-content aspects). C. Influence of Interaction Density The slices AM-1K-rich and GR-1K-rich are constructed with a text-centric stress-test in mind. Both are extremely sparse in terms of user-item interactions. To study the influence of sparseness in isolation, we created two larger samples of both datasets, one still sparse by design and the other denser in terms of users sharing items. We refer to these as AM-10Ksparse and AM-10K-dense, and analogously for GR. All these samples cover 10K users: 10x more users than our previous text-rich slices, to allow more potential for learning from interactions. Table I in Section IV-B shows statistics. The datasets are constructed as follows. To ensure connectivity in the interaction graphs, we first select 500 users and sample 2000 books connected to these users, both uniformly at random. This is the seed for constructing two variants of data, based on the cumulative item degrees of users, that is, the sum of the #users per book for all books that the user has in her collection: • 10K-dense: We randomly select 10K (minus the initial 500) users from all users in proportion to the users’ cumulative item degrees. This favors users with many or popular books. • 10K-sparse: We select users inversely proportional to their cumulative item degrees, thus favoring sparse users. In this sensitivity study, we focus on comparing CF (based on matrix factorization) against our default CUPtext configuration (with idf-selected sentences) and the hybrid combination CUPtext +CF. Tables V and VI show the results for the AM10K data and GR-10K data, comparing the sparse vs. the dense variants with search-based evaluation (corresponding results with standard evaluation are on the project web page). Key insights are: • As the 10K-sparse data is already much denser than the stress-test 1K-rich slices, CF can achieve decent results on the sparse data. Especially in search-based mode, CF is almost on par with CUP. Note that these gains come from the seen items alone, as CF is bound to fail on unseen items. • CUPtext is still the clear winner on the sparse variant. The hybrid configuration, with text and CF vectors combined, is inferior to learning from text alone. • For the 10K-dense data, CF alone performs well, as this is the traditional regime for which CF has been invented. An interesting point arises for the search-based mode and with dense data. Here, the negative test points are closer to the positive sample (after the BM25 search), and pose higher difficulty for text cues alone to discriminate them. Thus, CF becomes more competitive. On GR, hybrid CUP+CF performs best. D. Efficiency of CUP Two architectural choices make CUP efficient: • input length restricted to 128 tokens, and • fine-tuning only the last layer of BERT and the FFN layers. For more analysis, we measured the training time and resulting NDCG on the GR-1K-rich data, comparing different input size budgets and choices of which parameters are tuned and which ones are kept frozen. Figure 2 shows the results. We observe that the 128-token configuration has the lowest training cost: significantly less time per epoch than the other variants and fast convergence (reaching its best NDCG already after 15 epochs in ca. 3000 seconds). The 256- and 512-token models eventually reach higher NDCG, but only by a small margin and after much longer training time. This confirms that concise user profiles, with a small token budget, are the best approach in terms of benefit/cost ratio. As for configurations with more or less tunable parameters, we observe that the variant with frozen BERT takes much longer to converge and is inferior to the preferred CUP method even after more than 50 epochs. The other extreme, allowing all of BERT parameters to be altered together with the FFN parameters, performs best after enough training epochs, better than the CUP configuration that tunes only the last BERT layer. However, it takes almost twice as much time per epoch. So again, from the benefit/cost perspective, our design choice hits a sweet spot in the spectrum of model configurations. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References V. EXPERIMENTAL RESULTS A. Comparison of CUP against Baselines A. Comparison of CUP against Baselines • LLM-Rec also performs poorly. Solely relying on the LLM’s latent knowledge about books is not sufficient when coping with long-tail items that appear sparsely in the LLM’s training data. Popularity and position bias [31] further aggravate this adverse effect. Thus, to address RQ1 (see Section I-B), merely and fully resorting to LLMs alone is not a viable solution. We also ran extended CUP configurations where precomputed CF vectors were incorporated (see Section III-B). These achieved only very minor gains in some of the settings, and there was even a detrimental effect on some cases. We omit these hybrid methods here. We will revisit them in Subsection V-C for sensitivity studies on data density. Another observation is that the weighted training almost always improves performance. Therefore, the following subsections focus on results with weighted training samples. B. Comparison of CUP Configurations • On the GR-1K-rich data, the overall performance is higher than for AM across all configurations. This can be attributed to the fact that GR has longer reviews, and these texts tend to be more informative than the ones in the AM data (which sometimes refer to packaging, shipment and other non-content aspects). C. Influence of Interaction Density C. Influence of Interaction Density The slices AM-1K-rich and GR-1K-rich are constructed with a text-centric stress-test in mind. Both are extremely sparse in terms of user-item interactions. To study the influence of sparseness in isolation, we created two larger samples of both datasets, one still sparse by design and the other denser in terms of users sharing items. We refer to these as AM-10Ksparse and AM-10K-dense, and analogously for GR. All these samples cover 10K users: 10x more users than our previous text-rich slices, to allow more potential for learning from interactions. Table I in Section IV-B shows statistics. The datasets are constructed as follows. To ensure connectivity in the interaction graphs, we first select 500 users and sample 2000 books connected to these users, both uniformly at random. This is the seed for constructing two variants of data, based on the cumulative item degrees of users, that is, the sum of the #users per book for all books that the user has in her collection: • 10K-dense: We randomly select 10K (minus the initial 500) users from all users in proportion to the users’ cumulative item degrees. This favors users with many or popular books. • 10K-sparse: We select users inversely proportional to their cumulative item degrees, thus favoring sparse users. In this sensitivity study, we focus on comparing CF (based on matrix factorization) against our default CUPtext configuration (with idf-selected sentences) and the hybrid combination CUPtext +CF. Tables V and VI show the results for the AM10K data and GR-10K data, comparing the sparse vs. the dense variants with search-based evaluation (corresponding results with standard evaluation are on the project web page). Key insights are: • As the 10K-sparse data is already much denser than the stress-test 1K-rich slices, CF can achieve decent results on the sparse data. Especially in search-based mode, CF is almost on par with CUP. Note that these gains come from the seen items alone, as CF is bound to fail on unseen items. • CUPtext is still the clear winner on the sparse variant. The hybrid configuration, with text and CF vectors combined, is inferior to learning from text alone. • For the 10K-dense data, CF alone performs well, as this is the traditional regime for which CF has been invented. An interesting point arises for the search-based mode and with dense data. Here, the negative test points are closer to the positive sample (after the BM25 search), and pose higher difficulty for text cues alone to discriminate them. Thus, CF becomes more competitive. On GR, hybrid CUP+CF performs best. D. Efficiency of CUP D. Efficiency of CUP Two architectural choices make CUP efficient: • input length restricted to 128 tokens, and • fine-tuning only the last layer of BERT and the FFN layers. For more analysis, we measured the training time and resulting NDCG on the GR-1K-rich data, comparing different input size budgets and choices of which parameters are tuned and which ones are kept frozen. Figure 2 shows the results. We observe that the 128-token configuration has the lowest training cost: significantly less time per epoch than the other variants and fast convergence (reaching its best NDCG already after 15 epochs in ca. 3000 seconds). The 256- and 512-token models eventually reach higher NDCG, but only by a small margin and after much longer training time. This confirms that concise user profiles, with a small token budget, are the best approach in terms of benefit/cost ratio. As for configurations with more or less tunable parameters, we observe that the variant with frozen BERT takes much longer to converge and is inferior to the preferred CUP method even after more than 50 epochs. The other extreme, allowing all of BERT parameters to be altered together with the FFN parameters, performs best after enough training epochs, better than the CUP configuration that tunes only the last BERT layer. However, it takes almost twice as much time per epoch. So again, from the benefit/cost perspective, our design choice hits a sweet spot in the spectrum of model configurations.

Recommendations by Concise User Profiles from Review Text: Experimental Results

Location, Location, Location! PostGIS and Heroku Postgres

অবস্থান, অবস্থান, অবস্থান! পোস্টজিআইএস এবং হেরোকু পোস্টগ্রেস

Lage, Lage, Lage! PostGIS und Heroku Postgres

¡Ubicación, ubicación, ubicación! PostGIS y Heroku Postgres

Emplacement, emplacement, emplacement ! PostGIS et Heroku Postgres

स्थान, स्थान, स्थान! PostGIS और Heroku Postgres

場所、場所、場所！PostGIS と Heroku Postgres

위치, 위치, 위치! PostGIS와 Heroku Postgres

Localização, localização, localização! PostGIS e Heroku Postgres

Местоположение, Расположение, Местоположение! PostGIS и Heroku Postgres

Konum, Konum, Konum! PostGIS ve Heroku Postgres

Vị trí, vị trí, vị trí! PostGIS và Heroku Postgres

位置，位置，位置！PostGIS 和 Heroku Postgres

Humans! One interesting twinkle in the dark, vacuous expanse of the cosmos. Trapped on a floating ball of rock to mope into space and be tormented by relentless, conflicting emotions. Prison within and prison without. Awaiting the summon of death. What the hell is this existence and what is its use? At least we have each other. Right? Yes, we do. Plus, the resources of the earth. And most importantly, there's that small voice that keeps telling us to become more. So, we stagger on. Nothing is certain in this great maze of existence but we don't just give in. We grope for purpose in the confusing unknown. We do what we do in search of fulfillment. Thus, we solve problems for those we love. We really dig in and harvest ingenious creations from within us. It makes us happy, so we keep going. We keep chasing that which our heart yearns for. What is it many have asked? What is it that we really want? Some of the smartest among us have given a name to it, and I dare agree with them. It is what it is; Freedom! Freedom from wants, death, and ourselves. Freedom from whatever dares limit us. Freedom from this great bondage that is us. Hence, in our relentless yearning, we build this great bridge of science. Into matter, into space, into life. Into technology! Oh that we may find that which we seek. Oh that we may find; Autonomy! Autonomy Has Always Driven Technology Humans have always been bound by limitations that we constantly rebel against. Imagine the caveman days. Small, slow, and weak as we were, we were completely bound by the harsh laws of the jungle. Dominated by powerful creatures. Yet, our big brain brought us to the very top of the food chain. Inventions like the spear, knife, and arrow with advanced hunting strategies helped us overcome the powerful beasts that ruled the wild. Then, we created our own societies and were ruled by tribal laws. Not very different jungle laws though but strong humans were in charge now. Then came the reign of religion, and priests were in charge. Then came countries with cunning politicians. Then came capitalism with wealthy businessmen who are now running the show. All these changes have been driven by advancements as humans try to take more control of their lives. From being ruled by wild beasts to strong men to priests to politicians and businessmen, people have always invented systems that gave them more control of their lives. And this progress is not only evident in the technology of our leadership but also in the everyday devices that we use. First, we had caves and stone tools, then came villages and metal tools, and then came towns and analog machines. Now, we have cities and digital technology. This drive for autonomy in our technology has culminated in what we call a decentralized internet or web3. Autonomy in Web3 Web 3 remains a relatively recent technology that is yet to be widely understood. Yet, it is already being widely adopted as the advantages are very obvious. Simply put, web3 aims to put the Internet and financial system under the control of everyone and not just a few giant corporations. Why is this necessary you may ask? Well, the answer is simple: "Power corrupts and absolute power corrupts absolutely!" The internet and financial systems represent the most critical technologies that humans have been able to develop. Hence, there is a great power in the hands of anyone who controls them. It's no wonder that top government officials and tech billionaires dominate the list of most powerful people in the world. This great power which allows such people to exercise control around the world is akin to the legendary absolute power that absolutely corrupts the wielders. And we have already seen this corruption at play. For example; , , , etc. Facebook has recorded multiple user data breaches Twitter has been accused many times of stifling free speech US politicians were caught cheating through insider trading in the stock market Hence, the internet and financial market which have lasting effects on people’s lives need some level of democracy. This is what web3 promises. Cryptocurrencies were introduced as a means for people to transact directly without passing through middlemen in the banking institutions who often benefit fraudulently from people’s money with little or no punishment. Combining crypto with blockchain technology provides the world with a global decentralized financial internet that no one fully controls and everyone can benefit from. Is Autonomy Truly Possible? Many reservations have been raised about the practicality of web3 and whether it can be truly autonomous. For example; fundamental blockchain infrastructures are often hosted on web2 servers with centralized control. Also, mining cryptocurrency is energy intensive which makes the entire system dependent on power companies. Would the power companies and server owners not be the new bosses in town? Well, that's a critical problem that is yet to be solved. However, blockchain technology is slowly solving the centralized server problem through compressed distributed storage of the technology on the devices of its users. Basically, a blockchain is a database, and every device that connects to the blockchain has a version of the database stored on it. This makes it possible for the billions of devices that users use to connect to the blockchain to function as servers distributed around the world instead of just one central server. Fingers crossed for what will come in the future. About the electricity problem, well, most cryptocurrencies have a limited amount of tokens to be mined, hence, mining is not a forever thing. Also, alternative versions of power like solar make it possible for miners to set up affordable sources of personalized power. Hence, centralized power companies can possibly be bypassed. With these considerations, the autonomy that web3 promises can foreseeably be accomplished. Future of Autonomy Picture this... You fall asleep now and awaken in the autonomous age. Robots run the world now. The planet is one big country run by the United Nations and headquartered in Earth City (a giant circular artificial island built in the Atlantic Ocean on longitude 0 degrees and latitude 0 degrees). Networks of underwater hyperloop lines and speed train bridges interconnect all the continents in minutes and the former countries are nothing but political provinces for proper documentation and security. Autonomous vehicles with little or no accidents dominate the roads and autonomous spaceships transverse space to transport people and resources around the solar system. Humans have finally colonized the solar system. The world lives in abundance as automated high-tech farms meet all our agricultural needs. Basic amenities like water, electricity, and education are free, and everyone gets a living wage to cover secondary needs like real estate around the planets, internet access, and transportation. Advanced amenities like technology, art, entertainment, exploration, and financial trading are where businesses operate. Web3 is fully operational. Hologram devices are all over the place for people to experience the dinterverse (decentralized internet metaverse) which uses all those devices as servers. DAOs, running on open-source technology, are the global means of voting, with Auth ID being the prominent open-source platform for decentralized technology. Industry-based crypto coins are the means of exchange (e.g., techon for technology, agrocoin for agriculture, muse for music, etc.) and cryptex trading (forex trading of the time) is the primary means of employment for everyone with people speculating on which industry will do better based on the season. Bitcoin is, however, immortalized as the only currency accepted around the world across all industries. Global poverty has been eradicated and financial inequality is very low around the world with cryptex trading available to everyone. A decentralized financial system was the game changer as it led to an explosion of prosperity through relatively easy cryptex trading. You investigate your trade on your holophone. You're doing very well and will be making another $50,000 this week. You smile to yourself as you glance out the window of your noiseless starjet (silent speed jets that ferry people around the world in minutes). The city below is a great wonder as people said. You're on vacation in Earth City. It's your first time. You came for it. The statue of autonomy or "the human spirit" as it is fondly called. Many weep upon first beholding its glory. The symbol of human victory on the earth. It appears on the horizon. The shiny bust of a crowned androgynous human child stops at the waist so it's neither male nor female. Completely transparent as if entirely made of glass, hence, the name human spirit. With both hands upraised in freedom, it holds Bitcoin (the first global crypto) in the right hand and a scroll in the left. Your ship does the ceremonial circling of the statue. Then it passes the scroll and you behold those legendary words. You read it in a low voice. "We humans hold that all of us are born free. Free to live, free to love, free to prosper!" A smile crawls up your face, and you resolve to uphold this great motto. Then you hear a phone ring. A familiar ancient music. It gets louder and louder. It's your phone. You were dreaming of course. You curse as you pick up your phone. It's your boss calling you to return to work. You cut the call and sigh in disappointment. That dream was just beautiful. Then it occurs to you: The current world is one big nightmare! WHAT DOES THIS MEAN Web3 is upon us. Cryptocurrencies are everywhere and blockchain infrastructure gets more sophisticated every day. Many crypto millionaires roam the earth to prove the real value of the technology. Decentralization is on its way. Below are the predictions you should know about web3; Decentralization is the future. Digital commodities e.g., NFTs, in-game items, virtual real estate, and decentralized exchange (DEX) will create a new industry. Currencies will become more globalized. Companies will become smaller and more personalized. Self-service will become the new way of operating the financial industry. HOW DO I PREPARE FOR AUTONOMY Educate yourself on cryptocurrencies, blockchain technology, and the metaverse. Invest in promising crypto coins. Participate in public web3 forums. Fund web 3 projects if you can. Learn how to make money on your own from the internet.

Behold: The Autonomy We Always Sought

দেখুন: যে স্বায়ত্তশাসন আমরা সবসময় চেয়েছি

Siehe: Die Autonomie, die wir immer gesucht haben

He aquí: la autonomía que siempre buscamos

Voici : l’autonomie que nous avons toujours recherchée

देखिए: वह स्वायत्तता जो हम हमेशा से चाहते थे

見よ、私たちが常に求めていた自律性

보라: 우리가 항상 추구했던 자율성

Eis: a autonomia que sempre buscamos

Вот: автономия, к которой мы всегда стремились

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