Byte Tree

bytetree

Crypto founders share their views on whether physical currency will disappear altogether One day we will stumble across a £20 note in the back of an old drawer and think back to a time where this now tarnished piece of paper once represented a value that constituted the backbone of our world's economy. These are trying times for humanity, and with the use of cash actively discouraged during the pandemic, it's a more popular opinion within the crypto world that the move towards cashless societies’ looking increasingly likely. A 'death knell' of cash COVID-19's changing the way the world sees and uses money. It has accelerated the move to digital and contactless payments, driving forward the already steady decline in the use of cash and the number of ATM cash withdrawals over time. Throughout this pandemic, retailers that have historically been cash-only updated their card systems and embraced digital payments. Co-Founder of Ethereum and CEO of IOHK, Charles Hoskinson, has noticed a death knell toll for cash payments over the last few years, believing that COVID-19 could be the final nail in the coffin. He foresees that the values of decentralised digital payments and banking will overthrow those pertaining to what we now consider to be traditional. "With a decentralised infrastructure in place, people would have 24-hr remote access to vital products and services. These services could be accessed from any location, regardless of potential transport disruptions or banks. Decentralised banking would also allow for quicker and cheaper international transactions. People working abroad, for instance, would be able to send money to family in other countries without [the] fear of delays or inflated transfer fees." As of the 28th of May, Samsung Blockchain Wallet allows users in the U.S. and Canada to trade cryptocurrencies, thanks to a partnership with crypto exchange Gemini. Julian Sawyer, Co-Founder of Starling Bank and Gemini's Managing Director for Europe, said, "As we look to the future of crypto payments, it's important that we apply blockchain technology in places where it will have the most potential to revolutionise existing pain points. Look at the remittance, cross-border payments, and foreign exchange payments areas, for example. These are all very inefficient processes that will greatly benefit from a decentralised system. In many of these applications, the end-user may not even realise that the underlying technology is [the] blockchain - they will simply choose the newer solution if it’s faster, cheaper, and more convenient." What poses to be the most exciting prospect of decentralised banking is that it would allow us to finally 'bank the unbanked.' "Where 1.7 billion people globally don't have access to a bank account, a large proportion of this population does have a cell phone." Hoskinson continued. "Using third-generation blockchains, we can extend financial services to these individuals who have previously been excluded from the financial system. Secure blockchain wallets can be accessed online or via cell-phones, and can grant users access to essential services without the middleman involved, democratising finance on a global scale." University research facilities at Hoskinson' new company, IOHK, have, in fact, already created third-generation blockchains ultimately capable of processing over a million transactions a second, no matter the transaction size. Such advances, when rolled out, could mean that transaction fees could be eliminated and funds transferred almost instantaneously. A decentralised planet CEO of split-second crypto and fiat trading app, TAP Global, Arsen Torosian, is confident that a cashless society would have more of a positive impact in the long run, due to what we have already learned throughout this pandemic. "It's important to consider the downsides to using fiat as we emerge from this pandemic. Physical cash is a great way to transmit diseases and has often proved costly for businesses to process. Many now don't even accept it! However, there will remain an element of society that lives only using physical cash. We can't leave them behind." Torosian believes that challenger banks have an important role in ensuring that those who transition between physical and digital realms are efficiently accommodated. His pessimistic outlook toward the movement is also quite apparent. "There will have to be a grand overhaul whilst building some sort of safety net/banking option when moving these people over to a fully cashless society. It’s the duty of challenger banks to make the shift as inviting, easy, and safe as possible. Ultimately, a cashless society is inevitable." James Bennett, CEO of leading blockchain analytics company, ByteTree, has observed the fundamental place of crypto. "Crypto will always have a place as long as there’s distrust in government-issued currencies (or fiat currencies). Purchase volumes in crypto-asset, such as bitcoin, have shown to increase with political instability. This has been seen through Venezuela's on-going hyperinflation, Iran's heavily sanctioned economy, and Hong Kong, as its citizens battle to retain sovereignty from China. P2P bitcoin volumes have increased by around 15% worldwide over the last 30 days." There’s an inevitable need for digital currencies, and a real coming of age for blockchain technology shows that a move to decentralised banking could be on the cards much sooner than some think. Ultimately, humans are creatures of habit. It's uncertain whether we will see banks decentralised and a fully digital means of living in our lifetimes as it would require significant cultural change. Bennett added, "I believe physical cash is predominantly used for two reasons. It can be trusted (it's hard currency), and it allows consumers to maintain a level of privacy. With the rapid innovation of digital payments and challenger banks, alongside the increasing coverage of smartphone users across the world, users can gain access to both a trusted service as well as private transactions - with certain crypto-assets." Crypto founders share their views on whether physical currency will disappear altogether One day we will stumble across a £20 note in the back of an old drawer and think back to a time where this now tarnished piece of paper once represented a value that constituted the backbone of our world's economy. These are trying times for humanity, and with the use of cash actively discouraged during the pandemic, it's a more popular opinion within the crypto world that the move towards cashless societies’ looking increasingly likely. A 'death knell' of cash COVID-19's changing the way the world sees and uses money. It has accelerated the move to digital and contactless payments, driving forward the already steady decline in the use of cash and the number of ATM cash withdrawals over time. Throughout this pandemic, retailers that have historically been cash-only updated their card systems and embraced digital payments. Co-Founder of Ethereum and CEO of IOHK , Charles Hoskinson , has noticed a death knell toll for cash payments over the last few years, believing that COVID-19 could be the final nail in the coffin. He foresees that the values of decentralised digital payments and banking will overthrow those pertaining to what we now consider to be traditional. IOHK Charles Hoskinson "With a decentralised infrastructure in place, people would have 24-hr remote access to vital products and services. These services could be accessed from any location, regardless of potential transport disruptions or banks. Decentralised banking would also allow for quicker and cheaper international transactions. People working abroad, for instance, would be able to send money to family in other countries without [the] fear of delays or inflated transfer fees." As of the 28th of May, Samsung Blockchain Wallet allows users in the U.S. and Canada to trade cryptocurrencies, thanks to a partnership with crypto exchange Gemini. Julian Sawyer , Co-Founder of Starling Bank and Gemini's Managing Director for Europe, said, "As we look to the future of crypto payments, it's important that we apply blockchain technology in places where it will have the most potential to revolutionise existing pain points. Julian Sawyer Starling Bank Gemini's Look at the remittance, cross-border payments, and foreign exchange payments areas, for example. These are all very inefficient processes that will greatly benefit from a decentralised system. In many of these applications, the end-user may not even realise that the underlying technology is [the] blockchain - they will simply choose the newer solution if it’s faster, cheaper, and more convenient." What poses to be the most exciting prospect of decentralised banking is that it would allow us to finally 'bank the unbanked.' "Where 1.7 billion people globally don't have access to a bank account, a large proportion of this population does have a cell phone." Hoskinson continued. "Using third-generation blockchains, we can extend financial services to these individuals who have previously been excluded from the financial system. Secure blockchain wallets can be accessed online or via cell-phones, and can grant users access to essential services without the middleman involved, democratising finance on a global scale." University research facilities at Hoskinson' new company, IOHK, have, in fact, already created third-generation blockchains ultimately capable of processing over a million transactions a second, no matter the transaction size. Such advances, when rolled out, could mean that transaction fees could be eliminated and funds transferred almost instantaneously. A decentralised planet CEO of split-second crypto and fiat trading app, TAP Global , Arsen Torosian , is confident that a cashless society would have more of a positive impact in the long run, due to what we have already learned throughout this pandemic. "It's important to consider the downsides to using fiat as we emerge from this pandemic. TAP Global Arsen Torosian Physical cash is a great way to transmit diseases and has often proved costly for businesses to process. Many now don't even accept it! However, there will remain an element of society that lives only using physical cash. We can't leave them behind." Torosian believes that challenger banks have an important role in ensuring that those who transition between physical and digital realms are efficiently accommodated. His pessimistic outlook toward the movement is also quite apparent. "There will have to be a grand overhaul whilst building some sort of safety net/banking option when moving these people over to a fully cashless society. It’s the duty of challenger banks to make the shift as inviting, easy, and safe as possible. Ultimately, a cashless society is inevitable." James Bennett , CEO of leading blockchain analytics company, ByteTree , has observed the fundamental place of crypto. "Crypto will always have a place as long as there’s distrust in government-issued currencies (or fiat currencies). James Bennett ByteTree Purchase volumes in crypto-asset, such as bitcoin, have shown to increase with political instability. This has been seen through Venezuela's on-going hyperinflation, Iran's heavily sanctioned economy, and Hong Kong, as its citizens battle to retain sovereignty from China. P2P bitcoin volumes have increased by around 15% worldwide over the last 30 days." There’s an inevitable need for digital currencies, and a real coming of age for blockchain technology shows that a move to decentralised banking could be on the cards much sooner than some think. Ultimately, humans are creatures of habit. It's uncertain whether we will see banks decentralised and a fully digital means of living in our lifetimes as it would require significant cultural change. Bennett added, "I believe physical cash is predominantly used for two reasons. It can be trusted (it's hard currency), and it allows consumers to maintain a level of privacy. With the rapid innovation of digital payments and challenger banks, alongside the increasing coverage of smartphone users across the world, users can gain access to both a trusted service as well as private transactions - with certain crypto-assets."

Sounding The Death Knell for Cash Money: How Far Is It?

The cryptocurrency industry has just experienced the most anticipated event, Bitcoin (BTC) 2020 halving. The last 12.5 Bitcoin block has been mined by and encoded the message of “NYTimes 09/Apr/2020 With $2.3T Injection, Fed’s Plan Far Exceeds 2008 Rescue.” paying tribute to Satoshi Nakamoto. was in luck and mined the first 6.25 Bitcoin block. F2Pool Antpool The Bitcoin 1st halving happened in Nov 2012, mined by , and the 2nd halving happened in July 2016, mined by . For Bitcoin, we have now officially entered the 6.25 block subsidy era, and the 4th Bitcoin halving started to count down. Slushpool F2Pool Bitcoin network hash rate experienced some decline, but the first difficulty adjustment post-halving will still see a positive adjustment The market experienced some volatility during the last 60 blocks leading up to the halving, however, the Bitcoin price stayed within the $8,500–$9,000 range for a 24-hour window during the event. Bitcoin Price by Block Height, Source: ByteTree, TokenInsight The average block production time was roughly 7.5–8.5 mins per block 24 hours prior to the halving, and we see some increase in block time post-halving, with an average of 11 minutes per block in 12 hours following the halving. With the available sample, we can safely conclude that the network has experienced a hash rate decline post-halving, as indicated by longer block production times than the pre-halving average. to the Bitcoin difficulty estimator, immediately post halving the network pace was roughly 52 blocks ahead of schedule. This may be due to miners pushing block production faster in the 24 hours leading up to the halving. With the current estimate, the next difficulty change will happen in roughly a few days, with a negative adjustment. However, the block production time could shift significantly within the period as miners try to adjust their operations. According Slight uptick of transaction fees to subsidize miners’ revenue around the 2020 halving The number of transactions were consistently showing an upward trend in the 24 hours leading up to the halving, with an average of 1,959 transactions contained per block. No. of Transactions by Block Height, Source: ByteTree, TokenInsight Total transaction fees roughly doubled around the halving from a fraction of a Bitcoin to more than one Bitcoin, and eventually dropped back to the pre-halving level. Total Transaction Fees by Block Height, Source: ByteTree, TokenInsight The network has seen a slight uptick of average fees per transaction during the last 50 blocks, and in the first 20 blocks after the halving, the average fees have been decreasing and stayed relatively stable. Transaction Fees (Mean) by Block Height, Source: ByteTree, TokenInsight Prior to the halving, miners’ revenue from fees only accounted for a single digit of the total revenue that they will receive. There was an immediate jump in miner revenue from fees post-halving due to the reward being cut in half. Twelve hours into the 6.25 block subsidy era saw miners’ revenue from fees dropping to a lower level, but they stayed about 10% per block on average. Miner Revenue from Fees by Block Height: ource: ByteTree, TokenInsight It is also worth noting that Slushpool the 630,001st block, which contains almost 18% transactions fees, which is roughly 1.369 BTC in fees. It is one of a few blocks with the highest miners’ revenue from fees so far since the halving. mined Happy 2020 Bitcoin halving! Now counting down to 2024 Bitcoin was born during a time when banks and the traditional finance industry faced turmoil. Yet, more than 11 years later, the industry is working together to bring the Bitcoin and cryptocurrency industry to the world stage. The encoded coinbase parameter for block 629,999, “NYTimes 09/Apr/2020 With $2.3T Injection, Fed’s Plan Far Exceeds 2008 Rescue” echoes Satoshi Nakamoto’s encoded message for block 0, “The Times 03/Jan/2009 Chancellor on brink of second bailout for banks.” This reminds us why Bitcoin was created: To provide a more trustworthy monetary system.We look forward to witnessing future Bitcoin halvings with the whole world. This is just the beginning. Johnson Xu and Chou — By

Bitcoin's Post-Halving Hash Rate [Analyzed]

, an industrial partner with the University of California, Berkeley (“Berkeley”) FHL Vive Center, today announced that the partnership has led to the launch of , a new decentralized AI infrastructure project. ByteTrade Lab GaiaNet GaiaNet is a decentralized network that provides secure, censorship-resistant, and monetizable AI agents that incorporate each individual’s proprietary knowledge and skills while preserving privacy. Instead of setting up centralized servers, GaiaNet is building a distributed network of edge-computing nodes controlled by individuals and businesses, to host fine-tuned AI models, based on the node operator’s proprietary domain knowledge and expertise. Each GiaiaNet node is a digital twin of a real-world expert or personality, such as a university teaching assistant, a high school teacher, a crypto trader, or a customer service agent, performing skilled knowledge tasks on behalf of its creator or operator. GaiaNet boldly challenges the centralized industry giants that have dominated the AI lifecycle, from data collection to model training and fine-tuning. GaiaNet recognizes the advancement of edge AI research at Berkeley, particularly at the FHL Vive Center where it drives the adoption of small language models for offering interactive and educational AI teaching assistant services to the broad student community. GaiaNet envisions that its decentralized infrastructure will help to onboard more educational content and more educators in a decentralized fashion, and spread the use of AI teaching and learning applications outside the elite higher-education institutions to professional development and professional services markets. “I am honored to collaborate with Berkeley in our shared mission to revolutionize learning through decentralized AI. GaiaNet is not just a network; it's a paradigm shift towards a more secure, censorship-resistant future while preserving privacy and autonomy”, stated Shashank Sripada， Co-founder of GaiaNet. “GaiaNet is also committed to simplicity, ensuring that individuals and businesses can easily set up their own nodes and fine-tune their AI models with their proprietary knowledge. Through GaiaNet's intuitive interface and streamlined processes, accessing cutting-edge AI technology has never been easier”. As an esteemed academic institution, UC Berkeley Vive Center is primed to leverage its expertise to openly support the advancement of decentralized AI, especially with its partner ByteTrade to explore the technologies behind GaiaNet. This collaboration exemplifies the University's dedication to staying at the forefront of the most significant challenges and innovations in Artificial Intelligence. About GaiaNet Gaianet stands as a trailblazer in decentralized AI technology, dedicated to reshaping the tech industry with a focus on user privacy, transparency, and control. Our cutting-edge open-source infrastructure empowers users by ensuring privacy and censorship resistance in daily AI activities through a dynamic network of peer-to-peer edge nodes. This framework fosters the creation and collaboration of innovative AI business ecosystems, benefiting both private data holders and developers. Gaianet aims to revolutionize the user experience with AI chatbots, seamlessly introducing transformative changes while preserving the intuitive technology and satisfaction currently existing in the Web2 era. | | Website Github Twitter About ByteTrade Lab is inventing the infrastructure to decentralize the internet application and protocol layer for developers and users, ushering in a new internet paradigm that achieves the full Web 3.0 value proposition. ByteTrade’s innovative Web 3.0 Operating System-Terminus OS, running atop a network of decentralized personal servers protected by personal private keys, will build a new decentralized internet to empower users in controlling their own data and services. The company is backed by Susquehanna International Group (SIG)’s Asia Venture Capital Fund, and leading institutional investors including INCE Capital, BAI Capital, Sky9 Capital, BlueRun, and PWC with a $50mil USD A round financing. ByteTrade Lab | Website Twitter About UC Berkeley FHL Vive Center Connecting the world of individuals and businesses in a fully decentralized and trustless fashion has generated tremendous excitement to develop new solutions in the blockchain and DeFi space. Berkeley FHL Vive Center aims to support the long-term growth of decentralized technologies and applications by offering our expertise to create new education and degree programs for motivated students; promote cross-disciplinary research to address urgent technology bottlenecks and provide a Berkeley platform for academic and industrial partners to collaborate. Together, we can shape our society in a digital world that is more immersive, more connected, and more fair. Website This story was distributed as a release by BTCWire under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/

ByteTrade Lab and UC Berkeley Partner to Explore the Next Generation of Decentralized AI

বাইটট্রেড ল্যাব এবং UC বার্কলে অংশীদার বিকেন্দ্রীভূত AI এর পরবর্তী প্রজন্মের অন্বেষণ করতে

ByteTrade Lab und UC Berkeley arbeiten zusammen, um die nächste Generation dezentraler KI zu erforschen

ByteTrade Lab y UC Berkeley se asocian para explorar la próxima generación de IA descentralizada

ByteTrade Lab et UC Berkeley s'associent pour explorer la prochaine génération d'IA décentralisée

बाइटट्रेड लैब और यूसी बर्कले ने विकेंद्रीकृत एआई की अगली पीढ़ी का पता लगाने के लिए साझेदारी की

ByteTrade Lab とカリフォルニア大学バークレー校が次世代の分散型 AI の探索で提携

ByteTrade Lab과 UC Berkeley, 차세대 분산 AI 탐색을 위해 파트너십 체결

ByteTrade Lab e UC Berkeley fazem parceria para explorar a próxima geração de IA descentralizada

ByteTrade Lab и Калифорнийский университет в Беркли сотрудничают в изучении следующего поколения децентрализованного искусственного интеллекта

ByteTrade Lab ve UC Berkeley, Yeni Nesil Merkezi Olmayan Yapay Zekayı Keşfetmek İçin İşbirliği Yapıyor

Phòng thí nghiệm ByteTrade và UC Berkeley hợp tác để khám phá thế hệ tiếp theo của AI phi tập trung

ByteTrade Lab 与加州大学伯克利分校合作探索下一代去中心化人工智能

SAN FRANCISCO, United States, July 1st, 2024/Chainwire/--Nubila,a leading data oracle specializing in accurate and actionable Environmental, Social, and Governance (ESG) data for the Decentralized Physical Infrastructure Network (DePIN) ecosystem, has kicked off its fundraising round. The on-going round is led by IoTeX and VeChain + Boston Consulting Group, with the participation from OrangeDAO, MH Ventures, Future Money Group, SNZ Capital, DePINsurf, Kronos Research, Taisu Ventures, Bytetrade, WaterDrip, Deepin Lab, Assembly Partners, Cyber, Radiance Ventures and Genopets, Nubila has raised $2.5 million to date. This significant investment underscores the industry's recognition of Nubila's potential to leverage AI and tokenization to empower decision-making, drive sustainable development, and build financial products with ESG data. "At Nubila, we are committed to building the infrastructure for the MachineFi revolution, serving as the 'data sensor layer' for our planet," said Ben, the founder of Nubila. "Our vision is to create a comprehensive network of advanced weather stations and sensors that capture critical environmental data. Integrating real-time ESG data into an increasing number of AI models, such as Microsoft’s recently released Aurora atmospheric model, will enhance ESG assessments and drive more informed and sustainable decision-making across industries. With this funding, we are poised to scale our operations and expand our global reach, ensuring that we contribute significantly to the sustainability and well-being of our planet.” The funds will be strategically used to scale Nubila’s operations, further develop its cutting-edge ESG Data Oracle, and pursue key strategic goals. These initiatives include enhancing data accuracy, expanding the network’s reach, increasing user engagement, and the awareness of sustainability investment. "Nubila is at the forefront of leveraging DePIN and AI to transform ESG data management," said Raullen Chai, Founder of IoTeX. "Their approach to creating a decentralized ESG network of community-owned IoT devices, delivering hyperlocal weather data to enhance predictions for various industries is revolutionary. By providing actionable environmental data, Nubila is not only enhancing ESG assessments but also enabling more informed and sustainable decision-making across various industries. We are excited to support their mission and look forward to seeing the significant impact they will make on the sustainability landscape.” This network features advanced weather stations, the only ones equipped with high-precision sensors and cameras, dedicated to gathering detailed environmental data for accurate ESG assessments. To date, Nubila has deployed 16,000 stations worldwide, collectively capturing over 100TB in weather data. About Nubila Headquartered in San Francisco, Nubila is a pioneer in the Environmental, Social, and Governance (ESG) data sector, dedicated to transforming sustainability initiatives through innovative solutions. By building the largest ESG Data Oracle within the DePIN ecosystem, Nubila leverages AI and ESG tokenization to empower decision-making, drive sustainable development and build financial products. Contact Marketing Head Darren Goh Nubila marketing@nubila.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here, SAN FRANCISCO, United States, July 1st, 2024/Chainwire/--Nubila,a leading data oracle specializing in accurate and actionable Environmental, Social, and Governance (ESG) data for the Decentralized Physical Infrastructure Network (DePIN) ecosystem, has kicked off its fundraising round. The on-going round is led by IoTeX and VeChain + Boston Consulting Group, with the participation from OrangeDAO, MH Ventures, Future Money Group, SNZ Capital, DePINsurf, Kronos Research, Taisu Ventures, Bytetrade, WaterDrip, Deepin Lab, Assembly Partners, Cyber, Radiance Ventures and Genopets, Nubila has raised $2.5 million to date. This significant investment underscores the industry's recognition of Nubila's potential to leverage AI and tokenization to empower decision-making, drive sustainable development, and build financial products with ESG data. "At Nubila, we are committed to building the infrastructure for the MachineFi revolution, serving as the 'data sensor layer' for our planet," said Ben, the founder of Nubila. "Our vision is to create a comprehensive network of advanced weather stations and sensors that capture critical environmental data. Integrating real-time ESG data into an increasing number of AI models, such as Microsoft’s recently released Aurora atmospheric model, will enhance ESG assessments and drive more informed and sustainable decision-making across industries. With this funding, we are poised to scale our operations and expand our global reach, ensuring that we contribute significantly to the sustainability and well-being of our planet.” "At Nubila, we are committed to building the infrastructure for the MachineFi revolution, serving as the 'data sensor layer' for our planet," said Ben, the founder of Nubila. "Our vision is to create a comprehensive network of advanced weather stations and sensors that capture critical environmental data. Integrating real-time ESG data into an increasing number of AI models, such as Microsoft’s recently released Aurora atmospheric model, will enhance ESG assessments and drive more informed and sustainable decision-making across industries. With this funding, we are poised to scale our operations and expand our global reach, ensuring that we contribute significantly to the sustainability and well-being of our planet.” The funds will be strategically used to scale Nubila’s operations, further develop its cutting-edge ESG Data Oracle, and pursue key strategic goals. These initiatives include enhancing data accuracy, expanding the network’s reach, increasing user engagement, and the awareness of sustainability investment. "Nubila is at the forefront of leveraging DePIN and AI to transform ESG data management," said Raullen Chai, Founder of IoTeX. "Their approach to creating a decentralized ESG network of community-owned IoT devices, delivering hyperlocal weather data to enhance predictions for various industries is revolutionary. By providing actionable environmental data, Nubila is not only enhancing ESG assessments but also enabling more informed and sustainable decision-making across various industries. We are excited to support their mission and look forward to seeing the significant impact they will make on the sustainability landscape.” "Nubila is at the forefront of leveraging DePIN and AI to transform ESG data management," said Raullen Chai, Founder of IoTeX. "Their approach to creating a decentralized ESG network of community-owned IoT devices, delivering hyperlocal weather data to enhance predictions for various industries is revolutionary. By providing actionable environmental data, Nubila is not only enhancing ESG assessments but also enabling more informed and sustainable decision-making across various industries. We are excited to support their mission and look forward to seeing the significant impact they will make on the sustainability landscape.” This network features advanced weather stations, the only ones equipped with high-precision sensors and cameras, dedicated to gathering detailed environmental data for accurate ESG assessments. To date, Nubila has deployed 16,000 stations worldwide, collectively capturing over 100TB in weather data. About Nubila Headquartered in San Francisco, Nubila is a pioneer in the Environmental, Social, and Governance (ESG) data sector, dedicated to transforming sustainability initiatives through innovative solutions. By building the largest ESG Data Oracle within the DePIN ecosystem, Nubila leverages AI and ESG tokenization to empower decision-making, drive sustainable development and build financial products. Nubila Nubila Contact Marketing Head Darren Goh Nubila marketing@nubila.ai 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

Nubila Kickstarts Fundraising Round Led By IoTeX, VeChain And Other Leading Investors

Machine Learning: Your Ultimate Feature Selection Guide Part 2 - Select the Real Best

Efficient Microservices: Harnessing the Power of Data Compression in Direct Service-to-Service.

The choice of a suitable network topology is critical for effective and stable multiplayer. We'll talk about the pros and cons of the main network topologies for real-time multiplayer: P2P, authoritative servers, relay servers, and more. Greetings again everyone, I'm Dmitrii Ivashchenko, Lead Software Engineer at MY.GAMES. Our article series on the landscape of Unity realtime multiplayer in 2023 continues — and today's topic concerns the main network topologies for realtime multiplayer. Network Topology The key factor in ensuring the effective and stable operation of a multiplayer game is the choice of a suitable network architecture, or topology. This decision affects both the way data is transmitted and processed in the game, as well as performance and resource consumption. Let's look at some possible models. P2P (Peer-to-Peer) P2P, or peer-to-peer architecture, is a model in which each player connects to every other player and exchanges data about game state and events. In “pure” P2P, there is no single "host". Instead, each client is responsible for managing its own game character (or units) while receiving updates about events from each of the other clients. Advantages of P2P: No need to develop or maintain dedicated servers. Clients create direct connections with each other, providing the most direct connection (which usually, but not always, corresponds to a low ping). Disadvantages of P2P: The absence of a single authoritative host makes cheat detection significantly more difficult. This also makes compensation for delays less effective. Additionally, a "desynchronization" of clients is possible, leading to differences in game state between them. Each client creates multiple outgoing connections and accepts incoming connections, so each client has to deal with firewall settings and port forwarding. Authoritative servers In this model, one of the nodes of the game network (this is usually initially selected or based on network connection features) becomes the "authoritative server", controlling the game state and processing all game events. The other nodes (clients) send their actions to this server and receive updated game state from it. Server-client with the authoritative dedicated game server (DSG) In a client-server architecture with an authoritative dedicated game server, all clients connect to a single game server. The dedicated game server (DGS) runs the game engine and is the source of the game's final state. It receives updates from connected clients, incorporates them into its game state, and sends the resulting state to each client. MMORPGs and other games with persistent online worlds also fall into this category. In such cases, DGS also includes databases that store data about the persistent world. Advantages of DGS: The game server is "authoritative": it has the final say on whether events created by clients are valid or not. This allows for a wide range of delay compensation mechanisms and fraud prevention, as well as eliminating synchronization issues, as the server overrides the state of clients. Disadvantages of DGS: It is necessary to develop and manage a dedicated game server, and doing this has certain costs associated with it. Leveraging a client-server architecture with an authoritative dedicated game server is one of the most common approaches in large multiplayer games and provides a high degree of control over the state of the game and combating fraud. Server-client with authoritative client-host In a client-server architecture with an authoritative client-host, instead of using a dedicated game server to maintain authoritative game state, it's possible to use one of the client machines as a server. This client becomes a client-host; simultaneously the host of the game session as well as a participant in the game. Advantages of client-host: Client-host has advantages as a dedicated game server but without the additional cost of running one. Disadvantages of client-host: The host machine must perform additional calculations to simultaneously perform host and client functions. Locally connected clients have much lower connection latency to the host compared to other clients, which can create a competitive advantage (known as "host advantage"). The client-server architecture with an authoritative client-host allows players to independently organize multiplayer sessions without the need to connect to a dedicated game server. However, it's necessary to consider the load on the host machine and potential issues with "host advantage". Relay server In architecture with a relay/matchmaking server for finding opponents, the server exists to facilitate connections and data transmission between clients, but does not actively participate in the game. Although a dedicated server is still required because it does not need to run a game engine, creation and management of this can be relatively inexpensive. Moreover, most P2P games can easily switch to using a relay server, since the network code only differs in the way clients connect to each other. : Advantages of relay servers Since an external server is used, clients only need to establish outgoing connections, so they don't need to configure port forwarding or firewalls. : Disadvantages of relay servers Relay severs have the same drawbacks as P2P, but the delay is higher because the connections are no longer direct. The operation of a relay server still requires certain costs, although they are lower than those of a dedicated game server. The relay/server architecture for finding opponents provides a simple and inexpensive solution for ensuring connections and data transmission between clients. It avoids problems related to port forwarding and firewalls, but it may have some delay due to the intermediate server. Here's an example of the class in C# that uses TCP to listen for incoming client connections and then add them to a list of active clients. The server then asynchronously handles each client connection by reading data and forwarding it to all connected clients: RelayServer public class RelayServer
{
    private TcpListener _listener;
    private List<TcpClient> _clients = new List<TcpClient>();

    public async Task StartAsync(int port)
    {
        _listener = new TcpListener(IPAddress.Any, port);
        _listener.Start();
        Console.WriteLine($"Server started on port {port}");

        while (true)
        {
            TcpClient client = await _listener.AcceptTcpClientAsync();
            _clients.Add(client);

            Task.Run(() => HandleClientAsync(client));
        }
    }
} The code below represents the function , which is responsible for processing each connected client in asynchronous mode. HandleClientAsync First, the function creates a buffer for reading data and obtains a stream for reading and writing data. Next, in an infinite loop, the function asynchronously reads data from the stream. If no data is received (i.e. the client disconnected), the function removes the client from the list and stops processing. Otherwise, the received data is converted to a string and sent to all connected clients: private async Task HandleClientAsync(TcpClient client)
{
    byte[] buffer = new byte[1024];
    NetworkStream stream = client.GetStream();

    while (true)
    {
        int bytesRead = await stream.ReadAsync(buffer, 0, buffer.Length);

        if (bytesRead == 0)
        {
            _clients.Remove(client);
            return;
        }

        string message = Encoding.UTF8.GetString(buffer, 0, bytesRead);
        BroadcastMessage(message);
    }
} To send the received message to all connected clients, we will need the function : BroadcastMessage private void BroadcastMessage(string message)
{
    foreach (var client in _clients)
    {
        NetworkStream stream = client.GetStream();
        byte[] buffer = Encoding.UTF8.GetBytes(message);
        stream.Write(buffer, 0, buffer.Length);
    }
} The function takes a message as a string, iterates through the list of connected clients, and for each of them converts the message to bytes and writes it to the corresponding network connection stream. This way, the message is successfully transmitted to all clients. Conclusion In this article, we discussed the main network topologies for real-time multiplayer games — including P2P, authoritative server, and relay server. Each topology has its advantages and disadvantages, such as direct connections and low ping in P2P, and effective cheat detection and fraud prevention in authoritative server; relay server provides a simple and inexpensive solution for ensuring connections and data transmission between clients. Choosing the right topology is crucial for ensuring a smooth gaming experience in real-time multiplayer games. But this decision also concerns infrastructure cost and project scalability. In the next article, we will consider the main patterns of network architecture in different game genres.

Unity Realtime Multiplayer, Part 6: Game Network Topologies

- that's what the experts say. And what is left for the naughty ones who want to efficiently search through a huge number of templates? "Don't use regular expressions, otherwise you'll have 2 problems instead of 1" Sure, there are cool solutions like or for this specific problem. However, for my project it seemed impractical to master these fine technologies for the time being. Ragel re2c In this article, we will look at alternatives to the standard regexp library in Go and benchmark them for speed and memory consumption. We will also consider the differences between them from a practical point of view. Analogues At the moment, I've found the following working alternatives to the default that can be used to find patterns in Go (the versions used in the benchmarks are given in brackets): regexp (1.3.0) - as simple as possible replaces the default regexp. Uses to improve performance when dealing with large inputs or complex expressions; go-re2 C++ re2 (1.10.0) - a feature-rich regexp engine for . It does not have runtime guarantees like the built-in regexp package, but is compatible with Perl5 and .NET; regexp2 Go (1.0.0) - provides support for compatible regular expressions using or . JIT compilation is available, making this fork much faster than its counterparts. On the downside, you'll need the dependency; go-pcre Perl libpcre libpcre++ libpcre3-dev (regex 1.9.3) - uses the regex engine with bindings. The downside is a library dependency that needs to be compiled; rure-go Rust CGo Rust (1.2.2 + hs5.4.1) - regex engine designed for high performance. It is implemented as a library that provides a simple . It also requires compilation and linking of third-party dependencies; gohs C-API - A tool for identifying and classifying malware samples. Although has functionality for templating and regular expressions, it is very limited, so I will not include this library in the upcoming tests. go-yara YARA Existing benchmarks Before we start comparing the aforementioned solutions, it is worth to show how bad things are with the standard regex library in . I found the where the author compares the performance of standard regex engines of various languages. The point of this benchmark is to repeatedly run 3 regular expressions over a predefined text. came in in this benchmark! Go project Go 3rd place From the end... Language Email(ms) URI(ms) IP(ms) Total(ms) Nim Regex 1.32 26.92 7.84 36.09 Nim 22.70 21.49 6.75 50.94 Rust 26.66 25.70 5.28 57.63 ... ... ... ... ... Python PyPy3 258.78 221.89 257.35 738.03 Python 3 273.86 190.79 319.13 783.78 Go 248.14 241.28 360.90 850.32 C++ STL 433.09 344.74 245.66 1023.49 C# Mono 2859.05 2431.87 145.82 5436.75 . From other benchmarks I can highlight the following: As far as I can tell, benchmarking regex engines is not the easiest topic, because you need to know implementation and algorithm details for a correct comparison - comparison of engines with optimized versions for each language. For example, is no longer at the bottom, but it is still far from ideal there... And of course it does not use a native library, but a wrapper over PCRE - , which was specified in analogues. Benchmarks Game Go go-pcre - Comparison of different regex engines (PCRE, PCRE-DFA, TRE, Oniguruma, RE2, PCRE-JIT). Performance comparison of regular expression engines - here the author tries to compare engines using different regular expressions, which can complicate things depending on the engine implementation. In this benchmark, the author's top three engines are: Hyperscan, PCRE (with JIT compilation) and Rust regex ( uses it): A comparison of regex engines rure Benchmark #1 Now let's try to compare the analogues with default regex engine libraries of other languages. And also see how much faster they are compared to the default . To do this, I updated the above project by adding new libraries code. Here is what I got after running it on my machine: Go regex Language Email(ms) URI(ms) IP(ms) Total(ms) Go Rure 2.61 2.11 3.33 8.05 C++ SRELL 1.74 3.03 10.67 15.45 Rust 11.66 1.70 5.13 18.48 Nim 13.98 14.35 3.13 31.46 PHP 14.43 14.63 4.87 33.93 Go PCRE 14.18 14.98 6.21 35.37 C# .Net Core 10.83 5.10 26.71 42.64 Javascript 42.78 30.17 0.92 73.87 Go Re2 35.81 37.86 33.79 107.46 Go Hyperscan 90.17 31.64 8.68 130.49 Perl 92.51 66.42 22.51 181.44 Dart 73.48 63.60 80.52 217.59 C PCRE2 110.87 100.57 10.50 221.94 Kotlin 120.31 163.53 293.69 577.53 Java 205.14 201.55 295.68 702.36 Python 3 246.26 176.01 303.08 725.34 C++ STL 328.87 273.43 230.35 832.64 Go 270.19 275.73 504.79 1050.71 Go Regexp2 1703.51 1482.60 64.46 3250.57 C# Mono 2543.82 2139.44 110.37 4793.64 . Ps. Shortened the table a bit, so please check the to see all the results. project fork Pss. In this case, virtualization might have affected the results (the tests were performed in a Docker environment on Windows 11). Still, you can see how much faster regexps can be with some libraries! We even outperformed Rust with a Go library that uses the Rust library 🥴🙆🏼‍♂️. Perhaps this is what the author of this solution is trying to explain to us in his . repository As a result, almost all alternative solutions give us a speedup of times! Except for , which turns out to be slower than the standard library. 8-130 Regexp2 Benchmark #2 Questions While studying the existing benchmarks and the results of , I was lacking the answers to the following questions: Benchmark#1 How fast do the above libraries process large files? How much faster is the processing when grouping regular expressions? How fast will regular expressions with no matches in the text be processed? How much memory do the different libraries use? How many regexps will I be able to compile using grouping? Benchmarker differences To answer these questions, I wrote a small benchmarking program that can be used to compare different regex engines in terms of speed and memory usage. If you want to test it yourself or evaluate the correctness of the used methods, here is the . code The following features of this benchmark are worth mentioning: In the tests below, I used different regular expressions: 5 allRegexps["email"] = `(?P<name>[-\w\d\.]+?)(?:\s+at\s+|\s*@\s*|\s*(?:[\[\]@]){3}\s*)(?P<host>[-\w\d\.]*?)\s*(?:dot|\.|(?:[\[\]dot\.]){3,5})\s*(?P<domain>\w+)`
allRegexps["bitcoin"] = `\b([13][a-km-zA-HJ-NP-Z1-9]{25,34}|bc1[ac-hj-np-zAC-HJ-NP-Z02-9]{11,71})`
allRegexps["ssn"] = `\d{3}-\d{2}-\d{4}`
allRegexps["uri"] = `[\w]+://[^/\s?#]+[^\s?#]+(?:\?[^\s#]*)?(?:#[^\s]*)?`
allRegexps["tel"] = `\+\d{1,4}?[-.\s]?$?\d{1,3}?$?[-.\s]?\d{1,4}[-.\s]?\d{1,4}[-.\s]?\d{1,9}` In a good way, I should have used tricky regular expressions, like other benchmark authors do - to check the "weaknesses" of the algorithms. But I don't have much insight into the under-the-hood details of engines, so I used general-purpose regexps. That's why I think it should be possible to evaluate different parameters of libraries from a practical point of view. Instead of a static file, we will use a string containing our matches, repeated many times in memory to simulate files of different sizes: var data = bytes.Repeat([]byte("123@mail.co nümbr=+71112223334 SSN:123-45-6789 http://1.1.1.1 3FZbgi29cpjq2GjdwV8eyHuJJnkLtktZc5 Й"), config.repeatScanTimes) In addition to running the regexps sequentially over the data, there will be a separate run with named grouping of regular expressions, where they will take the following form: `(?P<name_1>regexp_1)|(?P<name_2>regexp_2)|...|(?P<name_N>regexp_N)` By the way, Hyperscan has a special functionality where we can build a database of regexps and use it against data. In the benchmarks I will use this method. Unlike in , for each regexp I will measure the time to find the result, without taking into account the compilation time; Benchmark#1 In the end, we will get the results for each library and each regexp in the following form: Generate data...
Test data size: 100.00MB

Run RURE:
  [bitcoin] count=1000000, mem=16007.26KB, time=2.11075s 
  [ssn] count=1000000, mem=16007.26KB, time=62.074ms 
  [uri] count=1000000, mem=16007.26KB, time=69.186ms 
  [tel] count=1000000, mem=16007.26KB, time=83.101ms 
  [email] count=1000000, mem=16007.26KB, time=172.915ms 
Total. Counted: 5000000, Memory: 80.04MB, Duration: 2.498027s
... 1-2. Processing large files and grouping regular expressions By "large file" I mean the amount of data generated from our predefined string in memory equal to . Of course it's hardly a big file, but I didn't want to wait hours for results in some cases 😅. 100MB Also, it makes sense to try grouping all regular expressions into one and see how much it affects performance. The hypothesis is that a sequential run of regexps over the data should take longer than a single run with a grouped regexp. Well, let's run the benchmark and see how fast each library processes the data. You can do this with my tool as follows: # Bench all libs, repeat the line 1000000 times (100MB)
regexcmp 1000000 
# Bench individual lib
regexcmp 1000000 rure
# See all options
regexcmp -h As a result, we have the following data: Lib Email Tel URI SSN Bitcoin Total Total-group Rure 0.173s 0.083s 0.069s 0.062s 2.11s 2.49s 10.13s PCRE 49.5s 0.367s 2.92s 2.07s 2.19s 57.07s 9.94s Re2 0.954s 0.63s 0.956s 0.945s 1.05s 4.54s 3.24s Hyperscan 0.469s 1.09s 0.669s 0.174s 1.97s 4.38s 4.97s Regexp2 126.4s 1.02s 21.51s 2.63s 3.38s 154.9s 20.65s Go regexp 11.22s 1.52s 3.62s 2.66s 3.32s 22.36s 26.49s The plot below shows the processing time of 100MB of data by all regexps in sequential mode and using grouping: : Conclusions Grouping can indeed significantly improve execution speed, but in some cases it can make it worse :); The fastest in sequential processing turned out to be - , with grouping - ; Rure Re2 Certain regexps may cause problems with some libraries (time to find in and ); email Regexp2 PCRE Now it is hard to say that some solutions are 180 times faster than the standard library, the maximum gain is . x8-9 3. Non-matching regexps In the previous case, we simulated the ideal situation where there are always matches in the data. But what if there are no hits for regexp in the text, how much will that affect performance? In this test, I additionally added modified regexps for SSN that do not match the data. In this case, means regexp, and - . Not a big difference? But let's see how it affects the time it takes to find all the matches: 5 SSN \d{3}-\d{2}-\d{4} Non-matching \d{3}-\d{2}-\d{4}1 Lib SSN Non-matching Total Total-group Rure 0.06s 0.083s 2.93s 15.68s PCRE 2.06s 4.21s 81.02s 13.52s Re2 0.960s 0.449s 6.75s 3.26s Hyperscan 0.175s 0.043s 4.61s 4.94s Regexp2 2.73s 3.09s 171.1s 15.72s Go regexp 2.66s 2.57s 35.13s 37.66s The plot below shows the time taken to process all regexps, sorted by processing time: 10 Non-matching : Conclusions This time is the same: the fastest in sequential processing was - , with grouped expressions - ; Rure Re2 is once again different, with time to process regexps in sequential mode; PCRE 2x non-matching Some algorithms are much faster when there are no matches ( , ); Re2 Hyperscan 4. Memory consumption Now let's see how much memory the different solutions consume when processing a 100MB file. Below, I have provided the results for each individual regexp and the total amount of memory consumed: Lib Email, MB Tel, MB URI, MB SSN, MB Bitcoin, MB Non-matching, KB Total, GB Total-group, GB Rure 16 16 16 16 16 0.0001 0.08 0.08 PCRE 186 186 186 186 186 0.38 0.93 0.9 Re2 254 254 254 255 254 100473 1.77 0.97 Hyperscan 314 314 314 314 314 0.68 1.57 1.7 Regexp2 997 854 854 854 902 396006 6.44 5.23 Go regex 191 144 144 160 208 3.62 0.78 1.8 The graph below shows the memory used by the libraries to process regexps (as in the last test), sorted by the time of `non-mathcing': 10 Conclusions: surprises with its practically zero memory consumption; Rure is resource hungry, consuming significantly more memory than its competitors; Regexp2 , despite its speed, is not the most memory-efficient solution; Re2 is on the good side, with relatively low costs in sequential mode. Go regex 5. Maximum number of regexps The main questions seem to be answered. Now let's see the maximum number of regexps we can compile with different solutions. In this case we will take a single regexp and repeat it many times in groups. For this purpose I will use the regexp: URI `[\w]+://[^/\s?#]+[^\s?#]+(?:\?[^\s#]*)?(?:#[^\s]*)?` Next, I've listed the results of compiling the regexps, along with the memory they use. The numbers in the first line are the number of expressions in the group: URI Lib 100 250 500 1000 5000 10000 Rure 0.38MB ❌ ❌ ❌ ❌ ❌ PCRE 0.40MB 2.37MB ❌ ❌ ❌ ❌ Re2 7.10MB 15.51MB 38.35MB 92.79MB 1181MB ❌ Hyperscan 0.03MB 0.06MB 0.12MB 0.25MB 1.21MB 2.52MB Regexp2 1.06MB 3.96MB 12.56MB 43.93MB 926MB 3604MB Go regex 1.29MB 4.52MB 14.02MB 47.18MB 942MB 3636MB : Conclusions As we can see, some solutions have limitations on the size of compiled regexps; not only allows to use a large number of regexps, but also uses minimal memory for compiled regexps; Hyperscan and have comparable memory consumption and also allow to compile a large number of regexps; Regexp2 Go Regex consumes the most memory at compile time. Re2 Conclusion I hope it was useful for you to learn about alternative solutions for regexps in , and based on the data I presented, everyone will be able to draw some conclusions for themselves, which will allow you to choose the most suitable regex solution for your situation. Go We can compare these libraries, the algorithms they use, their best/worst sides in details and for a long time. I just wanted to show the difference between them in the most general cases. Thus, I would advise you to look at for maximum speedup of regexps, but if you need the easiest library installation without dependencies, that is . And in the case of handling a large number of regexps would be a good choice. Also, don't forget the cost of using in some libraries. rure-go go-re2 hyperscan CGo As for me, I will definitely use these "findings" in my future :). project

Best Regexp Alternative for Go; Benchmarks and Plots

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An ethereum address is the last 20 bytes of the keccack256 of the public key. The public key algorithm is secp256k1, the same used in bitcoin. Because it is an elliptic curve algorithm, the public key is an (x, y) pair corresponds to a point on the elliptic curve. Generate the Elliptic Curve Public Key. The public key is the concatenation of x and y, and that is what we take the hash of. The code is provided below. from ecpy.curves import Curve
from ecpy.keys import ECPublicKey, ECPrivateKey
from sha3 import keccak_256

private_key = 0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80

cv = Curve.get_curve('secp256k1')
pv_key = ECPrivateKey(private_key, cv)
pu_key = pv_key.get_public_key()

# equivalent alternative for illustration:
# concat_x_y = bytes.fromhex(hex(pu_key.W.x)[2:] + hex(pu_key.W.y)[2:])

concat_x_y = pu_key.W.x.to_bytes(32, byteorder='big') + pu_key.W.y.to_bytes(32, byteorder='big')
eth_addr = '0x' + keccak_256(concat_x_y).digest()[-20:].hex()

print('private key: ', hex(private_key))
print('eth_address: ', eth_addr) The ecpy library is here . This library implements the elliptic curve math in python, so it won't be as fast as a wrapper around the bitcoin C implementation, which is used by the library. https://github.com/cslashm/ECPy coincurve However, the python implementation allows you to see step by step the elliptic curve math used to derive the public key. You can use this code to generate an ethereum vanity address with brute force, but be mindful that if your source of randomness is not secure or has insufficient randomness, you may fall victim to a hack similar to . this Generate private key with python and coin flips or dice You can generate an Ethereum address from a private key yourself by flipping a coin 256 times and writing to a string a 1 if it’s heads and 0 if it is tails. Let’s say you get the following outcome result = b'1100001011001101010001001100101000001111101101111011001000110001101100011101101011010001011000101111100110010101001001101110111011001000100001010101111100001100100110010010111110110100000010011111100000110101001110000101100101011111001101010001100001000' You can convert that to a private key using the following python code # 2 means base 2 for binary

private_key = hex(int(result, 2))

# private key is 0x1859a89941f6f646363b5a2c5f32a4ddd910abe19325f6813f06a70b2be6a308 Then, plug that private key into the code from the above section and you’ve generated your address with your own randomness. The same thing can be accomplished faster by rolling a 16-sided dice 64 times and writing out the hex string that is produced character by character. Be mindful that most dice don't have a representation for the number zero, so you'll have to subtract 1 from each result. If you only have traditional six-sided, you can write out a string in base 6 (don't forget to subtract 1 from each roll) and do a base conversion to binary. You'll need to keep rolling until you have at least 256 bits for your private key. If you are particularly paranoid about randomness, you can use casino grade dice. Be cautious using the built-in random number library for Python. It's not intended to be cryptographically secure. We recommend familiarizing yourself with if you are new to the topic. cryptographically secure randomness Generally, you cannot initialize a hardware wallet using this method because the 24 word recovery phrase they use is not the same thing as a private key used to sign transactions. The 24 word recovery phrase is used to derive multiple private keys for different types of crypto the wallet holds. Math: About secp256k1: Public Key from Private Key There is already a lot of material online describing what curve addition looks like , so we will describe it at a lower level here. visually The shape of the Curve Secp256k1 defines the shape of the elliptic curve y^2 = x^3 + y (mod p) where p is the prime number 115792089237316195423570985008687907853269984665640564039457584007908834671663 or 2^256 – 2^32 – 977 The prime should not be confused with the of the curve. Not every value 0 < n < p satisfies the equation above. However, operations on the curve are guaranteed to be . That is, if two valid points are added or multiplied, the result will be a valid number on the curve. p order closed The starting point The other important parameter in secp256k1 is the starting point G. Since G is a point on the elliptic curve, it is 2-dimensional and has the parameters x = 55066263022277343669578718895168534326250603453777594175500187360389116729240
y = 32670510020758816978083085130507043184471273380659243275938904335757337482424 To see G is a valid point, we can plug the numbers into python x = 55066263022277343669578718895168534326250603453777594175500187360389116729240
y = 32670510020758816978083085130507043184471273380659243275938904335757337482424
p = 115792089237316195423570985008687907853269984665640564039457584007908834671663

assert pow(y, 2, p) == (pow(x, 3) + 7) % p To create a public / private key pair, a random number is created (this is the secret key). The point G is added to itself times and the new point (x, y) is the public key. It is not feasible to derive from G. s s s Adding G to itself s times is the same as multiplying s * G. In fact, we can see this operation at a lower level in by stripping away some of the abstractions the library is providing. from ecpy.curves import Curve
from sha3 import keccak_256

private_key = 0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80

cv     = Curve.get_curve('secp256k1')
pu_key = private_key * cv.generator # just multiplying the private key by generator point (EC multiplication)

concat_x_y = pu_key.x.to_bytes(32, byteorder='big') + pu_key.y.to_bytes(32, byteorder='big')
eth_addr = '0x' + keccak_256(concat_x_y).digest()[-20:].hex()

print('private key: ', hex(private_key))
print('eth_address: ', eth_addr) The public key is simply the private key multiplied by the point G on the secp256k1 elliptic curve. That's it. Learn More Check out our advanced today and become a blockchain developer who knows the hard stuff other coders don't. blockchain bootcamp Also published . here

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