3M Company conducts operations in electronics, telecommunications, industrial, consumer and office, health care, safety, and other markets. The Company businesses share technologies, manufacturing operations, marketing channels, and other resources. 3M serves customers worldwide.

3M Company operates as a diversified technology company worldwide. It operates through four segments: Safety and Industrial; Transportation and Electronics; Health Care; and Consumer. The Safety and Industrial segment offers industrial abrasives and finishing for metalworking applications; autobody repair solutions; closure systems for personal hygiene products, masking, and packaging materials; electrical products and materials for construction and maintenance, power distribution, and electrical original equipment manufacturers; structural adhesives and tapes; respiratory, hearing, eye, and fall protection solutions; and natural and color-coated mineral granules for shingles. The Transportation and Electronics segment provides ceramic solutions; attachment tapes, films, sound, and temperature management for transportation vehicles; premium large format graphic films for advertising and fleet signage; light management films and electronics assembly solutions; packaging and interconnection solutions; and reflective signage for highway, and vehicle safety. The Healthcare segment offers food safety indicator solutions; health care procedure coding and reimbursement software; skin, wound care, and infection prevention products and solutions; dentistry and orthodontia solutions; and filtration and purification systems. The Consumer segment provides consumer bandages, braces, supports and consumer respirators; cleaning products for the home; retail abrasives, paint accessories, car care DIY products, picture hanging, and consumer air quality solutions; and stationery products. It offers its products through e-commerce and traditional wholesalers, retailers, jobbers, distributors, and dealers. The company was founded in 1902 and is based in St. Paul, Minnesota.

Rising world demand for personal protective equipment is driving a 90,000-square-foot expansion of <strong>3M</strong>&#x27;s plant in Valley, Nebraska, including new production lines, equipment and a warehouse.

$67 million expansion to boost production, create 40 jobs at 3M plant in Valley, Nebraska

With <strong>3M</strong> and Leggett &amp; Platt cutting their dividends, the 5 highest-yielding Dividend Aristocrats are listed here.

With 3M Out, Here Are the New Highest-Yielding Dividend Aristocrats - 24/7 Wall St.

HackerNoon curated the internet's most cited 3M+ tech company news articles and blog posts about the 3k+ most valuable tech companies in 2022 and 2023. These stories were curated to power HackerNoon.com/Companies, where we update daily news on top technology companies like Microsoft, Google, and HuggingFace. This dataset is open sourced under the Creative Common License on HuggingFace as Tech Company News Data Dump. Please use this tech company news data freely for your project :-) How the tech companies were curated Our team made a list of the most valuable technology companies, and added companies as they started to trend in the news and on HackerNoon. The first one and half thousand were public companies based on market cap. Then as companies got mentioned in HackerNoon stories and performed well in our startup of the year voting, we added created tech company news pages for them. Once a tech company news page is created, our system curates and stores the trending news, articles and blog posts about that company based on our rules and prompts  that define what is a trending story. How the stories, articles and blog posts were sourced A combination of custom rules, prompts and conditions for relevance, specificity and trendiness using the Bing News API, the Brave News API, and the HackerNoon API. We drilled down industry match for each company, and heavily favored more trusted high ranking sites while also allowing for relevant lower ranking niche publishers. For each company, we surface the most relevant 10-20 stories on their main /company page (Microsoft as an example), and then feature the complete list of the company’s news, stories, mentions, articles and notable links in internet history on company-name/news (Google as an example). How this tech company news data is organized The columns are companyName, company URL, publishedAT, (story) url, title, featured image, and (meta) description. This follows how we organize data in our database. Every article is connected to at least one company. Some companies have more articles than other based on their share of voice, for example using the dataset viewer you can see Google has 99,152 results, 3M has 20,608 results, Adobe has 13,449 results, and NVIDIA has 19,811 results. Without even downloading the data, you can search for company or publication names in the dataset viewer, like NVIDIA pictured below: This dataset is open sourced under the Creative Common License on HuggingFace as Tech Company News Data Dump. Please use this tech company news data freely for your project :-)  You could quantify a company’s aggregate share of voice online, you could measure sentiment analysis of a company’s digital news coverage, you could train your model to predict what headlines will publish about what companies in future, or whatever other research about large tech companies and media coverage your heart desires. Check it out this open data here: https://huggingface.co/datasets/HackerNoon/tech-company-news-data-dump?embedable=true HackerNoon curated the internet's most cited 3M+ tech company news articles and blog posts about the 3k+ most valuable tech companies in 2022 and 2023. These stories were curated to power HackerNoon.com/Companies , where we update daily news on top technology companies like Microsoft , Google , and HuggingFace . This dataset is open sourced under the Creative Common License on HuggingFace as Tech Company News Data Dump . Please use this tech company news data freely for your project :-) HackerNoon HackerNoon HackerNoon.com/Companies HackerNoon.com/Companies Microsoft Microsoft Google Google HuggingFace HuggingFace HuggingFace as Tech Company News Data Dump How the tech companies were curated Our team made a list of the most valuable technology companies, and added companies as they started to trend in the news and on HackerNoon. The first one and half thousand were public companies based on market cap. Then as companies got mentioned in HackerNoon stories and performed well in our startup of the year voting , we added created tech company news pages for them. Once a tech company news page is created, our system curates and stores the trending news, articles and blog posts about that company based on our rules and prompts  that define what is a trending story. startup of the year voting tech company news page How the stories, articles and blog posts were sourced A combination of custom rules, prompts and conditions for relevance, specificity and trendiness using the Bing News API, the Brave News API, and the HackerNoon API. We drilled down industry match for each company, and heavily favored more trusted high ranking sites while also allowing for relevant lower ranking niche publishers. For each company, we surface the most relevant 10-20 stories on their main /company page ( Microsoft as an example ), and then feature the complete list of the company’s news, stories, mentions, articles and notable links in internet history on company-name/news ( Google as an example ). Microsoft as an example Google as an example How this tech company news data is organized The columns are companyName, company URL, publishedAT, (story) url, title, featured image, and (meta) description. This follows how we organize data in our database. Every article is connected to at least one company. Some companies have more articles than other based on their share of voice, for example using the dataset viewer you can see Google has 99,152 results , 3M has 20,608 results , Adobe has 13,449 results , and NVIDIA has 19,811 results . the dataset viewer Google has 99,152 results 3M has 20,608 results Adobe has 13,449 results NVIDIA has 19,811 results Without even downloading the data, you can search for company or publication names in the dataset viewer, like NVIDIA pictured below : NVIDIA pictured below This dataset is open sourced under the Creative Common License on HuggingFace as Tech Company News Data Dump . Please use this tech company news data freely for your project :-)  You could quantify a company’s aggregate share of voice online, you could measure sentiment analysis of a company’s digital news coverage, you could train your model to predict what headlines will publish about what companies in future, or whatever other research about large tech companies and media coverage your heart desires. HuggingFace as Tech Company News Data Dump Check it out this open data here: https://huggingface.co/datasets/HackerNoon/tech-company-news-data-dump?embedable=true https://huggingface.co/datasets/HackerNoon/tech-company-news-data-dump?embedable=true

Tech Company News Data Dump on HuggingFace: 3M Most Cited Posts About 3k Most Valued Tech Companies

টেক কোম্পানি নিউজ ডাটা ডাম্প অন হাগিংফেস: 7M সর্বাধিক উদ্ধৃত পোস্টগুলি 3k সর্বাধিক মূল্যবান প্রযুক্তি সংস্থাগুলি সম্পর্কে

Tech-Unternehmens-News-Datendump auf HuggingFace: 7 Millionen meistzitierte Beiträge über 3.000 am meisten geschätzte Technologieunternehmen

Volcado de datos de noticias de empresas tecnológicas en HuggingFace: 7 millones de publicaciones más citadas sobre las 3.000 empresas tecnológicas más valoradas

Dump de données d'actualités sur les entreprises technologiques sur HuggingFace : 7 millions d'articles les plus cités sur les 3 000 entreprises technologiques les plus appréciées

हगिंगफेस पर टेक कंपनी समाचार डेटा डंप: 3 हजार सबसे मूल्यवान टेक कंपनियों के बारे में 7M सर्वाधिक उद्धृत पोस्ट

HuggingFace のテクノロジー企業ニュース データ ダンプ: 最も価値のある 3,000 のテクノロジー企業に関する最も引用された 700 万件の投稿

기술 회사 뉴스 HuggingFace의 데이터 덤프: 가장 가치 있는 기술 회사 3,000개에 대한 가장 많이 인용된 게시물 700만 개

Despejo de dados de notícias de empresas de tecnologia no HuggingFace: 7 milhões de postagens mais citadas sobre 3 mil empresas de tecnologia mais valiosas

Свалка данных новостей технологических компаний на HuggingFace: 7 миллионов самых цитируемых публикаций о 3 тысячах самых ценных технологических компаний

Teknoloji Şirketi Haberleri HuggingFace'teki Veri Dökümü: 3 Bin En Değerli Teknoloji Şirketi Hakkında 7 Milyon En Çok Alıntı Yapılan Gönderi

Kết xuất dữ liệu tin tức công ty công nghệ trên HuggingFace: 7 triệu bài viết được trích dẫn nhiều nhất về 3k công ty công nghệ có giá trị nhất

HuggingFace 上的科技公司新闻数据转储：关于 3000 家最有价值科技公司的 700 万条被引用次数最多的帖子

When I wrote 2 days ago this tweet https://twitter.com/NFTrebels/status/1663528306145128457?embedable=true I had no idea that just a few hours later, a new story would unfold, and I’d decide to cover it. But not a single day is boring in web3 and one ever knows what’s coming. So here I am after a day of research on the matter cracking open a bottle of beer and writing you this story. Let’s not waste time and start. Context/backstory In the past few weeks, everyone was talking about ben.eth. The guy basically tweeted out his wallet address telling people to send $ETH to it if they wanted to be part of the pre-sale of his upcoming new coin. I did not cover that story but a short version for context: BitBoy endorsed the coin, ben.eth received millions on that wallet and the whole space was astonished and kept speculating if it was a rug, marketing genius operations, or an illustration of how desperate the whole space had become. Since then ben.eth has launched/released 3 tokens so far: $PSYOP, $BEN, and now $LOYAL. It’s not about him now - but his story probably set the first precedent of people sending millions to a personal wallet with no smart contract and no idea of what the project is about and completely trusting a human vs the blockchain/code. (may be wrong - so feel free to correct me if that’s the case). Fast forward a few weeks and another influencer borovik.eth tweeted this after his phone kept crashing while he was on Twitter spaces. As a result, he ended up getting way more than what he needed. So that, too, became a popular topic in the Crypto Twitter. I kept seeing “I need x please send ETH to this address” tweets all over my timeline and it was becoming a meme already… Add to that the usual complaints about “this space has become a joke”, “so many people are so stupid”, “we have hit the bottom”, etc. as a backdrop and we have the full scene to start the story. The trigger Just like the above, this time, too, it all started with a tweet https://twitter.com/Pauly0x/status/1663527537174036482?s=20&embedable=true Pauly has probably been one of the most controversial characters in the space. I found out about him while writing my story about Phunks (here are and ) since he was among the most active members of the community and later founded Not LarvaLabs with a few other Phunks and launched its own marketplace. parts 1 2 Later many found out about him because of his and Ryder Ripps’ fight against the founders of BAYC (both have been sued by Yuga Labs). He’s eccentric, loud, raw and unfiltered. Or as he describes himself “I'm very stubborn and opinionated and I take feedback from certain people who I trust, but I don't really care what the peanut gallery has to say.” Many may argue that he is very toxic too. Pauly keeps fighting VCs, centralized exchanges and marketplaces, influencers… anything and anyone he thinks are wrong. It feels like whatever he does gets amplified by the 2 extreme camps of people: those who are his true fans and those who are his haters. As a result, no matter what the proportions of those camps are  - he wins in this attention economy. His popularity grew with , since he was one of the most active community members who promoted the meme coin from day 1 (some even speculate that he may even be the founder of $Pepe…).  Many found out early about Pepe thanks to his tweets. $PEPE Anyway, his tweet got out there, and maybe at first, no one gave it much thought… but then people started sending him money… and in less than 24 hours, there was more than $1 MILLION worth of crypto in that wallet. So the next day, I spoke to Pauly for this article. We started by Pauly saying that this whole thing reminded him of the Potato Salad campaign on Kickstarter that had stuck in his memory. (for those who have not heard of it, you can check it out The guy raised $55k to make a potato salad) here. I asked Pauly if it was something he had planned and if he expected what happened to be the outcome. “It started as a joke,” Pauly responded. “And it just, you know, kind of organically developed further. It wasn't something that I thought of. I'm super impulsive with, like, Twitter and stuff like that. I usually just post what's on my mind, but, yeah, I definitely saw a lot of ridiculous stuff happening where people were doing all this illegal sh*t and, like, sending money for, like, a scam or sending money because someone was begging for money. I don't know, I guess I just looked at that. It's, something to comment on or play with. But I wasn't expecting this, I wasn't. I was honestly shocked when there was three hundred dollars in it. I was like, Wow, that's a lot of money. Three hundred dollars. And then it went up a lot. And then it started getting traction. Now I'm having too much fun with it to stop. So no, it wasn't planned or thought out at all. Pauly really seems to be having fun with all the memes, jokes and unfolding the whole theme of “nothing”. Feels like he is very explicit that people should not expect anything in return, but at the same time, here and there you might perceive some tweets as hints of something actually coming. I thought that in a similar situation, one might feel psychological pressure caused by people’s expectations or feel obliged to do something in return, so I wanted to know if he felt so or not. “No, not at all. Like, I'm already being sued by a four billion dollar company that is trying to ruin my life and I don't care. And yes, maybe it was just a joke, but it captured people’s attention, triggered their curiosity and in many cases made people pissed. Many don’t understand how something like this happens? It doesn’t make sense at all, right?” The Confusion Even the guy from Coinbureau is not sure about … what he is missing I personally think that if you have been really deep into this space - and what I mean by that is not only being focused on the tech and building but also spending time with the people in it, embracing the culture and following the sentiments and the conversations in this space, probably it wouldn’t be a complete mystery to you. At the end of the day it’s all about human nature… So what’s behind this? Why are people taking part in this crazy thing? Why did “nothing” become something to talk about? If blockchain is really a stage… What is this story about then? Is it a blockchain performance art? A social experiment? Yes, it started as a joke… but what is it becoming? What’s even more important: why are people supporting it? So I thought that the fastest way to find out was speaking to those who had sent money to Pauly. Shot a tweet, and people reached out, confirming that they sent crypto to yougetnothing.eth (some multiple times) and explained their reasoning behind it. But first, let’s see if those people are expecting anything in return or not. Well… feels like some do have expectations or other motives. A few used this as part of their marketing efforts (2 examples below) Some others did not seem to believe nothing meant literally nothing, so they are waiting to get something after all… I also received messages from people who don’t expect anything in return and are completely fine not to receive anything in return. And they all gave different variations of the following reasons: They resonate with Pauly’s narrative (anti-VC, anti-influencers, etc.) and want to support him, because they trust him to fight for them + they think he has already done a lot for the space They are having fun and are supporting this as a meme for the culture They were introduced to $Pepe by Pauly and ended up making life-changing money from it. So now they are more than happy to show their appreciation. Accusations Obviously, it’s not only about fans or speculators here. I received claims that Pauly has copied the concept and the way he’s promoting “nothing” from . You can check and judge for yourself… this project here Others think that it’s another story of wash trading and avoiding taxes. I got this comment too implying that it was all planned. Results so far So far yougetnothing.eth wallet has received over $1,3M worth of crypto from 10,862 transactions. People still keep sending small amounts and the anticipation is building up. Seems like no one knows what’s gonna happen next and I did not even bother asking Pauly cause I am certain the answer would be “nothing”. All we gotta do is wait and see how the story ends. In any case I think this is yet another proof of the things I’ve kept talking about for many years already: the importance of the community of die hard fans, being yourself and not taking things too seriously, knowing your audience really well and telling the stories that resonate with them + building movements, not just code. Disclaimers Since my audience grew a bit, some did not feel happy about my choices of stories I cover, or the way I cover them, etc. Just wanted to reinforce that I am writing for my own newsletter exactly for that reason: to write whatever I want the way I want it and not have a boss/editor decide that for me I never shill anything and always do my best to stay unbiased. I am not promoting coins, projects or people - I just document stories I always reveal if I am part of a story in any way (eg. own coins, etc) - and that being said, no I have not sent anything to Pauly And that’s a wrap. Cheers rebels!

The Life Changing Magic Of.. Nothing — How Everyone Spent $1.3M to Receive 'Nothing' in Return

দ্য লাইফ চেঞ্জিং ম্যাজিক অফ .. কিছুই নয় — বিনিময়ে 'কিছুই না' পেতে সবাই কীভাবে $1.3M খরচ করেছে

Die lebensverändernde Magie des Nichts – Wie jeder 1,3 Millionen US-Dollar ausgab, um im Gegenzug „Nichts“ zu erhalten

La magia que cambia la vida de... nada: cómo todos gastaron $1.3 millones para recibir "nada" a cambio

La magie qui change la vie de... Rien - Comment tout le monde a dépensé 1,3 million de dollars pour ne rien recevoir en retour

जीवन बदलने वाला जादू.. कुछ नहीं - बदले में 'कुछ नहीं' पाने के लिए कैसे सभी ने $1.3M खर्च किए

人生を変える魔法の何もない — 誰もが「何もない」を受け取るために 130 万ドルを費やした方法

인생을 바꾸는 마법..아무것도 없음 - 모두가 '아무것도'를 받기 위해 130만 달러를 쓴 방법

A mágica que muda a vida do nada - como todos gastaram US $ 1,3 milhão para receber 'nada' em troca

Hiçbir Şey'in Hayatı Değiştiren Büyüsü - Karşılığında 'Hiçbir Şey' Almak İçin Herkes Nasıl 1,3 Milyon Dolar Harcadı

Phép thuật thay đổi cuộc sống của .. Không có gì — Mọi người đã chi 1,3 triệu đô la như thế nào để nhận lại 'Không có gì'

改变生活的魔力……一无所有——每个人如何花费 130 万美元却“一无所获”

With global markets plummeting, NFT trading volumes and crypto wallet activity are down across the board. Despite this, . Reddit just onboarded more people into web3 over the past 3 months than OpenSea has in the past five years They subtly handed their users custodial wallets and airdropped them free NFTs. Since launch, their NFT collections are also up an average of 300%. What’s their secret? Read on to find out. What Is Reddit? In case you haven’t heard, Reddit is “social news aggregation, content rating, and discussion website,” and it’s huge. the Every fifth person you meet has a Reddit account. The platform has: 430 million monthly active users. Over 1.5 billion registered users. Around 50 million daily users. NFTs have certainly been a topic of discussion on Reddit for a while now, and Reddit users’ disdain for digital assets is no secret. The apathy towards NFTs follows the well-known path of a social media hate storm echoed in particular subreddits. However, despite this, the platform has managed to influence three million of its users, and the majority of new sign-ups, to open a Polygon wallet and hold an NFT. Reddit’s Genius NFT Strategy To be fair, Reddit’s not entirely new to crypto. The platform has accepted cryptocurrency payments since 2013. They tokenized points on Layer2 blockchain, Arbitrum, for select subreddits in 2020. They even launched a four-piece 1/1 NFT collection in June 2021. The collection was named “CryptoSnoos”, based on their iconic ‘Snoo’ logo. The NFTs sold for 70–175 ETH. However, Reddit’s most recent crypto endeavor is what has caught everyone’s attention. They solved the million-dollar question of how to onboard the masses to crypto. The simplicity is genius. Here’s what they did. They ditched the “wallet” terminology, instead calling this wallet a “Vault.” First, Reddit created a custodial Polygon-based wallet. Cleverly, they didn’t call them NFTs. They referred to them as “collectible avatars.” The avatars could be used to customize Redditors’ profile pictures (PFPs). Second, Reddit started to offer their top users free NFTs. This was an enticing status symbol for engaged Reddit users who innately understand the value of community points, karma, and reputation. Except Redditors weren’t aware they were opening a Polygon wallet. They opened a “Vault” account. They weren’t taken to a third-party website to do this. Third, to collect their avatar, Reddit required users to set up a Polygon-based wallet account. They didn’t have to write down a seed phrase or go through onerous KYC procedures. They didn’t need to hold MATIC for gas fees (Reddit covered gas fees). Ultimately, there were no web3 hurdles. The wallet setup took two clicks and the seed phrase was simply embedded into their Reddit accounts. At this stage, very few Redditors were actually interacting with their NFTs or using them to customize their PFPs. The blockchain protocols, wallets, and infrastructure were disguised through user-friendly language and web2 UX. Reddit released Reddit Collectible Avatars Season One in July. Finally, Reddit had already been partnering with artists to release NFT PFP collections at a low cost, purchasable in USD. The NFTs, disguised as collectible avatars, were sold for between $10-$100, and the proceeds automatically went to artists. Owners of multiple avatars could pull traits from the different avatars to customize their Reddit profile pictures. The NFTs were semi-fungible, with each of the 29 PFPs containing 87 of the same NFTs. The updated profiles would display a hexagon profile picture with their relevant avatar. The process was seamless and akin to an all-too-familiar e-commerce transaction. The Results? A Slow Burn, Then an Explosion Reddit Collectible Avatars Season One launched in July and was very slow to sell out. However, airdropping free avatars in August lit the match for the collectible avatars’ explosion. The vast majority of the 3 million existing Reddit Vault wallets were created to collect avatars. From August to October, the number of collectible avatar holders rose from 12,940 to 2.87M. In fact, more people have opened Reddit Vaults to collect avatars than have ever traded on OpenSea, the most popular NFT platform. Reddit Collectible Avatars Season Two launched in October and sold out within 24 hours. Sales volume skyrocketed, increasing from less than $10/day at the beginning of August to a peak of $209,000 in October. This represents a 2,089,900% increase and a cumulative sales volume of over $10m. The Reddit Collections Have Admirable Traits That All NFT Collections Strive For: Of the 2.869M wallets that hold avatars, just 25,009 hold more than one. That statistic represents idle wallet holders. That means that there are 2.84M wallet holders that have NFTs but haven’t been fully engaged in trading. Maybe they are just holding, or maybe they mindlessly accepted a free avatar. However, the potential to engage these users is endless, and they have the capabilities to interact in the web3 ecosystem. It’s important to note that a continued and sustained engagement with those 2.84M wallets is necessary to claim an overwhelming web3 success. The foundations have been set. How Did They Do It? Web3 has a notoriously bad user experience, and it’s no secret that focusing relentlessly on user experience is a priority. However, Reddit took it a step further. : PFPs (profile pictures). Rightfully so. To onboard people to web3, blockchain choices, cryptographic wallets, and token standards like NFTs’ ERC721 complicate the use case. First, Reddit removed crypto jargon to put emphasis on the use case In web2, no one cares whether you use .pdf, HEIC, .jpeg, or .png. They simply care that they can digitally view, share, and store their photo. NFTs, cryptographic wallets, and the blockchain are all means to different, but related, ends. However, Reddit users often only care about their stored ownership of their avatars. To that end, it is important to be solution-oriented. What do NFTs, wallets, and the Polygon blockchain enable? Unique digital assets (collectible avatars), A way to access these unique digital assets (Vaults), and A place to store, trade, and verify the authenticity of these assets. If Reddit publicly referred to avatars as NFTs, the results would have likely been less impressive. The Trojan horse style approach and dressing up NFTs in a disguise, simply using the technology as a means to an end, is where the space should go. This acts as good validation for the use case of NFTs. Second, Reddit made the onboarding process hassle-free. To get Redditors to open crypto wallet accounts, they offered them Snoo-themed avatars which could be used to customize their PFPs. free Reddit dangled an NFT in front of active users to onboard them into their web3 ecosystem, however, they ensured that users themselves didn’t have to jump through any web3 hoops. The process to set up a Vault and accept the avatar took a couple of seconds and the same number of clicks. The process was seamless. Reddit covered the gas fees which meant that no one needed to figure out how to purchase MATIC with fiat and then how to send that MATIC to their Vault wallet. Reddit did all the legwork for Redditors. Compare this with Twitter’s attempt to get people to use NFTs for PFPs. Twitter users needed to go to OpenSea, have a MetaMask or other wallet integrated with the platform, own crypto, own NFTs, have ETH for gas, and finally return to Twitter. The comparison easily shows how Reddit’s relentless focus on user experience is a recipe for success. If Twitter took a similar approach, how might it have fared? . In Reddit’s case, product and community are largely the same thing. Community is the product. Third, Reddit already has a productized community that aligns itself well with Web3 principles. Brands with the same should take note Status symbols are present in most communities. Within Reddit, status is measured by your interactions within the platform and endorsements from other users in the form of karma points. NFTs introduce another way to represent status within Reddit. Avatars are scarce, which is a common feature of NFT collections. Airdropping avatars to top users initially anointed them as a symbol of a user’s engagement as they were able to flex them on their profile. Then, as collections grew, keen users wanted to also own this status symbol. Heightened demand for scarce resources only increased the collectibles’ monetary and status value. The community structure of Reddit aligns well with the NFT use case. Avatars enable Redditors to interact with Reddit in a non-detrimental way while supporting artists. The NFTs enhance Reddit users’ experience on the platform but are not integral to Reddit’s product. In this way, Reddit’s move has been low-risk. Using Reddit’s Snoo logo, tying utility to the avatars, periodically dropping new collections, and enabling a way to easily show them off within the platform should create a recurring loop of interactivity which will increase the adoption of NFTs within Reddit’s 430-million strong community. If it doesn’t, that’s fine. Reddit will not lose any users. The Avatar NFT strategy was well positioned to not deter from existing community features, but simply amplify them. Avatars are not essential for Reddit’s community to enjoy Reddit’s product. What Can You Learn From Reddit? Reddit’s move was brilliant because of its simplicity. It represents the first proven web3 experiment that worked without technical confusion and learning. Onboarding the masses to web3 is the golden chalice. Reddit seems to have found a way to prove that it’s possible at scale. The nature of the product at Reddit makes this an innate use case, however, easy-to-use purchasing and wallet integration remove any friction points. Reddit laid the foundations to provide additional utility to avatar holders. We’re already seeing this with holders of certain collections receiving airdrops. Using Polygon has enabled Reddit’s avatars to be used within the wider Ethereum ecosystem. The journey from here is an opportunity to shine the light on the brilliance of blockchain composability and asset liquidity. While only 25,009 individuals hold more than one avatar, which could be taken as a measure of interaction with the collections, by getting 2.8 million people to open Polygon wallets, Reddit has opened the Trojan gates. Avatar holders can more easily explore the wider web3 ecosystem. Thousands have done this already, millions more can. We look forward to welcoming them when they arrive. References: ; ; ; ; ; https://en.wikipedia.org/wiki/Reddit https://thrivemyway.com/reddit-statistics/#:\~:text=Subreddit Statistics,124%2C000 new subscribers each month https://page1.substack.com/p/the-reddit-nfts-thesis https://nft.reddit.com/ https://cointelegraph.com/news/reddit-nft-trading-volume-hits-all-time-high-as-wallet-holders-near-3-million https://www.redditfloor.com/

How to Get 3M People to Own NFTs Without Them Knowing

Cómo hacer que las personas de 3M sean dueñas de NFT sin que lo sepan

Comment amener 3 millions de personnes à posséder des NFT sans qu'elles le sachent ?

कैसे 3M लोगों को उनके बारे में जाने बिना NFT के मालिक बनने के लिए प्राप्त करें

知らないうちに 300 万人に NFT を所有してもらう方法

Como fazer com que as pessoas da 3M adquiram NFTs sem que elas saibam

Cách để 3M người sở hữu NFT mà họ không biết

如何让 300 万人在不知情的情况下拥有 NFT

Capsule Corp Labs supports large corporates, web2 companies and web3 start-ups in creating next-gen NFT use cases on Ternoa, the open-source blockchain dedicated to NFTs The seed round was led by deeptech and web3 VC investors Omnes Capital, REVAM and DFG Ternoa is poised to grow at an international scale, and will participate in efforts launched by Dubai government to create a next gen blockchain & metaverse ecosystem Paris, October 12, 2022— , specialized in the creation and development of utility NFTs, secures €7.3 million in a seed round, including €4.8 million in equity from , web3 funds and (DFG), and French business angels from the Web3 ecosystem. The funding is completed by a €2.5 million token sale from Ternoa ( ), the parent company of Capsule Corp Labs. Capsule Corp Labs Omnes Capital REVAM Digital Finance Group $CAPS The seed funding round led by expert investors in deeptech and Web3, follows Ternoa’s successful token sale in 2021. As the parent company of Capsule Corp Labs, which develops a blockchain dedicated to utility NFTs. The funding allows Capsule Corp Labs to widen its scale by strengthening its R&D and expanding its team. Moving from Collector to Utility NFTs Until now, NFTs have mainly found success as collector's items, especially in the luxury and art industries. Capsule Corp Labs develops NFT solutions for companies in all sectors, enabling them to integrate the value propositions of the Web3: ownership of data and digital assets, and integration and engagement of user communities. A Blockchain Dedicated to NFTs Launched in 2022, Capsule Corp Labs is the main builder on Ternoa, a layer-1 blockchain infrastructure. Open-source and decentralized, it provides developers with an independent framework to create scalable utility NFTs. Start-Up Studio, Web3 Consulting & Tech provider Capsule Corp Labs' mission is to help companies of all sizes develop their NFT-based business model and get a foot into web3. Unlike the current NFT market, which operates very much like the art market– based on scarcity value, we believe that NFTs will increasingly have utility value. NFTs will initially be used in the development of sectors such as video games, music, entertainment, or the metaverse, and then gradually spread to other sectors of activity. Our role is to enable this transition from Web 2.0 to Web3, which can be technologically complex - explains Clément Téqui, CEO of Capsule Corp Labs and Co-Founder of Ternoa. Capsule Corp Labs offers a "business as a service" model to Web3 start-ups by providing them with human and technological resources such as developers and product managers to help them develop NFTs. In the space of six months, Capsule Corp Labs has already helped dozens of startups, large corporates, or gaming companies to design and launch their first web3 product. Developing International Reach To keep building pace, Ternoa intends on conquering new markets. To this end, Ternoa and Capsule Corp. Labs support Dubaï’s efforts to become a global hub for metaverse blockchain ecosystems. The UAE is developing programs and systems to establish itself as an incubator for Metaverse technology and adoption. Ternoa, leveraging this fresh infusion of funding, will position itself as the vanguard wave by developing operations in Dubai. UAE recently established a Higher Committee for Future Technology and Digital Economy. In July, Sheikh Hamdan bin Mohammed, Crown Prince of Dubai, vowed to "promote Dubai's supremacy in the digital economy globally”. He expects the initiative to lead to 40,000 jobs and a 4 billion dollar boost to the UAE's economy over the next five years. About Capsule Corp. Labs Capsule Corp. Labs is the main developer on Ternoa blockchain. The labs fosters the development of new dapps and use cases on Ternoa chain and works with large corporate accounts and web3 startups to facilitate their integration on Ternoa. Capsule Corp. belongs to Ternoa’s ecosystem and is led by Clément TEQUI, CEO and co-founder of Ternoa. More information about the Labs can be found . here About Ternoa Ternoa is a Substrate-based blockchain dedicated to Utility NFTs. Ternoa makes NFTs’ mass adoption possible thanks to standardized on-chain NFT functions and a javascript SDK to reach out to the largest possible number of developers. Ternoa was founded by Mickael CANU, CEO and serial entrepreneur. More information about Ternoa can be found here. About Omnes is a major player in private equity and infrastructure. With more than €5 billion in assets under management, Omnes provides companies with the equity capital they need to grow through its three core businesses: Venture Capital, Expansion Capital & LBO and Infrastructure. Omnes is 100% owned by its employees. Omnes is committed to ESG issues. The company has created the Omnes Foundation in favor of children and youth. It is a signatory of the United Nations Principles for Responsible Investment (PRI). Omnes About DFG DFG is a global investment firm with more than $1B in AUM focused on empowering blockchain and Web3 companies. Since 2015, DFG has been committed to the long-term potential of blockchain technology and manages investments across the crypto ecosystem. DFG mainly runs three strategically dedicated funds: Venture Equity Fund, which follows traditional methods of investment in valuable CeFi service providers; Polkadot Ecosystem Fund, which empowers its Web3 innovation; Crypto Fund, which invests in promising solutions at an early stage. For more information about DFG, please visit our and follow us on Twitter website . @DFG_Official About REVAM is a leading hedge fund manager focusing on cryptocurrencies, tokens, and blockchain projects globally. REVAM

Ternoa Completes a $7.3M Seed to Foster NFT Mass Adoption and Launches Web3 Activity in Dubai, UAE

3moji NFTs are changing the meta on the Solana ecosystem and bringing true utility to Solana NFTs. The majority of the NFT ecosystem on is churning on the same meta that has prevailed since the rise of NFTs on Solana in 2021. However, there is a huge gap between the current state and infrastructure of NFTs and the grand vision for the NFTs. are an attempt to bridge that gap and pave the path to the other side. Solana 3moji NFTs What is 3moji? 3moji is an upgradeable NFT avatar that grows and evolves forever. Unlike traditional NFTs which can’t be easily changed or upgraded after minting, 3moji NFTs can evolve and grow forever with accessory NFTs. This is made possible with the , which upgrades NFTs without changing or altering the meta data. Unlike traditional upgrades, 3moji NFT upgrades are two way, which means you can both add and remove an accessory NFT from your avatar. Meta Blocks protocol The two-way customizability ensures that your 3moji avatar can grow with you and always stay relevant. The Philosophy Behind 3moji’s Upgradability The grand vision for NFTs is amazing. An avatar that you can own and take it across meta verses and video games. However, unlike video game avatars or our real selves, NFTs are rigid and do not evolve or grow overtime. Think of a game where you start you in a rookie town with rookie gear, but as you progress and chomp through the levels, you can’t upgrade or change your gear. That is the sad reality of NFTs. 3moji NFTs aim to change that. Our digital identities should evolve and grow over time, and that’s what 3moji does. The Utility Behind 3moji’s Upgradability NFTs are a form of expression and vanity. Think of it like a pair of Gucci shoes. If someone buys an expensive pair of Gucci, the first thing they’d do is show it off! Social events and parties are some of the best outlets to flex. Today, blue chip NFTs are vanity. If you owned a crypto punk or a BAYC, that’s a bigger flex than a puny Gucci. However, the standard outlet for flexing NFTs is just Twitter PFPs. 3moji NFTs bring your expression and vanity to your day to day communications in the form of stickers. If you own a 3moji NFT, then you’ll get custom stickers based off that NFT which can be used on apps like Discord, What’s App, Telegram and more. This brings both expression and vanity of owning an NFT to your daily life. 3moji NFTs as a Platform for other NFT Projects Third Party projects and artists can create their own 3moji compatible NFT accessories. https://twitter.com/SoulDogsCity/status/1533805803475329024 As a platform for other creators, 3moji NFTs pave a path for other collection to do consecutive drops as well as benefit from the 3moji’s unique utility. So, what’s the future of 3moji NFTs? The future of 3moji NFTs start with a base layer. A base layer is the face of your avatar where you can add accessory NFTs on top. Whitelist holders are entitled to a free mint of the base layer while the public mint cost is yet to be decided. Once the base layer mint is over, every holder will be airdropped a random accessory NFT that can be used to customize the base layer. Users can also purchase accessory NFTs by 3moji and third party creators. A full drop schedule of the drops will be released soon. The roadmap of the project can be found on . 3moji’s website Be A Part of 3moji’s Journey Twitter: @3mojiHQ Discord: Private Disclaimer: Nothing in this article constitutes professional investment advice. Please do your own thorough research before making any investment decisions.

3moji NFTs: Upgradeable NFT Avatars and NFT Emojis on Solana

3moji NFT: Avatares NFT actualizables y emojis NFT en Solana

4 Different Capital Tracks and $1.3M in Total Investment Available In 2020, like nearly every other business, the had to adapt to a new, remote reality.  As a team, we had made hundreds of community visits to learn what was going on in each town.  We had planned an eight-city, May-madness parade of pitch events that would all lead up to an in-person finals in Grand Junction during .  Come COVID, our plans were nearly the opposite of public health advice. Greater Colorado Venture Fund West Slope Startup Week So began the Greater Colorado Pitch Series.  We had invested remotely before, but never simultaneously in public.  As we leaned into the remote format, we kept our expectations tempered.  Frankly, we just hoped it successfully led to one investment for our fund. However, .  We ended up sourcing three investments (including the future of online media, rural-Colorado based, fiercely independent - !).  More interestingly, the online format uncovered a ton of companies across Rural Colorado. the event was a resounding success Hackernoon By making it a simple online application, the front door of GCVF became that much easier to walk through. While the value of in-person is not replaced, this demonstrated the obvious need for remote funding opportunities.  While we're aiming to solve geographic inequities in venture capital, we inadvertantly uphold them for founders whose locations don't coincide with our travel plans.  We're just three partners covering the better part of 104M square miles after all. This made launching the a no-brainer. 2021 Greater Colorado Pitch Series That doesn't mean there wasn't obvious room for improvement though. 4 Capital Options What's New for 2021? , we had to deliver a lot of No's last year.  Our job is to make investment decisions based on merit, and plenty of these No's were merit-based. That's just the nature of investing. With about 100 applications in 2020 However, plenty of these No's were delivered to really impressive business that simply didn't fit our particular capital product.  We are a seed stage venture fund.  It turns out, Rural Colorado is chocked full of amazing companies of all shapes and sizes.  Many of them don't need our seed stage venture check to continue to grow. Take for example.  They're a veteran-founder top 100 Amazon reseller based in Grand Junction.  They needed funding to buy more inventory.  Venture capital is really expensive for inventory, especially for a company with healthy revenues and operating history like theirs.  Fortunately, after the Pitch Series, we kept conversations going and were able to introduce them to .  Here's what Product Movement CEO Luke Sutherland had to say: Product Movement Technologies Greenline Ventures They seemed to actually understand our business and what we would use the capital for and we discussed the ROI on the inventory we would be purchasing compared to what the interest expense was. It worked for both parties and we closed the deal...We have grown enough to easily manage paying the note back when it comes due without sacrificing growth in the short term. It has been fantastic. Luke was no exception either.  It turns out, we had simply uncovered a lot of great businesses that needed the right capital partner.  So we went and found those capital partners. This year, applicants to the Greater Colorado Pitch Series can apply for four different capital tracks: Venture: Pre-seed: $50,000 – The Fund Rockies Venture: Seed: $250,000 – Greater Colorado Venture Fund Debt: Pre-seed: $50,000 – First Southwest Community Fund Debt: Growth-Stage: $1,000,000 – Greenline Ventures The Pitch Series event is . Applications close on June 18th. on July 16

$1.3M in Funding for Rural Colorado Founders via the Greater Colorado Pitch Series 2.0

We have all heard stories of how companies raise millions or of dollars to build the next hottest company. Many traditional tech companies have considered ICOs as an alternative to raise funds for their next expansion phase as compared to traditional venture capitals. This is because founders no longer have to give up stake in their startups to raise funds, and the ease to easily crowd fund the money they need to run the company. even billions As the VC industry is most disrupted industry by ICOs, we have seen a growing adoption in VCs and hedge funds turning to start their own cryptocurrency investment arms. Although the number of ICOs and amount raised have risen since last year ( compared to ), many companies find that they are no longer able to hit their hard cap in the fundraising process, or even raise enough funds to hit the project soft cap. This may be because of several reasons, and I’ll explain more of these reasons below. $6.3B raised since the start of the year till April ~$4b in 2017 1. Unsound Tokenomics Source: Thought Catelog on Unsplash There are more than , and much of them are 700 tokens on the Ethereum blockchain barely worth anything. Tokenomics are an important factor considered by participants in a token sale. Typically, investors favour projects that have both a speculative value and utility value. This value usually comes from building an application or ecosystem that will utilise the token that otherwise could not have been done with existing fiat or point systems. Many companies, enticed by the stories of raising easy millions in a cash grab, simply neglect the utility of their token or . Their tokens hold no value, does absolutely nothing or does not solve a compelling enough problem that existing systems can not do — this forms a sort of deterrence to your project. that their project does not need to exist on the blockchain Your investors are people looking for the next “moon”, and will not part with their money unless your project can promise that. Much of this comes from the potential speculative value, like from potential or existing partnerships with big brands that will also utilise your tokens or project on their platforms, or having the right advisors and team players to build a successful project. 2. Nobody knows about your project Source: Zachrie Friesen on Unsplash Marketing is an important aspect in any sort of business, and much of the crypto space is plagued by projects with a seemingly unlimited marketing budgets. To build a project that stands out is difficult in any space, and much more so in the crypto space. Getting funds in at an early stage is extremely difficult, and more so when nobody knows you. Much of the advertising in the crypto space is done by influencers, and they’re usually incentivised by bonuses (usually in additional tokens on large token purchases), which are then re-marketed to their groups in the form of pools. These influencers then take a cut of these bonuses as fees after investing a comfortable amount, and disperse the rest to their pools. Early adoption by influencers is extremely important as it breeds an invested interest in your project. Since their own money and reputation is at stake, they’re more inclined to aid in the successful fundraising of your project by marketing you to their communities or getting other influencers and communities on board. Early adoption by influencers is extremely important as it breeds an invested interest in your project. Since their own money and reputation is at stake, they’re more inclined to aid in the successful fundraising of your project by marketing you to their communities or getting other influencers and communities on board. 3. Your team/advisor(s) sucks Source: Charles Deluvio on Unsplash The success of any team is determined by the players behind the project, and not having an experienced enough team, or having team mates with a tarnished reputation may bring down the legitimacy of the project. Much of the blockchain space is very young and new and the communities extremely small and a bad reputation from a scam project goes a long way. 4. No product A year ago, you could write a whitepaper on your idea, launch an and investors will simply throw money at you. These days, fund raising through ICOs are getting increasingly difficult as investors become more savvy, and look into more details before deciding to invest in a project. ICO ICO typically invest a lot in the design and production of their initial website to drive investors onboard and take months to release an actual product, or simply just neglect the product altogether. Much of the space do not actually have a working product, and that has been one of the key issues to drive investors away from your token offering. 5. Bearish market conditions Source: Mark Basarab on Unsplash When the market conditions are bearish, investors are less likely to invest as they’re more cautious with protecting their initial investments. Much of the ICOs that raise huge amounts do so in great market conditions, and when the market takes a huge downturn, it impacts the amounts raised from different ICOs as investors are likely to reinvest profits from past ICOs into recent ones. With their recent ICOs underperforming when it hits the open market in exchanges, users simply do not have the capital to reinvest in other projects. 6. Your hard cap is too high or too low/uncapped ICOs Source: Kate Joie on Unsplash Investors are always concerned that if your ICO doesn’t sell out, or that if your market cap is too high that it will lack in demand from investors to buy post-ICO. Post ICO demand comes from investors that fail to take part in your ICO or investors that already bought during your ICO and would like to purchase more of your tokens. This drives demand in the market, and the lack of demand is a huge red flag for any ICO project since somebody will have to buy the tokens from your investors. If your market cap is too high, or that if you host an uncapped ICO (no obvious hard cap — these projects are very popular in 2017, but we do not see many of them thee days, example being Telegram’s ICO), the concern to investors is the lack of demand as everyone that wants your token already has your token, and that the supply is a lot greater than the demand in these tokens. Another concern is also if you’ll be raising enough to be able to pay for listing on credible exchanges. Exchange listings on Binance has been recently reported to be more than USD 5M, and listing on other Chinese exchanges are upwards of 6 figures. Conclusion The ICO landscape has grown to be a vastly different beast from what it was last year. Projects are no longer able to raise astronomical figures from just a simple whitepaper and investors are a lot more cautious and savvy with their investments. Regulations are clamping down in the form of KYC/AML frameworks, or even outright banning ICOs in China. When we conducted our $8M ICO for , it became clear that blockchain companies now need a lot more than hype to execute a successful token sale. Today, Switcheo is a decentralised exchange on both NEO and Ethereum blockchains with more than 25000 users. Switcheo The future of the ICO landscape is heavily reliant on what these projects may bring to existing blockchain ecosystems, and not just the creation of the next 10,000 shitcoins. As investors get more educated in the space, projects that are launching ICOs should also look into the contribution they may bring to the space, and not just as a tool for fundraising.

6 reasons why your ICO won’t raise more than 3M

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Republished on the Wire to 100+ Traditional Media Sites Just like the traditional press release wire services, we put your story on the wire. You should expect your technology press release to be re-published to ~100 more sites, such as Benzinga, Digital Journal, Yahoo Finance, and The AP. Prominent Distribution Across the HackerNoon Site, App, and Newsletter Technology Press Releases on HackerNoon are featured on the Homepage, the Category Page, 8 Tagged Pages, 12 Translation Homepages, the Mobile App, the Terminal App, the Lite Version, and the Noonification Newsletters (250k+ subscribers). Easy Content Uploading and Formatting with Staff Support We hate how difficult it is to upload content to archaic press release distribution services. The HackerNoon uploader and story editor are smooth AF. 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Hey everyone! and just like you, I’ve been fascinated with the recent progress of artificial intelligence. Realizing that I needed to stay abreast with all the developments happening, I decided to embark on a personal journey of learning, thus was born! With this series, I will be learning about LLMs and share ideas, experiments, opinions, trends & learnings through my blog posts. You can follow along the journey on HackerNoon or my personal website . In today’s article, we’ll be looking at the case study of how Klarna leveraged Gen AI and what other companies can learn from it. , I’m Nataraj 100 days of AI here here How is Klarna using generative AI? There are lots of ways generative AI is being used by companies, and we have explored some of them . In case of Klarna, they launched an AI customer support assistant that is available 24*7 and supports 30+ languages. They launched the assistant using Open AI as their LLM provider. If we have to guess, they have finetuned it using their own customer support interaction data set that they have collected over the years. in a previous post What features does the Klarna AI assistant support? The assistant is designed to handle wide range of questions related to refunds, returns, payment-related issues, cancellations, disputes, and invoice inaccuracies, ensuring swift and effective solutions. Like any tech feature, its available 24*7 across all geographies. For customers who want to know what they can afford and not afford, it also provides info related to purchase power, spending limits and the reasons why it exists. All the info of a customer account is with the assistant to help any questions related to it. Is the new AI assistant effective? Its cool to say that we have used gen AI and see the stock pop up, but the real question is, does the feature deliver value to the company. And from the numbers, it looks like it is beating expectations. Here are some of the numbers. Two-thirds of all customer service chats are handled by the assistant, which is an impressive 2.3M conversations. Customer satisfaction same as human conversations. 25% drop in repeat inquiries Estimated to drive $40M in profit improvements to the company. What are the takeaways from Klarna’s gen AI assistant? Its clear that generative AI is being adopted and creating value for companies at all stages. Customer support will get more effective and cheaper. If the new assistant is handling two-thirds of all conversations, it means the next set of companies which start their customer support teams will not require the same number of people in their teams. And this also means the existing customer support teams might be larger than needed. We will see a lot of growth stage startups follow Klarna to boost their profitability and reduce expenses. For companies like Klarna whose stocks are down due to the concern of profitability, companies with similar concerns will start to look at leveraging AI to cut costs and boost profitability. Open AI’s board drama didn’t effect its trust among companies to use them for production scale deployments. When Sam Altman was fired and re-hired at Open Ai, one of the concerns was that this would reduce the trust among developers to use Open Ai to create AI applications. When a large public company publishes such a case study its clear that the board drama at Open Ai didn’t effect Open AI’s abilities. Expect more companies to announce improvements because of using gen AI technology in fields outside customer support. More case studies using generative AI will come from more public companies showing how they are leveraging Ai. This is a function of every company wanting to get a piece of the AI narrative and the fact that gen AI features are actually delivering on value. If you are a company with large data set related to customer support, you should explore creating an AI assistant for support interactions. That’s it for Day 19 of 100 Days of AI. I write a newsletter called Above Average where I talk about the second order insights behind everything that is happening in big tech. If you are in tech and don’t want to be average, . subscribe to it Follow me on , or for latest updates on 100 days of AI or . If you are in tech you might be interested in joining my community of tech professionals . Twitter LinkedIn HackerNoon bookmark this page here

100 Days of AI, Day 19: Klarna’s AI Chatbot and The Rise of Generative AI in Startups

এআই-এর 100 দিন, 19তম দিন: ক্লারনার এআই চ্যাটবট এবং স্টার্টআপে জেনারেটিভ এআইয়ের উত্থান

100 Tage KI, Tag 19: Klarnas KI-Chatbot und der Aufstieg der generativen KI in Startups

100 días de IA, día 19: El chatbot de IA de Klarna y el auge de la IA generativa en las empresas emergentes

100 jours d'IA, jour 19 : le chatbot IA de Klarna et l'essor de l'IA générative dans les startups

एआई के 100 दिन, दिन 19: क्लार्ना का एआई चैटबॉट और स्टार्टअप्स में जनरेटिव एआई का उदय

100日間のAI、19日目: KlarnaのAIチャットボットとスタートアップにおける生成AIの台頭

AI 100일, 19일차: Klarna의 AI 챗봇과 스타트업에서 생성 AI의 부상

100 dias de IA, dia 19: AI Chatbot de Klarna e a ascensão da IA generativa em startups

100 дней искусственного интеллекта, день 19: чат-бот Klarna с искусственным интеллектом и рост генеративного искусственного интеллекта в стартапах

100 Gün Yapay Zeka, 19. Gün: Klarna'nın Yapay Zeka Sohbet Robotu ve Yeni İşletmelerde Üretken Yapay Zekanın Yükselişi

100 ngày của AI, Ngày 19: Chatbot AI của Klarna và Sự trỗi dậy của AI sáng tạo trong các công ty khởi nghiệp

人工智能 100 天，第 19 天：Klarna 的人工智能聊天机器人和初创企业中生成式人工智能的兴起

Introduction There are different ways to form an investment thesis in crypto. One is to reject the importance of fundamentals and subscribe to the view that token prices are driven mainly by quick-gain-100x-seeking behavior or "financial nihilism." Is all crypto simply a meme? Should your technically sophisticated, tier-1 VC-backed, and community-vetted utility/governance "meme" coin be valued more than mine? Which narrative will be picked up by the KOLs next and pumped up by the crowd? What are we aping into? These views and strategies have merit but don't work at scale and across market cycles. Disciplined investors and researchers must carefully examine the facts and start seeing the forest for the trees. Let's stick to the first principles and dive into the concept of re-staking. Is it another backed by token emission or a new sustainable economy? As the saying goes, the proof is in the pudding; let's look for it! yield-generating bubble Cosmos Interchain Security Eigen Layer has popularised the idea of restaking (renting ETH economic security); however, it was initially proposed and implemented within the Cosmos ecosystem (renting ATOM economic security). Chains started with the Cosmos SDK provide configurability and sovereignty. However, developers' main challenge is building their own operator set (100-150 validators based on Tendermint consensus), bootstrapping liquidity, and using a native token, which has the risk of inflation and security value loss. enables other Chains (Consumer Chains) to be secured by the Cosmos Hub's validator set and the ATOM token's total market cap. In other words, it allows Consumer Chains to rent the economic security from Cosmos Hub. Cosmos Interchain security With 248MM ATOM staked, Cosmos Hub provides 3.2Bn$ in Economic Security to Consumer Chains. This first version of CIS is called “Replicated Security.” We Can Identify the Within the CIS Model: Following Stakeholders Validators – Run Cosmos Hub nodes and additional nodes for Consumer Chains Stakers – Delegate Atom tokens to Validators Consumer Chains – Rent Security from Cosmos Hub In Q3 2023, Chorus One the actual economic data from the two Consumer Chains on Cosmos – Stride and Neutron and made the following findings: reviewed Cosmos Hub Rewards: Cosmos Hub minted 367M$ per year or 1M$ daily in ATOM tokens as rewards for Stakers. Out of which 37M$ per year went to Hub Validators. Revenue: Cosmos Hub generated 1.5k$ per day in revenue from transaction fees. Stride generated 1k$ per day, and Neutron 12$ per day for Cosmos Hub. Combined daily transaction fees (2.5k$) would need to be 400 times higher to offset daily ATOM token emission (1M$) Infrastructure Costs (Excluding DevOps Costs) It's important to note that Validators need to set up additional nodes to support Consumer Chains, i.e., the hardware cost scales linearly with the addition of new Consumer Chains: The monthly node cost per Cosmos chain or ICS is 600$ per month or 7.2k$ per year. Cosmos Hub Security infra cost – 180 validators x 7.2k$ = 1.3M$ per year. Single Consumer Chain infra cost is the same – 1.3M$ per year. With Stride generating 365k$ per year and 36.5k$ in commission going to validators, such revenue doesn't come close to offsetting the infra costs for validators (1.3M$ per year). 40% of Cosmos Hub validators are not profitable, and this percentage increases as more Consumer Chains are onboarded. The consumer Chain would need substantially more revenue to provide an to Atom stakers. For example, to generate an extra 2% APR on staked ATOM, ICS would need to distribute $65MM per year to the Cosmos Hub. additional yield Finally, the ICS model removes the utility from the Consumer Chain native token, as it can only be used as a governance token and cannot be used for staking or network operations. , while the original idea of Cosmos Interchain Security offered easier onboarding of new chains, it came with a trade-off of increased infrastructure costs, lack of utility for the Consumer Chain native tokens, slashing risk for Validators, and high cost of economic security and therefore high revenue requirements for Consumer Chains. In summary As a result, Cosmos Hub governance is unlikely to approve more projects to join CIS unless they are set to deliver meaningful revenue to Hub validators and stakers. Moreover, the Replicated Security model is being revised, and it’s proposed to use a more flexible version called “ ,” whereby Validators can decide whether to run the nodes for Consumer Chains and Consumer chains would use only the Economic Security of supporting validators. Partial Set Security Eigen Layer Like CIS, allows Ethereum node operators to opt in to validate the new software modules. Eigen Layer calls these "chains" – Actively Validated Services. Eigen Layer The key difference with the CIS model is that the operators' cost of AVS support is marginal compared to running the ETH node; adding the AVS node to the existing ETH node doesn’t increase server load. Therefore, AVSs only need to generate enough fees to provide additional yield on top of native ETH staking yield. There Are the Within the Eigen Layer Model: Following Stakeholders Operators – Run Ethereum Nodes and additional AVS modules Restakers – Delegate Eth to Operators, which in turn opt into AVSs AVSs – Rent Security from Ethereum Infrastructure Costs (Excluding DevOps Costs): It costs approximately 1k USD per year to run 1 x ETH node (32 ETH), and more ETH validator slots can be added without significantly increasing the server load. The additional cost of supporting AVS is marginal (close to zero) Eigen Layer Economic Security As seen in the CIS model, Consumer Chains need to rent the entire $3.2Bn$ of economic security from Cosmos Hub and are constrained by the number of Cosmos Hub Validators. Eigen Layer implements "elastic scaling of security" and allows AVSs to dynamically control payouts to ETH stakers. 25000 AVS nodes can provide the equivalent of Cosmos Hub 3.2bn$ economic security. Similar to CIS, it would cost 64MM$ annually for AVS to generate an extra 2% APR on staked ETH. However, the key difference is that AVS can start with a smaller operator set and run more sustainable economics. Therefore, while the Eigen Layer brings additional infra costs almost to zero, it introduces the complexity of dynamic operator set and operator payout management. Watchtower networks will play an instrumental role in this process. Other Have Been Currently Worked Out: Open Questions and Challenges AVS Selection Process Who will choose AVSs? Will it be Operators or users of restaking protocols through governance voting? Or both? Operators are motivated to implement internal risk control frameworks to choose more profitable AVSs and manage slashing risks. Restaking protocols aim to conduct similar due diligence on behalf of their users. How will these two groups match AVS strategies and dynamically support the on and off-boarding of AVSs in an automated way? Are there any potential compliance issues with Stakers or Operators supporting AVSs in certain jurisdictions? These are not trivial questions! Potential Operator Centralization As the incremental cost to support new AVSs for operators is marginal – it would be more profitable for operators to support all available AVSs (as long as slashing risk is adequate), leading to centralization. Similarly, if users choose to restake only with the major operators, it will also lead to centralization. Eigen Layer is developing incentives to promote diverse operator sets and solo staking for AVSs to resolve this. Slashing risk Stakers opt-in by granting the Eigen Layer smart contracts the ability to impose additional slashing conditions on their staked ETH. The core idea of the Eigen Layer is to use "pooled security," whereby the same staked ETH is used to support multiple AVSs simultaneously. However, if one of the AVSs has a bug that leads to slashing, it can affect the operations of other AVSs. Eigen Layer addresses this risk by ensuring the system has " "  and a veto committee can reverse the slashing resulting from non-malicious behaviors. training wheels In addition, AVSs can acquire "attributable security," which, unlike pooled security, is specific to each AVS and will only be slashable by one AVS. Business Model Eigen Layer provides more flexibility for AVSs when defining the utility of their native token. In a mode, AVSs can use the native token for staking and network operations and balance its use vs. ETH throughout the project's lifetime. dual staking Conclusion In summary, the comparison between Cosmos Interchain Security (CIS) and Eigen Layer highlights the evolution of economic security concepts within blockchain ecosystems. While CIS pioneered the concept, it revealed drawbacks such as increased infrastructure costs, a lack of utility for native tokens, and high revenue requirements for Consumer Chains. Eigen Layer represents a substantial improvement by introducing elastic security scaling and dual staking. However, this enhanced flexibility comes with added complexity in managing slashing, security scaling, and operator payouts. It's truly fascinating to observe the ongoing evolution of this space as the latest restaking ideas from Eigen Layer permeate into other ecosystems such as Bitcoin ( ), Cosmos ( ), Solana ( ), and . Babylon Ethos Composable Avalanche Thank you to for your valuable feedback and review. Kam Benbrik

Restaking Explored: The Pursuit of a Sustainable Crypto-Economic Model

Electric vehicle (EV) adoption globally has rapidly accelerated over the last few years. In this exploration, I dig into the data behind that growth, across various geographies and timeframes. in this research piece we use , a company for whom I am employed. Any time you see references to cells in code it’s because you can reference the entries in those cells in code. The full sheet upon which this analysis was built can be found in case you want to keep up with the code and visualizations alongside the article. Note/affiliated disclosure before we dive in: Quadratic here is roughly 9,500 rows with various regions and parameters, from 2010 to 2022 (updated annually). We'll chiefly explore sales growth of electric vehicles only (ignoring hybrids) through 2022 and the growth of charging infrastructure networks. The dataset (MIT license) Sales growth over time We start by getting the data from our spreadsheet into our Pandas DataFrame. From there, we filter our dataset for the powertrains we care about (fully electric, aka BEV) and directly use our spreadsheet to input the country we want to filter by in . The dataset also includes some projections, but in this analysis, we only care about historically recorded values, so we filter out the projections and keep historical entries. Python # get data from the spreadsheet into our DataFrame 
df = cells((0,0),(7,9542), 'EV Raw Data', first_row_header=True)

# if region cell is empty don't try to filter
if cell(2,2) != '':
    df = df.loc[df['region'] == cell(2,2)]

# filter by selected parameter 
df = df.loc[df['parameter'] == cell(2,1)]
# make this a historical analysis with none of the projections included 
df = df.loc[df['category'] == 'Historical'] We note massive growth starting in 2020. Note a clear trend, with 230,000 EV sales in 2020 in the US, 470,000 in 2021, and a whopping 800,000 in 2022. This growth from 2021 to 2022 is a 70% increase in YoY sales. The projections from our dataset assume continued growth, expecting 2025 sales in the US to come in close to 3M EV sales. Globally, we see a similar trend. 2021 to 2022 growth is a little muted relative to the US on a % growth basis, but still tremendous at 58% growth to go from 7.3M sales up from 4.6M sales the year prior. If you find these charts’ styling attractive, these are basic Plotly line charts with minor layout changes, as shown below. # title gets edited based on the value of the spreadsheet input at cell(2,1)
fig = px.line(df, x="year", y="value", title=c(2,1) + title_description)

# make chart prettier by removing background and making the margins less aggressive
fig.update_layout(
    plot_bgcolor="White",
    margin=dict(l=30, r=30, t=50, b=50),
)

# displaying chart to the sheet 
fig.show() Percentage of vehicle sales to EVs Before digging into market share, a significant bit of context is that China and the US are the overwhelming leaders in overall vehicle sales. The International Organization of Motor Vehicle Manufacturers cites China as the overall vehicle sales leader at over 25M units per year, the US in second at over 15M, and 3rd place Japan is down at over 4M sales per year. The EV growth from our prior analysis is impressive, but to understand its scope, we must compare its market share relative to all other vehicle types (gas and hybrid). Fortunately, the dataset includes a market share data point, so we don’t need to perform this calculation manually. We note the values again for the United States and globally (“World” in our dataset). For the US, we note an 8% EV share. Interestingly, the overall EV share % globally is much higher at 14%. Naturally, it’s time we expanded beyond the US and learned which countries contribute to this 14% share. Countries with the fastest adoption Instead of burying our heads in the sand and only looking at the United States and total global sales, we can also do a simple analysis to see which countries have garnered the highest % of their sales as EVs. As we learned earlier, China and the US dominate global vehicle sales, so if the US is doing 8% of its sales to EVs. Globally, the number is closer to 14%, so it’s fair to guess China’s EV sales as a % of their market share is much higher than the US, keeping this global % much higher as the US plays catch-up. To do our ranked analysis, we use a simple filter to filter down by sales share; we leave all countries and then apply a max() calculation to get the order of the highest EV share by country. # get our data from spreadsheet into Python 
df = cells((0,0),(7,9542), 'EV Raw Data', first_row_header=True)

# filter by selected parameter of market share
df = df.loc[df['parameter'] == cell(2,1)]
# make this a historical analysis with none of the projections included 
df = df.loc[df['category'] == 'Historical']
# filter just by the latest year available 
df = df.loc[df['year'] == '2022']
# exclude buses, vans, etc and just include cars 
df = df.loc[df['mode'] == 'Cars']

# sort by highest share of market 
df = df.sort_values(by=['value'], ascending=False)

# return dataframe to sheet 
df There are lots of fascinating insights from this list. Note that large vehicle sales countries like the US and Japan are dragging down total EV sales, so China pulls the % up with their #1 vehicle sales by country plus a high % of 29% of overall sales as EVs. We also note countries in Northern Europe whose sales are trending towards EV-only. Northern Europe has regulations that give VAT exemptions to EV producers, aggressive emissions targets, and other regulations that have resulted in this rapid transition to electric vehicles in Northern Europe. Charging networks As EV adoption accelerates it’s worth exploring how charging infrastructure is adapting. For context, there are 3 core types of chargers: Standard residential 120V AC outlet (what you use to plug in every day at-home items; this charging type is very slow, commonly doing 120V @ 14amps = ~1.7kW before efficiency losses for an 80 kW EV battery. This means a minimum of 47 hours for a full charge (realistically more due to efficiency losses/the actual power delivered to the battery), which is a fine enough solution for users who have less commuting and can use public infrastructure as needed. Level 1: Installable in residential applications (electric home dryers usually use 240V) and the most common public infrastructure charging stations; level 2 at home typically is up to 48A, sometimes lower. 240V @ 48A = 11.5 kW peak, realistically delivering less power. With level 2 infrastructure, an 80 kW battery can be charged in 5–9 hours. This is the best home solution as you can get a full charge easily overnight, and for public infrastructure, it allows topping up or fully charging if using prolonged parking. This is not the solution for trips requiring faster charging between stops. Level 2: DC fast charging is a purely commercial option that can’t be installed at home. These chargers get a vehicle from empty to full in under an hour. This is the charging solution that resembles topping up at the pump. This kind of charging has a correspondingly higher cost for convenience. Level 3 chargers range from 50kW to 350kW; at 350kW, an 80 kW battery could be fully charged in less than 20 minutes. Level 3 (DCFC): In our dataset, when the data provider refers to slow charging, they’re referring to publicly available Level 2 infrastructure. When referring to fast charging, they refer to Level 3 infrastructure. We start by visualizing charger growth in the US and China. We note that growth in China has an extremely impressive growth curve for fast charging, while the US has seen rapid growth for slow chargers and lagging growth for fast charging. charger growth in 2022 (China vs US) Fast China fast chargers installed/EV sale in 2022: 760k/4.4M (.17) US fast chargers installed/EV sale in 2022: 28k/800k (.035) charger growth in 2022 (China vs US) Slow China slow chargers installed/EV sale in 2022: 1M/4.4M (.22) US slow chargers installed/EV sale in 2022: 100k/800k (.125) Charging experiences vary. In some scenarios, lines or fully occupied chargers may exist. In others, chargers may see very little use. Factors like proximity to significant parking, pricing, etc, have obvious impacts. On average, chargers are going unused a high percentage of the time. For charging installation to be worth the capital expenditure, installers need to be able to project a usage rate that puts them in profitability on a reasonable timeline (like any investment). Summary EV adoption has accelerated in recent years at a rapid pace. As soon as the numbers for 2023 are released we’ll likely see similar growth to prior years. Go ahead and dig into this dataset and glean your own insights; our analysis is naturally incomplete — there’s lots of fields in the dataset we ignored that would provide all sorts of interesting insights (projections, sales of vans/trucks/other interesting vehicle types, etc). To access the dataset and analysis directly: https://app.quadratichq.com/file/4251cd97-e14d-4788-baf2-9d161eb10625 Dataset: https://www.kaggle.com/datasets/padmapiyush/global-electric-vehicle-dataset-2023 Other sources: Vehicle sales: Charging info: https://www.oica.net/category/production-statistics/2022-statistics/ https://www.transportation.gov/rural/ev/toolkit/ev-basics/charging-speeds

Data-Driven Analysis of Global EV Adoption

গ্লোবাল ইভি গ্রহণের ডেটা-চালিত বিশ্লেষণ

Datengesteuerte Analyse der weltweiten Einführung von Elektrofahrzeugen

Análisis basado en datos sobre la adopción mundial de vehículos eléctricos

Analyse basée sur les données de l'adoption mondiale des véhicules électriques

वैश्विक ईवी अपनाने का डेटा-संचालित विश्लेषण

世界のEV導入に関するデータ主導型分析

글로벌 EV 채택에 대한 데이터 기반 분석

Análise baseada em dados da adoção global de VE

Анализ глобального внедрения электромобилей на основе данных

Küresel Elektrikli Araç Benimsenmesinin Veriye Dayalı Analizi

Phân tích dựa trên dữ liệu về việc áp dụng EV toàn cầu

全球电动汽车采用情况的数据驱动分析

Authors: (1) Gopal Yadav, Department of Physics, Indian Institute of Technology & Chennai Mathematical Institute. Table of Links Abstract Acknowledgment PART I Chapter 1: Introduction Chapter 2: SU(3) LECs from Type IIA String Theory Chapter 3: Deconfinement Phase Transition in Thermal QCD-Like Theories at Intermediate Coupling in the Absence and Presence of Rotation Chapter 4: Conclusion and Future Outlook PART II Chapter 5: Introduction Chapter 6: Page Curves of Reissner-Nordström Black Hole in HD Gravity Chapter 7: Entanglement Entropy and Page Curve from the M-Theory Dual of Thermal QCD Above Tc at Intermediate Coupling Chapter 8: Black Hole Islands in Multi-Event Horizon Space-Times Chapter 9: Multiverse in Karch-Randall Braneworld Chapter 10: Conclusion and Future outlook APPENDIX A APPENDIX B APPENDIX C Bibliography CHAPTER 8 - BLACK HOLE ISLANDS IN MULTI-EVENT HORIZON SPACE-TIMES 8.1 Introduction and Motivation The information paradox of black holes has been investigated for black holes that are asymptotically flat, however the most recent observation indicates that our universe is currently in an accelerated phase. Because of this, it is only natural to wonder how the presence of a positive value for the cosmological constant Λ influences the information paradox problem. The information paradox of a stationary black hole with a positive cosmological constant Λ or the Schwarzschild de-Sitter black hole spacetime is something that attracted our attention. The information paradox of Schwarzschild de-Sitter black holes is important due to the fact that these black holes originated during the early inflationary phase of our universe (for example, [210–213]). It additionally offers a fantastic toy model for the global framework of isolated black holes of our universe, which is quite useful while keeping in mind the current period of accelerated expansion. Additionally, similar to the situation with black holes, there are regions of de Sitter space that are causally separated from one another. Therefore, an observer is only able to access the portions of the cosmos that are constrained by their own horizon. There are two event horizons associated with the Schwarzschild de-Sitter black hole: the cosmological event horizon (CEH) and the black hole event horizon (BEH). The global causal border of the de Sitter spacetimes has been provided by the cosmological event horizon. In comparison to Λ ≤ 0 single horizon spacetime, the thermodynamics of these event horizons are distinct [214–216]. Gibbons-Hawking radiation is emitted and absorbed by cosmological event horizon similar to the Hawking radiation of black holes. Comparatively speaking, the entropy creation of the cosmic horizon is an observer-dependent characteristic, in contrast to the entropy generation of the black hole. It is caused by people’s lack of knowledge regarding the things that exist beyond the cosmological horizon. In order to obtain the Page curve of the Schwarzschild de-Sitter black hole, we make use of the island concept. In the case of models that are not holographic, we are able to use the s-wave approximation for studying black holes in higher dimensions. Because we neglect the angular component of the metric when performing the s-wave approximation, we are left with a CFT metric that only has two dimensions. Because of this, we are able to compute the entanglement entropy of Hawking radiation using the formula for 2D CFT that is provided in [192, 193]. The entanglement entropy and complexity have been studied in [217–219] in the context of de-Sitter space. The purpose of this work was to investigate the information paradox and its resolution of black holes with multiple horizons. We considered the Schwarzschild de-Sitter black hole with two horizons and obtained the Page curves for the black hole and the de-Sitter patches by placing thermally opaque membranes on the two sides of the region of study. The “effect of temperature” on the Page curves and the scrambling time is something else that would be of interest to us. In this chapter, we will discuss the Page curve of black hole patch only. 8.2 Preliminary In this section, we will discuss the basics of Schwarzschild de-Sitter black hole, the concept of thermal opaque membrane and the effect of gravity near the aforementioned membrane in 8.2.1, 8.2.2, and 8.2.3 respectively. 8.2.1 Schwarzschild de-Sitter Black Hole The Schwarzschild de-Sitter (SdS) metric has the following form in spherical polar coordinates: and, where the Kruskal coordinates are: Therefore (8.7) and (8.8) do not have any coordinate singularities at r = rH and r = rC. It is not possible to remove the coordinate singularities of both event horizons simultaneously. Hence, we have to study the black hole patch by freezing the de-Sitter patch using thermal opaque membrane and vice-versa. 8.2.2 Thermal Opaque Membranes in SdS Space-Time The fact that the effective potential term disappears at both horizons and remains positive in between them is something that stands out as an interesting feature of the Schrödingerlike equation that was given before. Therefore, this bell-shaped potential acts as a barrier between the event horizons of the black hole and de-Sitter space. To be able to depict it in Penrose diagram of the extended Schwarzschild de-Sitter space-time, we will use (8.9) to describe the Kruskal timelike and spacelike coordinates as follows: implying Therefore, the line r =constant is a hyperbola linking i ±, and it is possible to draw it with regard to any of the Kruskal coordinates listed above. This appears in the Penrose diagram of the maximally extended SdS space-time. When we have put this hyperbola or the thermal opaque membrane, modes on each side of wall will be unable to pass through it and will continue to exist in their respective regions. A natural manifestation of this effective potential could be possible with the thermally opaque membrane. 8.2.3 Bulk Gravitational Effect Near Thermal Opaque Membranes In the context of this discussion, we are interested in the bulk gravitational influence on thermally opaque membranes. Using the island formula, the authors of [229, 230] studied the information paradox of pure de-Sitter space, evaporating and eternal black holes in the weak gravity domain. These setups do not include an exterior bath and the authors included an anchor curve that produces both an interior and an exterior. The island formula can be applied despite the fact that gravity is extremely weak on the exterior region. We suggest that we may think of an “anchor curve” going through the locations b ± 1,2 within our setup also where we have specified the boundaries of radiation regions in Figs. 8.1 and 8.2. Our justification is based on [229,230]. The interior and exterior in our setup are marked by these “anchor curves”. The “thermal opaque membrane” is being able of blocking the radiation coming from the region, that is not relevant to our research in any way. Therefore, the applicability of the island formula to our situation can only be considered notional at this point. The information paradox that arises from black holes in multi-event horizon spacetime can be solved using this approach. Considering the membrane that was discussed before is located at a significant distance from the region affected by the black hole, it is reasonable to suppose that the gravitational effects around these membranes is insufficiently strong. As a result, 2 CFT formulas can be utilized to determine the entanglement entropy of Hawking radiation. We have discussed another approach to get the Page curve of Schwarzschild deSitter black hole in chapter 9 using wedge holography. D 8.3 Black Hole Islands in Schwarzschild de-Sitter Black Hole The conformal factor and Kruskal coordinates, both of which exist in (8.13), are defined as follows: It has been calculated that thermal entropy of the black hole is: 8.3.1 No Island Phase In this case, the region of interest for the black hole patch is the area that is completely encompassed by the thermally opaque membrane on both sides in the C and L domains. Without island surface, we were able to calculate the entanglement entropy of Hawking radiation by utilizing the two-dimensional CFT formula that is given below [192, 193]: By utilizing (8.14), (8.5) and (8.17), we are able to simplify the equation (8.16). The form in its refined version is written as follows: The above equation has a significant physical meaning that it predicts that there is going to be a limitless quantity of Hawking radiation due to its linear time dependence upon entanglement entropy. This is in contrast to the fact that the Page curve of an eternal black hole predicts that there will only be a finite amount of Hawking radiation [132]. 8.3.2 Island Phase Taking into account the island surface as part of the setup, we will now calculate the entanglement entropy of Hawking radiation. With inclusion of the island surface, the entanglement entropy of the Hawking radiation can be determined using the formula [192, 193], which is the matter component of the generalized entropy (5.11). Simplifying the expression for the matter contribution to the generalized entropy (5.11) from the radiation and island regions by utilizing (8.13), and (8.17). We obtained: As a result, the area of the boundary of the island surface and the matter component (8.22) are added together to generate the generalized entropy (5.11): The previous equation has the following solution: Using the above equation, we can determine that the island exists at the following location: Since the second component in the above equation has a negative sign in front of it, the island must be found inside the black hole horizon. We have showed in the Penrose diagram where islands are beyond the black hole event horizon, but we have found that it lies within the horizon itself. Substituting a1 from (8.28) into the definition of generalized entropy yields: Because of this, we may deduce that the entanglement entropy of Hawking radiation remains constant when the island is incorporated into the black hole’s interior and is thus equal to twice the black hole’s thermal entropy (to leading order in G ). N Hawking radiation’s entanglement entropy without the island surface is equivalent to its entanglement entropy with the island surface present at a point in time called “Page time” as discussed in chapter 6. By equating (8.19) and (8.29), we obtained the Page time as given below: Page time: : The scrambling time is discussed in detail in chapter 6. For the black hole patch in Schwarzschild de-Sitter black hole, scrambling time can be obtained using the following equation: Scrambling time From (8.5) and (8.28), the black hole patch’s scrambling time is therefore obtained as: 8.3.3 Page Curves The black hole patch Page curves are now obtained using the results of 8.3.1 and 8.3.2. (8.19) and (8.29) are the key equations. When we incorporate the island surface, the entanglement entropy of Hawking radiation changes from a time-dependent function of entanglement entropy to a constant value of (2S BEH th ) (8.29). As a result, if we plot all of these contributions together, we get the Page curves of eternal black holes that are in accord with their unitary evolution. From the use of (8.2), (8.3), (8.19) and (8.29), we obtained the Page curves for black hole patch when Q = GN = 1, 3M √ Λ = 0.4 (green) and 3M √ Λ = 0.5 (magenta), shown in Fig. 8.3. As the mass of the black hole grows (as shown in 8.3), the Page curves move later in time. As a result, information recovered from large black holes takes much more time than that from smaller black holes. This finding can also be interpreted in terms of the temperature of black holes. Information can be recovered faster from black holes with higher temperatures than from those with lower temperatures because the black hole temperature is inversely related to black hole mass. To rephrase, island emerges earlier for the black holes with higher temperatures compared to the black holes with lower temperature. 8.4 Information Problem in SdS Black Hole as a Whole 8.5 Conclusion Using the Island concept, we have investigated the information paradox that is associated with the black hole patch in the Schwarzschild-deSitter black hole spacetime. On both sides of the black hole patch, we used thermally opaque membranes to create an isolation barrier between the black hole and the de-Sitter patches, and vice versa. We calculated the entanglement entropy of Hawking radiation without and with the inclusion of an island surface and found that, as is typical, the entanglement entropy has a linear time dependency as long as there isn’t an island surface, but it turns into constant value when there is an island surface present. As a result, we are able to produce the Page curve in a manner that accords with the unitary evolution of the black holes. In this particular instance, the island can be found inside the horizon of the black hole, in contrast to the universal finding, which states that the island must be found outside the horizon of the black hole in the case of eternal black holes [197]. Our results are likewise comparable to those presented in [235], in which the researchers calculated the Page curve of a one-sided asymptotically flat black hole and found that the island lies within the horizon of the black hole. In addition, we have investigated the “effect of temperature” on the Page curves depicted by the black hole patch. We noticed that as the temperature of the black hole patch rose, a shift toward later times occurred in the Page curves. When compared to the black hole patch with the greater temperature, the one with the lower temperature will take a significantly longer amount of time to send the information to the observer. This suggests that the island of the black hole patch doesn’t make its appearance until much later for the black hole patch with the lower temperature, but the island makes its debut much sooner for the black hole patch with the higher temperature. We are able to begin the process of recovering the information that was flung into the black holes horizon as soon as an island surface enters into the picture [194, 195]. As a result, we get to the conclusion that the temperature of both event horizons has a role in determining the “dominance of islands” and the “scrambling time”. This paper is under CC 4.0 license. available on arxiv

Black Hole Islands in Multi-Event Horizon Space-Times

I want to present to you an abstract provided after asking ChatGPT to synthesize specific ideas and concepts. I will present my additions in italics and I hope that after reading, you could envision the background of such a system of locks. Abstract In this proposal, we present a novel cryptographic communication system leveraging dynamic prime sets for secure information encoding and decoding. The system comprises two primary sets: primes and non-primes, each defined by specific intervals, both having equal cardinalities and dynamic sizes known only to authorized parties. Key Components: Dynamic Sets: The size of each set is dynamically determined by known intervals (e.g., 50, 100, 150). Cyclic Cipher: Primes and non-primes are encoded using a cyclic cipher. Each prime set begins with the symbol '3', and each non-prime set with '2'. Set Transition: Crucial transition between sets for security. Cipher cycles through different prime and non-prime sets, transitioning only upon successful access. The authorized personnel will know information about the state of the current cipher and thus, follow the steps to open it. Positional Significance: '3,2' sequence initiates communication. '(prime-nonprime=x)' indicates reaching the middle of the set, revealing a new cipher and starting position. When the internal logic fits the unlocking process, “2,3” will be called to check the real internal position of the cipher. Pattern Recognition: Parties recognizing set intervals can identify patterns, aiding in determining the starting position for decoding. Anyone without information about the sets will never be able to know the starting conditions, nor the rules, and thus, never know what they do. Since the cipher is changed to a new one after each entering, it ensures that any prior information given (L-R movement) about the unlocking is not useful in the next. Security Measures: Name Cycling: Sets cycle names to prevent external entities from deducing set significance. Dynamic Set Access: Cyclic transition occurs only upon successful access, reducing vulnerability to attacks. Summarily: The cryptographic system, based on dynamic prime sets and cyclic ciphers, offers a unique approach to secure communication. Incorporation of dynamic intervals and cyclic transitions adds layers of complexity, and positional symbols allow for a structured and secure information exchange. The proposal outlines a robust framework open for further exploration and refinement in practical secure communication systems. Operational Details First Iteration: Primes: 50th to 100th, Non-primes: 25th to 75th. Primes start at the last member of the group, non-primes at the first. First iteration requires 25 movements left for primes and 25 right for non-primes. Value of their difference serves as the key, revealed when the authorized person requires it. Possible Complexities: Iterating by 3: Adds complexity with the possibility of iterating by 3 at each step. May lead to a potential state of impossibility to open, limiting the number of allowed entries. If the 50th chyper iterates by 3 and it is impossible to reach the middle this way, then no allowance can be granted for further parties. No matter if they are authorized or not. Limitations: tate of impossibility to open serves as a limit on the number of entries. Prevents continuous attempts, maintaining system security. Overall Security Measures: Dynamic nature of set intervals and cyclic transitions. Limited information sharing, revealing only necessary entry details. Unique complexity of each iteration known only to authorized personnel. Incorporation of limits on certain iterations ensures a balance between security and accessibility. Summarily: Proposed cryptographic system, with intricate set intervals, cyclic transitions, and iterative puzzles, establishes a robust and secure communication framework. Background view provides insight into operational details and possible complexities, ensuring a delicate balance between accessibility and security. Presents a promising approach to secure communication with avenues for further exploration and refinement. Mathematical Model Set Representation: Primes: �={��∣�� is prime,�∈[50,100]} ={_pi_​∣_pi_​ is prime,_i_∈[50,100]} P Non-Primes: ��={���∣��� is non-prime,�∈[25,75]} ={_npi_​∣_npi_​ is non-prime,_i_∈[25,75]} NP Cyclic Cipher: Algebraically represented where '3' represents the prime set and '2' represents the non-prime set. Set Transition: Mathematically described for a cyclic pattern: �new=�old+�mod �_P_new​= , where �_k is the step size, and �_M_ is the total number of sets. P_old​+k_mod_M Positional Significance: Mathematically expressed as the '3,2' sequence at the start signifying the initiation of communication. Pattern Recognition: Mathematical identification of patterns within each set based on known intervals and transitions. Security Measures: Name Cycling: Mathematically expressed as a cycle of set names to prevent external inference. Dynamic Set Access: Cyclic transition upon successful access, represented as a mathematical condition. Limitations: Introducing limits on iterations expressed as a mathematical constraint: Limitimpossibility=some mathematical condition Sample Operation First Iteration: Mathematically represented as movement within set intervals: �start=[50,100]_P_start​=[50,100], ��start=[25,75]_NP_start​=[25,75] �end=�start−25_P_end​=_P_start​−25, ��end=��start+25_NP_end​=_NP_start​+25 Calculating the key: Key=�end−��endKey=_P_end​−_NP_end​ Iterating by 3: Mathematically expressed as �new=�old+3mod �_P_new​= P_old​+3mod_M Limitations: State of impossibility to open mathematically defined based on certain conditions. Security Measures in Mathematical Terms: Limited Information Sharing: Mathematical representation of minimal disclosure based on the principle of need-to-know. Unique Complexity: Each iteration is mathematically distinct and known only to authorized personnel. Conclusion The mathematical model provides a formal representation of the cryptographic system, incorporating set intervals, cyclic transitions, and operational details. The use of algebraic expressions and mathematical conditions ensures precision and clarity in defining the system's behavior and security measures. This mathematical view lays the foundation for rigorous analysis and potential optimizations of the proposed cryptographic system.”-Chat GPT

Cryptographic Communication Using Dynamic Prime Sets

When we think of debugging, we think of breakpoints in IDEs, stepping over, inspecting variables, etc. However, there are instances where stepping outside the conventional confines of an IDE becomes essential to track and resolve complex issues. This is where tools like DTrace come into play, offering a more nuanced and powerful approach to debugging than traditional methods. This blog post delves into the intricacies of DTrace, an innovative tool that has reshaped the landscape of and system analysis. debugging https://youtu.be/3M0AhZnVoUk?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my . If you want to get back to Java after a while, check out my Debugging book Java Basics book Java 8 to 21 book . DTrace Overview DTrace was first introduced by Sun Microsystems in 2004. DTrace quickly garnered attention for its groundbreaking approach to dynamic system tracing. Originally developed for Solaris, it has since been ported to various platforms, including MacOS, Windows, and Linux. DTrace stands out as a dynamic tracing framework that enables deep inspection of live systems – from operating systems to running applications. Its capacity to provide real-time insights into system and application behavior without significant performance degradation marks it as a revolutionary tool in the domain of system diagnostics and debugging. Understanding DTrace’s Capabilities DTrace, short for Dynamic Tracing, is a comprehensive toolkit for real-time system monitoring and debugging, offering an array of capabilities that span across different levels of system operation. Its versatility lies in its ability to provide insights into both high-level system performance and detailed process-level activities. System Monitoring and Analysis At its core, DTrace excels in monitoring various system-level operations. It can trace system calls, file system activities, and network operations. This enables developers and system administrators to observe the interactions between the operating system and the applications running on it. For instance, DTrace can identify which files a process accesses, monitor network requests, and even trace system calls to provide a detailed view of what's happening within the system. Process and Performance Analysis Beyond system-level monitoring, DTrace is particularly adept at dissecting individual processes. It can provide detailed information about process execution, including CPU and memory usage, helping to pinpoint performance bottlenecks or memory leaks. This granular level of detail is invaluable for performance tuning and debugging complex software issues. Customizability and Flexibility One of the most powerful aspects of DTrace is its customizability. With a scripting language based on C syntax, DTrace allows the creation of customized scripts to probe specific aspects of system behavior. This flexibility means that it can be adapted to a wide range of debugging scenarios, making it a versatile tool in a developer’s arsenal. Real-World Applications In practical terms, DTrace can be used to diagnose elusive performance issues, track down resource leaks, or understand complex interactions between different system components. For example, it can be used to determine the cause of a slow file operation, analyze the reasons behind a process crash, or understand the system impact of a new software deployment. Performance and Compatibility of DTrace A standout feature of DTrace is its ability to operate with remarkable efficiency. Despite its deep system integration, DTrace is designed to have minimal impact on overall system performance. This efficiency makes it a feasible tool for use in live production environments, where maintaining system stability and performance is crucial. Its non-intrusive nature allows developers and system administrators to conduct thorough debugging and performance analysis without the worry of significantly slowing down or disrupting the normal operation of the system. Cross-Platform Compatibility Originally developed for Solaris, DTrace has evolved into a cross-platform tool, with adaptations available for MacOS, Windows, and various . Each platform presents its own set of features and limitations. For instance, while DTrace is a native component in Solaris and MacOS, its implementation in Linux often requires a specialized build due to kernel support and licensing considerations. Linux distributions Compatibility Challenges on MacOS On MacOS, DTrace's functionality intersects with System Integrity Protection (SIP), a security feature designed to prevent potentially harmful actions. To utilize DTrace effectively, users may need to disable SIP, which should be done with caution. This process involves booting into recovery mode and executing specific commands, a step that highlights the need for a careful approach when working with such powerful system-level tools. We can disable SIP using the command: csrutil disable We can optionally use a more refined approach of enabling SIP without dtrace using the following command: csrutil enable --without dtrace Be extra careful when issuing these commands and when working on machines where dtrace is enabled. Back up your data properly! Customizability and Flexibility of DTrace A key feature that sets DTrace apart in the realm of system monitoring tools is its highly customizable nature. DTrace employs a scripting language that bears similarity to C syntax, offering users the ability to craft detailed and specific diagnostic scripts. This scripting capability allows for the creation of custom probes that can be fine-tuned to target particular aspects of system behavior, providing precise and relevant data. Adaptability to Various Scenarios The flexibility of DTrace's scripting language means it can adapt to a multitude of debugging scenarios. Whether it's tracking down memory leaks, analyzing CPU usage, or monitoring I/O operations, DTrace can be configured to provide insights tailored to the specific needs of the task. This adaptability makes it an invaluable tool for both developers and system administrators who require a dynamic approach to problem-solving. Examples of Customizable Probes Users can define probes to monitor specific system events, track the behavior of certain processes, or gather data on system resource usage. This level of customization ensures that DTrace can be an effective tool in a variety of contexts, from routine maintenance to complex troubleshooting tasks. Following in a simple hello world dtrace probe: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p 9999 The kernel is instrumented with hooks that match various callbacks. dtrace connects to these hooks and can perform interesting tasks when these hooks are triggered. They have a naming convention, specially: . In this case the provider is a system call in both cases. We have no module, so we can leave that part blank between the colon ( ) symbols. We grab a write operation and entries. When an application writes or tries to send a packet, the following code event will trigger. provider:module:function:name : sendto These things happen frequently, which is why we restrict the process ID to the specific target with . This means the code will only trigger for the PID passed to us in the command line. The rest of the code should be simple for anyone with basic C experience; it's a printf that would list the processes and the data passed. pid == $target Real-World Applications of DTrace DTrace's diverse capabilities extend far beyond theoretical use, playing a pivotal role in resolving real-world system complexities. Its ability to provide deep insights into system operations makes it an indispensable tool in a variety of practical applications. To get a sense of how dtrace can be used, we can use the command whose output on my Mac is below: man -k dtrace bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace
Tcl_CommandTraceInfo(3tcl), Tcl_TraceCommand(3tcl), Tcl_UntraceCommand(3tcl) - monitor renames and deletes of a command
bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace There's a lot here; we don't need to read everything. The point is that when you run into a problem, you can just search through this list and find a tool dedicated to debugging that problem. Let’s say you're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? rwbypid.d can help you with that. It can generate a list of processes and the number of calls they have for read/write based on the process ID, as seen in the following screenshot: We can use this information to better understand IO issues in our code or even in 3rd party applications/libraries. is another tool that helps us track IO operations but with more details: iosnoop In diagnosing elusive system issues, DTrace shines by enabling detailed observation of system calls, file operations, and network activities. For instance, it can be used to uncover the root cause of unexpected system behaviors or to trace the origin of security breaches, offering a level of detail that is often unattainable with other debugging tools. Performance optimization is the main area where DTrace demonstrates its strengths. It allows administrators and developers to pinpoint performance bottlenecks, whether they lie in application code, system calls, or hardware interactions. By providing real-time data on resource usage, DTrace helps in fine-tuning systems for optimal performance. Final Words In conclusion, DTrace stands as a powerful and versatile tool in the realm of system monitoring and debugging. We've explored its broad capabilities, from in-depth system analysis to individual process tracing and its remarkable performance efficiency that allows for its use in live environments. Its cross-platform compatibility, coupled with the challenges and solutions specific to MacOS, highlights its widespread applicability. The customizability through scripting provides unmatched flexibility, adapting to a myriad of diagnostic needs. Real-world applications of DTrace in diagnosing system issues and optimizing performance underscore its practical value. DTrace's comprehensive toolkit offers an unparalleled window into the inner workings of systems, making it an invaluable asset for system administrators and developers alike. Whether it's for routine troubleshooting or complex performance tuning, DTrace provides insights and solutions that are essential in the modern computing landscape. Also published . here

Advanced Debugging Techniques: Revisiting DTrace for Troubleshooting

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