Dexla

dexla

Solana has become a significant player in the transforming landscape of blockchain technology and decentralized finance (DeFi). Blockchain developers prefer it for its high performance, scalability, and vibrant ecosystem to build blockchain and crypto solutions. Crypto swapping and staking are crucial crypto solutions that developers build using Solana blockchain development services. They play a pivotal role in supporting and growing the network. Some of the most popular swapping and staking solutions run on the Solana blockchain. Understanding Swapping on Solana Swapping refers to exchanging one cryptocurrency for another, often facilitated by decentralized exchanges (DEXs) or automated market makers (AMMs). Solana's architecture facilitates speed and efficiency, making swapping on this blockchain network a seamless experience. It allows users to exchange one Solana asset for another effortlessly. This tool eliminates the need to create new frameworks or code structures. For instance, users can swap SOL for assets like Ethereum, Bitcoin, stablecoins such as USDC or USDT, or any token within Solana's SPL token program. To initiate a Solana token swap, users can turn to decentralized exchanges (DEX) that operate as automatic market makers (AMMs). Platforms like Raydium, Dexlab, and Mango Markets are popular choices for Solana token swaps. These exchanges leverage Solana's rapid transaction speed to facilitate instantaneous swaps with minimal fees. Overall, Solana's swapping mechanism on DEX platforms empowers users with the flexibility to trade a diverse array of tokens. It contributes to the dynamic DeFi ecosystem thriving on the Solana blockchain. Benefits of Swapping on Solana 1. High Speed Solana's high throughput enables speedy transaction confirmation, allowing for swift swaps. 2. Low Transaction Costs The Solana network boasts significantly lower transaction fees than many other blockchains, making swapping cost-effective. 3. Wide Range of Assets Solana supports diverse tokens, providing ample opportunities for swapping digital assets. 4. Robust Decentralized Exchanges (DEXs) Solana's ecosystem hosts several decentralized exchanges (DEXs) that operate as automated market makers (AMMs). These platforms, such as Raydium, Serum, and others, leverage Solana's speed and efficiency to offer seamless token swaps with high liquidity. 5. Integrated DeFi Ecosystem Swapping on Solana is not limited to basic token exchanges. The platform's integration with decentralized finance (DeFi) protocols allows for advanced trading features, such as liquidity provision, yield farming, impermanent loss protection, and access to various DeFi products and services. 6. Enhanced User Experience Solana's user-friendly interfaces, fast transaction speeds, and low fees create a smooth user trading experience. Whether you're a beginner or an experienced trader, Solana's swapping capabilities are accessible and efficient. 7. Scalability Solana's scalability allows the network to handle a high volume of transactions without compromising speed or efficiency. This scalability is crucial for maintaining optimal trading conditions, especially during increased market activity. Maximizing Returns through Staking on Solana Solana uses a consensus mechanism called Proof of Stake (PoS). Validators run this mechanism to ensure that all transactions are verified and secured. Solana allows individuals or entities, also known as ‘stakers,’ to lock up a certain amount of SOL directly with validators as collateral to support its consensus mechanism. This process is referred to as ‘staking.’ This economic incentive is vital because it enables the establishment of more high-quality validators, which improves the network's security and efficiency. Staking involves locking up your cryptocurrency holdings in a designated wallet to support network operations and validate transactions. In return, stakers earn rewards through additional tokens or transaction fees. Solana offers robust staking mechanisms, allowing users to participate in securing the network while earning passive income. Key Features of Staking on Solana Proof of Stake (PoS) Consensus Solana utilizes a Proof of Stake consensus mechanism, where validators validate transactions and secure the network. Stakers participate by delegating their tokens to validators, contributing to network security and decentralization. High Yield Potential Solana's staking rewards can be lucrative, allowing stakers to earn attractive returns on their holdings. Network Security By staking SOL (Solana's native token) or other compatible assets, participants contribute to the security and decentralization of the Solana blockchain. Flexibility and Accessibility Solana's staking protocols offer flexibility regarding delegation options and withdrawal mechanisms, catering to many staking preferences. Embracing the Future of Finance with Solana As blockchain technology revolutionizes the financial landscape, Solana remains at the forefront of innovation, offering users unparalleled speed, scalability, and DeFi opportunities. Whether you're a seasoned crypto enthusiast or a newcomer exploring the possibilities of decentralized finance, Solana's robust infrastructure and diverse ecosystem provide a solid foundation for your journey into swapping and staking. CoinGecko Research has recognized Solana as the most popular blockchain in 2024. It indeed stands as a testament to its growing influence and adoption in the crypto community. Final Thoughts Swapping and staking are integral to the vibrant Solana ecosystem, empowering users to optimize their crypto holdings, participate in network governance, and earn passive income. By leveraging Solana's advanced blockchain capabilities and embracing the principles of decentralized finance, you can unlock new avenues for financial growth and innovation in the digital asset space. Are you looking for top-notch Solana development services but confused about where to start? Look no further! Connect with our team of expert Solana developers at Oodles Blockchain. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ Solana has become a significant player in the transforming landscape of blockchain technology and decentralized finance (DeFi). Blockchain developers prefer it for its high performance, scalability, and vibrant ecosystem to build blockchain and crypto solutions. Crypto swapping and staking are crucial crypto solutions that developers build using Solana blockchain development services . They play a pivotal role in supporting and growing the network. Some of the most popular swapping and staking solutions run on the Solana blockchain. Solana blockchain development services Understanding Swapping on Solana Understanding Swapping on Solana Swapping refers to exchanging one cryptocurrency for another, often facilitated by decentralized exchanges (DEXs) or automated market makers (AMMs). Solana's architecture facilitates speed and efficiency, making swapping on this blockchain network a seamless experience. It allows users to exchange one Solana asset for another effortlessly. This tool eliminates the need to create new frameworks or code structures. For instance, users can swap SOL for assets like Ethereum, Bitcoin, stablecoins such as USDC or USDT, or any token within Solana's SPL token program. To initiate a Solana token swap, users can turn to decentralized exchanges (DEX) that operate as automatic market makers (AMMs). Platforms like Raydium, Dexlab, and Mango Markets are popular choices for Solana token swaps. These exchanges leverage Solana's rapid transaction speed to facilitate instantaneous swaps with minimal fees. Overall, Solana's swapping mechanism on DEX platforms empowers users with the flexibility to trade a diverse array of tokens. It contributes to the dynamic DeFi ecosystem thriving on the Solana blockchain. Benefits of Swapping on Solana 1. High Speed 1. High Speed Solana's high throughput enables speedy transaction confirmation, allowing for swift swaps. 2. Low Transaction Costs 2. Low Transaction Costs The Solana network boasts significantly lower transaction fees than many other blockchains, making swapping cost-effective. 3. Wide Range of Assets 3. Wide Range of Assets Solana supports diverse tokens, providing ample opportunities for swapping digital assets. 4. Robust Decentralized Exchanges (DEXs) 4. Robust Decentralized Exchanges (DEXs) Solana's ecosystem hosts several decentralized exchanges (DEXs) that operate as automated market makers (AMMs). These platforms, such as Raydium, Serum, and others, leverage Solana's speed and efficiency to offer seamless token swaps with high liquidity. 5. Integrated DeFi Ecosystem 5. Integrated DeFi Ecosystem Swapping on Solana is not limited to basic token exchanges. The platform's integration with decentralized finance (DeFi) protocols allows for advanced trading features, such as liquidity provision, yield farming, impermanent loss protection, and access to various DeFi products and services. 6. Enhanced User Experience 6. Enhanced User Experience Solana's user-friendly interfaces, fast transaction speeds, and low fees create a smooth user trading experience. Whether you're a beginner or an experienced trader, Solana's swapping capabilities are accessible and efficient. 7. Scalability 7. Scalability Solana's scalability allows the network to handle a high volume of transactions without compromising speed or efficiency. This scalability is crucial for maintaining optimal trading conditions, especially during increased market activity. Maximizing Returns through Staking on Solana Solana uses a consensus mechanism called Proof of Stake (PoS). Validators run this mechanism to ensure that all transactions are verified and secured. Solana allows individuals or entities, also known as ‘stakers,’ to lock up a certain amount of SOL directly with validators as collateral to support its consensus mechanism. This process is referred to as ‘staking.’ This economic incentive is vital because it enables the establishment of more high-quality validators, which improves the network's security and efficiency. Staking involves locking up your cryptocurrency holdings in a designated wallet to support network operations and validate transactions. In return, stakers earn rewards through additional tokens or transaction fees. Solana offers robust staking mechanisms, allowing users to participate in securing the network while earning passive income. Key Features of Staking on Solana Proof of Stake (PoS) Consensus Proof of Stake (PoS) Consensus Solana utilizes a Proof of Stake consensus mechanism, where validators validate transactions and secure the network. Stakers participate by delegating their tokens to validators, contributing to network security and decentralization. High Yield Potential High Yield Potential Solana's staking rewards can be lucrative, allowing stakers to earn attractive returns on their holdings. Network Security Network Security By staking SOL (Solana's native token) or other compatible assets, participants contribute to the security and decentralization of the Solana blockchain. Flexibility and Accessibility Flexibility and Accessibility Solana's staking protocols offer flexibility regarding delegation options and withdrawal mechanisms, catering to many staking preferences. Embracing the Future of Finance with Solana As blockchain technology revolutionizes the financial landscape, Solana remains at the forefront of innovation, offering users unparalleled speed, scalability, and DeFi opportunities. Whether you're a seasoned crypto enthusiast or a newcomer exploring the possibilities of decentralized finance, Solana's robust infrastructure and diverse ecosystem provide a solid foundation for your journey into swapping and staking. CoinGecko Research has recognized Solana as the most popular blockchain in 2024. It indeed stands as a testament to its growing influence and adoption in the crypto community. Final Thoughts Swapping and staking are integral to the vibrant Solana ecosystem, empowering users to optimize their crypto holdings, participate in network governance, and earn passive income. By leveraging Solana's advanced blockchain capabilities and embracing the principles of decentralized finance, you can unlock new avenues for financial growth and innovation in the digital asset space. Are you looking for top-notch Solana development services but confused about where to start? Look no further! Connect with our team of expert Solana developers at Oodles Blockchain. Solana developers This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ https://business.hackernoon.com/

Unlocking the Power of Swapping and Staking on Solana

সোলানাতে অদলবদল এবং স্টেকিংয়ের পাওয়ার আনলক করা

Die Macht des Swappings und Stakings auf Solana entfesseln

Liberando el poder de intercambiar y apostar en Solana

Libérer le pouvoir de l’échange et du jalonnement sur Solana

सोलाना पर स्वैपिंग और स्टेकिंग की शक्ति को अनलॉक करना

Solanaでスワッピングとステーキングのパワーを解き放つ

솔라나에서 스왑핑과 스테이킹의 힘을 활용해보세요

Desbloqueando o poder de trocar e apostar em Solana

Раскрытие возможностей обмена и ставок на Solana

Solana'da Takas ve Staking Gücünün Kilidini Açmak

Giải phóng sức mạnh của việc hoán đổi và đặt cược trên Solana

释放 Solana 上的兑换和质押功能

Can you believe it? A technology that has been around for a few years is now one of the top-rated FinTech in the blockchain industry. Solana, often abbreviated as SOL, is making waves in the world of blockchain technology. The technology is not only good but also aims to improve every year with its growing ecosystem, becoming a promising player in the blockchain and crypto spaces. And the same goes with the Chat GPT software, which is another favorite dessert for humankind. An AI tool that helps you in many ways and saves your time The tool that responds to human text inputs is simple and almost accurate. What if we mixed these two amazing technologies to create a better future? Isn’t that amazing? Hi, I am a contribute writer at this amazing site Hackernoon and i will talk about how we can create an amazing products if we merge these technologies Let's talk about it with facts and figures. Let's talk about Solana Solana is a blockchain platform that aims to perform fast and efficient results on decentralized applications. It uses a unique method called Proof of History" (PoH), which helps it process transactions quickly and increase scalability to support a large number of users. In simple words, Solana is a digital platform that gives developers a space to build decentralized applications that can be used by anyone with an internet connection. Here is the figure that will help you understand PoH and create an important impact on ChatGPT. Befor we talk about solana vs other big blockchain technologies with Chat GPT, lets understand an overview of ChatGPT. Overview of Chat GPT ChatGPT is a state-of-the art language model developed by OPEN AI that uses a neural network to generate human-like responses to natural language inputs. It is trained on a massive corpus of text data and has learned natural-sounding responses to a variety of topics. on the transformer architecture, which allows it to understand the context and meaning of text inputs, after which it generates an appropriate response to a given query. The tool has been pre-trained on a large amount of text data and can be used for specific tasks, such as language translation, sentiment analysis, and question answering. ChatGPT is built Ethereum might be popular, but when it comes to AI, Solana and Chat GPT are the real MVPs! Solana is well-suited for use with Chat GPT because of its high transaction throughput and low fees. Solana's unique consensus mechanism, Proof of History (PoH), allows it to process transactions quickly and efficiently, which is important when using Chat GPT for real-time interactions. Additionally, Solana's low fees make it an attractive option for developers looking to build and deploy decentralized applications without incurring high costs. You can also watch with Co-founder of Solana feat Anatoly Yakovenko and Scroll Researcher full podcast of the Solana vs Ethereum Debate On the other hand, Ethereum, while popular and widely used, has been facing scalability and congestion issues, which can lead to slow transaction times and high fees. This can be problematic when using Ethereum for real-time applications like Chat GPT, which require fast and efficient processing. Here is another figure that will help you understand top transactions. Despite facing several challenges like network outages and macroeconomic conditions, , a well-established player in the market. On-chain data from the second quarter of Solana's daily transactions have surpassed those of Ethereum 2022 indicates that Solana's daily transactions exceeded 40 million, while Ethereum was only able to handle 1 million daily transactions during the same period . This is a clear indication of Solana's growing popularity and adoption, as more and more users turn to its high-performance blockchain platform. It goes to show that sometimes hype does not equate to greater adoption, and Solana's success is a testament to its underlying technology and value proposition. Here are the top worldwide companies that are dealing with solana transactions. DEXLAB SOLDEX ATLAS DEX Raydium You can compare by reading articles to determine . Make confident decisions with the power of knowledge at your fingertips. which one is the best Read and compare before you buy! Don't settle for less - read up on your options, compare them, and choose the best with the power of informed decision-making! Future with ChatGPT There are several things that can be done using Chat GPT with Solana. Here are some of the top things: Chat GPT can be used to create intelligent chatbots that can communicate with users in natural language. Create intelligent chatbots: Chat GPT can be used to automate certain processes within decentralized applications built on Solana. Automate processes: Chat GPT can be used to improve the user experience of decentralized applications built on Solana. Improve user experience: Chat GPT can be used to provide personalized recommendations to users based on their preferences and behavior. Personalize recommendations: Chat GPT can be used to enhance the security of decentralized applications built on Solana. Enhance security: Conclusion Overall, by using , developers can create decentralized applications that are more intelligent, intuitive, and secure, providing a better user experience and unlocking new possibilities for decentralized technologies. Chat GPT with Solana

Experience Lightning-fast Transactions With Solana and Chat GPT at Your Fingertips!

In the cluttered cyber world of copycats & vaporware, Solana { } stands out from the crowd with its unique design & value proposition. SOL (sometimes jokingly called solami by the degen chads on ) CT *Disclaimer* → I hold in my portfolio & believe in the future of the project. $SOL → This is not an endorsement to buy the coin or use the network. (Do you) → Solana is home to a prolific NFT community… however, there will be of NFTs in this post because, at this time, there are too many failures, frauds, rugs & scams. *one exception being Sol domain names*. no mention → This is strictly a general overview of the network. Not about degens or monke pictures… ok, just a tiny bit about degening. 📜 A Bit of Background Conceptualized in 2017 by ex-Qualcomm engineer & birthed in march of 2020, Solana is a turning complete, smart contract platform & distributed ledger network that was created with the intention of displacing the legacy exchange infrastructure(not killing Ethereum). Anatoliy Yakoveno The Solana network was & continues to be one of the most exciting innovations in the space due to its dedication to the technological elements of its design. heavy 👨‍💻 The Technical Specs 👨‍💻 Monolithic Architecture ➡ Monolithic software architecture in blockchain means that all major operational activity; transaction execution, data storage/availability, and consensus, are condensed into a single endpoint; the chain itself. This design brings its own set of tradeoffs including better prospective value propositions for the native asset due to strong utility ties between the token<->network (NFA) while hindering its potential to be flexible. {Great resource on blockchain software architecture here} Example of another crypto with Monothlic architecture: Bitcoin (BTC). POH (Proof-Of-History) ➡ Proof-Of-History is a novel tweak to the POS consensus model through the implementation of as a module. Technically, Proof of History happens before state consensus is reached by allowing block producers the ability to submit blocks & organize/confirm them later. This is made possible by a combination of two things: time & 1) the application of pBFT + VDF (verifiable delay functions) the ( ). That helps in eliminating the issue of scalability and transaction ordering. Clustering of consensus nodes rather than 1 node doing all the work, on Solana, there are clusters of 25 nodes that are batching tx’s for consensus at any given time Asynchronicity in block production & contract execution ➡ Basically, the ability to operate at max capacity without bloating/interrupting the network. (outages mentioned in the next section) Partially made possible through node clustering & partially made possible through a parallel processing technique called . Sealeveling The technical aspect of this are far beyond the scope of this article, but if you want to dive deeper on it, make sure to check out this post Written in RUST programming language ➡ While not easy to learn for beginner developers, RUST is considered to be among the strongest of programming languages. At a very basic level, RUST is a low-level language that talks directly to hardware with an intense focus on memory allocation. Here is a great piece to understand the benefits of RUST as a language. * Multi-Client Architecture ( ) ➡ in development Within the context of blockchain, a is the software that must be utilized in order to connect a piece of hardware to the network. client A delicate nuance in distributed system design that is rarely (if ever) brought to the public’s consciousness, is that software clients are an important touchpoint for the security & decentralization of a network. If a network only has 1 software client, and if for any reason that client has a bug, then the whole network is at risk. The more clients, the more resilient the network becomes. Extremely Fast ➡ The block time is 400 milliseconds; that is approximately 30–32.5x faster than Ethereum(ETH) & 1,500x faster than Bitcoin (BTC). Huge & growing developer ecosystem ➡ Over 2,000 monthly active developers puts Solana as the 2nd most active network by developer activity; ahead of Polkadot & only behind Ethereum. High Throughput ➡ The network averages ~5,000 TPS on a day-to-day basis & has the capacity to scale, in theory, indefinitely. The network is only constrained by the amount & type of hardware used by the nodes. As hardware improves, the network becomes more efficient; as more nodes join, the network becomes more efficient. So then LFGrow. ⚠ 💨⚠ 💨 Headwinds ➡ Is a VC chain. Supported heavily by deep-pocketed venture capital firms. This is not necessarily a bad thing, but it does reduce the network's credibility in terms of decentralization. Was endorsed by (SBF) & FTX; ➡ As basically everybody in the world knows, Sam-Bankman Fried was the single biggest supporter of the Solana network. Actually recorded saying “ ” as well as injecting hundreds of millions of dollars into the project, the once heroic supporter has now become the sh*tstain across the windshield of Solana’s life. Sell me all of your Solana & then Fuck off Suffers from Outages ➡ The single biggest hurdle that keeps on messing with the project. This is not a small issue; if outages happen while people have money on the network, real-life problems occur. As of this writing, the network has faced roughly 10 full outages & countless partial outages. If you want to explore all of them, here is a great uptime resource. Community is… interesting. ➡ I love SOL. I love the developers. I love the project. I Even love Solana ! But I cannot for the life of me figure out how it keeps attracting low-IQ people to its user base. At first, I thought it was just wrought with bots… but then I found out… CT Dear reader, I SWEAR to you, I personally know real SOL users that pronounce SolanA as SolanO. I asked them why… they don't know… 🤦‍♂️ I digress. 🧰Ecosystem Resources: Since the turbulence of the recent market activity, many tools in the Solana ecosystem are sunsetting (r.i.p. Serum) while others are just dying off due to a lack of interest. Nevertheless, there still remain a handful of applications that have only strengthened their positions over this time. Below, I have tried my best to organize as many “valid” tools as possible: Wallets Phantom - https://phantom.app/ Solflare - https://solflare.com/ DEXes Raydium - https://raydium.io/ Orca - https://www.orca.so/ Saber - https://app.saber.so/ Lifinity - https://lifinity.io/ Atrix - https://atrix.finance/ Soldex - https://soldex.ai/ Saros - https://saros.finance/ Aldrin - https://aldrin.com/ DexLab - https://www.dexlab.space/ Crema - https://www.crema.finance/ Lending/Borrowing & Stablecoin Minting Larix - https://projectlarix.com/ Solend - https://solend.fi/ Apricot - https://apricot.one/ Port - https://port.finance/ Jet - https://www.jetprotocol.io/ PsyFI - https://www.psyfi.io/ Hubble - https://hubbleprotocol.io/ Ratio - https://ratio.finance/ Parrot - https://parrot.fi/mint/ Meteroa - https://www.meteora.ag/ Liquid Staking Marinade - https://marinade.finance/ Socean - https://www.socean.fi/en/ Yield Aggregators Tulip - https://tulip.garden/#home Cropper - https://cropper.finance/ Katana - https://katana.so/ Kamino - https://app.kamino.finance/ Sunny - https://app.sunny.ag/ Francium - https://francium.io/ Explorers & Analtyics Solana Explorer - https://explorer.solana.com/ Solana FM - https://solana.fm/ SolScan - https://solscan.io/ Solana Beach - https://solanabeach.io/ ChainCrunch - https://chaincrunch.cc/ Synthetic Assets Synthetify - https://synthetify.io/ Dashboard & Portfolio Sonar Watch - https://sonar.watch/ Sols Watch - https://sols.watch/portfolio Step Finance - https://www.step.finance/ Ape Bored - https://apeboard.finance/dashboard Domain Names Bonfida — https://naming.bonfida.org/ MISC. → Fantastic report by Messari on the S . tate of Solana in Q4 2022 → Great Interview with the Founder on the Future of Solana beyond 2023 I will end this all with a timeless quote ; (←POH pun intended) “Innovation distinguishes between a leader and a follower.” — Steve Jobs May your bags always be full & overflowing 🥂 Also published here

Everything You Need to Know About Solana: An Essential Guide

Angular: Exploring Imperative and Declarative Programming with RxJS and Signals

Apple is renowned for its unique design and approach to technology. Whether it is the Macintosh or the iPhone, every detail has value and significance in the technological aspect. On June 10th, 2024, at WWDC (Worldwide Developers Conference), Apple officially announced its entry into the AI competition, scheduled for September. Following this announcement, people are looking to Apple with hopes of something as revolutionary as the Macintosh or iPod. The tech giant named their AI ‘Apple Intelligence’ and promised a better user experience with an AI that understands the commands better and interacts with users more naturally. About Delay and Development of Apple Intelligence According to recent reports, Apple may miss the anticipated timeline for AI enhancements. The Apple Intelligence rollout may take until October, which will miss the initial launch of iOS18 and iPad18. When Apple Intelligence was announced in June at WWDC, it was anticipated that these enhancements would be released with the launch of the new iOS. However, recent development reports have shown that Apple will miss the bar. Meanwhile, Apple has made Apple Intelligence available for public testing but is now only available in developer beta. However, it won't be fully available until later this fall for a select group of iPhones, iPads, and Macs with the required chipsets; even then, it will only be available as a beta feature that users can choose to use. The new anticipation of AI enhancements is around October, with the first update of iOS 18, which will be iOS 18.1. Developers and other industry experts have assumed this is due to the delay in Apple Intelligence. What Enhancements were there in the Apple AI Update? The AI enhancements in Apple products were about improving the user experience rather than creating something new out of AI. Even the announced AI enhancements only enhanced the efficiency of earlier features. For instance, let’s follow up on the Apple AI technology enhancements that were announced during WWDC: A Better ‘Siri’ The AI integration into the operating system will help Siri answer questions with stumbled words. Siri will even answer questions regarding troubleshooting Apple products. Intelligent Writing Tools Tools integrated with AI will help users write text in a better format and with more meaningful sentences. They will even suggest better phrases and correct grammar to improve the sentence. AI-Generated Summaries Automatic AI-generated summaries of emails, texts, blogs, and articles that would save time for users. Also, any considerable amount of text on a website will be summarized to make the information more precise. OpenAI’s GPT Integration The biggest news of all the AI enhancements is the integration of OpenAI’s GPT with Apple’s iOS. Siri and other voice-operated features will be integrated with the generative pre-trained transformers. Who has Access to Apple Intelligence? The world awaits Apple’s fall event, where the company is expected to showcase the new iPhone and Apple Intelligence. Meanwhile, Apple Intelligence is currently a beta within a beta: it's part of the developer beta of iOS 18.1, iPadOS 18.1, and MacOS Sequoia 15.1, while the public beta available on iPhones, Macs, and iPads right now is still based on iOS 18.0, iPadOS 18.0, and MacOS Sequoia 15.0. During the WWDC, one of the major announcements was about the early release of the beta versions of iOS 18 for the reviews and other features. This beta version is only available for developers to test the features and submit their reviews. In very simple terms, Apple Intelligence is available only to developers until the fall event, and after that, it will be available to the whole world. Tech enthusiasts consider it to be beta testing of Apple Intelligence. Apple’s legacy is about providing a best-in-class user experience. Developer reviews and feedback will be necessary to improve user AI enhancements. According to the review, the required actions will be taken. GPT Powered Version of ‘Siri’ Apple is optimizing “Siri” by integrating AI. In simple terms, they are integrating AI into their chatbot, where you can consider ‘Siri’ as an iOS chatbot now integrated with OpenAI’s GPT. In very simple terms, to integrate your app with Siri and Apple Intelligence, you need to provide AppIntent, AppEntity, and AppEnum implementations that work well with Apple Intelligence's pre-trained models. After reviewing Apple’s official documentation, integrating Siri and Apple Intelligence may seem like a complex task. However, any third-party AI chatbot can be integrated with iOS apps using various chatbot libraries and tools. Reason for the Delay While much speculation exists about the delay, the company does not provide a specific reason. However, many reports suggest that the delay is caused by the European Union’s law regarding data privacy and protection. Also, some reports suggest that Apple is working on the bugs and minor flaws that came with the AI enhancements. Many assumptions about the launch delay are made, but the company has no concrete statement. Impact of the Delay Among Competitors Apple's AI announcement sparked a race among competitors to build AI features with better user experiences. From Samsung to Nothing, everyone is working to make better AI integrations. While AI enhancements are also booming economically, investors and tech giants are spending huge sums on AI to create a better user experience and make features more convenient. It is also opening a new gate of AI features for smartphones and laptops. At the same time, Apple’s competitors are trying to improve their encircling features, where someone has to draw a circle to find out more about something. ‘Nothing’ is tilted towards using AI integration to make the phone more minimalistic yet convenient. Every tech company is running for the title of best AI enhancement features. This is making the market size much better in terms of economics and technology. Conclusion On June 10th, 2024, At WWDC (WorldWide Developers Conference), Apple officially announced its entry into the AI competition in September.
The tech giant has named their AI ‘Apple Intelligence’ and promised a better user experience.
Apple may have missed the anticipated timeline for AI enhancements.
The new anticipation of AI enhancements is around October, with the first update of iOS 18, which will be iOS18.1.
Many reports suggest that the delay is caused by the European Union’s laws regarding data privacy and protection.
Apple's AI announcement could signal the start of a competition race to build AI features that deliver better user experiences. Apple is renowned for its unique design and approach to technology. Whether it is the Macintosh or the iPhone, every detail has value and significance in the technological aspect. On June 10th, 2024 , at WWDC (Worldwide Developers Conference), Apple officially announced its entry into the AI competition, scheduled for September. Following this announcement, people are looking to Apple with hopes of something as revolutionary as the Macintosh or iPod. June 10th, 2024 The tech giant named their AI ‘ Apple Intelligence ’ and promised a better user experience with an AI that understands the commands better and interacts with users more naturally. Apple Intelligence About Delay and Development of Apple Intelligence According to recent reports, Apple may miss the anticipated timeline for AI enhancements. The Apple Intelligence rollout may take until October, which will miss the initial launch of iOS18 and iPad18. iOS18 iOS18 When Apple Intelligence was announced in June at WWDC , it was anticipated that these enhancements would be released with the launch of the new iOS . However, recent development reports have shown that Apple will miss the bar. WWDC WWDC launch of the new iOS Meanwhile, Apple has made Apple Intelligence available for public testing but is now only available in developer beta. However, it won't be fully available until later this fall for a select group of iPhones, iPads, and Macs with the required chipsets; even then, it will only be available as a beta feature that users can choose to use. The new anticipation of AI enhancements is around October, with the first update of iOS 18, which will be iOS 18.1. Developers and other industry experts have assumed this is due to the delay in Apple Intelligence. What Enhancements were there in the Apple AI Update? The AI enhancements in Apple products were about improving the user experience rather than creating something new out of AI. Even the announced AI enhancements only enhanced the efficiency of earlier features. For instance, let’s follow up on the Apple AI technology enhancements that were announced during WWDC: A Better ‘Siri’ The AI integration into the operating system will help Siri answer questions with stumbled words. Siri will even answer questions regarding troubleshooting Apple products. Intelligent Writing Tools Tools integrated with AI will help users write text in a better format and with more meaningful sentences. They will even suggest better phrases and correct grammar to improve the sentence. AI-Generated Summaries Automatic AI-generated summaries of emails, texts, blogs, and articles that would save time for users. Also, any considerable amount of text on a website will be summarized to make the information more precise. OpenAI’s GPT Integration The biggest news of all the AI enhancements is the integration of OpenAI’s GPT with Apple’s iOS. Siri and other voice-operated features will be integrated with the generative pre-trained transformers. OpenAI’s GPT OpenAI’s GPT Who has Access to Apple Intelligence? The world awaits Apple’s fall event, where the company is expected to showcase the new iPhone and Apple Intelligence . Meanwhile, Apple Intelligence is currently a beta within a beta: it's part of the developer beta of iOS 18.1, iPadOS 18.1, and MacOS Sequoia 15.1, while the public beta available on iPhones, Macs, and iPads right now is still based on iOS 18.0, iPadOS 18.0, and MacOS Sequoia 15.0. Apple Intelligence Apple Intelligence During the WWDC, one of the major announcements was about the early release of the beta versions of iOS 18 for the reviews and other features. This beta version is only available for developers to test the features and submit their reviews. In very simple terms, Apple Intelligence is available only to developers until the fall event, and after that, it will be available to the whole world. Tech enthusiasts consider it to be beta testing of Apple Intelligence. Apple’s legacy is about providing a best-in-class user experience. Developer reviews and feedback will be necessary to improve user AI enhancements. According to the review, the required actions will be taken. GPT Powered Version of ‘Siri’ Apple is optimizing “Siri” by integrating AI. In simple terms, they are integrating AI into their chatbot, where you can consider ‘Siri’ as an iOS chatbot now integrated with OpenAI’s GPT. In very simple terms, to integrate your app with Siri and Apple Intelligence, you need to provide AppIntent , AppEntity , and AppEnum implementations that work well with Apple Intelligence's pre-trained models. AppIntent AppIntent AppEntity AppEntity AppEnum AppEnum After reviewing Apple’s official documentation , integrating Siri and Apple Intelligence may seem like a complex task. However, any third-party AI chatbot can be integrated with iOS apps using various chatbot libraries and tools. Apple’s official documentation Apple’s official documentation Reason for the Delay While much speculation exists about the delay, the company does not provide a specific reason. However, many reports suggest that the delay is caused by the European Union’s law regarding data privacy and protection . European Union’s law regarding data privacy and protection European Union’s law regarding data privacy and protection Also, some reports suggest that Apple is working on the bugs and minor flaws that came with the AI enhancements. Many assumptions about the launch delay are made, but the company has no concrete statement. Impact of the Delay Among Competitors Apple's AI announcement sparked a race among competitors to build AI features with better user experiences. From Samsung to Nothing, everyone is working to make better AI integrations. While AI enhancements are also booming economically, investors and tech giants are spending huge sums on AI to create a better user experience and make features more convenient. better user experience better user experience It is also opening a new gate of AI features for smartphones and laptops. At the same time, Apple’s competitors are trying to improve their encircling features, where someone has to draw a circle to find out more about something. ‘Nothing’ is tilted towards using AI integration to make the phone more minimalistic yet convenient. Every tech company is running for the title of best AI enhancement features. This is making the market size much better in terms of economics and technology. Conclusion On June 10th, 2024, At WWDC (WorldWide Developers Conference), Apple officially announced its entry into the AI competition in September. The tech giant has named their AI ‘Apple Intelligence’ and promised a better user experience. Apple may have missed the anticipated timeline for AI enhancements. The new anticipation of AI enhancements is around October, with the first update of iOS 18, which will be iOS18.1. Many reports suggest that the delay is caused by the European Union’s laws regarding data privacy and protection. Apple's AI announcement could signal the start of a competition race to build AI features that deliver better user experiences. On June 10th, 2024, At WWDC (WorldWide Developers Conference), Apple officially announced its entry into the AI competition in September. The tech giant has named their AI ‘Apple Intelligence’ and promised a better user experience. Apple may have missed the anticipated timeline for AI enhancements. The new anticipation of AI enhancements is around October, with the first update of iOS 18, which will be iOS18.1. update of iOS 18, Many reports suggest that the delay is caused by the European Union’s laws regarding data privacy and protection. Apple's AI announcement could signal the start of a competition race to build AI features that deliver better user experiences.

Apple's AI Enhancements: Delays, Development, and Developer Access

A mysterious botnet, identified by the user-agent string Mozilla/5.0 (X11; Linux x86_64), is aggressively scraping WordPress websites at an alarming rate, flagrantly disregarding the crawl delay directives set in the  robots.txt file. The botnet has been found to be causing significant server strain, bypassing web administrators' efforts to control access and protect their site resources. Despite the bot's user-agent string mimicking a legitimate Linux browser, its bad behavior is anything but lawful. Not only does it ignore robots.txt instructions, but it also carries out non-stop scraping activities that severely impact the performance and security of affected websites. The unregulated scraping can lead to slowed website loading times and increased bandwidth consumption. IP Addresses and ASN Sources Involved The IP addresses behind this botnet appear to originate from multiple autonomous system numbers (ASNs), primarily hosted by less-reputable service providers. The IPs exhibit coordinated behavior, systematically scraping WordPress websites with no regard for the implemented crawl-delay directives. The ASNs and their affiliated IP addresses are: ASN 13886 - Cloud-South


ASN 21769 - AS-COLOAM


ASN 263735 - Sociedad Buena Hosting, S.A.


ASN 52393 - Corporacion Dana S.A. 185.199.117.126
216.10.7.2
104.233.51.70
104.249.4.61
186.179.1.64
181.177.71.218
185.195.215.202
185.193.73.239
104.239.116.255
185.199.118.137
185.193.72.244
216.10.3.36
186.179.11.6
181.177.70.128
104.239.115.2
186.179.10.14
104.249.5.146
185.199.116.245
104.233.54.62
185.207.97.112
185.199.116.218
181.177.71.59
104.249.0.172
104.233.49.164
185.188.78.129
216.10.2.44
104.233.48.205
216.10.6.143
104.249.1.226
185.196.191.240
67.227.122.211
185.195.221.166
181.177.79.165
186.179.25.11
185.199.117.248
185.195.220.198
104.239.114.195
181.177.66.111
67.227.127.113
185.205.196.57
199.168.122.42
186.179.27.9
185.193.75.105
216.10.3.10
216.10.2.161
185.188.77.24
104.233.48.70
185.188.79.15
186.179.2.186
181.177.72.76
216.10.6.205
186.179.13.13
181.177.78.227
181.177.72.12
181.177.79.141
186.179.25.210
104.233.49.36
104.249.3.157
104.239.117.199
104.233.48.52
104.233.51.106
216.10.3.78
216.10.0.49
185.207.99.161
67.227.120.127
67.227.121.26
104.233.55.225
104.249.3.194
185.195.220.41
181.177.71.84
104.233.48.111
104.249.2.46
181.177.67.170
104.249.2.195
186.179.13.80
67.227.124.74
104.239.116.151
104.239.119.180
185.195.221.180
104.249.2.99
104.239.114.190
104.239.117.112
181.177.71.49
67.227.121.109
199.168.121.93
185.195.223.14
181.177.67.30
181.177.76.63
181.177.77.79
181.177.66.29
181.177.77.197
186.179.24.252
185.196.188.94
181.177.76.62
216.10.7.75
181.177.68.33
186.179.11.165
181.177.71.188
185.195.213.87
185.193.74.97
67.227.122.57
185.196.189.63
216.10.1.142
199.168.122.193
186.179.2.117
181.177.72.134
181.177.66.212
185.188.77.122
185.207.96.121
199.168.121.246
104.249.0.31
185.195.222.160 Potential Mitigation Strategies Web administrators are urged to monitor their logs for suspicious activity involving the Mozilla/5.0 (X11; Linux x86_64) user-agent string and IPs associated with the above ASNs. Immediate actions to consider include: Blocking or rate-limiting the offending IPs through firewall rules.


Implementing CAPTCHA systems for suspicious traffic.


Using bot protection plugins or services such as Cloudflare to prevent excessive scraping. The persistence of this botnet highlights the importance of continually refining web security measures to protect digital assets from unauthorized data scraping and potential attacks. This unknown botnet's activity is a pressing issue for WordPress site owners, and if left unchecked, its presence could lead to server overloads, data leaks, or even downtime for many websites. Interestingly, despite the aggressive nature of this botnet, none of the associated IPs have been flagged or reported in popular databases like AbuseIPDB. Even more surprising, these IPs are not natively blocked by Cloudflare’s managed rules, which typically catch such malicious behavior early on. This suggests that the botnet is currently flying under the radar, operating in a gray zone of anonymity. The lack of detection and reporting raises concerns about how sophisticated and stealthy this botnet may be. For now, it seems to be an entirely unknown entity in the cybersecurity landscape. However, it does seem look like it is scraping WordPress content. A mysterious botnet, identified by the user-agent string Mozilla/5.0 (X11; Linux x86_64) , is aggressively scraping WordPress websites at an alarming rate, flagrantly disregarding the crawl delay directives set in the  robots.txt file. The botnet has been found to be causing significant server strain, bypassing web administrators' efforts to control access and protect their site resources. Mozilla/5.0 (X11; Linux x86_64) Despite the bot's user-agent string mimicking a legitimate Linux browser, its bad behavior is anything but lawful. Not only does it ignore robots.txt instructions, but it also carries out non-stop scraping activities that severely impact the performance and security of affected websites. The unregulated scraping can lead to slowed website loading times and increased bandwidth consumption. robots.txt IP Addresses and ASN Sources Involved The IP addresses behind this botnet appear to originate from multiple autonomous system numbers (ASNs), primarily hosted by less-reputable service providers. The IPs exhibit coordinated behavior, systematically scraping WordPress websites with no regard for the implemented crawl-delay directives. The ASNs and their affiliated IP addresses are: ASN 13886 - Cloud-South ASN 21769 - AS-COLOAM ASN 263735 - Sociedad Buena Hosting, S.A. ASN 52393 - Corporacion Dana S.A. ASN 13886 - Cloud-South ASN 13886 - Cloud-South ASN 13886 - Cloud-South ASN 21769 - AS-COLOAM ASN 21769 - AS-COLOAM ASN 21769 - AS-COLOAM ASN 263735 - Sociedad Buena Hosting, S.A. ASN 263735 - Sociedad Buena Hosting, S.A. ASN 263735 - Sociedad Buena Hosting, S.A. ASN 52393 - Corporacion Dana S.A. ASN 52393 - Corporacion Dana S.A. ASN 52393 - Corporacion Dana S.A. 185.199.117.126
216.10.7.2
104.233.51.70
104.249.4.61
186.179.1.64
181.177.71.218
185.195.215.202
185.193.73.239
104.239.116.255
185.199.118.137
185.193.72.244
216.10.3.36
186.179.11.6
181.177.70.128
104.239.115.2
186.179.10.14
104.249.5.146
185.199.116.245
104.233.54.62
185.207.97.112
185.199.116.218
181.177.71.59
104.249.0.172
104.233.49.164
185.188.78.129
216.10.2.44
104.233.48.205
216.10.6.143
104.249.1.226
185.196.191.240
67.227.122.211
185.195.221.166
181.177.79.165
186.179.25.11
185.199.117.248
185.195.220.198
104.239.114.195
181.177.66.111
67.227.127.113
185.205.196.57
199.168.122.42
186.179.27.9
185.193.75.105
216.10.3.10
216.10.2.161
185.188.77.24
104.233.48.70
185.188.79.15
186.179.2.186
181.177.72.76
216.10.6.205
186.179.13.13
181.177.78.227
181.177.72.12
181.177.79.141
186.179.25.210
104.233.49.36
104.249.3.157
104.239.117.199
104.233.48.52
104.233.51.106
216.10.3.78
216.10.0.49
185.207.99.161
67.227.120.127
67.227.121.26
104.233.55.225
104.249.3.194
185.195.220.41
181.177.71.84
104.233.48.111
104.249.2.46
181.177.67.170
104.249.2.195
186.179.13.80
67.227.124.74
104.239.116.151
104.239.119.180
185.195.221.180
104.249.2.99
104.239.114.190
104.239.117.112
181.177.71.49
67.227.121.109
199.168.121.93
185.195.223.14
181.177.67.30
181.177.76.63
181.177.77.79
181.177.66.29
181.177.77.197
186.179.24.252
185.196.188.94
181.177.76.62
216.10.7.75
181.177.68.33
186.179.11.165
181.177.71.188
185.195.213.87
185.193.74.97
67.227.122.57
185.196.189.63
216.10.1.142
199.168.122.193
186.179.2.117
181.177.72.134
181.177.66.212
185.188.77.122
185.207.96.121
199.168.121.246
104.249.0.31
185.195.222.160 Potential Mitigation Strategies Web administrators are urged to monitor their logs for suspicious activity involving the Mozilla/5.0 (X11; Linux x86_64) user-agent string and IPs associated with the above ASNs. Immediate actions to consider include: Mozilla/5.0 (X11; Linux x86_64) Blocking or rate-limiting the offending IPs through firewall rules. Implementing CAPTCHA systems for suspicious traffic. Using bot protection plugins or services such as Cloudflare to prevent excessive scraping. Blocking or rate-limiting the offending IPs through firewall rules. Blocking or rate-limiting the offending IPs through firewall rules. Implementing CAPTCHA systems for suspicious traffic. Implementing CAPTCHA systems for suspicious traffic. Using bot protection plugins or services such as Cloudflare to prevent excessive scraping. Using bot protection plugins or services such as Cloudflare to prevent excessive scraping. The persistence of this botnet highlights the importance of continually refining web security measures to protect digital assets from unauthorized data scraping and potential attacks. This unknown botnet's activity is a pressing issue for WordPress site owners, and if left unchecked, its presence could lead to server overloads, data leaks, or even downtime for many websites. Interestingly, despite the aggressive nature of this botnet, none of the associated IPs have been flagged or reported in popular databases like AbuseIPDB. Even more surprising, these IPs are not natively blocked by Cloudflare’s managed rules, which typically catch such malicious behavior early on. This suggests that the botnet is currently flying under the radar, operating in a gray zone of anonymity. The lack of detection and reporting raises concerns about how sophisticated and stealthy this botnet may be. For now, it seems to be an entirely unknown entity in the cybersecurity landscape. However, it does seem look like it is scraping WordPress content. unknown entity

Unknown Botnet Using Mozilla/5.0 (X11; Linux x86_ User Agent Ignoring Crawl Delay on WordPress Sites

The Systemic Impact of Deplatforming on Social Media: Acknowledgements, Data, and References

Authors:
(1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally;
(2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk;
(3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Results Discussion & Conclusion Methods Acknowledgements, Data availability, and References METHODS Gettr data The data used for this study has been collected using GoGettr [76], a public client developed by the Stanford Internet Observatory to give access to the Gettr API. This API allows to query user interactions, including the posts they like or share. User profiles were initially collected through a snowball sampling, by using highly popular accounts on the platform as seed users, and using the API to query their follower and following list, before repeating the same process for a random sample of the newly retrieved users. Repeating this process many times ensures that our dataset is near-complete for the studied time period. To attract more users from Twitter, Gettr previously offered a feature that would automatically import a user’s tweets upon creating an account. However, due to Twitter blocking this capability on July 10, 2021[77], Gettr had to discontinue this feature. To ensure the accuracy of our results, any posts imported before July 10, 2021, and any Gettr post whose timestamp precedes the account’s creation date were removed from our dataset. To ensure our case study on the Brazilian rightwing group encompasses the Bras´ılia insurrection, we expanded the data collection time frame for any user associated with the Brazilian community. The data collection was run in July 2022 for every user whose profile we have retrieved, and in January 2023 for the users in the Brazilian cohort. Twitter data For each verified Gettr account where the Gettr API references their Twitter followers in the account metadata, we check that the Twitter account with the same username is active using the Twitter API (see https://developer.twitter.com/en/ products/twitter-api/academic-research). Then, for each active account we download their Twitter timeline including all tweets and retweets in the period July 2021 to May 2022. This totals approximately 12 million tweets. Data was collected between September and October 2022, preceding Elon Musk’s amnesty of suspended Twitter accounts. User labelling Throughout our analysis, we label verified Gettr users as being either “matched” or “banned”, depending on whether their corresponding Twitter account is active or suspended. Any verified user who decided to link their Twitter account on their Gettr page has their Twitter follower count displayed on their profile, which can also be retrieved from the Gettr API. We stress that this selfdeclaration permits cross-platform matching since users can “reasonably expect” that their Gettr accounts will be associated with their Twitter accounts. To match accounts across platforms, we assumed that users picked the same username on both Gettr and Twitter, and we used the Twitter API to retrieve their Twitter activity. A user is classified as “banned” if the Twitter user endpoint returned an error indicating that the account has been suspended. Note that for data privacy reasons, all analysis of users across platforms is aggregated at the cohort level; we do not present results for individual users. Calculating account survival The Kaplan-Meier estimate is a tool used to quantify the survival rate of a population (in our case, users active on a social platform) over time. For each time step t, we measure how many users become indefinitely inactive, and we quantify the survival rate as Topic Modelling Gettr and Twitter content is analysed using the BERTopic topic modelling library [32]. This method extracts latent topics from an ensemble of documents (in our case Gettr posts and Twitter tweets). The base model uses pre-trained transformer-based language models to construct document embeddings which are then clustered. These methods are known to struggle with very short documents which are common on micro-blogging sites. Hence, we train our topic model using exclusively Gettr posts which are longer than 100 characters. To avoid any single user dominating a specific topic, we limit the training set to no more than 50 posts from any given user. Latent ideology To calculate the latent ideology on Gettr, we use the method developed in [36, 37] and filtering procedures from [38]. The method uses a bipartite approach where it classifies Gettr accounts as influencers or regular users. It then generates an ordering of users based on the interaction patterns of regular users with the influencer set. In the current paper, we select the matched and banned user sets as our influencers, and the remaining set of Gettr users as our cohort of regular users. Two factors can conflate the latent ideology: (1) account geography, and (2) a lack of user-influencer interactions. Since we are interested in the segregation of the banned and matched cohorts based on political ideology, the former is problematic because country-specific communities on social media can appear structurally segregated from a related community in other countries, even if they are politically aligned. For this reason, we remove a small number of accounts associated with the UK and China from our set of Gettr influencers. In the latter case, a lack of user-influencer interactions can be problematic since influencers with few user interactions appear as erroneous outliers when computing the latent ideology, often because they do not take active part in the conversation. Hence, we restrict our influencer set to the 500 banned and matched accounts who receive the largest number of interactions from other users on Gettr. In the current study, we consider any interaction type including comments, shares and likes. The latent ideology is robust as long as the number of influencers used is larger than 200 accounts [38]. In order to assess the modality of the ideology distributions, we use Hartigan’s diptest. This approach is used to identify polarized social media conversations and echo-chambers [37, 38]. Hartigan’s diptest compares a test distribution against a matched unimodal distribution to assess distribution modality [39]. The test computes the distance, D, between the cumulative density of the test distribution and the cumulative density of the matched unimodal distribution. The D-statistic is accompanied by a p-value which quantifies whether the test distribution is significantly different to a matched unimodal distribution. A p-value of less than 0.01 indicates a multimodal distribution. Toxicity analysis The toxicity of Gettr and Twitter content is computed using the Google Perspective API [42], which has been used in several social media studies to assess platform toxicity [78–80]. Given a text input, the API returns a score between 0 and 1, indicating how likely a human moderator is to flag the text as being toxic. For our analysis, we used the flagship attribute “toxicity”, which is defined as “[a] rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion” [81]. When computing statistics for the toxicity of each user cohort, we apply a bootstrapping procedure to avoid erroneous results from variable cohort sizes. This is important since the distribution of post toxicity is fat tailed; there are far more posts with low toxicity than high toxicity. Therefore, a smaller set of posts from a user cohort may have a lower median toxicity, purely due to sampling effects. To avoid this conflation, bootstrapping is employed where equally sized samples are drawn from each cohort (usually 100 posts), and the median toxicity is computed for each sample. Then, the sampling procedure is repeated 100 times to compute the median and inter-quartile range for the sampled post toxicity. News media classification using Media Bias / Fact Check For the quote-ratio analysis in Fig. 5, we identify the Twitter handles of news media outlets and classify their political leaning using Media Bias / Fact Check (MBFC; see https://mediabiasfactcheck.com/). Ratings provided by MBFC are largely similar to other reputable media rating datasets [60]. MBFC classify news outlets under seven leaning categories: extreme left, left, center-left, least (media considered unbiased), center-right, right, extreme right. To ensure that we have enough news media outlets to enable the quote-ratio analysis, we group these classifications into four larger groups: Left – (extreme left, left, center-left), Least – (least), Right – (center-right, right), Far-right – (extreme right). Note that we have chosen to use the terminology “far-right” instead of “extremeright” since the former is more common in the academic literature. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Authors: Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Abstract and Introduction Results Results Discussion & Conclusion Discussion & Conclusion Methods Methods Acknowledgements, Data availability, and References Acknowledgements, Data availability, and References METHODS Gettr data The data used for this study has been collected using GoGettr [76], a public client developed by the Stanford Internet Observatory to give access to the Gettr API. This API allows to query user interactions, including the posts they like or share. User profiles were initially collected through a snowball sampling, by using highly popular accounts on the platform as seed users, and using the API to query their follower and following list, before repeating the same process for a random sample of the newly retrieved users. Repeating this process many times ensures that our dataset is near-complete for the studied time period. To attract more users from Twitter, Gettr previously offered a feature that would automatically import a user’s tweets upon creating an account. However, due to Twitter blocking this capability on July 10, 2021[77], Gettr had to discontinue this feature. To ensure the accuracy of our results, any posts imported before July 10, 2021, and any Gettr post whose timestamp precedes the account’s creation date were removed from our dataset. To ensure our case study on the Brazilian rightwing group encompasses the Bras´ılia insurrection, we expanded the data collection time frame for any user associated with the Brazilian community. The data collection was run in July 2022 for every user whose profile we have retrieved, and in January 2023 for the users in the Brazilian cohort. Twitter data For each verified Gettr account where the Gettr API references their Twitter followers in the account metadata, we check that the Twitter account with the same username is active using the Twitter API (see https://developer.twitter.com/en/ products/twitter-api/academic-research). Then, for each active account we download their Twitter timeline including all tweets and retweets in the period July 2021 to May 2022. This totals approximately 12 million tweets. Data was collected between September and October 2022, preceding Elon Musk’s amnesty of suspended Twitter accounts. User labelling Throughout our analysis, we label verified Gettr users as being either “matched” or “banned”, depending on whether their corresponding Twitter account is active or suspended. Any verified user who decided to link their Twitter account on their Gettr page has their Twitter follower count displayed on their profile, which can also be retrieved from the Gettr API. We stress that this selfdeclaration permits cross-platform matching since users can “reasonably expect” that their Gettr accounts will be associated with their Twitter accounts. To match accounts across platforms, we assumed that users picked the same username on both Gettr and Twitter, and we used the Twitter API to retrieve their Twitter activity. A user is classified as “banned” if the Twitter user endpoint returned an error indicating that the account has been suspended. Note that for data privacy reasons, all analysis of users across platforms is aggregated at the cohort level; we do not present results for individual users. Calculating account survival The Kaplan-Meier estimate is a tool used to quantify the survival rate of a population (in our case, users active on a social platform) over time. For each time step t, we measure how many users become indefinitely inactive, and we quantify the survival rate as Topic Modelling Gettr and Twitter content is analysed using the BERTopic topic modelling library [32]. This method extracts latent topics from an ensemble of documents (in our case Gettr posts and Twitter tweets). The base model uses pre-trained transformer-based language models to construct document embeddings which are then clustered. These methods are known to struggle with very short documents which are common on micro-blogging sites. Hence, we train our topic model using exclusively Gettr posts which are longer than 100 characters. To avoid any single user dominating a specific topic, we limit the training set to no more than 50 posts from any given user. Latent ideology To calculate the latent ideology on Gettr, we use the method developed in [36, 37] and filtering procedures from [38]. The method uses a bipartite approach where it classifies Gettr accounts as influencers or regular users. It then generates an ordering of users based on the interaction patterns of regular users with the influencer set. In the current paper, we select the matched and banned user sets as our influencers, and the remaining set of Gettr users as our cohort of regular users. Two factors can conflate the latent ideology: (1) account geography, and (2) a lack of user-influencer interactions. Since we are interested in the segregation of the banned and matched cohorts based on political ideology, the former is problematic because country-specific communities on social media can appear structurally segregated from a related community in other countries, even if they are politically aligned. For this reason, we remove a small number of accounts associated with the UK and China from our set of Gettr influencers. In the latter case, a lack of user-influencer interactions can be problematic since influencers with few user interactions appear as erroneous outliers when computing the latent ideology, often because they do not take active part in the conversation. Hence, we restrict our influencer set to the 500 banned and matched accounts who receive the largest number of interactions from other users on Gettr. In the current study, we consider any interaction type including comments, shares and likes. The latent ideology is robust as long as the number of influencers used is larger than 200 accounts [38]. In order to assess the modality of the ideology distributions, we use Hartigan’s diptest. This approach is used to identify polarized social media conversations and echo-chambers [37, 38]. Hartigan’s diptest compares a test distribution against a matched unimodal distribution to assess distribution modality [39]. The test computes the distance, D, between the cumulative density of the test distribution and the cumulative density of the matched unimodal distribution. The D-statistic is accompanied by a p-value which quantifies whether the test distribution is significantly different to a matched unimodal distribution. A p-value of less than 0.01 indicates a multimodal distribution. Toxicity analysis The toxicity of Gettr and Twitter content is computed using the Google Perspective API [42], which has been used in several social media studies to assess platform toxicity [78–80]. Given a text input, the API returns a score between 0 and 1, indicating how likely a human moderator is to flag the text as being toxic. For our analysis, we used the flagship attribute “toxicity”, which is defined as “[a] rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion” [81]. When computing statistics for the toxicity of each user cohort, we apply a bootstrapping procedure to avoid erroneous results from variable cohort sizes. This is important since the distribution of post toxicity is fat tailed; there are far more posts with low toxicity than high toxicity. Therefore, a smaller set of posts from a user cohort may have a lower median toxicity, purely due to sampling effects. To avoid this conflation, bootstrapping is employed where equally sized samples are drawn from each cohort (usually 100 posts), and the median toxicity is computed for each sample. Then, the sampling procedure is repeated 100 times to compute the median and inter-quartile range for the sampled post toxicity. News media classification using Media Bias / Fact Check For the quote-ratio analysis in Fig. 5, we identify the Twitter handles of news media outlets and classify their political leaning using Media Bias / Fact Check (MBFC; see https://mediabiasfactcheck.com/). Ratings provided by MBFC are largely similar to other reputable media rating datasets [60]. MBFC classify news outlets under seven leaning categories: extreme left, left, center-left, least (media considered unbiased), center-right, right, extreme right. To ensure that we have enough news media outlets to enable the quote-ratio analysis, we group these classifications into four larger groups: Left – (extreme left, left, center-left), Least – (least), Right – (center-right, right), Far-right – (extreme right). Note that we have chosen to use the terminology “far-right” instead of “extremeright” since the former is more common in the academic literature. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Systemic Impact of Deplatforming on Social Media: Methods

Authors:
(1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally;
(2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk;
(3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Results Discussion & Conclusion Methods Acknowledgements, Data availability, and References DISCUSSION & CONCLUSION In this paper, we have analysed self-declared user-level matched data to study the ban-induced platform migration from Twitter to Gettr. First, we showed that the banned cohort of users deplatformed from Twitter are more active on Gettr, and have higher platform retention, than the matched cohort who remain active on Twitter. Second, we revealed that Gettr content primarily discusses themes relevant to the US political right. Topics overepresented on Gettr are known to have resulted in account suspensions on Twitter. Third, we showed that the matched and banned cohorts share the same politically homogeneous Gettr audience. Fourth, we found that matched users are more toxic on Twitter than they are on Gettr, and that these toxic tweets often directly mention political opponents. Finally, we highlighted Gettr’s broader societal impact, revealing significant structural changes in the Gettr interaction network in the run-up to the Bras´ılia insurrections. The fact that the banned and matched cohorts appear similar in every regard, apart from their activity and retention on Gettr, is evidence of the systemic impact of deplatforming. Fringe platforms offer a safe haven where deplatformed users are free to capitalise on their supporters following suspension from Twitter. However, in this politically homogeneous environment, users are essentially confined to an ideological “echo-platform” [43, 44] where they cannot engage and confront their political opponents. Our results hint that this ability to interact with opponents may be part of Twitter’s appeal for farright social media users, although more work is needed to fully clarify this observation. When users are banned from mainstream platforms, they become wholly dependent on fringe alternatives. This poses a societal risk since fringe platforms can facilitate the emergence of radical narratives and the spread of hate speech [9, 20, 70–72]. A lack of monitoring can, therefore, mean that signs of collective upheaval are missed. The Bras´ılia insurrection is a clear example of this. Critically, these considerations are not only relevant for mainstream platforms, whose policies have received the most scrutiny, but for the whole social media ecosystem. Despite branding itself as a free speech platform, Gettr has also deplatformed users for posting radical content, most notably the white supremacist Nicholas Fuentes [73]. It is important to contextualise the scope of our findings, whose limitations are avenues for future work. First, the current study only considers the migration from Twitter to Gettr, since users of other platforms do not declare their Twitter use as standard. If data becomes available, future work should extend scrutiny to other platforms. Second, the Gettr matching feature only applies to verified users, a subset of the users who migrated from Twitter to Gettr. Analysing non-verified users migrating across platforms would clarify the differential impact of deplatforming on content creators as opposed to consumers. Finally, we cannot study tweets from the banned cohort since this data is not publicly available. Analysing this content would explain why some users are deplatformed, but others are not. Overall, our study highlights how Gettr struggles to compete with Twitter when users have free choice to use either platform. However, the decision by Twitter to deplatform a user impacts how those users view Gettr as an alternative. We anticipate that future work will build on these observations and speculate that other fringe platforms will likely show a similar dependence on their mainstream competitors. This work is urgently needed given the risks posed to democracy by poorly regulated social media [74, 75]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Authors: Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Abstract and Introduction Results Results Discussion & Conclusion Discussion & Conclusion Methods Methods Acknowledgements, Data availability, and References Acknowledgements, Data availability, and References DISCUSSION & CONCLUSION In this paper, we have analysed self-declared user-level matched data to study the ban-induced platform migration from Twitter to Gettr. First, we showed that the banned cohort of users deplatformed from Twitter are more active on Gettr, and have higher platform retention, than the matched cohort who remain active on Twitter. Second, we revealed that Gettr content primarily discusses themes relevant to the US political right. Topics overepresented on Gettr are known to have resulted in account suspensions on Twitter. Third, we showed that the matched and banned cohorts share the same politically homogeneous Gettr audience. Fourth, we found that matched users are more toxic on Twitter than they are on Gettr, and that these toxic tweets often directly mention political opponents. Finally, we highlighted Gettr’s broader societal impact, revealing significant structural changes in the Gettr interaction network in the run-up to the Bras´ılia insurrections. The fact that the banned and matched cohorts appear similar in every regard, apart from their activity and retention on Gettr, is evidence of the systemic impact of deplatforming. Fringe platforms offer a safe haven where deplatformed users are free to capitalise on their supporters following suspension from Twitter. However, in this politically homogeneous environment, users are essentially confined to an ideological “echo-platform” [43, 44] where they cannot engage and confront their political opponents. Our results hint that this ability to interact with opponents may be part of Twitter’s appeal for farright social media users, although more work is needed to fully clarify this observation. When users are banned from mainstream platforms, they become wholly dependent on fringe alternatives. This poses a societal risk since fringe platforms can facilitate the emergence of radical narratives and the spread of hate speech [9, 20, 70–72]. A lack of monitoring can, therefore, mean that signs of collective upheaval are missed. The Bras´ılia insurrection is a clear example of this. Critically, these considerations are not only relevant for mainstream platforms, whose policies have received the most scrutiny, but for the whole social media ecosystem. Despite branding itself as a free speech platform, Gettr has also deplatformed users for posting radical content, most notably the white supremacist Nicholas Fuentes [73]. It is important to contextualise the scope of our findings, whose limitations are avenues for future work. First, the current study only considers the migration from Twitter to Gettr, since users of other platforms do not declare their Twitter use as standard. If data becomes available, future work should extend scrutiny to other platforms. Second, the Gettr matching feature only applies to verified users, a subset of the users who migrated from Twitter to Gettr. Analysing non-verified users migrating across platforms would clarify the differential impact of deplatforming on content creators as opposed to consumers. Finally, we cannot study tweets from the banned cohort since this data is not publicly available. Analysing this content would explain why some users are deplatformed, but others are not. Overall, our study highlights how Gettr struggles to compete with Twitter when users have free choice to use either platform. However, the decision by Twitter to deplatform a user impacts how those users view Gettr as an alternative. We anticipate that future work will build on these observations and speculate that other fringe platforms will likely show a similar dependence on their mainstream competitors. This work is urgently needed given the risks posed to democracy by poorly regulated social media [74, 75]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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