PoplarML

poplarml

SAN FRANCISCO, CALIFORNIA - HackerNoon, the technology publishing company, is pleased to announce the nominees for its annual “Startups of the Year” award, featuring the most promising and innovative companies in the San Francisco startup ecosystem. The list, compiled by the HackerNoon team, showcases over 800 startups that are making significant contributions to the San Francisco economy and driving innovation in their respective industries. Nominations for the Startups of the Year 2023 are open! Nominate your company as the . startup of the year in San Fransisco here https://www.youtube.com/watch?v=ZqIW54yMsNc&t=2s&embedable=true Among the nominees in San Francisco for Startups of the Year 2023 are: PoplarML Nodle Cryenx Labs Willow Servicing Retain My Team Remio BabylonAI Fungify Monterey Openlayer Incoggo Polymath Feathery RunWhen Recruiterflow Beacon AI Booth.ai General Micro Technology Enterpret Lemonflow Pixly.tv Hyperstack HireQuotient Found Aviator Finch Dispatch Content Beta Zenpath ScreenMeet All of these startups have demonstrated remarkable growth and innovation in their fields. “We are thrilled to recognize the top startups in San Francisco and celebrate their achievements,” said . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO is a community-driven voting platform that recognizes startups that are changing technology and the world for the better. Starting May 8th, 2023, the internet can nominate and vote for the best ventures in their city. The voting period will remain open until December 31, 2023, and winners will be announced in January 2024 after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 In its , a total of 44,427 startups were nominated in 16 different regions, and the community cast 234,344 votes during a 6-month long campaign in favor of budding companies that were growing and trending across every major city in the world. The received perks from sponsors, like , and a from HackerNoon. previous iteration 1,382 winners 3,000 Brex Points tech company news page As a proponent of the open data movement, HackerNoon releases the unique social data gathered from Startups of the Year on GitHub as “ ” for valuable insights. /where-startups-trend Apart from validation from the world's greenest tech publication, all nominees relevant to their domain, region, and funding. They will also receive an and a free version of their very own on HackerNoon. get free interviews amazing discount package designed for their startup Tech Company News Page These pages include business overviews (such as head count, founding year, bio, social links, and domain ranking) coupled with dynamic updates every time the company is mentioned in HackerNoon stories or relevant headlines around the web. People can , and vote once per day per award for who they think should be Startup of the Year all the way up to December 31st. nominate their favorite startups until June 8th In addition to the award itself, winners receive exclusive interviews, an exclusive NFT from HackerNoon, and a for life. .Tech Domain For more information about the “Startups of the Year 2023” list and the nominated companies, please visit our . FAQ page About HackerNoon HackerNoon is a technology publication that aims to empower developers, entrepreneurs, and tech enthusiasts to share their knowledge and expertise with a global audience. With a readership of over 4M+ monthly visitors, HackerNoon provides a platform for startups and innovators to showcase their work and connect with a vibrant community of like-minded individuals. Companies that blog get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Startups Package Startups Of The Year 2023 is sponsored by . .Tech Domains Nominations for the Startups of the Year 2023 are open! Nominate your company as the . startup of the year in San Fransisco here

Startups of The Year 2023: 800+ Startups Nominated in San Francisco

THE POPLAR-WEEVIL

HackerNoon Reporter: Please tell us briefly about your background. I was enthralled with technology and entrepreneurship from a young age. I was born in Australia but grew up in Hong Kong. One of my favorite past times was wandering through the bustling electronics markets of Hong Kong, searching for different games or computer parts. When it came time for high school, I had my eye on the entrepreneurial action of Silicon Valley, so I convinced my parents to let me travel solo to the US and attend school in the San Francisco Bay Area. I ended up trying my luck on a few mobile app ideas after high school, including one that let people retrieve digital photo memories (before Snapchat, Facebook, and Google). I was accepted into a San Jose-based incubator called Teens In Tech. In the meantime, I fell in love with Santa Clara University after reading the story of a student named Diane Keng, who graduated after having built and sold multiple startups. That’s how I ended up at Santa Clara University, which is where we founded Poplar Homes (formerly Onerent). What's your startup called? And in a sentence or two, what does it do? Our company is (formerly Onerent). We’re a tech-enabled property management service for residential real estate. Poplar makes renting a home as easy as buying a book off Amazon and helps property owners manage properties like they manage their stocks. We do everything from listing a home for rent to fixing the pipes when there’s a leak! Poplar Homes What is the origin story? We founded Poplar Homes in 2014 after our own terrible housing experience as college students. My co-founders and I were all left without housing one year and figured that was a pretty fundamental problem also experienced by many of our peers. We set up shop in a garage on Poplar Street (part of the inspiration for our new name) and got to work. Our platform ended up renting 90% of the off-campus housing inventory. Eventually, we realized the big opportunity in modernizing not just off-campus housing, but property management as a business model. In late 2015, we expanded beyond college campuses to the general market and started offering full-service property management. Today we manage over 2,200 homes across 10 major markets in the US, doing everything from leasing to maintenance and rent collection. What do you love about your team, and why are you the ones to solve this problem? Our team is not afraid of taking big leaps of faith and weathering the storm. We‘re serious and persistent in our mission to simplify the entire real estate lifecycle. As founders when we started, we personally managed a small portfolio of around 20 properties. We’d go out and take photos, answer calls, clean up the homes, and even handle tricky situations like evictions all on our own. This in-the-field learning was essential for young founders who didn’t have decades of past experience in real estate in our back pocket. I think it gave us a lot of perspective on how difficult it is to be a landlord--even with all the bad rep people give them. We saw how strained relationships can become between a tenant and landlord. Eventually, we combined our organic experience with experienced real estate and tech operators, adding executives to our team from large US Real Estate Investment Trusts and some of the country’s largest property management businesses. Today, our executive team each carries over 10+ years of experience in real estate management, technology, and operations on portfolios as large as 50,000 homes managed across the country. Our advisory board includes Google’s Chief of AI as an investor and advisor. If you weren’t building your startup, what would you be doing? If I weren’t building this startup, I’d certainly be building another--likely in real estate as well. Real estate is a complex asset class. This is why it’s one of the lagging industries in technology adoption. Yet, it’s also the world’s largest asset class. I’m fascinated with building technology to bridge these complex offline-to-online experiences and I believe that’s the current era of innovation that we’re only beginning now. Even the most offline businesses can now benefit from technology and I see massive opportunity in that regard. At the moment, how do you measure success? What are your core metrics? Our primary measure of success is the number of doors (rental units) we have under management and customer satisfaction. As of this writing, we have around 2,200 doors under management and an 85% customer satisfaction rating (the industry average in property management is less than 40%). As we grow, more doors in our portfolio also means more opportunities for us to help renters and real estate investors in all steps of their journey—from renting, to buying, selling, and investing. What’s most exciting about your traction to date? We’ve grown 70% in the last six months as technology adoption in real estate was dramatically accelerated during the pandemic in 2020. We also added key new team members such as Travis Buchanan, our Chief Operating Officer who brings experience in managing tens of thousands of homes at American Homes 4 Rent (NYSE:AMH). What technologies are you currently most excited about, and most worried about? And why? The technology for anti-fraud and identity verification are critical in high-value transactions such as real estate. Over 5.2 million renters each year lose money in a scam when fraudsters spoof rental listings and pose as a landlord to rip-off unassuming renters. I’m excited and feel a sense of urgency around anti-fraud technology to stop the millions of dollars lost by renters. At Poplar, we built a tool called as one way to try and help renters identify scam listings. It’s important to put a stop to rental fraud. Scamlord.ai I would not fear or worry about technology if it weren’t for bad actors such as scammers. In that regard, I worry about the tech that these scammers have at their disposal. Software such as face manipulation or filter apps are concerning as we’ve seen fraudsters attempt to impersonate a stolen identity and bypass facial recognition tools. These data manipulation products, when in the wrong hands, could wreak havoc on people’s wallets and well-being. What drew you to get published on HackerNoon? What do you like most about our platform? We’re big fans of HackerNoon’s editorial team and contributors. As a young startup founder, it’s unrealistic to expect you’ll learn the tricks of the trade in a purely academic setting. Publications such as HackerNoon and the communities built around it help young entrepreneurs absorb so many strategic and tactical best practices that have real application in the company yielding process. What advice would you give to the 21-year-old version of yourself? Go beyond flashy apps and look at the dirtiest, unsexiest problems out there to solve. Look at fundamental human needs and identify what part of the supply chain for that basic need is broken. Often there are fewer people taking action on those problems. Because of that, there is a massive opportunity if your solution is the right one and picks up traction. What is something surprising you've learned this year that your contemporaries would benefit from knowing? In the last year, I learned that the foundation of company culture is what gets you through extremely difficult times like those we all faced in the last year. We’ve been through many of the ups and downs of company-building, but my co-founders and I have worked together on this with full commitment for seven years. The foundation of trust we’ve built between each other is crucial to the success of our team and company. I learned that with the underlying trust, you can act more quickly when there are major changes to your business. Vote Poplar Homes for Startup of the Year in San Jose, California

Go for the "dirtiest, unsexiest problems out there to solve," say Poplar Homes Co-Founder & CTO

Image Attributed to @LucianoInf Now second only to South Korea as the biggest crypto market in Asia, Hong Kong's future might be one of decentralised ledgers, Bitcoin and Crypto-Currencies. Hong Kong will always hold a special place in my heart. For just shy of two years from 2016 to 2018 I lived and worked in the city, and still today it sits close to the top of the list of my preferred jurisdictions for business incorporation. Hong Kong is, as ever, a great place to do business and with its grit and its tenacity is very much the New York City of East Asia, or at least, it was. Every so often a need arises to clear up some business or banking in the city and so I find myself landing at its world class airport and dining at its world class restaurants with old friends and contacts. People who are close to the action and keyed in to the city's inner workings. Yet, as an infrequent visitor my eyes and ears observe changes not so obvious to those stuck in the daily grind, in the same way the ageing process is invisible from one day to the next, but stark and profound as the years roll by. Hong Kong is struggling to reinvent itself. Before you boomers get all giddy with your 'muh China', hold on. This isn't an article about the Chinese Government, I rather want to lay out my thoughts around Hong Kong itself, as well as the city relative to its immediate region. I don't intend to make the case for Singapore, which, despite being arguably the greatest beneficiary of Hong Kong's headaches, is still a top contender for title of the most boring place in Asia. I intend only to outline the unique challenges and opportunities faced by one of the most magnificent - and least boring - cities in the world. Gateway to the Middle Kingdom Incorporating your business in Hong Kong still makes sense in many cases. Hong Kong is pragmatic, with systems and infrastructure that is well established to facilitate commerce and trade. Structures can be highly tax efficient, and while getting a bank account has become more difficult, once established, the banking services at your disposal are second-to-none. Hong Kong is still a respected jurisdiction globally. Yet for entrepreneurs and companies alike the case for incorporating in Hong Kong has often been as part of an overall strategy to do business in and with the mainland. Because of the prevalence of a legal system and system of governance with its foundations in the British style, foreigners could come to Hong Kong and feel just enough familiarity not to be scurrying back to wherever they came from (and often this meant quite a lot of familiarity was on offer). Recently a contact of mine who does business in the mainland linked me to a new structure in Shanghai for establishing 100% foreign owned companies. While the corporate tax rate on the mainland is higher (25% vs 16.5% in Hong Kong), the fees associated with establishing such an entity are far lower, and skipping Hong Kong to go directly to the supplier or client has the benefit of reduced friction and complexity. There is no obvious remedy to this dilemma for Hong Kong. As companies have become more confident engaging directly with the mainland, the appeal of Hong Kong in this respect has waned. Added to this the fact that Hong Kong itself is too small to be a genuine start-up hub, there exists a need for the city to take a new approach. Economic Woes At the same time as Hong Kong's role as gateway to the mainland has diminished, there has been an ever growing inflow of both capital and people from the mainland. Every year the prevalence of Mandarin on the streets of the city grows, but, in the wake of the pandemic, this growth has been offset by an exodus of both Hong Kongers (primarily to the UK, Canada and Australia) and Western Expats. This population shuffle fundamentally changes the fabric of Hong Kong society, and is a phenomenon seen elsewhere, including for example in Germany. Those leaving Hong Kong are by-and-large wealthier than the average Hong Konger, and with them goes their skills and capital worsening the city's already pessimistic demographic outlook (with a Total Fertility Rate of 0.7 according to the World Bank, Hong Kong's ageing population - an almost worldwide issue - is one of its greatest challenges in the years ahead). Arrivals from the mainland have helped to offset this exodus, but only so much, and with the collapse in the Yen, Tokyo is increasingly attractive to wealthy Chinese looking to diversify themselves both financially and by way of residency. The fact that Hong Kong remains one of the most expensive places in the world only adds fuel to the fire. As the saying goes, one man's (or city's) loss is another's gain, and as already mentioned, Singapore has been effective in attracting capital and businesses included in the exodus. At the same time, countries such as Malaysia with its sizeable Chinese diaspora and corporate structures offering tax rates as low as 3% (via the Labuan incorporation route), are also seeing an uptick in interest from those who might otherwise have stayed in the Fragrant Harbour. While the economy on the mainland has been sluggish, the PBOC has adjusted rates downwards. Hong Kong's peg however has dictated that the Hong Kong Monetary Authority has followed the Fed's increases over the past few years. This has resulted in a policy rate that sits at 5.25% vs 3.10% for the PBOC at the time of writing (5 years ago the PBOC rate stood at 4.31% while the HKMA policy rate was around 1%). While the increase in rates in the USA was implemented to reign in inflation, Hong Kong suffered no such comparable phenomenon - although it did suffer significant capital outflows - and the increase in rates has been in direct contradiction to conventional economic wisdom that may have encouraged a policy of easing rather than tightening over the last few years had it not been for the peg. Crypto In February 2022, with the invasion of Ukraine by Russia marking a dramatic escalation in hostilities, Chinese policy makers and tycoons alike were undoubtedly keenly observing the geopolitical fallout. If not already clear, this event and the close to three years since has demonstrated the closeness of these two civilisational states. However, and perhaps more importantly, it has exposed the extent to which the United States will go to both pursue sanctions and weaponise the financial system that it controls via the greenback. Many, including myself, did not think that Vladimir Vladimirovich Putin would do it, and many others believed that the Russia - which had popularly, and falsely, been described by many in the West as a 'gas station masquerading as a country' (John McCain, 2015) - would quickly crumble under the weight of economic exclusion. As is usually the case, pretty much nothing happened according to consensus. Russia which had been so successful in its quick, practically bloodless annexation of Crimea, did not scare the Ukrainians back to the negotiating table. Conversely, the Russian economy has actually experienced something of an economic boom. However, and the point I want to drive home, from the perspective of those in Asia, it was a test case for what Hong Kong may face in the wake of any escalation in the straits of Taiwan. China's approach to crypto-currencies has been mixed at best. While officially there are bans on crypto, it is no coincidence that Hong Kong, which initially pushed back so hard on this financial innovation, has now seen a dramatic uptick in crypto assets. In a recent report by Chainalysis Hong Kong saw the fastest growth in its region at 86% from July 2023 to June 2024. In light of the situation in Ukraine, it is possible that crypto could be considered a useful tool in mitigating the effects of an economic war, such as that which the West has waged on Russia. At the very least, it does provide an incentive for regulators to look the other way or turn a blind eye. Conclusion With approximately 7.5 million inhabitants, Hong Kong just doesn't have the scale to make it a stand-out start-up hub, and its role as a conduit into the mainland will in all probability be further eroded by the likes of Shanghai and Shenzhen. Nevertheless, its role as a critical financial centre may be reinvigorated by a realignment towards crypto-assets and the newly emerging financial paradigm unfolding before our very eyes. Whether Trump's reelection will inspire a rapprochement and deescalation of the now vivid geopolitical schism calving up the world is yet to be seen. Nevertheless, one thing is abundantly clear, if Hong Kong is to retain its prestige and replicate its past success, things will need to change. Image Attributed to @LucianoInf Image Attributed to @LucianoInf Now second only to South Korea as the biggest crypto market in Asia, Hong Kong's future might be one of decentralised ledgers, Bitcoin and Crypto-Currencies. Now second only to South Korea as the biggest crypto market in Asia, Hong Kong's future might be one of decentralised ledgers, Bitcoin and Crypto-Currencies. Hong Kong will always hold a special place in my heart. For just shy of two years from 2016 to 2018 I lived and worked in the city, and still today it sits close to the top of the list of my preferred jurisdictions for business incorporation. Hong Kong is, as ever, a great place to do business and with its grit and its tenacity is very much the New York City of East Asia, or at least, it was. Every so often a need arises to clear up some business or banking in the city and so I find myself landing at its world class airport and dining at its world class restaurants with old friends and contacts. People who are close to the action and keyed in to the city's inner workings. Yet, as an infrequent visitor my eyes and ears observe changes not so obvious to those stuck in the daily grind, in the same way the ageing process is invisible from one day to the next, but stark and profound as the years roll by. Hong Kong is struggling to reinvent itself. Before you boomers get all giddy with your 'muh China', hold on. This isn't an article about the Chinese Government, I rather want to lay out my thoughts around Hong Kong itself, as well as the city relative to its immediate region. I don't intend to make the case for Singapore, which, despite being arguably the greatest beneficiary of Hong Kong's headaches, is still a top contender for title of the most boring place in Asia. I intend only to outline the unique challenges and opportunities faced by one of the most magnificent - and least boring - cities in the world. Gateway to the Middle Kingdom Incorporating your business in Hong Kong still makes sense in many cases. Hong Kong is pragmatic, with systems and infrastructure that is well established to facilitate commerce and trade. Structures can be highly tax efficient, and while getting a bank account has become more difficult, once established, the banking services at your disposal are second-to-none. Hong Kong is still a respected jurisdiction globally. Yet for entrepreneurs and companies alike the case for incorporating in Hong Kong has often been as part of an overall strategy to do business in and with the mainland. Because of the prevalence of a legal system and system of governance with its foundations in the British style, foreigners could come to Hong Kong and feel just enough familiarity not to be scurrying back to wherever they came from (and often this meant quite a lot of familiarity was on offer). Recently a contact of mine who does business in the mainland linked me to a new structure in Shanghai for establishing 100% foreign owned companies. While the corporate tax rate on the mainland is higher (25% vs 16.5% in Hong Kong), the fees associated with establishing such an entity are far lower, and skipping Hong Kong to go directly to the supplier or client has the benefit of reduced friction and complexity. There is no obvious remedy to this dilemma for Hong Kong. As companies have become more confident engaging directly with the mainland, the appeal of Hong Kong in this respect has waned. Added to this the fact that Hong Kong itself is too small to be a genuine start-up hub, there exists a need for the city to take a new approach. Economic Woes At the same time as Hong Kong's role as gateway to the mainland has diminished, there has been an ever growing inflow of both capital and people from the mainland. Every year the prevalence of Mandarin on the streets of the city grows, but, in the wake of the pandemic, this growth has been offset by an exodus of both Hong Kongers (primarily to the UK, Canada and Australia) and Western Expats. This population shuffle fundamentally changes the fabric of Hong Kong society, and is a phenomenon seen elsewhere, including for example in Germany. Those leaving Hong Kong are by-and-large wealthier than the average Hong Konger, and with them goes their skills and capital worsening the city's already pessimistic demographic outlook (with a Total Fertility Rate of 0.7 according to the World Bank, Hong Kong's ageing population - an almost worldwide issue - is one of its greatest challenges in the years ahead). population shuffle population shuffle Arrivals from the mainland have helped to offset this exodus, but only so much, and with the collapse in the Yen, Tokyo is increasingly attractive to wealthy Chinese looking to diversify themselves both financially and by way of residency. The fact that Hong Kong remains one of the most expensive places in the world only adds fuel to the fire. As the saying goes, one man's (or city's) loss is another's gain , and as already mentioned, Singapore has been effective in attracting capital and businesses included in the exodus. At the same time, countries such as Malaysia with its sizeable Chinese diaspora and corporate structures offering tax rates as low as 3% (via the Labuan incorporation route), are also seeing an uptick in interest from those who might otherwise have stayed in the Fragrant Harbour. one man's (or city's) loss is another's gain While the economy on the mainland has been sluggish, the PBOC has adjusted rates downwards. Hong Kong's peg however has dictated that the Hong Kong Monetary Authority has followed the Fed's increases over the past few years. This has resulted in a policy rate that sits at 5.25% vs 3.10% for the PBOC at the time of writing (5 years ago the PBOC rate stood at 4.31% while the HKMA policy rate was around 1%). While the increase in rates in the USA was implemented to reign in inflation, Hong Kong suffered no such comparable phenomenon - although it did suffer significant capital outflows - and the increase in rates has been in direct contradiction to conventional economic wisdom that may have encouraged a policy of easing rather than tightening over the last few years had it not been for the peg. Crypto In February 2022, with the invasion of Ukraine by Russia marking a dramatic escalation in hostilities, Chinese policy makers and tycoons alike were undoubtedly keenly observing the geopolitical fallout. If not already clear, this event and the close to three years since has demonstrated the closeness of these two civilisational states. However, and perhaps more importantly, it has exposed the extent to which the United States will go to both pursue sanctions and weaponise the financial system that it controls via the greenback. Many, including myself, did not think that Vladimir Vladimirovich Putin would do it, and many others believed that the Russia - which had popularly, and falsely, been described by many in the West as a 'gas station masquerading as a country' (John McCain, 2015) - would quickly crumble under the weight of economic exclusion. As is usually the case, pretty much nothing happened according to consensus. Russia which had been so successful in its quick, practically bloodless annexation of Crimea, did not scare the Ukrainians back to the negotiating table. Conversely, the Russian economy has actually experienced something of an economic boom. However, and the point I want to drive home, from the perspective of those in Asia, it was a test case for what Hong Kong may face in the wake of any escalation in the straits of Taiwan. China's approach to crypto-currencies has been mixed at best. While officially there are bans on crypto, it is no coincidence that Hong Kong, which initially pushed back so hard on this financial innovation, has now seen a dramatic uptick in crypto assets. In a recent report by Chainalysis Hong Kong saw the fastest growth in its region at 86% from July 2023 to June 2024. In light of the situation in Ukraine, it is possible that crypto could be considered a useful tool in mitigating the effects of an economic war, such as that which the West has waged on Russia. At the very least, it does provide an incentive for regulators to look the other way or turn a blind eye. report Conclusion With approximately 7.5 million inhabitants, Hong Kong just doesn't have the scale to make it a stand-out start-up hub, and its role as a conduit into the mainland will in all probability be further eroded by the likes of Shanghai and Shenzhen. Nevertheless, its role as a critical financial centre may be reinvigorated by a realignment towards crypto-assets and the newly emerging financial paradigm unfolding before our very eyes. Whether Trump's reelection will inspire a rapprochement and deescalation of the now vivid geopolitical schism calving up the world is yet to be seen. Nevertheless, one thing is abundantly clear, if Hong Kong is to retain its prestige and replicate its past success, things will need to change.

Hong Kong and the Crypto Boom: Economic Woes, Money Flows and Sanctions

I ventured into the web3 ecosystem for the first time in 2020. It has been a turpsy-turvy journey up till now, and although I am grateful that I keyed in then, there are a lot of things that I’ve learned in my journey and that I’m still learning but wish someone had explained to me then. I started by reading books, but it was not nearly enough because I had to source for them myself and then relate them to the aspect of web3 that the particular community that I was in, which was called medic DAO, was bullish on at that time. Was it airdrops that people were gunning for? Was it discord whitelist grinding? Was it Web3 Twitter writing for the project’s recognition? I had to understand it by reading, and it was not so easy. Due to the fact that I settled on web3 writing, I’d be writing on a few of the endless Web3 writing aspects that are lucrative and how you can make use of these available opportunities and be successful in them. Before delving into that aspect, though, let’s talk about some of the Web3 basics. Basic Rules in The Web3 Space In Web3, you have the free will to invest as much as you’d like; in exchange, you’d either get profitable returns or lose money (you can always get it back through other investments), which is why you’d sometimes see the acronym D.Y.O.R., which simply means Do Your Own Research, meaning the writer, like me, is not giving financial advice; you should do your own research as well before investing in any coin, project, or asset. During my first NFT mint, I lost 50 dollars that I minted the project with. After minting, I held for a few hrs, however, when I went to check the project’s progress on OpenSea, I noticed that it had fallen way below the mint price, and by the time I realized, it had fallen so bad that I just had to sell it off, I sold at $10. Although, I minted a project that was free mint after that period, and the project tripled in floor price overnight. The point of this is to show you that the market is not predictable which further buttresses my point to always trade and do your own research at your own risk, in case things don’t go as planned, it would be based on your own decision to invest and not because you were coaxed. Another important rule in the Web3 ecosystem is to be very cautious of scams and weary of foreign links. There are a lot of phishing links out there; in a bid to take your funds, they would present as though you got a token and would ask you to claim it. The rooky mistake would be to claim it. However, as much as experience is said to be the best teacher, it would be wise not to click on it till you verify. A lot of times, after clicking, your account would most likely be hacked into and your funds might be wiped. Do not disclose your private key.


Do not share your passwords.


However, you can share your address, your Ethereum address, your Bitcoin address, and so on, to get funds or when needed. That would be how to get funds into your account from an external source, by giving out the required address.

This is an example: do not give a different address for a different cause; this was one of my mistakes back then, and this is just Ethereum and Bitcoin. There is Solana and the rest; take note of them. Lucrative Aspects in Web3 Writing Apart from being a moderator, a collab manager and so on, being a web3 writer is also very lucrative. I wrote my first web3 thread in 2020; then I worked as a web3 ghostwriter for a person whose main source of whitelist was to write threads. Let me start off by delving slightly into these aspect: Writing for Whitelists
Creating content for projects, whether upcoming or those already popularly known can be strategically focused, although it can go in many other ways, when you write good content and post on social media, it can go in every way but not badly. Although while creating anything or putting anything out, you should have a definite audience and goal.
In this case, it would be for the project that you’re writing about to notice you and consider your post worthy to be given whitelist, as there are specific whitelists that are given to early contributors and your contribution is more publicity to the project. You can put them out on platforms like Twitter, Medium, Facebook, and so on where they can get traction and people can come onboard. However, do not forget to include D.Y.O.R. at the end. Then on to the next: Ghostwriting
In this aspect, as the term implies, ghostwriting means writing for someone with no recognition to the actual writer with renumeration to them, in this case, it would be you. Writing for someone else to get whitelist is a form of ghostwriting, writing for web3 founders and influencers, blogs, speeches and so on. Web3 Newsletter writing You can be in the niche of writing Newsletters focused on Web3 topics, such as crypto markets, NFT trends, and decentralized governance. Writers with expertise in creating high-value content for subscription-based newsletters or educational platforms are well-compensated. An example is producing weekly newsletters that track new projects that are coming into the market or doing well. Lastly, would be: Airdrop writing Writing on the new airdrops in the markets would get people to swamp your page for more information as people are really interested in airdrops as it is free and only requires gas fee during lauch. All you’d have to do is to keep making your research and updating your page, and watch the magic play out. However, the point of any form of writing would be to captivate the audience, and for this aspect of writing, the goal would be to influence the audience with enough facts on the project to get them onboard without promising anything false; keep it simple and truthful so as to keep a profile of being trustworthy. There are many more aspects but I’d be stopping here. Thank you! I ventured into the web3 ecosystem for the first time in 2020. It has been a turpsy-turvy journey up till now, and although I am grateful that I keyed in then, there are a lot of things that I’ve learned in my journey and that I’m still learning but wish someone had explained to me then. I started by reading books, but it was not nearly enough because I had to source for them myself and then relate them to the aspect of web3 that the particular community that I was in, which was called medic DAO, was bullish on at that time. Was it airdrops that people were gunning for? Was it discord whitelist grinding? Was it Web3 Twitter writing for the project’s recognition? I had to understand it by reading, and it was not so easy. Due to the fact that I settled on web3 writing, I’d be writing on a few of the endless Web3 writing aspects that are lucrative and how you can make use of these available opportunities and be successful in them. Before delving into that aspect, though, let’s talk about some of the Web3 basics. Basic Rules in The Web3 Space Basic Rules in The Web3 Space Basic Rules in The Web3 Space In Web3, you have the free will to invest as much as you’d like; in exchange, you’d either get profitable returns or lose money (you can always get it back through other investments), which is why you’d sometimes see the acronym D.Y.O.R., which simply means Do Your Own Research, meaning the writer, like me, is not giving financial advice; you should do your own research as well before investing in any coin, project, or asset. In Web3, you have the free will to invest as much as you’d like; in exchange, you’d either get profitable returns or lose money (you can always get it back through other investments), which is why you’d sometimes see the acronym D.Y.O.R., which simply means Do Your Own Research, meaning the writer, like me, is not giving financial advice; you should do your own research as well before investing in any coin, project, or asset. During my first NFT mint, I lost 50 dollars that I minted the project with. After minting, I held for a few hrs, however, when I went to check the project’s progress on OpenSea, I noticed that it had fallen way below the mint price, and by the time I realized, it had fallen so bad that I just had to sell it off, I sold at $10. Although, I minted a project that was free mint after that period, and the project tripled in floor price overnight. The point of this is to show you that the market is not predictable which further buttresses my point to always trade and do your own research at your own risk, in case things don’t go as planned, it would be based on your own decision to invest and not because you were coaxed. Another important rule in the Web3 ecosystem is to be very cautious of scams and weary of foreign links. There are a lot of phishing links out there; in a bid to take your funds, they would present as though you got a token and would ask you to claim it. Another important rule in the Web3 ecosystem is to be very cautious of scams and weary of foreign links. There are a lot of phishing links out there; in a bid to take your funds, they would present as though you got a token and would ask you to claim it. The rooky mistake would be to claim it. However, as much as experience is said to be the best teacher, it would be wise not to click on it till you verify. A lot of times, after clicking, your account would most likely be hacked into and your funds might be wiped. Do not disclose your private key. Do not share your passwords. However, you can share your address, your Ethereum address, your Bitcoin address, and so on, to get funds or when needed. That would be how to get funds into your account from an external source, by giving out the required address.

This is an example: do not give a different address for a different cause; this was one of my mistakes back then, and this is just Ethereum and Bitcoin. There is Solana and the rest; take note of them. Do not disclose your private key. Do not disclose your private key. Do not share your passwords. Do not share your passwords. However, you can share your address, your Ethereum address, your Bitcoin address, and so on, to get funds or when needed. That would be how to get funds into your account from an external source, by giving out the required address. This is an example: do not give a different address for a different cause; this was one of my mistakes back then, and this is just Ethereum and Bitcoin. There is Solana and the rest; take note of them. However, you can share your address, your Ethereum address, your Bitcoin address, and so on, to get funds or when needed. That would be how to get funds into your account from an external source, by giving out the required address. This is an example: do not give a different address for a different cause; this was one of my mistakes back then, and this is just Ethereum and Bitcoin. There is Solana and the rest; take note of them. Lucrative Aspects in Web3 Writing Lucrative Aspects in Web3 Writing Lucrative Aspects in Web3 Writing Apart from being a moderator, a collab manager and so on, being a web3 writer is also very lucrative. I wrote my first web3 thread in 2020; then I worked as a web3 ghostwriter for a person whose main source of whitelist was to write threads. Let me start off by delving slightly into these aspect: Writing for Whitelists
Creating content for projects, whether upcoming or those already popularly known can be strategically focused, although it can go in many other ways, when you write good content and post on social media, it can go in every way but not badly. Although while creating anything or putting anything out, you should have a definite audience and goal.
In this case, it would be for the project that you’re writing about to notice you and consider your post worthy to be given whitelist, as there are specific whitelists that are given to early contributors and your contribution is more publicity to the project. You can put them out on platforms like Twitter, Medium, Facebook, and so on where they can get traction and people can come onboard. However, do not forget to include D.Y.O.R. at the end. Writing for Whitelists Creating content for projects, whether upcoming or those already popularly known can be strategically focused, although it can go in many other ways, when you write good content and post on social media, it can go in every way but not badly. Although while creating anything or putting anything out, you should have a definite audience and goal. In this case, it would be for the project that you’re writing about to notice you and consider your post worthy to be given whitelist, as there are specific whitelists that are given to early contributors and your contribution is more publicity to the project. You can put them out on platforms like Twitter, Medium, Facebook, and so on where they can get traction and people can come onboard. However, do not forget to include D.Y.O.R. at the end. Writing for Whitelists Writing for Whitelists Creating content for projects, whether upcoming or those already popularly known can be strategically focused, although it can go in many other ways, when you write good content and post on social media, it can go in every way but not badly. Although while creating anything or putting anything out, you should have a definite audience and goal. In this case, it would be for the project that you’re writing about to notice you and consider your post worthy to be given whitelist, as there are specific whitelists that are given to early contributors and your contribution is more publicity to the project. You can put them out on platforms like Twitter, Medium, Facebook, and so on where they can get traction and people can come onboard. However, do not forget to include D.Y.O.R. at the end. Then on to the next: Ghostwriting
In this aspect, as the term implies, ghostwriting means writing for someone with no recognition to the actual writer with renumeration to them, in this case, it would be you. Writing for someone else to get whitelist is a form of ghostwriting, writing for web3 founders and influencers, blogs, speeches and so on. Ghostwriting In this aspect, as the term implies, ghostwriting means writing for someone with no recognition to the actual writer with renumeration to them, in this case, it would be you. Writing for someone else to get whitelist is a form of ghostwriting, writing for web3 founders and influencers, blogs, speeches and so on. Ghostwriting Ghostwriting In this aspect, as the term implies, ghostwriting means writing for someone with no recognition to the actual writer with renumeration to them, in this case, it would be you. Writing for someone else to get whitelist is a form of ghostwriting, writing for web3 founders and influencers, blogs, speeches and so on. Web3 Newsletter writing Web3 Newsletter writing Web3 Newsletter writing You can be in the niche of writing Newsletters focused on Web3 topics, such as crypto markets, NFT trends, and decentralized governance. Writers with expertise in creating high-value content for subscription-based newsletters or educational platforms are well-compensated. An example is producing weekly newsletters that track new projects that are coming into the market or doing well. Lastly, would be: Airdrop writing Airdrop writing Airdrop writing Writing on the new airdrops in the markets would get people to swamp your page for more information as people are really interested in airdrops as it is free and only requires gas fee during lauch. All you’d have to do is to keep making your research and updating your page, and watch the magic play out. However, the point of any form of writing would be to captivate the audience, and for this aspect of writing, the goal would be to influence the audience with enough facts on the project to get them onboard without promising anything false; keep it simple and truthful so as to keep a profile of being trustworthy. There are many more aspects but I’d be stopping here. Thank you!

Web3 Hustle: A Win-Win Venture

Dreams to Reality - The AI Evolution Story

ህልሞች ወደ እውነታ - የ AI የዝግመተ ለውጥ ታሪክ

Snovi u stvarnost - priča o evoluciji umjetne inteligencije

De los sueños a la realidad: la historia de la evolución de la IA

رویاها به واقعیت - داستان تکامل هوش مصنوعی

Dreams to Reality - AI Evolution Story

सपनों से हकीकत तक - एआई विकास की कहानी

夢を現実に - AI の進化の物語

Армандардан шындыққа - AI эволюциясының тарихы

Svajonės į realybę – AI evoliucijos istorija

යථාර්ථයට සිහින - AI පරිණාමය කතාව

Amaphupha kwiNyaniso-Ibali le-AI Evolution

Target encoding, also known as mean encoding or likelihood encoding, is a technique used to convert categorical variables into numerical values based on the target variable in supervised learning tasks. This method is particularly useful in dealing with high cardinality categorical variables (i.e., variables with a large number of unique categories). For credit risk models, this approach is also applied to continuous variables and is popularly known as “Weight of Evidence” (in the context of binary classification problems). We will derive the above formula, mathematically, for WoE to see where it comes from in the first part of this story. In the second part, we will explore the coding part of it. The idea is to fit a “piecewise constant” model to the outcome binary variable. This is done by partitioning the variable space into non-overlapping regions such that there is a constant predicted value in each region. As shown in the figure above, the input space is partitioned into seven non-overlapping regions and the output is a piecewise constant for each region. This is expressed mathematically as follows: We have a single variable x whose value x_i can belong to a region R_j and we associate a parameter \beta_j with the region R_j. If we have k regions, we get a \beta vector with length k. The piecewise constants will be the estimated values of the parameters \beta_j. Note that the function I is an indicator function which is 1 when x_i belongs to R_j and 0 otherwise. Before we move further, intuition tells us that if we are fitting a constant value in a region, it should be the “average value” of the outcome in that region. Turns out, that is what we are supposed to get with the following model: The probability of outcome y_i =1 is given by p_i and is modeled as a function of piecewise constants passed through a logistic or sigmoid function. Note that decision trees work on a similar concept of partitioning of the variable space (they use binary partitions only though). Also note that Each x_i can be expanded to a vector of dummy variables, i.e., we can convert the input x to k dummy variables: However, we will avoid dealing with vectors and keep the x_i scalar, and use the indicator function to turn on \beta_j if x_i belongs to R_j. Although we don’t really need the summand before \beta_j we will keep it to formulate the change of j with the region R_j when x_i will fall under R_j. In general, the above equation for p_i can also be expressed as a function of any x. Before we move further to derive the WoE values, it is interesting to note that there is no “intercept” term in the above model. Whilst, we could’ve added an intercept term in the above model, turns out that its value will not be defined. The reason is similar to the problem of adding n-values of dummy variables instead of (n-1) values in a regression model, because then intercept can be expressed completely as a sum of all the n-dummy variables, therefore, it is “redundant”. Similarly, here the intercept term is “redundant”. However, it will appear later in the computation of WoE. We will start by defining the likelihood. Note that y, x, and \beta are vectors and the Likelihood is the joint pmf of y_1, y_2, .. y_n Now, since we know that If y_i = 1 then P(y_i) = p_i and if y_i = 0 then P(y_i) = 1-p_i, we use it to redefine L Note: the clever use of the indicator functions which will turn on the specific probability for each y_i. However, we can express this better by using the values y_i themselves. We can use the above substitution to make our likelihood expression unwieldy: We also take a log of the likelihood because we are working with probabilities and summing them avoids the overflow issues. Now we substitute our piecewise constant model And once we do that, we think of the likelihood as a function of the parameter vector because we want to maximize it later on. We get the following expression for the log-likelihood by substituting values of p_i and also the above expression Now comes the main part of maximizing log-likelihood by differentiating it with respect to \beta. For ease, we will partially differentiate it with respect to \beta_j. That is, we want to compute the following partial derivative, and we will do that separately for both the “first term” and the “second term” in the log likelihood expression. The partial derivative of the first term: x_i must belong to R_j; only then, we will get the terms associated with \beta_j, whose partial derivative with respect to \beta_j will be 1. Thus, we will be summing over y_i for all those values of i such that x_i belongs to R_j. This is represented by the set I_j. This completes our partial derivative of the first term. Before we move on to the partial derivative of the second term, we consider the following We use the aforementioned concepts and formulae to derive the partial derivative of the second term and we get the following: Of course, the “non \beta_j” terms get omitted, even if they are counted over the summand from i=1 to n, while partially differentiating with respect to \beta_j. The lines around I_j indicate the cardinality of the set I_j, i.e., the number of values in I_j. When we combine the first term and the second term differentials and set it to zero, we replace \beta_j with \hat{\beta_j} because that is the estimate of \beta_j when we maximize the likelihood function. Thus, Solving for \beta_j we get. which are the log-odds for the j-th region R_j Consider k = 1, i.e., R_1 = {x_1, x_2, …., x_n} which are the “Overall average” log-odds. This gives us the value of “weight of evidence” as WoE is the difference between the log-odds for the j-th region R_j and the “overall average” log-odds. Also, note that instead of using the dummy variable approach, we replace the variable x with is WoE values. Note: Since there are no restrictions placed on the parameters \beta_j, the regions R_j can have a non-monotonic relationship with respect to the outcome. Target encoding, also known as mean encoding or likelihood encoding, is a technique used to convert categorical variables into numerical values based on the target variable in supervised learning tasks. This method is particularly useful in dealing with high cardinality categorical variables (i.e., variables with a large number of unique categories). For credit risk models, this approach is also applied to continuous variables and is popularly known as “Weight of Evidence” (in the context of binary classification problems). We will derive the above formula, mathematically, for WoE to see where it comes from in the first part of this story. In the second part, we will explore the coding part of it. The idea is to fit a “piecewise constant” model to the outcome binary variable. This is done by partitioning the variable space into non-overlapping regions such that there is a constant predicted value in each region. As shown in the figure above, the input space is partitioned into seven non-overlapping regions and the output is a piecewise constant for each region. This is expressed mathematically as follows: We have a single variable x whose value x_i can belong to a region R_j and we associate a parameter \beta_j with the region R_j. If we have k regions, we get a \beta vector with length k. The piecewise constants will be the estimated values of the parameters \beta_j. Note that the function I is an indicator function which is 1 when x_i belongs to R_j and 0 otherwise. Before we move further, intuition tells us that if we are fitting a constant value in a region, it should be the “average value” of the outcome in that region. Turns out, that is what we are supposed to get with the following model: The probability of outcome y_i =1 is given by p_i and is modeled as a function of piecewise constants passed through a logistic or sigmoid function. Note that decision trees work on a similar concept of partitioning of the variable space (they use binary partitions only though). Also note that Each x_i can be expanded to a vector of dummy variables, i.e., we can convert the input x to k dummy variables: However, we will avoid dealing with vectors and keep the x_i scalar, and use the indicator function to turn on \beta_j if x_i belongs to R_j. Although we don’t really need the summand before \beta_j we will keep it to formulate the change of j with the region R_j when x_i will fall under R_j. In general, the above equation for p_i can also be expressed as a function of any x. Before we move further to derive the WoE values, it is interesting to note that there is no “intercept” term in the above model. Whilst, we could’ve added an intercept term in the above model, turns out that its value will not be defined. The reason is similar to the problem of adding n-values of dummy variables instead of (n-1) values in a regression model, because then intercept can be expressed completely as a sum of all the n-dummy variables, therefore, it is “redundant”. Similarly, here the intercept term is “redundant”. However, it will appear later in the computation of WoE. We will start by defining the likelihood. Note that y, x, and \beta are vectors and the Likelihood is the joint pmf of y_1, y_2, .. y_n Now, since we know that If y_i = 1 then P(y_i) = p_i and if y_i = 0 then P(y_i) = 1-p_i, we use it to redefine L Note: the clever use of the indicator functions which will turn on the specific probability for each y_i. However, we can express this better by using the values y_i themselves. We can use the above substitution to make our likelihood expression unwieldy: We also take a log of the likelihood because we are working with probabilities and summing them avoids the overflow issues. Now we substitute our piecewise constant model And once we do that, we think of the likelihood as a function of the parameter vector because we want to maximize it later on. We get the following expression for the log-likelihood by substituting values of p_i and also the above expression Now comes the main part of maximizing log-likelihood by differentiating it with respect to \beta. For ease, we will partially differentiate it with respect to \beta_j. That is, we want to compute the following partial derivative, and we will do that separately for both the “first term” and the “second term” in the log likelihood expression. The partial derivative of the first term: x_i must belong to R_j; only then, we will get the terms associated with \beta_j, whose partial derivative with respect to \beta_j will be 1. Thus, we will be summing over y_i for all those values of i such that x_i belongs to R_j. This is represented by the set I_j. This completes our partial derivative of the first term. Before we move on to the partial derivative of the second term, we consider the following We use the aforementioned concepts and formulae to derive the partial derivative of the second term and we get the following: Of course, the “non \beta_j” terms get omitted, even if they are counted over the summand from i=1 to n, while partially differentiating with respect to \beta_j. The lines around I_j indicate the cardinality of the set I_j, i.e., the number of values in I_j. When we combine the first term and the second term differentials and set it to zero, we replace \beta_j with \hat{\beta_j} because that is the estimate of \beta_j when we maximize the likelihood function. Thus, Solving for \beta_j we get. which are the log-odds for the j-th region R_j Consider k = 1, i.e., R_1 = {x_1, x_2, …., x_n} which are the “Overall average” log-odds. This gives us the value of “weight of evidence” as WoE is the difference between the log-odds for the j-th region R_j and the “overall average” log-odds. Also, note that instead of using the dummy variable approach, we replace the variable x with is WoE values. Note: Since there are no restrictions placed on the parameters \beta_j, the regions R_j can have a non-monotonic relationship with respect to the outcome. Note: Since there are no restrictions placed on the parameters \beta_j, the regions R_j can have a non-monotonic relationship with respect to the outcome.

How To Use Target Encoding in Machine Learning Credit Risk Models – Part 1

Entrepreneurs, Journalists, Researchers: The Ethereum Influencers to Follow to Stay Informed!

This paper is under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Juan Mera Men´endez; (2) Martin Bartlett. Table of Links Abstract and Introduction Initial Application Performance Tests Best Practices and Techniques Combinations Discussion Related Work Conclusion and References Abstract Despite its already widespread popularity, it continues to gain adoption. More and more developers and architects continue to adopt and apply the FaaS (Function as a Service) model in cloud solutions. The most extensively used FaaS service is AWS Lambda, provided by Amazon Web Services. Moreover, despite the new trends in programming languages, Java still maintains a significant share of usage. The main problem that arises when using these two technologies together is widely known: significant latencies and the dreaded cold start. However, it is possible to greatly mitigate this problem without dedicating too much effort. In this article, various techniques, strategies and approaches will be studied with the aim of reducing the cold start and significantly improving the performance of Lambda functions with Java. Starting from a system that involves AWS lambda, java, DynamoDB and Api Gateway. Each approach will be tested independently, analyzing its impact through load tests. Subsequently, they will be tested in combination in an effort to achieve the greatest possible performance improvement. —AWS Lambda, AWS, Amazon Web Services, Java, performance, cold start, FaaS, Function as a Service, Serverless, GraalVM, Snapstart, JAVA TOOL OPTIONS Index Terms I. INTRODUCTION FOLLOWING the trend of recent years, the serverless approach [9] continues to gain popularity due to its scalability, flexibility, agility, and other well-known benefits. According to the survey conducted by Datadog in August 2023 [13], one of the most popularly used services within this architectural model is AWS Lambda (provided by Amazon Web Services) and over a 10% of Lambda functions are written in Java. [11] Furthermore Java is the primary language for 30% of professional developers. Being the performance one of the main criteria when comparing solutions, the survey shows that cold start of java lambdas are roughly twice as long as the cold starts of other languages like Node.js and Python [3], [4](which are the most used languages with AWS lambda). Indeed, it becomes evident why performance is the first aspect brought to the table when discussing the combination of Java with AWS Lambda. And rightfully so, if it’s not taken into account when building the system, it’s common to encounter time-out issues in API Gateway due to excessively high latency of the functions. Furthermore, another noteworthy benefit of improving the performance of Lambda functions is the reduction in their cost. This is because the cost model for this service is based on computing time. By reducing this time, the cost is also reduced. Thus, the primary goal of the study is to optimize the way Java code is executed on AWS Lambda and provide a set of best practices that assist this approach in being competitive in terms of performance. To achieve that goal, a series of techniques, approaches or strategies are proposed with the aim of improving function performance and mitigating cold starts: • Profile the function to see which memory configuration is better suited in terms of performance. • Use Snapstart to reduce the cold-start latency. • Choose the Arm64 Lambda architecture against the default x86 64 architecture. • Use the AWS SDK v2 for Java. • Pre-compile the source code with GraalVM to avoid initialize all classes in runtime. • Java Lambda function customization settings: JAVA TOOL OPTIONS environment variable. • Other good practices The improvement caused by each technique will be measured independently since some of them are not compatible with each other. The advantages and drawbacks of the techniques will be discussed as well. For the implementation of each technique or approach, we start with an initial, unoptimized system, which will be explained in the following section. The performance of this system has also been measured and will be used to calculate the improvements experienced relative to it. After this, the techniques will be combined, and the result will be measured.

Performance Best Practices Using Java and AWS Lambda: Abstract and Introduction

Analysis of Network Graphs: Visualizing Hamilton Characters as a Social Network

নেটওয়ার্ক গ্রাফের বিশ্লেষণ: একটি সামাজিক নেটওয়ার্ক হিসাবে হ্যামিল্টন চরিত্রগুলিকে ভিজ্যুয়ালাইজ করা

Analyse von Netzwerkgraphen: Visualisierung der Hamilton-Charaktere als soziales Netzwerk

Análisis de gráficos de red: visualización de personajes de Hamilton como una red social

Analyse des graphiques de réseau : visualiser les personnages de Hamilton en tant que réseau social

नेटवर्क ग्राफ़ का विश्लेषण: हैमिल्टन के पात्रों को एक सामाजिक नेटवर्क के रूप में देखना

ネットワーク グラフの分析: ハミルトンの登場人物をソーシャル ネットワークとして視覚化する

네트워크 그래프 분석: 해밀턴 캐릭터를 소셜 네트워크로 시각화

Análise de gráficos de rede: visualizando personagens de Hamilton como uma rede social

Анализ сетевых графов: визуализация персонажей Гамильтона как социальной сети

Ağ Grafiklerinin Analizi: Hamilton Karakterlerinin Sosyal Ağ Olarak Görselleştirilmesi

Phân tích biểu đồ mạng: Trực quan hóa các nhân vật Hamilton như một mạng xã hội

网络图分析：将汉密尔顿特征可视化为社交网络

Stories, News & Mentions about POPLARML

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