Charli3is in the Blockchain Infrastructure Industry

Charli3

charli3

Things have been a bit quieter with Cardano and its token ADA recently. After a lot of buzz in late August and early September, the release of smart contracts, exciting news coming out every day and reaching a new all-time high of 3.1 USD, it certainly feels like Cardano lost its momentum. The chart above shows that ADA has been going sideways at around 2 – 2.2 USD for around 6 weeks now. Yet this makes me really bullish and excited about what’s likely to come next. These long consolidation phases are in fact very typical for ADA. As you can see on the chart below, we had similar patterns around November-December 2020, again in January 2021 and then from late February to late April. Each of these consolidation phases was between 4 and 8 weeks longs. The reason why I’m excited is because of what happened between these phases. Each time we observed an increase in price between 100% and 160%. If this pattern repeats itself again, we could see ADA going to around 4-5 USD on its next leg up. Of course, the main difference is that in the past, these price movements were based almost purely on speculation, while the next move up will also depend on the actual usability of the Cardano ecosystem. This is probably why we’re not seeing ADA moving much now. While I remain very bullish on Cardano and it’s still my second biggest investment after Bitcoin, what I’m excited more about are various projects within the Cardano ecosystem. Cardano’s market cap is around 66 billion USD, which makes it quite slow to move as any significant price movement requires a very large cash inflow. However, as Cardano started going sideways, perhaps even a little bit bearish you could say, a lot of alts dipped in price or didn’t recover much from the summer bear market. These alts present a great buying opportunity right now and once ADA starts going up, they are very likely to go up at a much faster pace due to their much lower market caps. Below are a few projects, which in my opinion are particularly interesting. For full disclosure, I either invested in recently or bought more due to their current low price or I’m considering buying them. – 90 mil market cap and currently around 86% down from its all-time high. These numbers alone suggest that it’s potentially a great buying opportunity. Cardstarter is a launchpad and every blockchain needs launchpads so it certainly has potential. Cardstarter – 128 mil market cap and down around 53% from its ATH. It’s a suite of DeFi protocols that include a launchpad, DEX and liquidity pools. DeFi protocols and in particular DEX are a key element of every crypto ecosystem. OccamFi – around 19 mil market cap and down around 75%. This makes it VERY attractive in terms of its potential for growth and it’s an oracle which similarly to launchpads is something needed for every blockchain. My only concern is that Chainlink – the biggest oracle - is going to be implemented on Cardano so this may potentially somewhat cripple Charli3’s potential. Charli3 – 20 mil market cap and 87% down. Non-custodial wallet plus DeFi platform. 
Another project with a tiny market cap and substantially down from its ATH, so with a huge potential to be the next moonshot. Gero Wallet – 506 mil market cap and only 12% down. This one is a bit different. COTI had a ton of bullish news in the last few weeks and is actually performing much better than most of the Cardano ecosystem. It’s probably the safest bet on this list but due to its relatively large market cap, it’s likely to move up at a much steadier pace. In my view, it’s a great mid-to-long term investment. COTI is a payment solutions platform that provides a whole array of options for online and offline payments, crypto and stable coins, credit cards, etc. They are also working on creating DJED – Cardano’s stablecoin. COTI To conclude, I think all of these projects have immense potential for growth. The ones with a lower market cap present the biggest opportunity but also come with the highest risk. But at a 20 mil market cap, all it takes is just one mention from a big crypto influencer and the price can shoot up 3-5x in a matter of hours. Aside from pure speculation, there’s no doubt that the Cardano ecosystem is gaining utility, slowly maturing and it will eventually explode. While I expect ADA to start moving up before the end of this year, I think the real pump will come much later, potentially as late as mid or late 2022. As always – the key to making big profits in crypto is being ahead of the game and looking at what is likely to pump next. If you’re investing now in meme coins, you’re already late to the game and you have to be extremely careful. Gaming cryptos have much more long-term potential but some of them have already pumped a lot so again – you have to be careful. On the other hand, the Cardano ecosystem is going slow and NFTs are currently in their own bear market and that’s why my attention is on these projects. If you found this article interesting, please check out my – I post new market commentaries and thoughts about altcoins on Mondays, NFT updates on Tuesdays, and tutorials on Fridays. I also mention a lot of altcoins and NFTs on my . YouTube channel Twitter --- Disclaimer The content covered in this article is NOT to be considered investment advice. I’m NOT a financial adviser. These are only my own speculative opinions, ideas and theories. Do NOT trade or invest based purely upon the information presented in this article.

Why You Should Invest in the Cardano Ecosystem Now

Hey Hackers! I’m Charlie Gedeon and I’m the co-founder of in Montreal. Pragmatics Studio First of all, I’d like to say a huge thank you to the community for recognizing me as the winner of “ ” HackerNoon 2021 HackerNoon Contributor of the Year - ALGORITHMS If you have made a video acceptance speech on YouTube  (whether a comedic one or a serious one), please post the link here: tasteful I can’t say that I have anything recorded specifically for this, but I’d be remiss not to share a video of my celebratory dance: https://www.youtube.com/watch?v=cQKjAsguhA0 Reporter: What does it mean for you to win this title? HackerNoon Honestly I’m pretty surprised that this happened! I often repost articles about technology in several places, and HackerNoon is a great place for more technical stories. This is surprising because I didn’t nominate myself nor did any of my friends know about this, so to all the strangers who voted for me: thank you! I’d love to talk to you, so please reach out on and send me a message. LinkedIn How do you intend to embrace the responsibility of this title in 2022? Recently my writing slowed down as I focused on other projects. I’ve been telling myself to get back into writing again, so the email in my inbox today with this award is an awesome motivational boost! What are some of the goals you’re looking forward to accomplishing in 2022 (whether it be through company initiatives or your personal journey)? A good friend and I have been talking about how we’ve been feeling disconnected from our work as designers. For a couple of years now we thought about starting a design studio after working together on several projects, and so we finally made the jump a couple of weeks ago. In fact, we just bought the domain and made a , and we’re very excited to start working. landing page As you look at the variety of concerning trends in the world around you, what solutions can you think of? Name three trends and three solutions. Be as brief or as detailed as you like. Three areas I’d like to have more impact with are: Helping people who want to tackle anything related to the environment, particularly if they’re working with food, reforestation or water. Educating people about technology so that they can design and build better futures for us. Getting more involved with biotech and medicine so that people can live healthier lives. If I could add a fourth it would the growing disconnection between people, and help reduce loneliness. This is one that I feel most helpless about but it hurts me to hear about how only 1 in 2 people in North America can say they and the . have a close friend rising teen suicide rates After participating in this journey, is there anything that you would like the team to know? HackerNoon I love what the team at HackerNoon has done with the publication since its humble beginnings on Medium. I would say that I’d love to see more diversity of thought, especially being wary of the overt technosolutionism we see coming from certain communities. As a publication about technology, we need to hear both the good and the bad of tech. What is something special about you or your company that you’ve rarely had the chance to properly share but really wanted to? (Give this one a proper think, I’ll know if you didn’t!) Whenever I think about a little known moment in my life, I think back to the time I got to stare a Galapagos tortoise in the face while I was in the Galapagos. That was all thanks to a back in 2014. I typically don’t take many pictures of special moments because I like to be present so I can’t share the wonderful creature’s face, but there’s nothing like the deep hollow breathing of a being that’s over two centuries old. competition that my friends and I won What song or music album are you currently enjoying? Most of the time I’m discovering new music on Spotify’s incredible Discover Weekly playlist, or Shazaming things at my favorite little cafe down the street from my house. Today I Shazamed a track by which is perfect for work. Otherwise, a classic playlist I go to when I need a pick me up is my “ ” playlist. It’s favorite of mine and many of my friends! Tommy Guerrero Like Totally Girly What are some concepts you wish people knew more about? Often I go back to wishing people were better at understanding bad arguments. It’s troubling to watch people make circular arguments and appeal to the bandwagon without even realizing it. The best resource on this topic has to be by Ali Almossawi. Discourse will die if people don’t educate themselves better on how to identify flawed logic—and we’re seeing this happen every passing day in our society. An Illustrated Book of Bad Arguments Is there anything else you’d like me to know? It can be anything. The only thing on my mind right now is how cool it is to get an award for something you didn’t even know you were running in! I really appreciate the community’s support. I’d also like to thank the team at HackerNoon for continuing to grow the site and explore ideas like web monetization, using NFTs for the awards, and generally pushing the idea of independent publishing in new directions. Thank you for everything that you do! – Team. HackerNoon P.S. If you enjoy getting interviewed (AKA, the ol’ intellectual stimulation, check out some more interviews !) here For more information: Read about the Noonies . To see all of the winners you may visit , pick any award, and view the name that appears at the top. Or follow for posts with the list of names released over the course of the week of February 14, 2022. here this site this account Read the FAQs about or . To see all of the winners you may visit , pick any city and view the name that appears at the top. Or follow for posts with the list of names released over the course of the week of February 14, 2022. Startups here here this site this account Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! : Startups , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network : Noonies The Noonies Awards are sponsored by: , , and hosted by: Bybit Avast TECH Domains HackerNoon

We Need to Hear Both the Good and Bad Sides of Tech - Interview with Charlie G

Charlie Shrem is and The Winklevoss Twins are subject to ridicule more than once. 15 years ago, they were outsmarted by Mark Zuckerberg who allegedly stole their idea of a social network. Just last week they announced that Charlie Shrem embezzled US$32 Million by taking their Dollars and not giving them Bitcoin back in 2012 (Took a long time to go public with that). Time to recall who which pivotal role he played in the history of Bitcoin. “ In prison, the more of a celebrity you are, the less respect you get. You’re judged on who you are as a person, and I like being judged like that because I feel like I’m a I’m a pretty okay person. People liked me and I was judged on that, not on the Bitcoin stuff.” “ ” I am one of the first 10 people to come into contact with Bitcoin. I am a capitalist and I wanted to earn money. And that’s what we did. These two statements come from Charlie Shrem, who has had a remarkable career. From solid US middle class, he becomes a millionaire at the age of 22 thanks to Bitcoin, only to work as a dishwasher for 8 dollars an hour at 25 after a prison sentence for money laundering. But since the Bitcoin world is like a roller coaster, Charlie is already on top again. Even in high school, Charlie earns a few hundred dollars a week fixing and configuring neighbors’ computers and software. “ My father went out of his way to make sure that I always understood the value of a dollar.” ( . .” “ The day I turned 18, I got credit, I was still in high school. I had a $6000 credit limit. And I was I was taking people to Vegas. No, not really but I was I was spending -like the first month I spent it. In pizza stores and uh just stuff I took like um lessons to learn how to fly a plane that cost a bunch of money When Charlie graduates high school, he owes $10,000. When Charlie’s father finds out, he has to take out a loan on the house. Charlie pays off the debt for two years and is sure: from now on he has to be reasonable. At 19, he builds a web shop for a department store that wants to sell rejected goods on the Internet. He works on a commission basis and the site becomes a small success. Other wholesalers call and want to sell as well. Charlie earns 600 dollars a week. Had the Austrians Known About Bitcoin… . Ludwig von Mises Institute [ ] , via Wikimedia Commons Ludwig von Mises GFDL Shrem attends Brooklyn College’s evening school and takes business lessons. There he gets to know the Austrian School of Economics, a circle of Austrian economists led by Ludwig von Mises and Friedrich von Hayek. Already at the end of the 19th century, the theorists put forward the thesis that a state works best when the government keeps out of everything and lets the market decide. The Austrians are the heroes of the Bitcoin enthusiasts; after all, the crypto currency is no state currency, but purely market-based. “ ” I realized that bitcoin was taking all of that Austrian economic theory and putting it into practice. Charlie feels that he must distance himself from his strong Jewish family and their intolerance of the world as a whole. “ Um, all of a sudden, I went from having this one family, and now I have this new family. The Bitcoin community. but the Bitcoin community was a — especially back then when it was still so small. It was a real life working example of a community where everyone was equal because we all had our common agenda of revolutionizing the financial system.” Charlie has the right idea at the right time. He founds BitInstant and, together with payment partners, makes it possible for customers of supermarkets such as Walmart or 7–11 to buy Bitcoin conveniently and easily in vending machines. BitInstant receives a few percent commission. “ Within a few months, we enabled people to be able to buy bitcoin at almost a million locations in the US.” BitInstant is growing one and a half times — every month. Investors get in, the office gets bigger, 30 employees are added. Within a short time, 30% of all Bitcoin purchases worldwide are made via BitInstant. Charlie Shrem makes a deal when everyone else is still thinking about what to do with Bitcoin. Screenshot of Bitinstant. “We made Bitcoin what it is today. That’s my legacy.” Charlie gets rich and he gets cocky. “Picture this: I was like 22 years old. I had half a million dollars sitting in the bank. I owned a nightclub. I lived above it. I was dating a beautiful girl. Like, life was great. I would walk around like my shit didn’t stink.” The Sharks are Closing in The mills of the state grind slowly, but they grind. In March 2013, the US government enacts a regulatory law on Bitcoin. This makes BitInstant legally a money remitter — similar to Western Union — and requires a license, which of course it does not have. After E-Gold, this is another company that is subsequently caught up in crime. But BitInstant has a more pressing problem. “My lawyers told me that we are in a dangerous situation. One of my clients bought Bitcoin from me and resold it on (the Darknet site) Silk-Road. resell those Bitcoins on Silk Road so then those people would then go buy drugs and stuff like that. And I knew about it I didn’t really care enough to stop it, because I was making money. And um my lawyers eventually sent me a letter and they said, if you don’t shut the company down right now we can’t represent you anymore.” Screenshot of drug market place Silk Road. On that day in July 2013, Charlie Shrem follows the counsel of lawyers. “ I thought I was good because we shut down the company. I wasn’t caught with my hand in the cookie jar.” Crime and Punishment Eight months later, Charlie is arrested at a New York Airport and charged with money laundering and unlicensed money transfers. One of the evidence against him is an e-mail to Bitcoin salesman Robert Faiella: …” Some E-mails are dangerous, even if they are well-intentioned. “ “ I know you’re on Silk Road I know you’re reselling these Bitcoins on Silk Road. Like you better stop, or else That was the smoking gun because that email I admitted that I knew what he was doing. Charlie hires the best lawyer. “ ” He pleads guilty and is sentenced to two years in prison, one of them on probation. they cost hundreds of thousands of dollars. I walked into prison with sixteen thousand dollars to my name. “There are no friends in prison. Everyone has his own goals. Sometimes inmates overlap their own goals. Sometimes people help you. I have read a lot and always left the cell light on. All of a sudden this like huge guy, he’s known as a leader of some crew walks into my cell and walks up really close to me and I’m laying on my top bunk. Right? And he’s a tall guy. He comes up all right to my face. And he says, ‘Hey Shrem, you know we’re trying to sleep right now. And your light’s on really bright.’ And I’m like shaking, right. He goes, ‘Here borrow my book light. Give it back to me when you get your own’.” Every punishment comes to an end. Even Charlie Shrem’s: “ ”. You prepare yourself for the day when you are released. You know that a few weeks before. You wake up in the morning, they take all your things, then it’s on to Processing. It takes a little longer. I get scared if they really let me out. But then I get my box and think to myself: “Let’s get out of here quickly before they get to different minds “ I was guilty. I did my time. I worked my ass off to do that time. And I moved on now. ” Outside I didn’t turn on the computer for the first two months. No mobile phone, I haven’t even told people that I’m outside. I wasn’t ready. I was just happy to have a cup of coffee in the morning. I did my job as a dishwasher because I had to have a real job for six months. One that had nothing to do with the Internet. At first I thought that prison was a lesson in humility, but to wash plates 11 hours a day… Charlie Shrem lives the American dream backwards, from millionaire to dishwasher. It sounds strange, but for Charlie, prison means being financially independent. “ Afterwards, I didn’t want to be the slave of money for a long time. Pay rent, cell phone bills and such.” And today? Charlie buys and sells Bitcoin. His investment strategy is: one third in Bitcoin, one third in hard currencies, one third in real estate. “ ” Bitcoin is an experiment that could also fall to 0. I can’t risk everything. Nevertheless, Shrem is more a blockchainer than ever. “ “ I’m thrilled to show [people] the light and preach the gospel. Afterthought Of course, the reality of Charlie Shrem is more like riches-to-rags-to-riches again. His name carries weight in an economy that has little else to offer than names. In the height of ICO mania, Charlie Shrem lends his name as an advisor to token sales such as Slovenia’s Viberate. As sources close to Viberate revealed, Shrems name to the company’s press efforts had made a $10 million ICO campaign. Good for Viberate, good for Charlie Shrem. The gospel is heard. Sources: http://historyofbitcoin.org/ http://www.wnyc.org/story/a-bitcoin-mogul-goes-broke-death-sex-money/?utm_medium=social&utm_source=tw&utm_content=dsm&hootPostID=28d133498557388923d3452cf9c308ec http://markets.businessinsider.com/news/stocks/A-26-year-old-Bitcoin-entrepreneur-was-handed-prison-time-and-the-experience-only-confirmed-his-belief-in-the-cryptocurrency-1001572621 https://www.youtube.com/watch?v=pUCJQLpy8ms&feature=youtu.be

Charlie Shrem: a Bitcoin Story From Riches-to-Rags

A Geek in Prison is Bitcoin Pioneer Charlie Shrem’s account of his experience going from being a force for increasing adoption of Bitcoin before the world had heard of cryptocurrency to a 15-month stint in federal prison for selling it to the wrong people. In his excitement to spread the word about Bitcoin, Charlie fell afoul of the law and acknowledges that he committed the crime. He has since gone on to found Crypto.IQ , an educational and investment firm. There is no foolproof way to prepare for prison. On March 30, 2015, I self surrendered to Lewisburg Federal Prison Camp after I was sentenced to two years for aiding and abetting an unlicensed money transmitting business, for which I was guilty. In my case, the only thing I could do to prepare myself was to embrace the idea that, for the foreseeable future, I wouldn’t be around. But I was optimistic because the only way to get through it, was to fully embrace it and take it head on. Which is what I did. On March 5, 2015, about 30 days before my self surrender, I wrote this on my blog: I’ve been dreading writing this blog post, and since I’ve written it, it’s been sitting as a draft for a few weeks. Nevertheless, it’s time I get on with it. On March 30th, I’ll be self surrendering to Lewisburg Federal Prison Camp in Pennsylvania. It’s been a long hard fight, from getting arrested at JFK airport while landing home, to solitary confinement and being under house arrest the for the past 14 months. When the government indicted me and requested 30 years, I kept my head up with the help of friends, family, and the Bitcoin community. While some distanced themselves, most stood by and fought. I owe my life to those people. Of course I don’t look for sympathy. I did the crime and I will do the time. They say those who stand by you in the bad times deserve to be with you in the good times. Good times are coming, and I look forward to it. Optimism No matter how sad I was feeling, I needed to stay positive with my head up, because once I fell into a spiral of self pity and depression, there would be no coming out. So I started taking notes from other people who had been to Lewisburg to find out everything could to prepare myself for this experience. Keep books/newspapers under 13 oz. Keep it to 4 per package. Get USA Today delivered. It’s dropped off directly. Always use Amazon for books. Bring a list of people to communicate with: Name/Mailing Address/ Email Address/ Phone number Understand that I get 300 minutes every month for the phone, maximum. Nov/Dec it’s 400 minutes. Email/Phone are busiest mostly after dinner. Get a watch $360 a month is max you can spend on commissary, not including phone and email. Buy sneakers. Try to get an education job, teaching other inmates. Don’t gamble Get there 8:45 a.m. Then complete strip search, get temporary clothes, do medical intake, sign forms, and meet a counselor. Get Corrlinks app for iPhone; it becomes like instant messaging. Can print emails. Write physical letters. Stay optimistic. But what was prison going to be like? What was my day going to consist of from waking up until I went to sleep? No one could answer these questions except for me. I needed to accept that I was going to be in prison, and I was going to use the time wisely. There was a lot to do. What do I do with my email account? How do I secure my Bitcoin wallet. How do I keep my phone number? How will I maintain a relationship with my family? All these questions were burning in my head, and I couldn’t find anything online to help me. On December 19, 2014, I stood up facing the judge and said: “Bitcoin is what I love and all I have. It’s my whole life. It’s what I’m on this earth to do, is to help the world see a financial system that does not discriminate and provide for corruption, and I think that Bitcoin will do to money what email did to the postal service. It allowed everyone to be equal. People in Africa, the Middle East, Asia, will have the same opportunities now with Bitcoin, and because of this now, because you can move money instantly and information on a peer-to-peer system. And I think that’s really important. And if your honor grants me that, I’d love to be back out there, helping the world and making sure people don’t do stupid things like I did.” In a letter to the Judge a few days earlier, my lawyer had written: “Had the Greeks known of crypto-currencies and of certain provincial Brooklyn neighborhoods, a tragedy could have been written about a boy who, through Dionysian passion and a little hubris, helped nurture an idea — Bitcoin — that was new to the world and that could change how the world — the whole world — passed value from one person to another.” This new idea would take the boy from his neighborhood. The boy would see himself as an almost sacred guardian of this new idea, charged with the awesome responsibility of bringing it out of the darkness and into the light of widespread, mainstream acceptance. However, in the chaos of developing the new idea, he would drop his guard and allow the dark forces to caste it in shadow. He would be to blame. He would be viewed not as its protector but as its destroyer, the destroyer of the one thing — the idea he loved most. He would be sent back to his provincial neighborhood and, for a while, would live in his parents’ basements, all the while dreaming of the time he could return to his lifelong task of helping — of being just one of many — to bring this new idea further into light. The rest of the story remains to be written. However as fate would have it, I was heading to prison. The courtroom cleared out while I stood back to take it all in. Putting all those factors, as well as all the other facts under Section 3553(a), which the Court had considered, into the equation. The Court thinks that the appropriate sentence is two years. Accordingly, the defendant will be sentenced to two years in prison, 24 months. Two years in Federal prison. At this point I had been under house arrest for about 18 months, and I thought I would get probation or maybe another year of house arrest, but two years? The New York Times wrote: Before Thursday’s hearing in New York federal court in downtown Manhattan, Mr. Shrem, in jeans and a dark suit jacket, sat alone on a bench outside courtroom 14B, his right leg crossed over his left as he read a book. He appeared calm, fidgeting only slightly once he entered the courtroom. Known for his charisma, he was instead stoic, his voice unwavering Looking back, this day, December 19, 2014 was one of the most important days of my life and even one of the happiest. Why? Finally there was light at the end of the tunnel. I was facing 30 years and went to sleep every night since my arrest thinking that I would spend the rest of my adult years in prison. When the Judge said two years, I breathed a sigh of relief. After good behavior, it would be a little over a year. I could make it, I think. It was agreed. I would be given 90 days to get my life in order and prepare for my self surrender on March 30th. At this time, Courtney and I were living in New York City, just enjoying things that I wouldn’t be able to do once I was in jail. We spent the days and nights living like it was our last. I continued to be apart of the Bitcoin community, skyped into conferences and consulted for different companies to pass the time until my impending incarceration. Two months later, it’s March 1, and I’m not ready. I had been living life as if nothing was wrong, and I wasn’t about to go to prison. How do you mentally prepare yourself for this? You don’t. You physically get prepared, drive up to the prison, and walk in. So that’s what I did. Called up the moving company and packed up my life into boxes. Cancelled my lease, suspended my cell phone number, added multi factor to my email accounts, Bitcoin accounts, and hoped to g\God that a hacker wouldn’t get into any of my accounts while I was away. Cell phone: I called up Verizon and had my phone number suspended. I needed to keep my number and many cell phone companies allow you to pay $10 a month to keep the line in a suspended state. Gmail: I made sure two- factor is working and gave backup codes to my loved ones. It was important to make sure I had enough storage to cover a year’s worth of emails. I had to pay Google $10 a month for extra storage on my various email accounts. Bitcoin wallet: Without giving too much away, I had to secure various wallets. I even set up honeypot wallets assuming someone would try and break in (Spoiler- someone tried). Physical stuff: In our apartment, the furniture was rented, so scheduling a pickup was easy. We had a moving truck come a day before and pickup the rest of our things. Bank accounts: Spoke to my bank and let them know my situation. At the time, I was banking with the Internet Archive Credit Union and was good friends with their staff. They understood and suspended any transfers in or out of my account. Power of Attorney: Very important before going to prison, or even a surgery. Have someone able to make decisions on your behalf. March 30th, 5:30 a.m. It was freezing in New York City. We all packed into the car and drove up to Lewisburg, PA. After a quick breakfast at Panera, which would be my last as a free man, we drove up to the prison. My goodbye to Courtney was quick. I told her I loved her and would see her at the first visit. I didn’t cry even though I wanted to. I needed to be strong. I walked through those doors and did not look back. It would be 13 months before I walked through those doors again. ……Next post: Prison Day 1 Charlie

A Geek in Prison — A Life Series by Charlie Shrem (Part 1)

Understanding and Generating Dialogue between Characters in Stories: Limitations and References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Zhe Liu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (2) Chunyang Chen, Monash University, Melbourne, Australia; (3) Junjie Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author; (4) Mengzhuo Chen, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (5) Boyu Wu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (6) Zhilin Tian, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (7) Yuekai Huang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (8) Jun Hu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (9) Qing Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author. Table of Links Abstract and Introduction Motivational Study and Background Approach Experiment Design Results and Analysis Discussion and Threats to Validity Related Work Conclusion and References 8 CONCLUSION Automated testing is crucial for helping improve app quality. Despite the dozens of mobile app GUI testing techniques, how to automatically generate the diversified unusual text inputs for fully testing mobile apps remains a challenge. This paper proposes InputBlaster which leverages the LLM to produce the unusual inputs together with the mutation rules which serve as the reasoning chains. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. The evaluation is conducted for both effectiveness and usefulness, with 136% higher bug detection rate than the best baselines, and uncovering 37 new crashes. In the future, we plan to further analyze the root causes and repair strategy of these input-related bugs, and design automated bug repair methods. REFERENCES [1] 2022. Crash bug text. https://www.theguardian.com/technology/iphone-crashbug-text imessage-ios. [2] 2022. Crash bug text in ios. https://tech.hindustantimes.com/tech/news/becareful-a-new-text bomb-is-making-whatsapp-crash-and-will-hang-yourphone-71599532897852.html. [3] 2022. F-droid. https://f-droid.org/. [4] Toufique Ahmed and Premkumar Devanbu. 2022. Few-shot training LLMs for project-specific code-summarization. ASE (2022). [5] Nadia Alshahwan and Mark Harman. 2011. Automated web application testing using search based software engineering. In ASE. IEEE, 3–12. [6] Saswat Anand, Mayur Naik, Mary Jean Harrold, and Hongseok Yang. 2012. Automated concolic testing of smartphone apps. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering. 1–11. 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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Conclusion and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Conclusion and Limitations We gave a new and far simpler method for computing posterior moments, marginals and samples in massively parallel importance sampling based on differentiating a slightly modified marginal likelihood estimator. The method has limitations, in that while it is considerably more effective than e.g. VI, HMC and global importance sampling, it is more complex. Additionally, at least a naive implementation may be quite costly in terms of memory consumption, limiting how the number of importance samples we can draw for each variable. That said, it should be possible to eliminate almost all of this overhead by careful optimizations to avoid allocating large intermediate tensors, following the strategy in KeOps (Charlier et al. 2021). References Aitchison, L. 2019. Tensor Monte Carlo: particle methods for the GPU era. Advances in Neural Information Processing Systems, 32. Albarghouthi, A.; D’Antoni, L.; Drews, S.; and Nori, A. V. 2017. Fairsquare: probabilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA): 1–30. Andrieu, C.; Doucet, A.; and Holenstein, R. 2010. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): 269–342. Bidyuk, B.; and Dechter, R. 2007. Cutset sampling for Bayesian networks. Journal of Artificial Intelligence Research, 28: 1–48. Bornschein, J.; and Bengio, Y. 2014. Reweighted wakesleep. arXiv preprint arXiv:1406.2751. Bradbury, J.; Frostig, R.; Hawkins, P.; Johnson, M. J.; Leary, C.; Maclaurin, D.; Necula, G.; Paszke, A.; VanderPlas, J.; Wanderman-Milne, S.; and Zhang, Q. 2018. JAX: composable transformations of Python+NumPy programs. Burda, Y.; Grosse, R.; and Salakhutdinov, R. 2015. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519. Charlier, B.; Feydy, J.; Glaunes, J. A.; Collin, F.-D.; and Du- ` rif, G. 2021. 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The quantum theory of fields, volume 2. Cambridge university press. Zavatone-Veth, J.; Canatar, A.; Ruben, B.; and Pehlevan, C. 2021. Asymptotics of representation learning in finite Bayesian neural networks. Advances in neural information processing systems, 34: 24765–24777. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Conclusion and Limitations We gave a new and far simpler method for computing posterior moments, marginals and samples in massively parallel importance sampling based on differentiating a slightly modified marginal likelihood estimator. The method has limitations, in that while it is considerably more effective than e.g. VI, HMC and global importance sampling, it is more complex. Additionally, at least a naive implementation may be quite costly in terms of memory consumption, limiting how the number of importance samples we can draw for each variable. That said, it should be possible to eliminate almost all of this overhead by careful optimizations to avoid allocating large intermediate tensors, following the strategy in KeOps (Charlier et al. 2021). References Aitchison, L. 2019. Tensor Monte Carlo: particle methods for the GPU era. Advances in Neural Information Processing Systems, 32. Albarghouthi, A.; D’Antoni, L.; Drews, S.; and Nori, A. V. 2017. Fairsquare: probabilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA): 1–30. Andrieu, C.; Doucet, A.; and Holenstein, R. 2010. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): 269–342. Bidyuk, B.; and Dechter, R. 2007. Cutset sampling for Bayesian networks. Journal of Artificial Intelligence Research, 28: 1–48. Bornschein, J.; and Bengio, Y. 2014. Reweighted wakesleep. arXiv preprint arXiv:1406.2751. Bradbury, J.; Frostig, R.; Hawkins, P.; Johnson, M. J.; Leary, C.; Maclaurin, D.; Necula, G.; Paszke, A.; VanderPlas, J.; Wanderman-Milne, S.; and Zhang, Q. 2018. JAX: composable transformations of Python+NumPy programs. Burda, Y.; Grosse, R.; and Salakhutdinov, R. 2015. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519. Charlier, B.; Feydy, J.; Glaunes, J. A.; Collin, F.-D.; and Du- ` rif, G. 2021. Kernel Operations on the GPU, with Autodiff,  without Memory Overflows. Journal of Machine Learning Research. Chatterjee, S.; and Diaconis, P. 2018. The sample size required in importance sampling. The Annals of Applied Probability, 28(2): 1099–1135. Claret, G.; Rajamani, S. K.; Nori, A. V.; Gordon, A. D.; and Borgstrom, J. 2013. Bayesian inference using data flow anal- ¨ ysis. In Proceedings of the 2013 9th joint meeting on foundations of software engineering, 92–102. Corenflos, A.; Chopin, N.; and Sarkk ¨ a, S. 2022. De- ¨ Sequentialized Monte Carlo: a parallel-in time particle smoother. arXiv preprint arXiv:2202.02264. Crucinio, F. R.; and Johansen, A. M. 2023. Properties of Marginal Sequential Monte Carlo Methods. ArXiv:2303.03498 [math, stat]. Dawid, A. P. 1992. Applications of a general propagation algorithm for probabilistic expert systems. Statistics and computing, 2(1): 25–36. Dechter, R.; Broka, F.; Kask, K.; and Ihler, A. 2018. Abstraction Sampling in Graphical Models. AAAI. Del Moral, P.; Doucet, A.; and Jasra, A. 2006. Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society Series B: Statistical Methodology, 68(3): 411–436. DOE. 2023. Bus Breakdown and Delays. url https://data.cityofnewyork.us/Transportation/BusBreakdown-and-Delays/ez4e-fazm Accessed on: 05.05.2023. Doucet, A.; Johansen, A. M.; et al. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, 12(656-704): 3. Geffner, T.; and Domke, J. 2022. Variational Inference with Locally Enhanced Bounds for Hierarchical Models. arXiv preprint arXiv:2203.04432. Gehr, T.; Misailovic, S.; and Vechev, M. 2016. PSI: Exact symbolic inference for probabilistic programs. In International Conference on Computer Aided Verification, 62–83. Springer. Geldenhuys, J.; Dwyer, M. B.; and Visser, W. 2012. Probabilistic symbolic execution. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, 166–176. Gogate, V.; and Dechter, R. 2012. Importance samplingbased estimation over and/or search spaces for graphical models. Artificial Intelligence, 184: 38–77. Goodman, N. D.; and Stuhlmuller, A. 2014. The design and implementation of probabilistic programming languages. Gordon, N. J.; Salmond, D. J.; and Smith, A. F. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE proceedings F (radar and signal processing), volume 140, 107–113. IET. Harper, F. M.; and Konstan, J. A. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4): 1–19. Heap, T.; and Laurence, A. 2023. Massively parallel reweighted wake-sleep. UAI. Hoffman, M. D.; Gelman, A.; et al. 2014. The No-UTurn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15(1): 1593–1623. Holtzen, S.; Van den Broeck, G.; and Millstein, T. 2020. Scaling exact inference for discrete probabilistic programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA): 1–31. Jordan, M. I.; Ghahramani, Z.; Jaakkola, T. S.; and Saul, L. K. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2): 183–233. Kingma, D. P.; Welling, M.; et al. 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4): 307–392. Kuntz, J.; Crucinio, F. R.; and Johansen, A. M. 2023. The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems. ArXiv:2110.15782 [math, stat]. Lai, J.; Domke, J.; and Sheldon, D. 2022. Variational Marginal Particle Filters. In International Conference on Artificial Intelligence and Statistics, 875–895. PMLR. Le, T. A.; Igl, M.; Rainforth, T.; Jin, T.; and Wood, F. 2017. Auto-encoding sequential monte carlo. arXiv preprint arXiv:1705.10306. Le, T. A.; Kosiorek, A. R.; Siddharth, N.; Teh, Y. W.; and Wood, F. 2020. Revisiting reweighted wake sleep for models with stochastic control flow. In Uncertainty in Artificial Intelligence, 1039–1049. PMLR. Lindsten, F.; Johansen, A. M.; Naesseth, C. A.; Kirkpatrick, B.; Schon, T. B.; Aston, J.; and Bouchard-Cotˆ e, A. 2017. ´ Divide-and-conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics, 26(2): 445–458. Maddison, C. J.; Lawson, J.; Tucker, G.; Heess, N.; Norouzi, M.; Mnih, A.; Doucet, A.; and Teh, Y. 2017. Filtering variational objectives. Advances in Neural Information Processing Systems, 30. Naesseth, C.; Linderman, S.; Ranganath, R.; and Blei, D. 2018. Variational sequential monte carlo. In International conference on artificial intelligence and statistics, 968–977. PMLR. Narayanan, P.; Carette, J.; Romano, W.; Shan, C.-c.; and Zinkov, R. 2016. Probabilistic inference by program transformation in Hakaru (system description). In International Symposium on Functional and Logic Programming, 62–79. Springer Obermeyer, F.; Bingham, E.; Jankowiak, M.; Pradhan, N.; Chiu, J.; Rush, A.; and Goodman, N. 2019. Tensor variable elimination for plated factor graphs. In International Conference on Machine Learning, 4871–4880. PMLR. Pfeffer, A. 2005. The design and implementation of IBAL: A generalpurpose probabilistic programming language. In Harvard Univesity. Citeseer. Salvatier, J.; Wiecki, T. V.; and Fonnesbeck, C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2: e55. Publisher: PeerJ Inc. Sankaranarayanan, S.; Chakarov, A.; and Gulwani, S. 2013. Static analysis for probabilistic programs: inferring whole program properties from finitely many paths. In Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation, 447–458. Wang, D.; Hoffmann, J.; and Reps, T. 2018. PMAF: an algebraic framework for static analysis of probabilistic programs. ACM SIGPLAN Notices, 53(4): 513–528. Weinberg, S. 1995. The quantum theory of fields, volume 2. Cambridge university press. Zavatone-Veth, J.; Canatar, A.; Ruben, B.; and Pehlevan, C. 2021. Asymptotics of representation learning in finite Bayesian neural networks. Advances in neural information processing systems, 34: 24765–24777.

Using Autodiff to Estimate Posterior Moments: Conclusion, Limitations, and References

SINGAPORE, Singapore, April 3rd, 2024/Chainwire/-- Bitlayer Labs, the first Bitcoin Layer 2 solution based on BitVM, announced a seed round of $5M led by Framework Ventures and ABCDE Capital, participation by StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital and many more. The announcement comes as Bitlayer prepares the launch of its open-incentive program Ready Player One, inviting developers worldwide to augment, enhance, and build on the protocol. Bitlayer Labs, the developer of Bitcoin Layer 2 protocol , announced today the close of a $5 million seed funding round at an $80 million valuation, driving a vision to become the computation layer of Bitcoin. Bitlayer Investors & Ecosystem Partners Blockchain-based venture firms Framework Ventures and ABCDE Capital led the round, with participation from StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital, Kenetic Capital, Kestrel, Global Coin Research, Pivot Global, and Web3Port. Many other incredible investors added value to the seed round. Prominent angel investors including Messari CEO Ryan Selkis, Messari co-founder Dan McArdle, Asymmetric Capital founder Dan Held, Hacken CEO Dyma, Sky Mavis CEO Trung and CTO Andy, and Kyber Network founder Loi Luu, among others, joined in capitalizing the round. Bitlayer’s seed round was delivered soaring value-add support from ecosystem partners including Hacken, AWS Cloud, Ankr, Polyhedra, Babylon, Particle Network, Meson, Nubit, BitSmiley, TokenPocket, Xverse, Flash Protocol, Umoja.xyz, RunesTerminal, and more. The Bitlayer Network Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing completeness. The protocol aims to build a more scalable and interconnected Bitcoin ecosystem. The securing of new capital pushes Bitlayer's lead in paving the way for more Bitcoin Layer 2 use cases, a race that will propel Bitcoin to prevail over other blockchains in size, scalability, and security. The fund will also enable Bitlayer to grow its team by hiring across business development and engineering faculties to support global expansion efforts. Investment firms are increasingly optimistic about the Bitcoin ecosystem as Bitlayer is rapidly gaining prominence, capturing the industry's attention. Investor Remarks Roy Learner, partner at Framework Ventures, spoke on the augmentation of Bitlayer with BitVM: “We are excited to back Bitlayer as they launch the first Bitcoin Layer 2 network based on BitVM. Looking at the trade-off space, BitVM struck us as the most practical approach to bring scalability and expressiveness into the BTC ecosystem without sacrificing security.With over $1T of global wealth looking to productively utilize their BTC, Bitlayer opens up the design space to introduce novel DeFi use cases and applications. Led by a team of OGs, we've been impressed by Kevin and Charlie's blend of deep technical background and rich crypto experience and are excited to partner with them as they deliver on their vision of making Bitcoin a more productive asset.” Qiao Wang, co-founder of Alliance DAO, emphasized the future of Bitlayer and Bitcoin L2s in the emerging era: “I spent the vast majority of my intellectual, social, and financial capital in the Ethereum ecosystem in the last decade, but deep down I've always been a Bitcoiner. The possibility to enhance Bitcoin's expressiveness and scalability via L2s such as Bitlayer is the most exciting moment in the history of Bitcoin. That is because there's over $1T of wealth stored on Bitcoin, and the holders would want to do something productive with it. Greater expressiveness and scalability enable this. Bitlayer is one of the most experienced, passionate, and technically capable teams to tackle this problem.” In the midst of building Bitlayer, co-founder Charlie Hu shared his thoughts on the protocol’s milestone: "This support signifies investors’ and builders’ trust in Bitlayer and the team, and demonstrates a robust backing for the Bitcoin ecosystem and the future of Bitcoin DeFi at large." Bitlayer ventures into the future of Bitcoin with newly supplied capital, anticipating a rush of development activity and investor interest in the Bitcoin Layer 2 space. "Bitlayer is dedicated to bringing super scalability into the Bitcoin ecosystem, with hundreds of millions of potential users as an addressable market," said Kevin He, co-founder of Bitlayer. "We’ve created the best, easiest-to-use scalability product in order to give users better fee rates and faster actions, all based on Bitcoin’s security foundation." With the approach of the Bitcoin halving event, the need for a robust Bitcoin ecosystem becomes more important than ever. Bitlayer’s exacting design and rigorous security auditing in preparation for public mainnet launch primes the protocol for a flourishing builder ecosystem. Mainnet launches on April 5th, with more milestones arriving soon on the project’s development pipeline. Ready Player One– Ecosystem Incentive Program. To accelerate ecosystem development and incentivize projects to deploy on the Bitlayer mainnet, Bitlayer is launching a series of ecosystem incentive programs– starting with the first event, Ready Player One. The campaign is an open incentive program designed to distribute token rewards valued at over $50M to protocols and teams that deploy to the Bitlayer mainnet and demonstrate exceptional performance. Bitlayer’s growth is accelerating, urging strong projects to join its ecosystem to grow the protocol in exponential ways. Bitlayer commits to providing comprehensive ecosystem support for all projects, offering the following resources to builders: Potential investment from the foundation and institutions Initial liquidity support Comprehensive product development resources Mentorship and pitch opportunities from top-tier incubators Support from the Bitcoin community and OGs Ecosystem collaboration and co-creation Users can find the full article for more information. here About Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing-completeness. Bitlayer is committed to becoming the computation layer for Bitcoin. It introduces ultra-scalability and inherits Bitcoin’s L1 security, providing users with high throughput and a low-cost transaction experience. Bitlayer Github Contact Digital Marketing Manager Sky Harris Bitlayer skyler@bitlayer.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Bitlayer, a Leading Bitcoin L2, Nets $5M In Funding, Unveils $50M 'Ready Player One' Program

The comprehensive C# Cheat Sheet is designed to aid developers in mastering key syntax and concepts related to C# programming. Contents Basic Structure Data Types Variables Constants Conditional Statements Loops Arrays Lists Dictionaries Methods Classes & Objects Exception Handling Delegates, Events & Lambdas LINQ (Language-Integrated Query) Attributes Async/Await Miscellaneous String Manipulation File I/O Date & Time Generics Nullables Attributes & Reflection Extension Methods Dependency Injection Partial Classes Interoperability Anonymous Types Tuples Pattern Matching Local Functions Records with Expressions Indexers and Ranges using Declaration Nullable Reference Types (NRTs) Pattern-Based Using Property Patterns Default Interface Implementations Dynamic Binding 1. Basic Structure All C# programs follow a fundamental structure, outlined below: using System;
    
    public class HelloWorld
    {
        public static void Main(string[] args)
        {
            Console.WriteLine("Hello, World!");
        }
    } Starting with .NET 5, top-level statements simplify the Program.cs content: Console.WriteLine("Hello, World"); 2. Data Types C# supports various data types such as: Value Types: int, char, and float Reference Types: string, class, and array 3. Variables Variables are symbolic names for values: int age = 30; // integer variable
    string name = "John"; // string variable
    double PI = 3.14159; // double for floating-point numbers
    bool isLoggedIn = true; // boolean variable Use ‘var’ for type inference: var number = 5; // compiler infers type as int
    var message = "This is a message"; // compiler infers type as string 4. Constants Constants hold immutable values: const double GRAVITY = 9.81; // constant for gravitational acceleration
    const string COMPANY_NAME = "MyCompany"; // constant company name 5. Conditional Statements Control program flow based on conditions: int age = 20;
    
    if (age >= 18)
    {
        Console.WriteLine("You are eligible to vote.");
    }
    else
    {
        Console.WriteLine("You are not eligible to vote.");
    }
    
    switch (variable) { /*...*/ } // Switch statement 6. Loops Execute code repeatedly: for (int i = 1; i <= 5; i++)
    {
        Console.WriteLine(i);
    }
    
    foreach (var item in collection) { /*...*/ } // Foreach loop
    
    while (condition) { /*...*/ } // While loop
    
    do { /*...*/ } while (condition); // Do-while loop 7. Arrays Fixed-size collections of elements: string[] names = new string[3] { "Alice", "Bob", "Charlie" };
    Console.WriteLine(names[1]); // Output: Bob (accessing element at index 1) 8. Lists Dynamic collections similar to arrays: List<int> numbers = new List<int>();
    numbers.Add(1);
    numbers.Add(2);
    numbers.Add(3);
    
    foreach (var number in numbers)
    {
        Console.WriteLine(number);
    } 9. Dictionaries Key-value pairs for data association: Dictionary<string, string> phonebook = new Dictionary<string, string>();
    phonebook.Add("John Doe", "123-456-7890");
    phonebook.Add("Jane Doe", "987-654-3210");
    
    Console.WriteLine(phonebook["John Doe"]); // Output: 123-456-7890 10. Methods Encapsulate reusable logic: public class Rectangle
    {
        public double Width { get; set; }
        public double Height { get; set; }
    
        public double GetArea()
        {
            return Width * Height;
        }
    }
    
    public class Program
    {
        public static void Main(string[] args)
        {
            Rectangle rect = new Rectangle();
            rect.Width = 5;
            rect.Height = 10;
    
            double area = rect.GetArea();
            Console.WriteLine($"Area of rectangle: {area}");
        }
    } 11. Classes & Objects Classes define blueprints for objects: public class MyClass // Class definition
    {
        public string PropertyName { get; set; } // Properties store data
        public void MethodName() { /*...*/ } // Methods define actions
    }
    
    MyClass obj = new MyClass(); // Object creation 12. Exception Handling Manage runtime errors gracefully: public static int GetNumberInput()
    {
      while (true)
      {
        try
        {
          Console.WriteLine("Enter a number: ");
          string input = Console.ReadLine();
          return int.Parse(input);
        }
        catch (FormatException)
        {
          Console.WriteLine("Invalid input. Please enter a number.");
        }
      }
    }
    
    public static void Main(string[] args)
    {
      int number = GetNumberInput();
      Console.WriteLine($"You entered: {number}");
    } 13. Delegates, Events & Lambda For event-driven programming and method handling: public delegate void MyDelegate(); // Delegate declaration
    
    event MyDelegate MyEvent; // Event declaration
    
    public class Person
    {
      public string Name { get; set; }
      public int Age { get; set; }
    }
    
    public static void Main(string[] args)
    {
      List<Person> people = new List<Person>()
      {
        new Person { Name = "Alice", Age = 30 },
        new Person { Name = "Bob", Age = 25 },
        new Person { Name = "Charlie", Age = 40 },
      };
    
      people.Sort((p1, p2) => p1.Name.CompareTo(p2.Name));
    
      foreach (var person in people)
      {
        Console.WriteLine(person.Name); // Output: Alice, Bob, Charlie (sorted by name)
      }
    } 14. LINQ (Language-Integrated Query) Query capabilities for data manipulation: using System.Linq;
    
    public static void Main(string[] args)
    {
      List<int> numbers = new List<int>() { 1, 2, 3, 4, 5, 6 };
      var evenNumbers = numbers.Where(x => x % 2 == 0);
    
      foreach (var number in evenNumbers)
      {
        Console.WriteLine(number); // Output: 2, 4, 6
      }
    } 15. Attributes Add metadata to code elements: [Obsolete("Use the new DoSomethingV2 method instead.")]
    public void DoSomething()
    {
      // Implementation here
    }
    
    public void DoSomethingV2()
    {
      // New and improved implementation
    } 16. Async/Await For non-blocking code execution: using System.Threading.Tasks;
    
    public static async Task DownloadFileAsync(string url, string filePath)
    {
      // Simulate downloading data asynchronously
      await Task.Delay(2000); // Simulate a 2-second download
    
      // Write downloaded data to the file
      File.WriteAllText(filePath, "Downloaded content");
      Console.WriteLine($"File downloaded to: {filePath}");
    }
    
    public static void Main(string[] args)
    {
      string url = "https://example.com/data.txt";
      string filePath = "downloaded_data.txt";
    
      DownloadFileAsync(url, filePath);
    
      // Continue program execution while download happens in the background
      Console.WriteLine("Downloading file...");
      Console.WriteLine("Meanwhile, you can do other things...");
    } 17. Miscellaneous Additional language features: enum, interface, class, record, and struct dynamic, is, as, var, and nameof 18. String Manipulation Powerful string handling methods: string.Concat(); // Combine strings
    string.Join(); // Join elements
    str.Split(); // Split string
    str.ToUpper(); // Convert to uppercase
    str.ToLower(); // Convert to lowercase 19. File I/O Operations with files: using System.IO; // Required for File I/O
    
    File.ReadAllText(path); // Read file content
    File.WriteAllText(path, content); // Write to file
    File.Exists(path); // Check file existence 20. Date & Time Date and time manipulation: using System;
    
    public static void Main(string[] args)
    {
      DateTime startDate = DateTime.Parse("2024-03-10");
      DateTime endDate = DateTime.Now;
    
      TimeSpan difference = endDate - startDate;
      Console.WriteLine($"Time difference: {difference.Days} days, {difference.Hours} hours");
    } 21. Generics Type-safe data structures: public class Stack<T>
    {
      private List<T> items = new List<T>();
    
      public void Push(T item)
      {
        items.Add(item);
      }
    
      public T Pop()
      {
        T item = items[items.Count - 1];
        items.RemoveAt(items.Count - 1);
        return item;
      }
    }
    
    public static void Main(string[] args)
    {
      Stack<string> messages = new Stack<string>();
      messages.Push("Hello");
      messages.Push("World");
    
      string message = messages.Pop();
      Console.WriteLine(message); // Output: World
    } 22. Nullables Allow value types to be null: int? nullableInt = null; // Nullable integer 23. Attributes & Reflection Metadata and type introspection: public class Person
    {
      public string Name { get; set; }
      public int Age { get; set; }
    }
    
    public static void Main(string[] args)
    {
      Type personType = typeof(Person);
      PropertyInfo[] properties = personType.GetProperties();
    
      foreach (PropertyInfo property in properties)
      {
        Console.WriteLine(property.Name); // Output: Name, Age
      }
    } 24. Extension Methods Add methods to existing types: public static class StringExtensions
    {
      public static string ToUppercase(this string str)
      {
        return str.ToUpper();
      }
    }
    
    public static void Main(string[] args)
    {
      string message = "Hello, world!";
      string uppercased = message.ToUppercase(); // Using the extension method
      Console.WriteLine(uppercased); // Output: HELLO, WORLD!
    } 25. Dependency Injection Loosely coupled code design: public interface ILogger
    {
      void LogMessage(string message);
    }
    
    public class MyService
    {
      private readonly ILogger _logger;
    
      public MyService(ILogger logger)
      {
        _logger = logger;
      }
    
      public void DoSomething()
      {
        _logger.LogMessage("Doing something...");
      }
    }
    
    // Implementing the ILogger interface (example)
    public class ConsoleLogger : ILogger
    {
      public void LogMessage(string message)
      {
        Console.WriteLine(message);
      }
    }
    
    public static void Main(string[] args)
    {
      ILogger logger = new ConsoleLogger();
      MyService service = new MyService(logger);
      service.DoSomething();
    } 26. Partial Classes Splitting a single class definition: public partial class MyClass { /*...*/ } // Partial class definition 27. Interoperability Interop with other languages: using System;
    using System.Runtime.InteropServices;
    
    [DllImport("user32.dll")]
    public static extern int MessageBox(IntPtr hWnd, string lpText, string lpCaption, uint uType);
    
    public static void Main(string[] args)
    {
      MessageBox(IntPtr.Zero, "Hello from C#!", "Interop Example", 0);
    } 28. Anonymous Types Creating unnamed types:csharpCopy code var person = new { Name = "John", Age = 30 };
    Console.WriteLine($"Name: {person.Name}, Age: {person.Age}"); 29. Tuple Data structures with a specific number of elements: (string Name, int Age) person = ("Alice", 30);
    Console.WriteLine($"Name: {person.Name}, Age: {person.Age}"); // Accessing elements using Item1 and Item2 30. Pattern Matching Simplifies certain programming tasks: object obj = new Person { Name = "Bob", Age = 25 };
    
    if (obj is Person { Name: "Bob", Age >= 18 })
    {
      Console.WriteLine("Bob is an adult.");
    } 31. Local Functions Encapsulate logic within methods: public static int Calculate(int number)
    {
      int Factorial(int n)
      {
        if (n == 0) return 1;
        return n * Factorial(n - 1);
      }
    
      return Factorial(number);
    }
    
    public static void Main(string[] args)
    {
      int result = Calculate(5);
      Console.WriteLine($"5! = {result}");
    } 32. Records Concise syntax for reference types: public record Person(string Name, int Age);
    
    public static void Main(string[] args)
    {
      Person person1 = new Person("Alice", 30);
      Person person2 = new Person("Alice", 30);
    
      // Records provide default equality comparison
      if (person1 == person2)
      {
        Console.WriteLine("People are equal");
      }
    } 33. with Expressions Non-destructive mutation for records: var john = new Person("John", 30);
    var jane = john with { Name = "Jane" }; // Non-destructive mutation 34. Indexers and Ranges Flexible data access: int[] arr = {0, 1, 2, 3, 4, 5};
    var subset = arr[1..^1]; // Indexer and range usage 35. using Declaration Dispose of IDisposable objects: using var reader = new StreamReader("file.txt"); // using declaration 36. Nullable Reference Types (NRTs) Avoid null reference exceptions: public class Person
    {
      public string Name { get; set; }
      public int Age { get; set; }
    }
    
    public static void Main(string[] args)
    {
      Person person = new Person() { Age = 30 };
    
      // NRTs require null checks before accessing properties
      if (person?.Name != null)
      {
        Console.WriteLine(person.Name);
      }
      else
      {
        Console.WriteLine("Name is null");
      }
    } 37. Pattern-Based Using More patterns in the using statement: public ref struct ResourceWrapper { /*...*/ } // Resource wrapper
    
    using var resource = new ResourceWrapper(); // Pattern-based using 38. Property Patterns Deconstruct objects in pattern matching: if (obj is Person { Name: "John", Age: var age }) { /*...*/ } // Property pattern matching 39. Default Interface Implementations Interfaces with default method implementations: public interface IPerson { /*...*/ } // Interface with default method
    public class MyClass : IPerson { /*...*/ } // Class implementing interface 40. Dynamic Binding Runtime type resolution: dynamic d = 5; // Dynamic binding
    d = "Hello"; // No compile-time type checking Conclusion This structured C# Cheat Sheet concludes with advanced topics and techniques, providing a comprehensive reference for developers aiming to enhance their C# programming skills. For detailed examples and further exploration, refer to the specific sections outlined in this guide. Happy coding! C# Programming🚀 Thank you for being a part of the C# community! Before you leave: If you’ve made it this far, please show your appreciation with a clap, and follow the author! 👏️️ Follow us: | | | | | X LinkedIn Dev.to Hashnode Newsletter Tumblr Visit our other platforms: | | | | GitHub Instagram Tiktok Quora Daily.dev Inspired By: https://zerotomastery.io/cheatsheets/csharp-cheat-sheet/#constants Also published here

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C#: от основ к продвинутым методам — шпаргалка для начинающих

C#: Temel Bilgilerden İleri Tekniklere - Başlangıç Seviyesine Uygun Bir Hile Sayfası

C#: Từ cơ bản đến kỹ thuật nâng cao - Bảng cheatsheet thân thiện với người mới bắt đầu

C#：从基础知识到高级技术 - 适合初学者的备忘单

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vianney Brouard, ENS de Lyon, UMPA, CNRS UMR 5669, 46 All´ee d’Italie, 69364 Lyon Cedex 07, France; E-mail: vianney.brouard@ens-lyon.f. Table of Links Abstract & Introduction and presentation of the model Main results and biological interpretation First-order asymptotics of the mutant sub-populations for an infinite mono-directional grap First-order asymptotics of the mutant sub-populations for a general finite trait space (Theorem 2.1) Convergence for the stochastic exponents (Theorem 2.2) Acknowledgements & References Acknowledgements The author would like to thank H´el`ene Leman for inspiring and helpful discussions and feedback. References [1] Guillaume Achaz. Frequency spectrum neutrality tests: one for all and all for one. Genetics, 183(1):249–258, 2009. [2] Jochen Blath, Tobias Paul, and Andr´as T´obi´as. A stochastic adaptive dynamics model for bacterial populations with mutation, dormancy and transfer. 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A general multi-scale description of metastable adaptive motion across fitness valleys. arXiv preprint arXiv:2112.12675, 2021. [15] Jasmine Foo and Kevin Leder. Dynamics of cancer recurrence. The Annals of Applied Probability, 23(4):1437–1468, 2013. [16] Jasmine Gamblin, Sylvain Gandon, Fran¸cois Blanquart, and Amaury Lambert. Bottlenecks can constrain and channel evolutionary paths. Genetics, 224(2):iyad001, 2023. [17] Einar Bjarki Gunnarsson, Kevin Leder, and Jasmine Foo. Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability. Theoretical population biology, 142:67–90, 2021. [18] Einar Bjarki Gunnarsson, Kevin Leder, and Xuanming Zhang. Limit theorems for the site frequency spectrum of neutral mutations in an exponentially growing population. arXiv preprint arXiv:2307.03346, 2023. [19] Agnes Hamon and Bernard Ycart. Statistics for the luria-delbr¨uck distribution. Electronic Journal of Statistics, 2012. [20] Siˆan Jones, Wei-dong Chen, Giovanni Parmigiani, Frank Diehl, Niko Beerenwinkel, Tibor Antal, Arne Traulsen, Martin A Nowak, Christopher Siegel, Victor E Velculescu, et al. Comparative lesion sequencing provides insights into tumor evolution. Proceedings of the National Academy of Sciences, 105(11):4283–4288, 2008. [21] Peter Keller and Tibor Antal. Mutant number distribution in an exponentially growing population. Journal of Statistical Mechanics: Theory and Experiment, 2015(1):P01011, 2015. [22] David G Kendall. Birth-and-death processes, and the theory of carcinogenesis. Biometrika, 47(1/2):13–21, 1960. [23] David A Kessler and Herbert Levine. Scaling solution in the large population limit of the general asymmetric stochastic luria–delbr¨uck evolution process. Journal of statistical physics, 158(4):783– 805, 2015. [24] Jean-Fran¸cois Le Gall et al. Brownian motion, martingales, and stochastic calculus, volume 274. Springer, 2016. [25] Douglas E Lea and Charles Alfred Coulson. The distribution of the numbers of mutants in bacterial populations. Journal of genetics, 49:264–285, 1949. [26] Shaoping Ling, Zheng Hu, Zuyu Yang, Fang Yang, Yawei Li, Pei Lin, Ke Chen, Lili Dong, Lihua Cao, Yong Tao, et al. Extremely high genetic diversity in a single tumor points to prevalence of non-darwinian cell evolution. Proceedings of the National Academy of Sciences, 112(47):E6496– E6505, 2015. [27] Salvador E Luria and Max Delbr¨uck. Mutations of bacteria from virus sensitivity to virus resistance. Genetics, 28(6):491, 1943. [28] Michael D Nicholson and Tibor Antal. Competing evolutionary paths in growing populations with applications to multidrug resistance. PLoS computational biology, 15(4):e1006866, 2019. [29] Michael D Nicholson, David Cheek, and Tibor Antal. Sequential mutations in exponentially growing populations. PLOS Computational Biology, 19(7):e1011289, 2023. [30] Tobias Paul. The canonical equation of adaptive dynamics in individual-based models with power law mutation rates, 2023. [31] Andrea Sottoriva, Haeyoun Kang, Zhicheng Ma, Trevor A Graham, Matthew P Salomon, Junsong Zhao, Paul Marjoram, Kimberly Siegmund, Michael F Press, Darryl Shibata, et al. A big bang model of human colorectal tumor growth. Nature genetics, 47(3):209–216, 2015. [32] Subramanian Venkatesan and Charles Swanton. Tumor evolutionary principles: how intratumor heterogeneity influences cancer treatment and outcome. American Society of Clinical Oncology Educational Book, 36:e141–e149, 2016. [33] Marc J Williams, Benjamin Werner, Chris P Barnes, Trevor A Graham, and Andrea Sottoriva. Identification of neutral tumor evolution across cancer types. Nature genetics, 48(3):238–244, 2016. [34] Kai Zeng, Yun-Xin Fu, Suhua Shi, and Chung-I Wu. Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics, 174(3):1431–1439, 2006.

Genetic Composition of Supercritical Branching Populations: Acknowledgements & References

AI is growing faster than ever. MarketsandMarkets that the artificial intelligence market will skyrocket to $407 billion by 2027. In this constantly changing environment, you either adapt or risk falling behind. As a result, companies and businesses around the world are incorporating AI into their day-to-day operations. According to a conducted by Forbes Advisor, a whopping 64% of businesses are confident that artificial intelligence will boost their overall productivity. A very optimistic outlook, isn’t it? predicts survey Today, it’s hard to imagine any aspect of our lives without AI, and influencer marketing is no exception. According to a by the micro-influencer platform SocialPubli, recent study 82% of marketers view AI as an opportunity for growth and success, with 76% already using AI in their work. Last year, Meta's of partnerships with "cultural icons and influencers" sparked many discussions. The company created AI-powered chatbots with unique interests and personalities and their own Instagram and Facebook profiles. Some resemble such celebrities as Snoop Dogg, Charli D’Amelio, Kendall Jenner, Tom Brady, Paris Hilton, and others. announcement While some see AI as a threat that could eventually take over human jobs, it's important to understand the many benefits that it can offer to marketers. Right now, However, the potential of AI certainly spreads far beyond these areas. AI is in influencer marketing to brainstorm ideas (45%), write social media posts (47%), create images and videos (39%), do editing (36%), and conduct research (35%). already being used How AI Can Revolutionize Influencer Marketing Discover relevant influencers with a relevant target audience When it comes to choosing influencers for collaborations, understanding their target audiences is key. While some platforms claim to be well-fitted for this task, manual review and verification seem to be more reliable at the moment. AI tools are already here to help, but in my experience, their current data doesn't quite match up with actual sales performance. They either miss important factors, or their methods are off. So, let's just imagine that AI keeps getting better and will one day reach a point of perfection. How could it help with searching for relevant influencers? When working with influencers, you should always watch out for certain red flags like low-quality followers, low frequency of posts, or low engagement rates. AI could help by performing initial checks on influencers and eliminating those who don't meet these standards. I can’t even begin to explain how much time it could save. Moreover, AI could check subscriber engagement, comment frequency, the sentiment of comments, any involvement in scandals, and alignment of political views with the brand's policy. In theory, interests could be analyzed as well. But automatic assessment of interests can be very complex because each audience category can have many subcategories. For example, imagine trying to sell a healthy vegan product. The system might suggest a list of influencers interested in healthy eating, but what if one of them supports healthy living but opposes veganism? You'd only catch these nuances through manual review. AI could also consolidate data from various platforms. Instead of manually copying and pasting links and calculating average metrics, AI could streamline this process by integrating all the data. It would also be a significant time-saver. Create and edit content According to the , 52% of video content creators are already incorporating AI into their workflow. However, I don't believe in AI replacing creative professionals anytime soon. I’d say that at this stage, it primarily produces content at the level of a junior manager. Artlist Trend Report 2024 Nevertheless, there are numerous areas where AI can be very beneficial. For example, many creators struggle with writer's block or feel lost when they need to come up with a cutting-edge and highly effective creative. AI can inspire and generate ideas as a starting point. AI is very accessible and very cost-effective. This makes content creation more budget-friendly, as even basic free versions of AI can offer some guidance. However, most AI platforms today have significant limitations and require validation. You need to check their outputs carefully because results can be very different — some are just funny, while others are alarming. Streamline your daily workflow Once again, AI is still in its early stages, so there are plenty of mistakes, and most of the data isn’t very accurate. While AI can provide guidance and explain some processes, you still need to proofread and verify everything carefully. But we're here to dream about the future. AI could help create spreadsheets, develop formulas, or even generate reports. Imagine you could simply say, "I need to design a spreadsheet for this advertising campaign. I'm stuck on the layout; can you help?" And it would create a template for you. Moreover, if you work with many managers or agencies, they are likely to send you information in various formats, and AI can help you standardize it. I once had an experience where AI proved incredibly effective for a similar task. I was collaborating with different agencies and needed to create a stop list of influencers to ensure they wouldn't be approached with conflicting proposals. There were two main requirements: the list had to be dynamic, and it had to be easy to input new influencers and check if they were already on the list. AI helped me develop a script for the spreadsheet. As a result, this tool has been refined and it’s being widely used today, saving a lot of time. Analyze Campaigns and Launches AI could monitor and analyze engagement, click-through, and conversion rates, as well as other metrics, providing insights for future strategies. Today, many businesses are flooded with data that includes sales figures, lead generation statistics, influencer performance, various marketing channels, methodologies, and timeframes. If we imagine that the future is here and ChatGPT-6 is already available, you could just ask, "Hey, I'd like to know the impact of these content creators on our overall business performance," or "I want to determine the most and the least effective creators." In general, every brand employs at least one analyst dedicated to tasks like these, spending days or even weeks writing scripts, utilizing SQL, and extracting data. But with AI, you could just write a query, and you're good to go. In my personal experience, AI helped me build regression analysis models to examine the correlation between sales and influencer marketing campaigns. It suggested certain tools, allowing me to discuss them with an analyst and implement them later on. Detect Fraud Currently, most fraud detection is handled manually by managers. For instance, if an influencer produces boring content but has a lot of likes, it can raise suspicion. Other red flags include frequent spelling mistakes, abnormal likes, or comments that don't match the post. Managers have to manually verify each account, checking for consistent content or a detailed profile description. Sometimes, it's easy to spot fake accounts, like those with a single profile picture, a mismatched description, or a random link. AI could automate this process using mathematical models. It would analyze subscription patterns, saving managers time and allowing them to focus on more important tasks. Increase outreach One of the most crucial parts of the influencer sales funnel is outreach. Your goal is to enhance the funnel's early stages with basic messages like, "Hi, I work with this amazing brand, and we have this fantastic product. We'd like to collaborate with you." Such tasks can be easily delegated to a bot, ideally programmed to simulate human interactions. After the initial contact, a manager can take over. Certainly, AI’s conversion rate may not match that of your top-performing managers. Moreover, since you're not making personalized approaches to content creators, there's also a risk of missing out. For instance, if you simply send a generic message to an influencer saying, "Hey, let's collaborate," they might ignore it. However, if you had taken the time to mention something specific about their content, it would have been more personalized, increasing the chance of a response. The risks of using AI Some experts argue that AI lacks authenticity and emotional connection. But I disagree, and I believe you can customize it to have a specific tone of voice and even add some humor. However, the ethical implications are significantly more important. Let's face it: not everyone enjoys talking with a bot, especially when it pretends to be human. The issue of ethics can arise when content creators are contacted by a bot and later feel deceived because they thought they were interacting with a real person. Moreover, as I’ve already mentioned many times, current AI platforms are not flawless, and depending on them for analytical tasks or calculations can be risky. To make things worse, some errors may not be immediately obvious. For instance, while mistakes in math formulas can be identified and corrected, it’s not that easy to see errors in influencer selection. You might think you have received a comprehensive list while actually missing out on some valuable influencers. Lastly, there are some concerns about data security. There's a risk that AI might start uploading data inputs from other users, possibly including your own. Avoid using confidential sales data, revenue figures, or other sensitive information. However, most influencer statistics, such as links, reach, likes, and comments, can generally be used without concern. The Recipe for Success How can everyone in influencer marketing use AI to boost their performance and make their work more effective right now instead of waiting for the perfect AI tools of the future? Instead of 'Google it,' try 'AI it': Use AI as your search engine, especially if you have an unresolved issue. AI can guide you to some unconventional solutions. Study all market cases: Understand AI's capabilities and gather case studies to anticipate future development. Technologies evolve at a rapid pace, and falling behind means having to play catch-up for a long time. Engage with AI when you're bored: Sometimes, interacting with AI and asking random questions can be quite intriguing. Like, what was the first-ever social media post? Exercise critical thinking: Since all AI platforms have their limitations, it's essential to validate their outputs. Stay curious and open-minded: While AI isn't an immediate game-changer or a genius, it's a valuable tool that can significantly simplify and speed up many tasks.

6 Game-Changing Ways AI Can Boost Your Influencer Marketing

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs References Alberto Abadie, Susan Athey, Guido W Imbens, and Jeffrey Wooldridge. When should you adjust standard errors for clustering? Technical report, National Bureau of Economic Research, 2017. Vivi Alatas, Abhijit Banerjee, Rema Hanna, Benjamin A Olken, and Julia Tobias. Targeting the poor: evidence from a field experiment in indonesia. American Economic Review, 102 (4):1206–1240, 2012. Peter M Aronow and Cyrus Samii. Estimating average causal effects under general interference, with application to a social network experiment. The Annals of Applied Statistics, 11(4):1912–1947, 2017. Susan Athey, Dean Eckles, and Guido W Imbens. Exact p-values for network interference. Journal of the American Statistical Association, 113(521):230–240, 2018. Sarah Baird, J Aislinn Bohren, Craig McIntosh, and Berk Ozler. Optimal design of experi- ¨ ments in the presence of interference. Review of Economics and Statistics, 100(5):844–860, 2018. G Basse and A Feller. Analyzing multilevel experiments in the presence of peer effects. arXiv preprint arXiv, 1608, 2016. Guillaume W Basse and Edoardo M Airoldi. Limitations of design-based causal inference and a/b testing under arbitrary and network interference. Sociological Methodology, 48(1): 136–151, 2018a. Guillaume W Basse and Edoardo M Airoldi. Model-assisted design of experiments in the presence of network-correlated outcomes. Biometrika, 105(4):849–858, 2018b. Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008, 2008. Paul S Bradley, Kristin P Bennett, and Ayhan Demiriz. Constrained k-means clustering. Microsoft Research, Redmond, 20(0):0, 2000. Yann Bramoull´e, Habiba Djebbari, and Bernard Fortin. Identification of peer effects through social networks. Journal of econometrics, 150(1):41–55, 2009. Emily Breza, Arun G Chandrasekhar, Tyler H McCormick, and Mengjie Pan. Using aggregated relational data to feasibly identify network structure without network data. American Economic Review, 110(8):2454–2484, 2020. Jing Cai, Alain De Janvry, and Elisabeth Sadoulet. Social networks and the decision to insure. American Economic Journal: Applied Economics, 7(2):81–108, 2015. Aureo De Paula, Seth Richards-Shubik, and Elie Tamer. Identifying preferences in networks ´ with bounded degree. Econometrica, 86(1):263–288, 2018. Dean Eckles, Brian Karrer, and Johan Ugander. Design and analysis of experiments in networks: Reducing bias from interference. Journal of Causal Inference, 5(1), 2017. Dennis Egger, Johannes Haushofer, Edward Miguel, Paul Niehaus, and Michael Walker. General equilibrium effects of cash transfers: experimental evidence from kenya. Econometrica, 90(6):2603–2643, 2022. Stefan Faridani and Paul Niehaus. Rate-optimal linear estimation of average global effects. arXiv preprint arXiv:2209.14181, 2022. Paul Goldsmith-Pinkham and Guido W Imbens. Social networks and the identification of peer effects. Journal of Business & Economic Statistics, 31(3):253–264, 2013. Keisuke Hirano and Jack R Porter. Asymptotics for statistical treatment rules. Econometrica, 77(5):1683–1701, 2009. David Holtz, Ruben Lobel, Inessa Liskovich, and Sinan Aral. Reducing interference bias in online marketplace pricing experiments. arXiv preprint arXiv:2004.12489, 2020. Daniel Hong, Hyunwoo Lee, and Alex Wei. Optimal solutions and ranks in the max-cut sdp. arXiv preprint arXiv:2109.02238, 2021. Michael G Hudgens and M Elizabeth Halloran. Toward causal inference with interference. Journal of the American Statistical Association, 103(482):832–842, 2008. Ravi Jagadeesan, Natesh S Pillai, and Alexander Volfovsky. Designs for estimating the treatment effect in networks with interference. 2020. Igor Kabiljo, Brian Karrer, Mayank Pundir, Sergey Pupyrev, Alon Shalita, Alessandro Presta, and Yaroslav Akhremtsev. Social hash partitioner: a scalable distributed hypergraph partitioner. arXiv preprint arXiv:1707.06665, 2017. Hyunseung Kang and Guido Imbens. Peer encouragement designs in causal inference with partial interference and identification of local average network effects. arXiv preprint arXiv:1609.04464, 2016. Brian Karrer, Liang Shi, Monica Bhole, Matt Goldman, Tyrone Palmer, Charlie Gelman, Mikael Konutgan, and Feng Sun. Network experimentation at scale. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3106– 3116, 2021. Toru Kitagawa and Guanyi Wang. Who should get vaccinated? individualized allocation of vaccines over sir network. Journal of Econometrics, 2021. Lihua Lei. Unified ℓ2→∞ eigenspace perturbation theory for symmetric random matrices. arXiv preprint arXiv:1909.04798, 2019. Lihua Lei, Xiaodong Li, and Xingmei Lou. Consistency of spectral clustering on hierarchical stochastic block models. arXiv preprint arXiv:2004.14531, 2020. Michael P Leung. Treatment and spillover effects under network interference. Review of Economics and Statistics, 102(2):368–380, 2020. Michael P Leung. Causal inference under approximate neighborhood interference. Econometrica, 90(1):267–293, 2022. Michael P Leung. Network cluster-robust inference. Econometrica, 91(2):641–667, 2023. Shuangning Li and Stefan Wager. Random graph asymptotics for treatment effect estimation under network interference. The Annals of Statistics, 50(4):2334–2358, 2022. Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J Bickel, and Elizaveta Levina. Hierarchical community detection by recursive partitioning. Journal of the American Statistical Association, 117(538):951–968, 2022. Shuyang Ling and Thomas Strohmer. Certifying global optimality of graph cuts via semidefinite relaxation: A performance guarantee for spectral clustering. Foundations of Computational Mathematics, 20(3):367–421, 2020. Elena Manresa. Estimating the structure of social interactions using panel data. Unpublished Manuscript. CEMFI, Madrid, 2013. Charles F Manski. Identification of endogenous social effects: The reflection problem. The review of economic studies, 60(3):531–542, 1993. Charles F Manski. Identification of treatment response with social interactions. The Econometrics Journal, 16(1):S1–S23, 2013. Karthik Muralidharan and Paul Niehaus. Experimentation at scale. Journal of Economic Perspectives, 31(4):103–24, 2017. Mark EJ Newman. Community detection and graph partitioning. Europhysics Letters, 103 (2):28003, 2013a. Mark EJ Newman. Spectral methods for community detection and graph partitioning. Physical Review E, 88(4):042822, 2013b. Elizabeth L Ogburn, Oleg Sofrygin, Ivan Diaz, and Mark J van der Laan. Causal inference for social network data. arXiv preprint arXiv:1705.08527, 2017. Jean Pouget-Abadie. Dealing with Interference on Experimentation Platforms. PhD thesis, 2018. Fredrik S¨avje, Peter Aronow, and Michael Hudgens. Average treatment effects in the presence of unknown interference. Annals of statistics, 49(2):673, 2021. Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. 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Causal Clustering: Design of Cluster Experiments Under Network Interference: References

273 Stories To Learn About Venture Capital

I wrote this series of posts primarily for myself, or at least someone who is trying to make something electronic to sell. If that’s you, then I hope this helps you on your journey! If you just want to see the end product then you can find it at . https://hortus.dev/products/social-battery This is a long post, so I have broken it up into chunks to make it a bit easier to digest. These are: (this article) Designing and Prototyping the Board Selling the Prototypes Online Going from Prototype to Production Introduction I wanted to experiment with JLC PCB’s assembly service - whilst I’ve designed and manufactured bare PCBs before placing and soldering components manually, I’ve got some future projects in mind that will be impractical to solder up by hand due to both the quantity and size of components. It’s kind of amazing how cheap this service is when you think about what’s involved, but it’s still expensive enough that it can be a bit daunting handing over your money, and then waiting to find out whether you’ve made some mistake that will ruin the end result. I figured I’d get started with something simple, something I could test the waters of not just PCB assembly but also e-commerce. My plan was to come up with a small item that I could realistically design and submit for manufacture in less than a day, then hopefully sell reasonably easily on a marketplace like Etsy. I was expecting the design and manufacturing side of things to be the hard part and listing the product on Etsy to be easy, but it was very much the other way around as you will see if you keep reading! The Product I had a quick scan of Etsy to look at the kinds of things people were successfully selling that I could make. I found a couple of examples of people making electronic pin badges - the perfect project! These mostly consisted of LEDs with either a random or pre-set flashing pattern on a novelty-shaped board. These looked alright to me, but I wanted to try something a bit more interactive and meaningful that I could sell for enough money to make a sensible margin on a small number of initial units. Looking at the regular pin badges being sold on Etsy, I saw a bunch of enamel ‘Social Battery’ pins with a sliding indicator. These immediately jumped out at me as something that a) I could identify with personally (people make endless jokes about my social battery…), b) would be really fun as an electronic version, and c) would be easy to design - just a few LEDs, a switch, and a microcontroller to tie it all together! Designing and Prototyping the Board Confident in my skills, I threw together a quick circuit diagram in Kicad. I decided to use an ATtiny13A - mainly because I had a few on hand from a previous project, and because I have a decent amount of experience with similar chips. If you’re not familiar with it, the ATtiny13A is a small 8-bit microprocessor with 6 IO pins and is part of the AVR family of MCUs. It’s similar to the ATmega chips that have historically been at the core of most Arduinos, except its capabilities are much more limited. The benefit is that the ATtiny range of chips are smaller and cheaper, so if you don’t need a lot of memory or peripherals, then they are great! (although possibly a bit dated now with the endless variety of ARM chips that are available). I laid out the PCB for my circuit in KiCad, then got it to spit out the gerbers and drill files (which are used to manufacture the PCB), and the bill of materials and placement files (which are used to assemble components onto PCBs). I submitted them to JLC PCB to see if they were able to process them correctly. The BoM and placement files needed a bit of tweaking from the default to get them in the right format (turns out I did this the hard way, and there’s a much easier plugin for KiCad that does everything perfectly in a single click). JLC PCB maintains a pretty large library of components that they keep in stock for assembly orders. However, in my case, the ATtiny13a was not available, so I had to order them in. This was pretty straightforward using their global sourcing service. I was able to find the supplier with the best price for the quantity I needed and then let JLC PCB order them to their warehouse on my behalf. Whilst I waited, I figured I might as well breadboard my design and start working on the code, and I’m glad I did because I immediately discovered an issue! My design used five of the IO pins available on the ATtiny13a to drive the LEDs directly (with the sixth being used to monitor the button). What I didn’t realize/remember from the last time I made this mistake (yes, It’s happened before), was that one of those IOs is also the reset pin. You can use it, but it’s not able to supply much current, and by tying it to the ground through an LED, I was keeping the chip in a permanent reset state. One way to get around this is to burn a fuse on the chip that permanently disables the reset functionality of the pin transforming it into a regular IO. The issue with this though is that you can only program the chip once (unless you own a high-voltage programmer, which I don’t), and given my propensity for learning things the hard way, that seemed potentially quite wasteful! The other option is to find a way to do more with fewer pins so that the reset pin can be left alone. This can be achieved using a technique called which allows you to address many more LEDs than the number of available pins. charlieplexing You can then scan through these LEDs, turning them on and off individually at a high rate to make it appear as if multiple are on at once through . persistence of vision In my case, I’m driving the four green LEDs from 3 pins, and the red LED with a dedicated pin. This isn’t the most efficient example of charlieplexing, as I could drive all the LEDs from the three pins, but keeping red on a dedicated pin allows for simpler code when it comes to programming. I revised my circuit diagram and PCB design, then re-exported the necessary files and sent them off to JLC PCB to manufacture and assemble an initial set of five prototypes. A little over a week later, I received my prototypes, and I was really pleased with them! There were a few things that I could see I needed to change: I hadn’t really planned how I was going to program the boards. I had just broken out the AVR programming pins and hoped for the best. This was fine for five prototypes as I could tack the programming wires on with the tiniest bit of solder, but this would get very tedious very quickly at scale. The spike for the fastener on the back was soldered onto a ground pad. This would have been fine except the thermal mass of the spike and the ground plane on the board made it difficult to make a good join. It also sticks out very close to the positive metal cage of the battery holder which meant that there was a good chance of accidental shorts occurring if people placed/attached the badge on/to conductive surfaces. The negative contact for the battery wasn’t prominent enough, so I had to add a dab of solder to it to make a good connection. Again - not the end of the world for a small number of prototypes, but a pain if I had to do this for loads. These were all simple fixes to implement. I added a proper programming header that could be used with a pogo pin jig for quick and repeatable programming. I disconnected the spike from the ground plane so that it was its own small disconnected island of copper that would heat up more easily and not cause shorts. And I expanded the negative contact for the battery so that it would have more surface area to make a solid connection. The code is pretty simple. I keep track of the mode the badge is in which is represented by an integer that gets decremented every time the button is pressed. I then loop and blink each LED on my way around as dictated by the mode. When mode 0 is reached, I reset it back to the original number and then put the ATtiny into sleep mode. Pressing the button again triggers the interrupt which wakes the chip and starts the whole process again. Overall, I was really pleased with the results, which meant it was time to see if I could sell them! You can read about my experience doing this in . the next post in this series

How I Manufactured and Sold Electronic Badges - Part 1: Designing and Prototyping the Board

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Issue: column encoding is altered when saving a DataFrame from Python to Oracle When dealing with databases, it's important to pay attention to data types to ensure that the table structure is preserved when saving a DataFrame from . The following is a basic code snippet to save a DataFrame to an Oracle database using SQLAlchemy and pandas: Python import pandas as pd
from sqlalchemy import create_engine

# Define the table structure
data = {
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie'],
    'hire_date': ['2020-01-15', '2019-05-20', '2021-02-10'],
    'insert_datetime': ['2021-09-15 10:00:00', '2021-09-16 11:30:00', '2021-09-17 09:45:00']
}

df = pd.DataFrame(data)

# Display the DataFrame and Data Types
print(df)
print(df.dtypes)

# Create SQLAlchemy engine
engine = create_engine('oracle://username:password@hostname:port/service_name')

# Use pd.to_sql() to replace/update the table in Oracle
df.to_sql('employee', con=engine, if_exists='replace', index=False) id     name   hire_date      insert_datetime
0   1    Alice  2020-01-15  2021-09-15 10:00:00
1   2      Bob  2019-05-20  2021-09-16 11:30:00
2   3  Charlie  2021-02-10  2021-09-17 09:45:00

id                  int64
name               object
hire_date          object
insert_datetime    object
dtype: object However, running this code may reveal a bug where the column encoding is altered in the Oracle database table. Upon inspection, it becomes apparent that only the first column retains its integer data format, while the remaining columns , , and are changed to (Character Large Object) encoding in Oracle. id name hire_date insert_datetime CLOB Solution: using the parameter when invoking dtype to_sql() To ensure that the correct data types are preserved when writing a DataFrame to an Oracle database, one approach is to explicitly define the data types for each column when using . This can be achieved by creating an SQLAlchemy Table object with specified data types and then using the parameter when invoking . to_sql() dtype to_sql() import pandas as pd
from sqlalchemy import create_engine, types

# Define the table structure
data = {
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie'],
    'hire_date': ['2020-01-15', '2019-05-20', '2021-02-10'],
    'insert_datetime': ['2021-09-15 10:00:00', '2021-09-16 11:30:00', '2021-09-17 09:45:00']
}

df = pd.DataFrame(data)

# convert data type to datetime
df['hire_date'] = pd.to_datetime(df['hire_date'], format='%Y-%m-%d')
df['insert_datetime'] = pd.to_datetime(df['insert_datetime'], format='%Y-%m-%d %H:%M:%S')

# Create SQLAlchemy engine
engine = create_engine('oracle://username:password@hostname:port/service_name')

# Set data types
dtype_dic = {'id': types.INTEGER(), 'name': types.NVARCHAR(length=50),'hire_date': types.DATE(),'insert_datetime': types.DateTime()}

# Use pd.to_sql() to replace/update the table in Oracle
df.to_sql('employee', con=engine, schema='TMP01', if_exists='replace', index=False, dtype=dtype_dic) Ensure that the data type has been converted to datetime before using . types.DATE() Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on . Happy exploring!👋 LinkedIn

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