Integritee provides a platform to enable collaboration between businesses on sensitive data while giving all parties control and transparency about what their data is used for.

Integritee

integritee

Redefining AI Data Provenance In the fast changing field of artificial intelligence and decentralised networks, a new actor has emerged: Rivalz. This revolutionary platform is more than simply a technology development; it is a leader in AI data provenance. Imagine a world in which each bit of data has a clear and verifiable past, similar to a well-documented pedigree. This is the world Rivalz is building with its AI-powered DePIN RollApp, which uses cutting-edge Dymension technology. Rivalz's native token, $RIZ, allows transactions, powers node infrastructure, and enables data monetization, lays the groundwork for a powerful AI-app ecosystem. The technology is intended to serve as a foundation for the creation of an ideal AI data market, with an emphasis on assuring data authenticity and integrity—key qualities that are sometimes difficult to achieve in digital settings. The Team and Vision Rivalz is guided by individuals that have strong roots in artificial intelligence, data management, and Web3 technologies. The team that guides Rivalz consists of approximately thirty professionals. They have been at the forefront of developing solutions that combine these sectors since the year 2019, with the intention of bringing about a new age of data-driven artificial intelligence applications. The initiative is bolstered by the assistance of well-known organisations in the industry, such as Magnus Capital and Delphi Ventures, amongst others. This provides an additional layer of legitimacy and financial support, which promises considerable growth and innovation. Key Components of Rivalz - RX - Rivalz Platform: Here, users can create AI Personas, essentially digital identities that can interact within the AI ecosystem, expanding into roles such as AI NPCs (Non-Player Characters), AI Companions, and AI Agents. - RECO - Rivalz Ecosystem Collaboration: This segment focuses on fostering collaborations across various fields such as DID (Digital Identity), GameFi (Gaming Finance), and SocialFi (Social Finance), creating a network of partners leveraging the Rivalz infrastructure. - RNET - Rivalz Network: Dedicated to enhancing neural network infrastructure, RNET supports the development and operation of modular AI applications and agents. At its core, the Rivalz network relies on rNodes and Validator zNodes, essential for maintaining network integrity and efficiency. These nodes are analogous to the foundations of a building, crucial for the stability and functionality of the entire structure. Why Follow Rivalz? A window into the future of artificial intelligence and blockchain integration is provided by Rivalz, which is accessible to enthusiasts, developers, and casual spectators alike. Because of the ever-increasing complexity of the digital world, platforms such as Rivalz are absolutely necessary in order to guarantee the dependability and safety of the data that is used to power artificial intelligence applications. This dedication to the provenance of data is what distinguishes Rivalz as a pioneer in the field of artificial intelligence technologies for the future. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Redefining AI Data Provenance In the fast changing field of artificial intelligence and decentralised networks, a new actor has emerged: Rivalz. This revolutionary platform is more than simply a technology development; it is a leader in AI data provenance. Imagine a world in which each bit of data has a clear and verifiable past, similar to a well-documented pedigree. This is the world Rivalz is building with its AI-powered DePIN RollApp, which uses cutting-edge Dymension technology. Rivalz's native token, $RIZ, allows transactions, powers node infrastructure, and enables data monetization, lays the groundwork for a powerful AI-app ecosystem. The technology is intended to serve as a foundation for the creation of an ideal AI data market, with an emphasis on assuring data authenticity and integrity—key qualities that are sometimes difficult to achieve in digital settings. The Team and Vision The Team and Vision Rivalz is guided by individuals that have strong roots in artificial intelligence, data management, and Web3 technologies. The team that guides Rivalz consists of approximately thirty professionals. They have been at the forefront of developing solutions that combine these sectors since the year 2019, with the intention of bringing about a new age of data-driven artificial intelligence applications. The initiative is bolstered by the assistance of well-known organisations in the industry, such as Magnus Capital and Delphi Ventures, amongst others. This provides an additional layer of legitimacy and financial support, which promises considerable growth and innovation. Key Components of Rivalz Key Components of Rivalz - RX - Rivalz Platform : Here, users can create AI Personas, essentially digital identities that can interact within the AI ecosystem, expanding into roles such as AI NPCs (Non-Player Characters), AI Companions, and AI Agents. RX - Rivalz Platform - RECO - Rivalz Ecosystem Collaboration : This segment focuses on fostering collaborations across various fields such as DID (Digital Identity), GameFi (Gaming Finance), and SocialFi (Social Finance), creating a network of partners leveraging the Rivalz infrastructure. RECO - Rivalz Ecosystem Collaboration - RNET - Rivalz Network : Dedicated to enhancing neural network infrastructure, RNET supports the development and operation of modular AI applications and agents. RNET - Rivalz Network At its core, the Rivalz network relies on rNodes and Validator zNodes, essential for maintaining network integrity and efficiency. These nodes are analogous to the foundations of a building, crucial for the stability and functionality of the entire structure. Why Follow Rivalz? Why Follow Rivalz? A window into the future of artificial intelligence and blockchain integration is provided by Rivalz, which is accessible to enthusiasts, developers, and casual spectators alike. Because of the ever-increasing complexity of the digital world, platforms such as Rivalz are absolutely necessary in order to guarantee the dependability and safety of the data that is used to power artificial intelligence applications. This dedication to the provenance of data is what distinguishes Rivalz as a pioneer in the field of artificial intelligence technologies for the future. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Unlock the Secrets of AI: How Rivalz Is Transforming Data Integrity Forever!

এআই-এর গোপনীয়তা আনলক করুন: কীভাবে রিভালজ ডেটা অখণ্ডতাকে চিরতরে রূপান্তরিত করছে!

Lüfte die Geheimnisse der KI: Wie Rivalz die Datenintegrität für immer verändert!

Descubra los secretos de la IA: ¡Cómo Rivalz está transformando la integridad de los datos para siempre!

Découvrez les secrets de l'IA : comment Rivalz transforme l'intégrité des données pour toujours !

एआई के रहस्यों को अनलॉक करें: कैसे रिवाल्ज़ डेटा अखंडता को हमेशा के लिए बदल रहा है!

AI の秘密を解き明かす: Rivalz がデータ整合性を永遠に変革する方法!

AI의 비밀을 풀어보세요: Rivalz가 데이터 무결성을 영원히 변화시키는 방법!

Desvende os segredos da IA: como a Rivalz está transformando a integridade dos dados para sempre!

Раскройте секреты искусственного интеллекта: как Rivalz навсегда меняет целостность данных!

Yapay Zekanın Sırlarını Açığa Çıkarın: Rivalz, Veri Bütünlüğünü Sonsuza Kadar Nasıl Dönüştürüyor?

Mở khóa bí mật của AI: Rivalz đang biến đổi tính toàn vẹn dữ liệu mãi mãi như thế nào!

解锁 AI 的秘密：Rivalz 如何永远改变数据完整性！

Supra, a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers. Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Supra , a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Supra GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. ApeChain ApeChain Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an Supra Supra vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers . Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. in-depth manuals in-depth manuals technical whitepapers technical whitepapers Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Supra Integrates First Ever Oracle Data Feed and Verifiable Randomness on ApeChain

ApeChain-এ সুপ্রা প্রথম ওরাকল ডেটা ফিড এবং যাচাইযোগ্য এলোমেলোতা একীভূত করে

Supra integriert den ersten Oracle-Datenfeed und verifizierbare Zufälligkeit auf ApeChain

Supra integra la primera fuente de datos de Oracle y aleatoriedad verificable en ApeChain

Supra intègre le tout premier flux de données Oracle et le caractère aléatoire vérifiable sur ApeChain

सुप्रा ने एपचेन पर पहली बार ऑरेकल डेटा फीड और सत्यापन योग्य रैंडमनेस को एकीकृत किया

Supra、ApeChainで初のOracleデータフィードと検証可能なランダム性を統合

Supra는 ApeChain에 최초의 Oracle 데이터 피드와 검증 가능한 무작위성을 통합합니다.

Supra integra o primeiro feed de dados Oracle e aleatoriedade verificável no ApeChain

Supra интегрирует первый в мире поток данных Oracle и проверяемую случайность в ApeChain

Supra, ApeChain'e İlk Oracle Veri Beslemesini ve Doğrulanabilir Rastgeleliği Entegre Ediyor

Supra tích hợp nguồn cấp dữ liệu Oracle đầu tiên và tính ngẫu nhiên có thể xác minh được trên ApeChain

Supra 在 ApeChain 上集成首个 Oracle 数据源和可验证随机性

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 24 of 38. Line of Effort 7: Defend Information Integrity Information integrity challenges are not new, but determined foreign state adversaries and rapid technological advances, especially AI-enabled human-machine interactions, create complex dynamics that compound information risks by enabling rapid, large-scale, and targeted dissemination of AI-enabled synthetic content. Building a resilient information environment—one in which there is open, free public debate and consistent access to diverse sources of fact-based information—is an ongoing priority for the United States and its allies and partners. These features are essential for citizens to inform their opinions and exercise their human rights, including freedom of expression, freedom of peaceful assembly and association, and the right to vote. Information manipulation is destabilizing and can harm national security, democratic processes, economic welfare, the environment, crisis response, human rights, and public health. While foreign actors seeking to interfere with or manipulate the information environment pose significant risks, there are additional challenges open societies face around the quality of information online and deteriorating trust. With allies and partners, the Department of State will continue to work to build civic information resilience, counter foreign state and non-state extremist propaganda online, and mitigate risks of AI to information integrity while protecting freedom of expression. The U.S. Government will work to protect the integrity of elections and other democratic processes across the globe. At the TTC, OECD, and G7, the United States develops shared approaches to building healthy and resilient information ecosystems. The United States and France are co-chairing the DIS/MIS Information Resource Hub, the OECD’s leading information integrity initiative. At the Hub, the Department of State is focused on increasing cooperation around sharing of best practices and strengthening information resilience, both among OECD and non-OECD countries, and developing a framework to guide whole-of-society efforts in this area. Through the Promoting Information Integrity and Resilience Initiative (Pro-Info), USAID aims to bolster healthy information ecosystems and help address information manipulation through multi-stakeholder engagement, donor coordination, and capacity building efforts. At the third Summit for Democracy in 2024, the United States launched a democratic roadmap for building civic resilience to global digital manipulation that highlights the importance of the digital information manipulation challenge as a threat to the functionality and vitality of society; recognizes that building information integrity can be consistent with freedom of opinion and expression; reinforces private sector digital platforms’ ability to strengthen civic resilience; and prioritizes efforts to address generative AI (GAI)—particularly in the context of global 2024 elections. The United States has also endorsed the Global Declaration on Information Integrity Online, launched by Canada and the Netherlands. The Declaration, grounded in international human rights law, establishes high-level international commitments by participating states to protect and promote information integrity online. In addition, the Department of State has announced a Framework to Counter Foreign State Information Manipulation. This Framework seeks to develop a common understanding of the threat and establish a common set of action areas from which the United States, with its allies and partners, can develop coordinated responses to foreign information manipulation and protect free and open societies. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 24 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 24 of 38. here Line of Effort 7: Defend Information Integrity Information integrity challenges are not new, but determined foreign state adversaries and rapid technological advances, especially AI-enabled human-machine interactions, create complex dynamics that compound information risks by enabling rapid, large-scale, and targeted dissemination of AI-enabled synthetic content. Building a resilient information environment—one in which there is open, free public debate and consistent access to diverse sources of fact-based information—is an ongoing priority for the United States and its allies and partners. These features are essential for citizens to inform their opinions and exercise their human rights, including freedom of expression, freedom of peaceful assembly and association, and the right to vote. Information manipulation is destabilizing and can harm national security, democratic processes, economic welfare, the environment, crisis response, human rights, and public health. While foreign actors seeking to interfere with or manipulate the information environment pose significant risks, there are additional challenges open societies face around the quality of information online and deteriorating trust. With allies and partners, the Department of State will continue to work to build civic information resilience, counter foreign state and non-state extremist propaganda online, and mitigate risks of AI to information integrity while protecting freedom of expression. The U.S. Government will work to protect the integrity of elections and other democratic processes across the globe. At the TTC, OECD, and G7, the United States develops shared approaches to building healthy and resilient information ecosystems. The United States and France are co-chairing the DIS/MIS Information Resource Hub, the OECD’s leading information integrity initiative. At the Hub, the Department of State is focused on increasing cooperation around sharing of best practices and strengthening information resilience, both among OECD and non-OECD countries, and developing a framework to guide whole-of-society efforts in this area. Through the Promoting Information Integrity and Resilience Initiative (Pro-Info), USAID aims to bolster healthy information ecosystems and help address information manipulation through multi-stakeholder engagement, donor coordination, and capacity building efforts. At the third Summit for Democracy in 2024, the United States launched a democratic roadmap for building civic resilience to global digital manipulation that highlights the importance of the digital information manipulation challenge as a threat to the functionality and vitality of society; recognizes that building information integrity can be consistent with freedom of opinion and expression; reinforces private sector digital platforms’ ability to strengthen civic resilience; and prioritizes efforts to address generative AI (GAI)—particularly in the context of global 2024 elections. The United States has also endorsed the Global Declaration on Information Integrity Online, launched by Canada and the Netherlands. The Declaration, grounded in international human rights law, establishes high-level international commitments by participating states to protect and promote information integrity online. In addition, the Department of State has announced a Framework to Counter Foreign State Information Manipulation. This Framework seeks to develop a common understanding of the threat and establish a common set of action areas from which the United States, with its allies and partners, can develop coordinated responses to foreign information manipulation and protect free and open societies. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

U.S. and Allies Launch Efforts to Defend Information Integrity Against AI-Enabled Threats

Decentralized finance (DeFi) continues innovating, with platforms integrating popular messaging apps like Telegram. This convergence brings financial services directly to users, tapping into Telegram's extensive user base and intuitive interface. Projects such as Storm Trade, Telegram Wallet, Ston.Fi and Dedust pioneer this approach, offering trading, wallet, and exchange services within the Telegram ecosystem. As traditional finance and decentralized solutions intersect, Telegram-integrated DeFi platforms simplify complex financial services, potentially driving mass adoption. Storm Trade, a decentralized derivatives trading platform built on the TON blockchain, has been making waves in the Telegram-integrated DeFi space. With a 24-hour trading volume of $8,749,409 and a total value locked (TVL) of $11,198,846. Storm Trade aims to attract the next 10 million DeFi users by offering leverage trading up to 50x directly through the Telegram messaging app. In the coming months, Storm Trade is set to launch its native token, $STORM. Holders of the platform's Market Makers NFT will have the first opportunity to purchase the token at a fixed price. As Storm Trade prepares for this token launch, we had the chance to sit down with Denis Vasin, Co-Founder & Lead Dev of Storm Trade, to discuss the project's vision, the role of Telegram integration, and the challenges of operating in the rapidly evolving DeFi space. Can you tell us about the unique features of Storm Trade that set it apart from other derivatives trading platforms? In fact, Storm Trade is the first derivative DEX right on Telegram. By DEX I don't mean 2 buttons and a chart, I'm talking about a serious tool along with a mode for professional traders. Storm Trade is a social-fi solution that aims to make trading not only convenient but also not boring, adding advanced mechanics in the form of trading tournaments, squads, NFT collections, while adding professional tools in the form of cross-margin, copy-trading and much more. How does the direct integration with Telegram benefit users and contribute to the growth of Storm Trade? Telegram means almost limitless possibilities for all messenger users. This helps Storm Trade go down the path of social-fi trading by adding different competition mechanics based on Telegram channels and chats, increasing brand awareness in various Telegram app centres and reducing the execution time of trade ideas for traders with their own channels. In addition to 900m potential users, it is an opportunity to be embedded in advertising integrations with preset bidding parameters. Here's how it works. For example, the author of a Telegram channel assumes that the market will go up and opens a LONG position in BTC pair with a certain stop loss and take profit. Usually, in order for someone to repeat such an entry, they need to register on the exchange, deposit funds there, find the right pair, in general, do a lot of things. Deep integration of Storm Trade in Telegram implies that having created a position, the author of the channel will be able to share it with already set parameters in his/her Telegram channel. And those who want to repeat the same entry will only be able to follow the link and enter the amount of collateral they are ready to trade with. Storm Trade - a real breakthrough in futures trading technology. The $STORM token is launching soon. Can you share more details about the token's utility and how it fits into the Storm Trade ecosystem? Launching a token is one of the most important events for every product. It is often the case that the token exists separately from the product without making a significant contribution to the ecosystem. For STORM, this approach doesn't seem close. STORM is the heart of the ecosystem, integrated into the exchange directly, and helping to increase the efficiency of the protocol economy. STORM has two very powerful utilities: staking, whose profit is made up of the protocol's fees, and a liquidity buffer, which will maintain a good APR for liquidity providers. You mentioned that only a small percentage of the total token supply will be available for sale. Can you explain the reasoning behind this decision? There are several reasons for 4% allocation to IDO. Unlike memecoins, which traditionally allocate a large volume of token for sale, STORM attracts institutional funding and does not need overfunding from retail. How do the Market Makers NFTs play a role in the token launch, and what advantages do NFT holders have? First, you have to understand what Market Makers NFT is. Market Makers is a unique, hand-drawn NFT community collection designed to be embedded in the social-fi mechanics, rewarding the best traders and activists on the platform. Ownership of NFT will provide the owner with a guaranteed spot on the whitelist for token purchases, and in the future will add discounts on trading commissions, avatars for the user profile, and many other uses. Since all NFT holders have access to the whitelist at the same time, this will serve as a great opportunity to buy STORM tokens the very first at a fixed starting price. Each NFT will have a certain allocation to the purchase amount, and the more NFTs you have in your wallet, the higher the allocation to IDO will be. After IDO, collection opportunities will only increase from reduced trading fees to VIP community membership. As a derivatives trading platform, how does Storm Trade ensure the safety and security of users' assets? It is worth remembering that Storm Trade is a DEX, where all internal work is built on smart contracts. All smart contracts are audited by several trusted organisations. Since building smart contracts on TON is quite complex, it is not easy to find auditors. Our first audit is from the TON Tech campaign. Additional audits are planned for the future. Can you elaborate on the staking mechanism and how it rewards token holders who support the platform? The staking mechanism will be quite simple. The stakeholder will have two options: to put liquidity on the Ston.Fi and introduce tokens to the market, or lock tokens in staking on Storm Trade and withdraw tokens from the market. The mechanisms will balance each other, creating a deflationary token economy. Storm Trade has partnerships with notable organizations like Gotbit, Cointelegraph, and ToncoinFund. How do these partnerships contribute to the project's development and success? All of the above partnerships help us develop in one area or another. Cointelegraph is aimed at media support, while Gotbit and ToncoinFund provide us with investment and advisory support. What are the long-term goals for Storm Trade, and how do you envision the platform evolving in the future? Storm Trade's long-term goals are to build the most exciting platform in Telegram, bringing together both experienced traders and beginners, both those who came for the spirit of competition and those who came for passive earnings. Storm Trade aims to become the most balanced place for trading with easy onboarding and amazing Social-fi products. Decentralized finance ( DeFi ) continues innovating, with platforms integrating popular messaging apps like Telegram . This convergence brings financial services directly to users, tapping into Telegram's extensive user base and intuitive interface. Projects such as Storm Trade, Telegram Wallet, Ston.Fi and Dedust pioneer this approach, offering trading, wallet, and exchange services within the Telegram ecosystem. As traditional finance and decentralized solutions intersect, Telegram-integrated DeFi platforms simplify complex financial services, potentially driving mass adoption. DeFi Telegram Storm Trade , a decentralized derivatives trading platform built on the TON blockchain, has been making waves in the Telegram-integrated DeFi space. With a 24-hour trading volume of $8,749,409 and a total value locked (TVL) of $11,198,846. Storm Trade aims to attract the next 10 million DeFi users by offering leverage trading up to 50x directly through the Telegram messaging app. Storm Trade Storm Trade In the coming months, Storm Trade is set to launch its native token, $STORM. Holders of the platform's Market Makers NFT will have the first opportunity to purchase the token at a fixed price. As Storm Trade prepares for this token launch, we had the chance to sit down with Denis Vasin, Co-Founder & Lead Dev of Storm Trade, to discuss the project's vision, the role of Telegram integration, and the challenges of operating in the rapidly evolving DeFi space. Can you tell us about the unique features of Storm Trade that set it apart from other derivatives trading platforms? Can you tell us about the unique features of Storm Trade that set it apart from other derivatives trading platforms? In fact, Storm Trade is the first derivative DEX right on Telegram. By DEX I don't mean 2 buttons and a chart, I'm talking about a serious tool along with a mode for professional traders. Storm Trade is a social-fi solution that aims to make trading not only convenient but also not boring, adding advanced mechanics in the form of trading tournaments, squads, NFT collections, while adding professional tools in the form of cross-margin, copy-trading and much more. How does the direct integration with Telegram benefit users and contribute to the growth of Storm Trade? How does the direct integration with Telegram benefit users and contribute to the growth of Storm Trade? Telegram means almost limitless possibilities for all messenger users. This helps Storm Trade go down the path of social-fi trading by adding different competition mechanics based on Telegram channels and chats, increasing brand awareness in various Telegram app centres and reducing the execution time of trade ideas for traders with their own channels. Telegram means almost limitless possibilities for all messenger users. This helps Storm Trade go down the path of social-fi trading by adding different competition mechanics based on Telegram channels and chats, increasing brand awareness in various Telegram app centres and reducing the execution time of trade ideas for traders with their own channels. In addition to 900m potential users, it is an opportunity to be embedded in advertising integrations with preset bidding parameters. In addition to 900m potential users, it is an opportunity to be embedded in advertising integrations with preset bidding parameters. Here's how it works. For example, the author of a Telegram channel assumes that the market will go up and opens a LONG position in BTC pair with a certain stop loss and take profit. Here's how it works. For example, the author of a Telegram channel assumes that the market will go up and opens a LONG position in BTC pair with a certain stop loss and take profit. Usually, in order for someone to repeat such an entry, they need to register on the exchange, deposit funds there, find the right pair, in general, do a lot of things. Usually, in order for someone to repeat such an entry, they need to register on the exchange, deposit funds there, find the right pair, in general, do a lot of things. Deep integration of Storm Trade in Telegram implies that having created a position, the author of the channel will be able to share it with already set parameters in his/her Telegram channel. And those who want to repeat the same entry will only be able to follow the link and enter the amount of collateral they are ready to trade with. Deep integration of Storm Trade in Telegram implies that having created a position, the author of the channel will be able to share it with already set parameters in his/her Telegram channel. And those who want to repeat the same entry will only be able to follow the link and enter the amount of collateral they are ready to trade with. Storm Trade - a real breakthrough in futures trading technology. Storm Trade - a real breakthrough in futures trading technology. The $STORM token is launching soon. Can you share more details about the token's utility and how it fits into the Storm Trade ecosystem? The $STORM token is launching soon. Can you share more details about the token's utility and how it fits into the Storm Trade ecosystem? Launching a token is one of the most important events for every product. It is often the case that the token exists separately from the product without making a significant contribution to the ecosystem. For STORM, this approach doesn't seem close. STORM is the heart of the ecosystem, integrated into the exchange directly, and helping to increase the efficiency of the protocol economy. STORM has two very powerful utilities: staking, whose profit is made up of the protocol's fees, and a liquidity buffer, which will maintain a good APR for liquidity providers. You mentioned that only a small percentage of the total token supply will be available for sale. Can you explain the reasoning behind this decision? You mentioned that only a small percentage of the total token supply will be available for sale. Can you explain the reasoning behind this decision? There are several reasons for 4% allocation to IDO. Unlike memecoins, which traditionally allocate a large volume of token for sale, STORM attracts institutional funding and does not need overfunding from retail. How do the Market Makers NFTs play a role in the token launch, and what advantages do NFT holders have? How do the Market Makers NFTs play a role in the token launch, and what advantages do NFT holders have? First, you have to understand what Market Makers NFT is. Market Makers is a unique, hand-drawn NFT community collection designed to be embedded in the social-fi mechanics, rewarding the best traders and activists on the platform. Ownership of NFT will provide the owner with a guaranteed spot on the whitelist for token purchases, and in the future will add discounts on trading commissions, avatars for the user profile, and many other uses. Since all NFT holders have access to the whitelist at the same time, this will serve as a great opportunity to buy STORM tokens the very first at a fixed starting price. Each NFT will have a certain allocation to the purchase amount, and the more NFTs you have in your wallet, the higher the allocation to IDO will be. After IDO, collection opportunities will only increase from reduced trading fees to VIP community membership. As a derivatives trading platform, how does Storm Trade ensure the safety and security of users' assets? As a derivatives trading platform, how does Storm Trade ensure the safety and security of users' assets? It is worth remembering that Storm Trade is a DEX, where all internal work is built on smart contracts. All smart contracts are audited by several trusted organisations. Since building smart contracts on TON is quite complex, it is not easy to find auditors. Our first audit is from the TON Tech campaign. Additional audits are planned for the future. Can you elaborate on the staking mechanism and how it rewards token holders who support the platform? Can you elaborate on the staking mechanism and how it rewards token holders who support the platform? The staking mechanism will be quite simple. The stakeholder will have two options: to put liquidity on the Ston.Fi and introduce tokens to the market, or lock tokens in staking on Storm Trade and withdraw tokens from the market. The mechanisms will balance each other, creating a deflationary token economy. Storm Trade has partnerships with notable organizations like Gotbit, Cointelegraph, and ToncoinFund. How do these partnerships contribute to the project's development and success? Storm Trade has partnerships with notable organizations like Gotbit, Cointelegraph, and ToncoinFund. How do these partnerships contribute to the project's development and success? All of the above partnerships help us develop in one area or another. Cointelegraph is aimed at media support, while Gotbit and ToncoinFund provide us with investment and advisory support. What are the long-term goals for Storm Trade, and how do you envision the platform evolving in the future? What are the long-term goals for Storm Trade, and how do you envision the platform evolving in the future? Storm Trade's long-term goals are to build the most exciting platform in Telegram, bringing together both experienced traders and beginners, both those who came for the spirit of competition and those who came for passive earnings. Storm Trade aims to become the most balanced place for trading with easy onboarding and amazing Social-fi products.

Pioneering Telegram-Integrated DeFi: An Interview with Storm Trade Co-Founder Denis Vasin

অগ্রগামী টেলিগ্রাম-ইন্টিগ্রেটেড ডিফাই: স্টর্ম ট্রেডের সহ-প্রতিষ্ঠাতা ডেনিস ভ্যাসিনের সাথে একটি সাক্ষাত্কার

Pionierarbeit für Telegram-integriertes DeFi: Ein Interview mit Storm Trade-Mitbegründer Denis Vasin

Pionero DeFi integrado en Telegram: una entrevista con el cofundador de Storm Trade, Denis Vasin

DeFi pionnier intégré à Telegram : entretien avec le cofondateur de Storm Trade, Denis Vasin

अग्रणी टेलीग्राम-एकीकृत DeFi: स्टॉर्म ट्रेड के सह-संस्थापक डेनिस वासिन के साथ एक साक्षात्कार

テレグラム統合型 DeFi の先駆者: ストームトレード共同創設者デニス・ヴァシン氏へのインタビュー

선구적인 텔레그램 통합 DeFi: Storm Trade 공동 창립자 Denis Vasin과의 인터뷰

DeFi pioneiro integrado ao Telegram: uma entrevista com o cofundador da Storm Trade, Denis Vasin

Новаторская DeFi-интеграция с Telegram: интервью с соучредителем Storm Trade Денисом Васиным

Öncü Telegram Entegre DeFi: Storm Trade Kurucu Ortağı Denis Vasin ile Röportaj

Tiên phong về DeFi tích hợp Telegram: Cuộc phỏng vấn với Denis Vasin, người đồng sáng lập Storm Trade

开创性的 Telegram 集成 DeFi：对 Storm Trade 联合创始人 Denis Vasin 的采访

Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about. That doesn’t mean these tools are strictly unsafe, but it does warrant attention. You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about . That doesn’t mean these tools are strictly unsafe, but it does warrant attention. you should know about You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 14 states have data privacy laws 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. recognized quantum-proof encryption methods 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. it increases visibility and reduces downtime Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. more than a hundred apps You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. outlines software supply chain security steps 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. 95% of cybersecurity issues Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf

How Can You Integrate Cybersecurity Into Your Content Automation Process?

Unlike traditional centralized exchanges, instant crypto exchanges allow users to trade cryptocurrencies directly from their wallets, without the need to deposit funds into an exchange account. Also, many of them help to exchange crypto between different blockchains. Integrating instant crypto exchange functionality into applications through opens up new possibilities for developers. By using these APIs, developers can offer their users the ability to seamlessly swap between cryptocurrencies without leaving the application. This eliminates the need for users to navigate multiple platforms and chains providing access to a wide range of trading pairs. And of course APIs allow developers to benefit from exchange fees, creating a new revenue stream for their applications. APIs In this article, I will explore the process of integrating a ChangeNow API into an application. I will cover the essential steps, including obtaining an API key, authenticating requests, retrieving available trading pairs, executing trades, and monitoring transaction status. By the end of this guide, you'll have a clear understanding of how to leverage such APIs to add powerful trading capabilities to your application. So, let's dive in? Step 1: Sign Up for a ChangeNOW API Key To get started with the ChangeNOW API, you first need to sign up for an API key. This key will be used to authenticate your API requests and ensure secure access to the ChangeNOW exchange services. Navigate to the ChangeNOW page and click on the "Start your integration" button. crypto exchange API Fill in the required information, such as your email address and password, to create a new account. Once you have successfully created an account, log in to your ChangeNOW account dashboard. In the account dashboard, locate the "API" section. This is where you can manage your API keys and access the necessary information for integrating the ChangeNOW API into your application. Click on the "Generate New API Key" button to create a new API key. You may be prompted to provide additional information or verify your account before proceeding. After successfully generating a new API key, you will see it displayed in the API section of your account dashboard. Make sure to copy and store this API key securely, as you will need it for authenticating your API requests. With your ChangeNOW API key generated, you are now ready to proceed to the next step of setting up your development environment and starting to integrate the ChangeNOW API into your application. Remember to keep your API key confidential and avoid sharing it with others. If you suspect that your API key has been compromised, you can always generate a new one from your account dashboard and update your application accordingly. Step 2: Set Up Your Development Environment Before you start integrating the ChangeNOW API into your application, you need to set up your development environment. This involves choosing a programming language and an Integrated Development Environment (IDE) that you are comfortable with, and installing the necessary libraries for making HTTP requests. For this guide, I will provide examples in . Python Install Python on your machine if you haven't already. You can download the latest version of Python from the official and follow the installation instructions for your operating system. website Choose an for Python development. Some popular options include PyCharm, Visual Studio Code, and Sublime Text. IDE Install the requests library, which is a popular library for making HTTP requests in Python. You can install it using pip, the package installer for Python, by running the following command in your terminal or command prompt: pip install requests Create a new Python file in your IDE and import the requests library at the top of your file: import requests With your development environment set up and the necessary libraries installed, you are now ready to start making API requests to the ChangeNOW API. Step 3: Authenticate Your API Requests To authenticate your API requests and ensure secure communication with the ChangeNOW API, you need to include your API key in the x-changenow-api-key header of each request. This header tells the ChangeNOW API that you are an authorized user and grants you access to the API endpoints. Here's how you can set the API key header in Python: import requests

api_key = 'YOUR_API_KEY'  # Replace with your actual API key

headers = { 'x-changenow-api-key': api_key }

response = requests.get('https://api.changenow.io/v1/endpoint', headers=headers) Replace 'YOUR_API_KEY' with the actual API key you obtained in Step 1. The headers object is created with the x-changenow-api-key header set to your API key. This header is then included in the API request using the headers parameter of the requests.get() method. By including the API key header in each request, you authenticate yourself as an authorized user of the ChangeNOW API. The API server will validate your API key and grant access to the requested endpoints if the key is valid. Step 4: Retrieve Available Currency Pairs To start using the ChangeNOW API, you need to know which currency pairs are available for exchange. The ChangeNOW API provides an endpoint to retrieve the list of available currency pairs. To retrieve the available currency pairs, you need to make a GET request to the /v1/market-info/available-pairs/ endpoint. Here's how you can send the request and parse the response: import requests

api_key = 'YOUR_API_KEY'

headers = {

'x-changenow-api-key': api_key }

response = requests.get('https://api.changenow.io/v1/market-info/available-pairs/', headers=headers)

if response.status_code == 200:

available_pairs = response.json()

print("Available currency pairs:")

for pair in available_pairs: print(pair) 

else: print(f"Error: {response.status_code}") The code sends a GET request to the /v1/market-info/available-pairs/ endpoint, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON array of available currency pairs. The code then parses the response and extracts the list of currency pairs. The response.json() method is used to parse the JSON response into a Python list. The code then iterates over the list and prints each currency pair. If the request fails, the code prints an error message along with the status code. By retrieving the available currency pairs, you can provide your users with the options to choose from when performing an exchange. Step 5: Check Minimum and Maximum Exchange Amounts Before performing an exchange, it's important to know the minimum and maximum amounts allowed for a specific currency pair. The ChangeNOW API provides an endpoint to retrieve this information. To check the minimum and maximum exchange amounts, you need to make a GET request to the /v2/exchange/range endpoint, providing the required parameters: fromCurrency and toCurrency. Here's how you can construct the request URL with query parameters and parse the response: import requests

api_key = 'YOUR_API_KEY'

headers = { 'x-changenow-api-key': api_key }

from_currency = 'btc'  # Replace with the source currency

to_currency = 'eth'    # Replace with the target currency

url = f'https://api.changenow.io/v2/exchange/range?fromCurrency={from_currency}&toCurrency={to_currency}'

response = requests.get(url, headers=headers)

if response.status_code == 200:

data = response.json() 

min_amount = data['minAmount'] 

max_amount = data['maxAmount'] 

print(f"Minimum exchange amount: {min_amount}") 

print(f"Maximum exchange amount: {max_amount}") 

else: print(f"Error: {response.status_code}") Set the fromCurrency and toCurrency variables to the desired source and target currencies, respectively. The code constructs the request URL by appending the fromCurrency and toCurrency parameters as query parameters to the /v2/exchange/range endpoint URL. It then sends a GET request to the constructed URL, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON object with the minAmount and maxAmount fields. The code parses the response and extracts these values. Tthe response.json() method is used to parse the JSON response into a Python dictionary. The minAmount and maxAmount values are then accessed and printed. By checking the minimum and maximum exchange amounts, you can ensure that the user's requested exchange amount falls within the allowed range for the selected currency pair. Step 6: Estimate Exchange Amounts Before proceeding with an exchange, you may want to provide an estimate of the exchange amount to the user based on their input. The ChangeNOW API offers an endpoint to estimate the exchange amount for a given currency pair and amount. To estimate the exchange amount, you need to make a GET request to the /v2/markets/estimate endpoint, providing the required parameters: fromCurrency, toCurrency, fromAmount or toAmount, and type. Here's how you can build the request URL, send the request, and handle the response: import requests

api_key = 'YOUR_API_KEY'

headers = { 'x-changenow-api-key': api_key }

from_currency = 'btc'   # Replace with the source currency

to_currency = 'eth'     # Replace with the target currency 

from_amount = 1.5       # Replace with the source amount

url = f'https://api.changenow.io/v2/markets/estimate?fromCurrency={from_currency}&toCurrency={to_currency}&fromAmount={from_amount}&type=direct'

response = requests.get(url, headers=headers)

if response.status_code == 200:

data = response.json() 

estimated_amount = data['toAmount'] 

print(f"Estimated exchange amount: {estimated_amount} {to_currency}") 

else: print(f"Error: {response.status_code}") Set the fromCurrency, toCurrency, and fromAmount variables to the desired source currency, target currency, and source amount. The code builds the request URL by appending the fromCurrency, toCurrency, fromAmount, and type parameters as query parameters to the /v2/markets/estimate endpoint URL. It then sends a GET request to the constructed URL, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON object with the estimated exchange amount in the toAmount field. The code parses the response and extracts the estimated amount. The response.json() method is used to parse the JSON response into a Python dictionary. The toAmount value is then accessed and printed along with the target currency. If the request fails, the code prints an error message along with the status code. By estimating the exchange amount, you can provide the user with an idea of how much they will receive in the target currency based on their input amount in the source currency. Step 7: Create an Exchange Transaction Once the user has provided the necessary information, such as the source and target currencies, the amount to exchange, and the destination address, you can create an exchange transaction using the ChangeNOW API. To create an exchange transaction, you need to make a POST request to the /v2/exchange/refund endpoint, including the required parameters in the request body: id, address, and extraId (if applicable). Here's how you can construct the request payload, send the POST request, and handle the API response: import requests

api_key = 'YOUR_API_KEY'

headers = { 'x-changenow-api-key': api_key, 'Content-Type': 'application/json' }

transaction_id = 'YOUR_TRANSACTION_ID'  # Replace with the actual transaction ID

refund_address = 'YOUR_REFUND_ADDRESS'  # Replace with the actual refund address 

extra_id = 'YOUR_EXTRA_ID'  # Replace with the actual extra ID (if applicable)

payload = {

'id': transaction_id, 

'address': refund_address, 

'extraId': extra_id 

}

response = requests.post('https://api.changenow.io/v2/exchange/refund', headers=headers, json=payload)

if response.status_code == 200:

data = response.json() 

refund_status = data['result'] 

print(f"Refund status: {refund_status}") 

else: print(f"Error: {response.status_code}") Set the transactionId, refundAddress, and extraId variables to the appropriate values for the exchange transaction. The code constructs the request payload as a JSON object containing the id, address, and extraId fields. It then sends a POST request to the /v2/exchange/refund endpoint, including the API key in the x-changenow-api-key header and the payload in the request body. If the request is successful (status code 200), the response will contain a JSON object indicating the refund status in the result field. The code parses the response and extracts the refund status. The response.json() method is used to parse the JSON response into a Python dictionary. The result value is then accessed and printed. By creating an exchange transaction, you initiate the process of exchanging the user's funds from the source currency to the target currency. The API will handle the exchange process and send the exchanged funds to the specified destination address. Step 8: Monitor Transaction Status After creating an exchange transaction, you can monitor its status to keep track of the progress and provide updates to the user. The ChangeNOW API allows you to retrieve the status and details of transactions using the /v2/exchanges endpoint. To monitor the status of a transaction, you need to make a GET request to the /v2/exchanges endpoint. You can optionally include query parameters to filter and sort the transactions. Here's how you can build the request URL with query parameters, send the request, and parse the response to retrieve transaction details and status: import requests

api_key = 'YOUR_API_KEY'  # Replace with your actual API key

headers = { 'x-changenow-api-key': api_key }

url = 'https://api.changenow.io/v2/exchanges?limit=10&offset=0&sortField=createdAt&sortDirection=DESC'

response = requests.get(url, headers=headers)

if response.status_code == 200:

data = response.json() 

transactions = data['exchanges'] 

for transaction in transactions: 

transaction_id = transaction['requestId'] 

status = transaction['status'] 

print(f"Transaction ID: {transaction_id}, Status: {status}") 

else: print(f"Error: {response.status_code}") The code builds the request URL with optional query parameters. In this example, the limit parameter is set to 10 to retrieve a maximum of 10 transactions, the offset parameter is set to 0 to start from the beginning, the sortField parameter is set to 'createdAt' to sort the transactions by creation date, and the sortDirection parameter is set to 'DESC' to sort in descending order. It then sends a GET request to the /v2/exchanges endpoint, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON object with an array of transactions in the exchanges field. The code parses the response and iterates over the transactions. The response.json() method is used to parse the JSON response into a Python dictionary. The code then iterates over the exchanges array and prints the transaction ID and status for each transaction. By monitoring the transaction status, you can keep track of the progress of the exchange and provide relevant updates to the user. You can periodically poll the API to fetch the latest status of the transaction until it reaches a final state (e.g., "finished" or "failed"). Congratulations! You have successfully learned how to set up and use the ChangeNOW API to integrate cryptocurrency exchange functionality into your application. If you have any problems or need further assistance while implementing the ChangeNOW API, please feel free to leave a comment below. There are on HackerNoon, but you'd like to see a similar step-by-step guide for integrating any other API into your application, let me know in the comments section. many guides Keep exploring, keep learning, and happy coding!

Step by Step Guide on How to Integrate ChangeNOW API

Weather tracking is a common requirement for many web applications, ranging from personal projects to commercial services. PHP, a server-side scripting language, can be a powerful tool for retrieving and displaying weather information on websites. In this beginner's guide, we'll explore how you can use PHP to track weather data and integrate it into your web applications. Getting Started with Weather APIs To track Weather using PHP, we'll need access to weather data from a reliable source. Fortunately, there are several weather APIs available that provide developers with access to real-time and forecast weather information. One popular weather API is OpenWeatherMap. It offers a wide range of weather data, including current weather conditions, forecasts, and historical weather data. To get started, you'll need to sign up for an API key, which you can obtain by registering on the OpenWeatherMap website. Retrieving Weather Data with PHP Once you have obtained your API key, you can use PHP to retrieve weather data from the OpenWeatherMap API. Here's a simple example of how you can make a request to the API and display the current weather conditions: phpCopy code<?php

// Replace 'YOUR_API_KEY' with your actual OpenWeatherMap API key
$apiKey = 'YOUR_API_KEY';

// City and country code for the location you want to retrieve weather data for
$city = 'London';
$countryCode = 'UK';

// API endpoint URL
$url = "http://api.openweathermap.org/data/2.5/weather?q={$city},{$countryCode}&appid={$apiKey}";

// Make a request to the API
$response = file_get_contents($url);

// Decode the JSON response
$data = json_decode($response, true);

// Check if the request was successful
if ($data && $data['cod'] === 200) {
    // Extract relevant weather information
    $weatherDescription = $data['weather'][0]['description'];
    $temperature = round($data['main']['temp'] - 273.15, 1); // Convert temperature from Kelvin to Celsius

    // Display weather information
    echo "<h2>Current Weather in {$city}, {$countryCode}</h2>";
    echo "<p><strong>Temperature:</strong> {$temperature} °C</p>";
    echo "<p><strong>Description:</strong> {$weatherDescription}</p>";
} else {
    // Display error message if request fails
    echo 'Failed to retrieve weather data.';
}

?> In this example, we construct a URL with the desired city and country code, along with our API key. We then make a request to the OpenWeatherMap API using and decode the JSON response using . Finally, we extract relevant from the response and display it on the webpage. file_get_contents() json_decode() weather information Enhancements and Considerations : It's important to implement error handling to gracefully handle situations where the API request fails or returns unexpected data. Error Handling : Consider implementing caching mechanisms to reduce the number of API requests and improve performance. Caching : You can enhance the display of weather information by incorporating CSS styling and additional details such as wind speed, humidity, and atmospheric pressure. Display Formatting : Make your weather-tracking application accessible to users worldwide by supporting multiple languages and units of measurement. Localization : Keep your API key secure by avoiding hardcoding it directly into your PHP files. Consider using environment variables or configuration files to store sensitive information. Security Conclusion Tracking weather using PHP can be a valuable addition to your web applications, providing users with up-to-date weather information for their desired locations. By leveraging weather APIs such as OpenWeatherMap and incorporating PHP to retrieve and display weather data, you can create dynamic and engaging experiences for your website visitors. With the foundational knowledge provided in this guide, you can explore further customization and integration possibilities to meet the specific needs of your projects.

How to Integrate Weather Tracking into Web Applications with PHP

OFX, aka , is an open standard for creating 2D visual effects and video compositing. It operates in a plugin-like application development model. Essentially, it serves as both a Host - an application providing a set of methods, and a Plug-in - an application or module implementing this set. OFX Image Processing API This configuration offers the potential for unlimited expansion of the host application's functionality. DaVinci Resolve and Metal Applications such as Final Cut X and DaVinci Resolve Studio, starting from version 16, fully support Apple Metal pipelines. Similar to OpenCL and Cuda, in the case of OFX, you can obtain a descriptor or handler of a platform-specific command queue. The host system also takes responsibility for allocating a pool of such queues and balancing calculations on them. Moreover, it places the source and target image clip data in GPU memory, significantly simplifying the development of extensible functionality. OFX Version Support in Resolve With Resolve, things are slightly more complicated. DaVinci announces support for OFX v1.4, albeit with some limitations. Specifically, some methods for working with interface functions are not available for use. To determine which method is available, OFX allows you to examine the supported suite through key/value queries. Publishing methods in the plugin code are based on . But we will use the OpenFXS C++ shell adapted for C++17. For convenience, I've compiled everything into one repository: taken from the open-source . C calls dehancer-external Dehancer project OFXS Concept In this project, I will be using OpenFXS, a C++ extension to OpenFX that was originally written by and has become popular over time in commercial and open-source video processing projects. Bruno Nicoletti The original was not adapted to modern C++ dialects, so I updated it to make it compatible with . OpenFXS C++17 OFX, and consequently OFXS, is a standalone software module that is dynamically loaded by the host program. Essentially, it is a dynamic library that is loaded when the main application starts. OpenFXS, like OFX, must publish method signatures. Hence, we use one C method from the code. To start developing in OpenFXS, you need to agree to a few common sets of classes that are used to create new functionality in your application. Typically, in a new project, you need to inherit from these classes and implement or override some virtual methods. To create your own plugin on the host system, let's start by familiarizing ourselves with the following public classes and the same method: is a basic template for creating a plugin's data structure suite and control panel (although it can be left empty). The inherited class creates a singleton object that registers a set of parameters and presets in the host system, with which the developer registers his module; OFX::PluginFactoryHelper - base container class for creating and storing structure properties; OFX::ParamSetDescriptor - a container of properties used when manipulating graphic data when calling data processing procedures. Used by the host application to save the context of processing parameters in the internal database and work with plugin properties defined for each of its instances; OFX::ImageEffectDescriptor - a set of settings that allows you to manipulate the registered data structure; OFX::ParamSet - a set of settings for effects on graphical data, inherited from OFX::ParamSet; OFX::ImageEffect - in the child class, it is necessary to implement data stream processing: images or videos; OFX::MultiThread::Processor - method of registering a plugin (factory) in the host application; OFX::Plugin::getPluginIDs False Color One feature that distinguishes the process of shooting video from simply capturing an image in a photo is the dynamic change of scenes and lighting of both scenes as a whole and areas in the image. This determines the way exposure is controlled during the shooting process. In digital video, there is a control monitor mode for operators in which the exposure level of areas is mapped into a limited set of zones, each tinted with its own color. This mode is sometimes called "predator" or False Color mode. The scales are usually referenced to the IRE scale. Such a monitor allows you to see the exposure zones and avoid significant mistakes when setting camera shooting parameters. Something similar in meaning is used when exposing in photography - zoning according to Adams, for example. You can measure a specific target with an exposure meter and see in which zone it is located, and in real-time we see the zones, neatly tinted for ease of perception. The number of zones is determined by the objectives and capabilities of the control monitor. For instance, a monitor used with cameras can incorporate up to 6 zones. Arri Alexa Adding Extensions Before proceeding with the example, we need to add some simple proxy classes to implement OpenFXS as a platform for processing source data, such as Metal textures. These classes include: : A proxy class for OFX clip data. imetalling::Image : A functor for transferring data from the clip buffer into a Metal texture. From DaVinci, you can extract a buffer of any structure and packaging of image channel values into the plugin, and it should be returned in a similar form. imetalling::Image2Texture To make working with the stream format in OFX easier, you can request the host to prepare data of a specific type in advance. I will use floats packed in RGBA - red/green/blue/alpha. : A reverse functor for transforming a stream into a host system buffer. As you can see, there is potential for significant optimization of calculations if you teach the Metal computing cores to work not with the texture, but directly with the buffer. imetalling::ImageFromTexture We inherit the OFXS base classes and write our functionality without going into the details of how the Metal core works: : Here, we implement the stream transformation and initiate the processing. imetalling::falsecolor::Processor : This will be our specific part of the suite description for the plugin. We need to implement several mandatory calls related to setting up the structure and create an instance of the OFX::ImageEffect class with specific functionality, which we divide into two subclasses in the implementation: Interaction and Plugin. imetalling::falsecolor::Factory : Implementation of the interactive part of working with effects. Essentially, this is the implementation of only virtual methods from OFX::ImageEffect related to processing changes in plugin parameters. imetalling::falsecolor::Interaction : Implementation of thread rendering, that is, launching imetalling::Processor. imetalling::falsecolor::Plugin Additionally, we will need several utility classes built on top of Metal to logically separate the host code and the kernel code on MSL. These include: : A base class that obscures work with the Metal command queue. The main parameter will be the name of the kernel in the MSL code, and the executor of the kernel call. imetalling::Function : A general class for transforming a source texture into a target texture, extending Function to simply set the parameters for calling the MSL kernel. imetalling:Kernel : Bypass kernel. imetalling::PassKernel : Our main functional class, a "predator" emulator that posterizes (downsamples) to a specified number of colors. imetalling::FalseColorKernel The kernel code for the "predator" mode could look like this: static constant float3 kIMP_Y_YUV_factor = {0.2125, 0.7154, 0.0721};
constexpr sampler baseSampler(address::clamp_to_edge, filter::linear, coord::normalized);
 
inline float when_eq(float x, float y) {
  return 1.0 - abs(sign(x - y));
}
 
static inline float4 sampledColor(
        texture2d<float, access::sample> inTexture,
        texture2d<float, access::write> outTexture,
        uint2 gid
){
  float w = outTexture.get_width();
  return mix(inTexture.sample(baseSampler, float2(gid) * float2(1.0/(w-1.0), 1.0/float(outTexture.get_height()-1))),
             inTexture.read(gid),
             when_eq(inTexture.get_width(), w) // whe equal read exact texture color
  );
}
 
kernel void kernel_falseColor(
        texture2d<float, access::sample> inTexture [[texture(0)]],
        texture2d<float, access::write> outTexture [[texture(1)]],
        device float3* color_map [[ buffer(0) ]],
        constant uint& level [[ buffer(1) ]],
        uint2 gid [[thread_position_in_grid]])
{
  float4  inColor = sampledColor(inTexture,outTexture,gid);
  float luminance = dot(inColor.rgb, kIMP_Y_YUV_factor);
  uint      index = clamp(uint(luminance*(level-1)),uint(0),uint(level-1));
  float4    color = float4(1);
 
  if (index<level)
    color.rgb = color_map[index];
 
  outTexture.write(color,gid);
} Initialization of the OFX Plugin We will begin by defining the class In this class, we will set a single parameter - the status of the monitor (either on or off). This is necessary for our example. imetalling::falsecolor::Factory. We will inherit from and overload five methods: OFX::PluginFactoryHelper : This method is invoked to globally configure the instance when the plugin is first loaded. Overloading this method is optional. load() : This method is invoked when an instance is unloaded, for instance, to clear memory. Overloading this method is also optional. unload() : This is the second method that the OFX host calls when the plugin is loaded. It is virtual and must be defined in our class. In this method, we need to set all the properties of the plugin, regardless of its context type. For more details about the properties, refer to the code. describe(ImageEffectDescriptor&) ImageEffectDescriptor : Similar to the method, this method is also called when the plugin is loaded and must be defined in our class. It should define properties associated with the current context. describeInContext(ImageEffectDescriptor&,ContextEnum) describe The context determines the type of operations the application works with, such as filter, paint, transition effect, or frame retimer in a clip. : This is the most crucial method that we overload. We return a pointer to an object of type . In other words, our in which we have defined all the functionalities, both with regard to user events in the host program and rendering (transforming) the source frame into the target one: createInstance(OfxImageEffectHandle, ContextEnum) ImageEffect imetalling::falsecolor::Plugin OFX::ImageEffect *Factory::createInstance(OfxImageEffectHandle handle,OFX::ContextEnum) {
     return new Plugin(handle);
   } Handling Events At this stage, if you compile a bundle with the OFX module, the plugin will already be available in the host application, and in DaVinci, it can be loaded onto the correction node. However, to work fully with a plugin instance, you need to define at least the interactive part and the part associated with processing the incoming video stream. To do this, we inherit from the class and overload virtual methods: OFX::ImageEffect - This method allows us to define the logic for handling the event. The event type is determined by the value of OFX::InstanceChangedArgs::reason and can be: eChangeUserEdit, eChangePluginEdit, eChangeTime - the event occurred as a result of a property being edited by the user, changed in a plugin or host application, or as a result of a change in the timeline. changedParam(const OFX::InstanceChangedArgs&, const std::string&) The second parameter specifies the string name that we defined at the plugin initialization stage, in our case, it is one parameter: . false_color_enabled_check_box - This method allows us to define the logic for reacting to an event, and return a state that determines whether something has changed and whether rendering makes sense. The method must return false or true. This is a way to optimize and reduce the number of unnecessary calculations. isIdentity(...) You can read the implementation of interactive interaction with OFX in the code. As you can see, we receive pointers to the clips: the source one and the memory area in which we will put the target transformation. Interaction.cpp Implementation of Rendering Launch We will add another logical layer on which we will define all the logic for launching the transformation. In our case, this is the only method for overriding so far: - Here, you can find out the properties of the clips and decide how to render them. Also, at this stage, the Metal command queue and some useful attributes associated with the current timeline properties become available to us. render(const OFX::RenderArguments& args) Processing At the launch stage, an object with useful properties became available to us: we have at least a pointer to the video stream (more precisely, a memory area with frame image data), and, most importantly, a queue of Metal commands. Now, we can construct a generic class that will bring us closer to a simple form of reusing kernel code. The OpenFXS extension already has such a class: OFX::ImageProcessor; we just need to overload it. In the constructor, it has the OFX::ImageEffect parameter, i.e., in it, we will receive not only the current state of the plugin parameters but everything necessary for working with the GPU. At this stage, we just need to overload the method and initiate the processing of kernels already implemented on Metal. processImagesMetal() Processor::Processor(
            OFX::ImageEffect *instance,
            OFX::Clip *source,
            OFX::Clip *destination,
            const OFX::RenderArguments &args,
            bool enabled
    ) :
            OFX::ImageProcessor(*instance),
            enabled_(enabled),
            interaction_(instance),
            wait_command_queue_(false),
            /// grab the current frame of a clip from OFX host memory
            source_(source->fetchImage(args.time)),
            /// create a target frame of a clip with the memory area already specified in OFX
            destination_(destination->fetchImage(args.time)),
            source_container_(nullptr),
            destination_container_(nullptr)
    {
 
      /// Set OFX rendering arguments to GPU
      setGPURenderArgs(args);
 
      /// Set render window
      setRenderWindow(args.renderWindow);
 
      /// Place source frame data in Metal texture
      source_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, source_);
 
      /// Create empty target frame texture in Metal
      destination_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, destination_);
 
      /// Get parameters for packing data in the memory area of the target frame
      OFX::BitDepthEnum dstBitDepth = destination->getPixelDepth();
      OFX::PixelComponentEnum dstComponents = destination->getPixelComponents();
 
      /// and original
      OFX::BitDepthEnum srcBitDepth = source->getPixelDepth();
      OFX::PixelComponentEnum srcComponents = source->getPixelComponents();
 
      /// show a message to the host system that something went wrong
      /// and cancel rendering of the current frame
      if ((srcBitDepth != dstBitDepth) || (srcComponents != dstComponents)) {
        OFX::throwSuiteStatusException(kOfxStatErrValue);
      }
 
      /// set in the current processor context a pointer to the memory area of the target frame
      setDstImg(destination_.get_ofx_image());
    }
 
    void Processor::processImagesMetal() {
 
      try {
 
        if (enabled_)
          FalseColorKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();
        else
          PassKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();

        ImageFromTexture(_pMetalCmdQ,
                         destination_,
                         destination_container_->get_texture(),
                         wait_command_queue_);
 
      }
      catch (std::exception &e) {
        interaction_->sendMessage(OFX::Message::eMessageError, "#message0", e.what());
      }
    } Building the Project To build the project, you will need CMake, and it must be at least version 3.15. Additionally, you will require Qt5.13, which aids in the easy and convenient assembly of the bundle with the plugin installer in the system directory. To initiate cmake, you must first create a build directory. After creating the build directory, you can execute the following command: cmake -DPRINT_DEBUG=ON -DQT_INSTALLER_PREFIX=/Users/<user>/Develop/QtInstaller -DCMAKE_PREFIX_PATH=/Users/<user>/Develop/Qt/5.13.0/clang_64/lib/cmake -DPLUGIN_INSTALLER_DIR=/Users/<user>/Desktop -DCMAKE_INSTALL_PREFIX=/Library/OFX/Plugins .. && make install Afterward, the installer, called , will appear in the directory that you specified in the parameter. Let's go ahead and launch it! Once the installation is successful, you can start DaVinci Resolve and begin using our new plugin. IMFalseColorOfxInstaller.app PLUGIN_INSTALLER_DIR You can find and select it in the OpenFX panel on the color correction page, and add it as a node. External links False Color OFX Plugin Code The Open Effects Association - OFX header file version and OFXS library code under Resolve + examples Download DaVinci Resolve

An Easy Way to Develop Your Own Apple Metal Plugin and Integrate It Into DaVinci Resolve

আপনার নিজস্ব অ্যাপল মেটাল প্লাগইন বিকাশ করার এবং এটিকে DaVinci সমাধানে সংহত করার একটি সহজ উপায়

Eine einfache Möglichkeit, Ihr eigenes Apple Metal Plugin zu entwickeln und es in DaVinci Resolve zu integrieren

Una forma sencilla de desarrollar su propio complemento Apple Metal e integrarlo en DaVinci Resolve

Un moyen simple de développer votre propre plugin Apple Metal et de l'intégrer dans DaVinci Resolve

अपना स्वयं का Apple मेटल प्लगइन विकसित करने और इसे DaVinci रिज़ॉल्यूशन में एकीकृत करने का एक आसान तरीका

独自の Apple Metal プラグインを開発して DaVinci Resolve に統合する簡単な方法

나만의 Apple Metal 플러그인을 개발하고 DaVinci Resolve에 통합하는 쉬운 방법

Uma maneira fácil de desenvolver seu próprio plug-in Apple Metal e integrá-lo ao DaVinci Resolve

Простой способ разработать собственный плагин Apple Metal и интегрировать его в DaVinci Resolve

Kendi Apple Metal Eklentinizi Geliştirmenin ve DaVinci Resolve'ye Entegre Etmenin Kolay Yolu

Một cách dễ dàng để phát triển Plugin Apple Metal của riêng bạn và tích hợp nó vào DaVinci Resolve

开发您自己的 Apple Metal 插件并将其集成到 DaVinci Resolve 中的简单方法

How to Integrate React and AngularJS via Webpack Module Federation

**NEW YORK CITY, New York, February 16th, 2024/Chainwire/-- Introducing Kadena SpireKey, a human-friendly and secure way to seamlessly interact with any application by removing complex signing processes. Kadena Spirekey “Unlike complex Web3 wallets today where you have to remember every wallet you’ve ever created, Kadena’s SpireKey uses WebAuthn, a technology that has been developed by Google and Apple over the last 20 years, to help anyone securely digital assets directly on your phone or computer. It’s as easy as receiving a prompt on your mobile device and providing a fingerprint as a signature. Even that uncle who asks you about crypto every family holiday dinner can do it. With Kadena, we've made using applications accessible to everyone, no matter if you’re an experienced "degen" or using blockchain for the first time,” Said Kadena CMO, Mike Herron. SpireKey creates a seamless interaction between humans and technology, providing a Web2 experience with Web3 innovation. With SpireKey, users can sign transactions and Web3 applications, just like how it works on Apple Pay or Google Pay. However, it can be done directly on the user's device without opening multiple windows or copying and pasting keypairs, which eliminates potential vulnerabilities seen in traditional wallets. Added Security with Built-in Multi-sig “Kadena's built-in multi-sig signing enables an additional layer of security for SpireKey that only we can provide through our original language, Pact. With multi-sig, SpireKey allows for multiple signatures to be required for certain transaction types. For example, if you want to send over $10,000, you can set up parameters to require signatures from three different devices – your phone, laptop, and cold storage wallet. The multi-sig feature reduces the risk of compromised accounts because a bad actor would need access to three devices. It mitigates the risk of another attack vector, and adds to the overall security,” Said Stuart Popejoy, Kadena Co-Founder and CEO. SpireKey displays how Kadena thinks about the level of usability that Web3 technology should be at. At its core, SpireKey connects humans to interact with the blockchain and with one another. SpireKey believes that showing working examples will inspire all humans alike to see how Spirekey can impact the rest of the world beyond Web3. https://www.youtube.com/watch?v=paweE0nxAZU&embedable=true About Kadena is a blockchain technology protocol that was founded in 2017 by Stuart Popejoy and Will Martino. Kadena is the industry’s only scalable Layer-1 Proof of Work (PoW) blockchain. This scalability enables Kadena to deliver infrastructure-grade performance for any blockchain project. Kadena Along with Kadena's own smart contract language Pact, Kadena’s platform provides the world with the tools and environment to turn ideas and ambitions into reality. Founded by Stuart Popejoy and William Martino, who created JP Morgan’s first blockchain and led the SEC’s Crypto Committee, Kadena aims to allow for true blockchain mass adoption. For more information, users can follow Kadena's: | | | Twitter Telegram Discord YouTube Contact Kadena Press press@kadena.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Kadena SpireKey Integrates With WebAuthn To Provide Seamless Web3 Interactions

DUBAI, UAE, February 8th, 2024/Chainwire/-- The Kinetex team is proud to announce an integration with 1inch. Despite the numerous decentralized exchanges (DEXes) that have emerged in the market, users still require assistance in locating the appropriate liquidity and the most effective trading routes for their crypto assets. This is unsurprising, as the DeFi industry and its services can be difficult to navigate, particularly for those with little or no experience, due to the abundance of platforms and tools available. Fortunately, the DeFi space can be made more user-friendly and accessible with solutions developed by the Kinetex team. Kinetex works to connect different chains more seamlessly for an effortless and favorable trading experience. Liquidity Aggregation is one such solution created to simplify the search for adequate liquidity across the blockchain ecosystem. It combines liquidity from hundreds of sources, including 1inch. According to Tigran Bolshoi, co-founder of Kinetex, 1inch stands out as one of the top liquidity aggregators in the DeFi market. Their API grants access to rates and liquidity across DeFi, enabling users to trade easily and quickly. Combined with Kinetex's advanced algorithms, this integration provides users with the best swap routes, whether on-chain or cross-chain. With nearly 12 million users and a trading volume of $420 billion, 1inch is currently among the crypto industry's trailblazers that are shaping the future of Web3. Therefore, working with them is a great honor for the Kinetex team and an exciting possibility to add even more liquidity to the efficient Liquidity Aggregation solution. Besides Liquidity Aggregation, the Kinetex team continues to explore and develop new cross-chain solutions that can help overcome even more problems often met in the cross-chain space. For instance, they are finishing the innovative Flash Trade, which will revolutionize how users engage with crypto. About Kinetex Network: Kinetex Network provides highly efficient cross-chain trading, enabling instant crypto asset transfers between networks through Zero-Knowledge proofs. Kinetex Network: | Website Kinetex dApp Contact Public Relations Jane Mayer Kinetex Network hello@kinetex.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Kinetex to Integrate With 1inch to Boost Liquidity In Cross-Chain Swaps: What You Need to Know

This is a whole-journey guide for users, especially those from the financial sector, which requires a high level of data security and availability. If you don't know how to build a real-time data pipeline and make the most of the Apache Doris functionalities, start with this post, and you will be loaded with inspiration after reading. Apache Doris This is the best practice of a non-banking payment service provider that serves over 25 million retailers and processes data from 40 million end devices. sources include MySQL, Oracle, and MongoDB. They were using Apache Hive as an offline data warehouse but feeling the need to add a real-time data processing pipeline. Data After introducing Apache Doris, they increase their data ingestion speed by 2~5 times, ETL performance by 3~12 times, and query execution speed by 10~15 times. In this post, you will learn how to integrate Apache Doris into your data architecture, including how to arrange data inside Doris, how to ingest data into it, and how to enable efficient data updates. Plus, you will learn about the enterprise features that Apache Doris provides to guarantee data security, system stability, and service availability. Building a real-time data warehouse with Apache Doris Choice of data models Apache Doris arranges data with three data models. The main difference between these models lies in whether or how they aggregate data. : for detailed data queries. It supports ad-hoc queries of any dimension. Duplicate Key model : for use cases with data uniqueness constraints. It supports precise deduplication, multi-stream upserts, and partial column updates. Unique Key model : for data reporting. It accelerates data reporting by pre-aggregating data. Aggregate Key model The financial user adopts different data models in different data warehouse layers: : As a payment service provider, the user receives a million settlement data every day. Since the settlement cycle can span a whole year, the relevant data needs to be kept intact for a year. Thus, the proper way is to put it in the Duplicate Key model, which does not perform any data aggregations. An exception is that some data is prone to constant changes, like order status from retailers. Such data should be put into the Unique Key model so that the newly updated record of the same retailer ID or order ID will always replace the old one. ODS - Duplicate Key model : Data in the DWD and DWS layers are further abstracted, but it is all put in the Unique Key model so that the settlement data can be automatically updated. DWD & DWS - Unique Key model : Data is highly abstracted in this layer. It is pre-aggregated to mitigate the computation load of downstream analytics. ADS - Aggregate Key model Partitioning and bucketing strategies The idea of partitioning and bucketing is to "cut" data into smaller pieces to increase data processing speed. The key is to set an appropriate number of data partitions and buckets. Based on their use case, the user tailors the bucketing field and bucket number to each table. For example, they often need to query the dimensional data of different retailers from the retailer flat table, so they specify the retailer ID column as the bucketing field and list the recommended bucket number for various data sizes. Multi-source data migration In the adoption of Apache Doris, the user had to migrate all local data from their branches into Doris, which was when they found out their branches were using and had , so the migration could be a mess. different databases data files of very different formats Luckily, Apache Doris supports a rich collection of data integration methods for both real-time data streaming and offline data import. : Apache Doris fetches MySQL Binlogs in real-time. Part of them is written into Doris directly via Flink CDC, while the high-volume ones are synchronized into Kafka for peak shaving and then written into Doris via the Flink-Doris-Connector. Real-time data streaming : This includes more diversified data sources and data formats. Historical data and incremental data from S3 and HDFS will be ingested into Doris via the method, data from Hive or JDBC will be synchronized to Doris via the method, and files will be loaded to Doris via the Flink-Doris-Connector and Flink FTP Connector. (FTP is how the user transfers files across systems internally, so they developed the Flink-FTP-Connector to support the complicated data formats and multiple newline characters in data.) Offline data import Broker Load Insert Into Full data ingestion and incremental data ingestion To ensure business continuity and data accuracy, the user figures out the following ways to ingest full data and incremental data: : Create a temporary table of the target schema in Doris, ingest full data into the temporary table, and then use the statement for atomic replacement of the regular table with the temporary table. This method prevents interruptions to queries on the frontend. Full data ingestion ALTER TABLE t1 REPLACE WITH TABLE t2 alter table ${DB_NAME}.${TBL_NAME} drop partition IF EXISTS p${P_DOWN_DATE};
ALTER TABLE ${DB_NAME}.${TBL_NAME} ADD PARTITION IF NOT EXISTS  p${P_DOWN_DATE} VALUES [('${P_DOWN_DATE}'), ('${P_UP_DATE}'));

LOAD LABEL ${TBL_NAME}_${load_timestamp} ... : Create a new data partition to accommodate incremental data. Incremental data ingestion Offline data processing The user has moved part of their offline data processing workload to Apache Doris and thus . increased execution speed by 5 times : The old Hive-based offline data warehouse used the TEZ execution engine to process 30 million new data records every day. With 2TB computation resources, the whole pipeline took 2.5 hours. Before : Apache Doris finishes the same tasks within only 30 minutes and consumes only 1TB. Script execution takes only 10 seconds instead of 8 minutes. After Enterprise features for financial players Multi-tenant resource isolation This is required because it often happens that the same piece of data is requested by multiple teams or business systems. These tasks can lead to resource preemption and, thus, performance decrease and system instability. Resource limit for different workloads The user classifies their analytics workloads into four types and sets a resource limit for each of them. In particular, they have four different types of Doris accounts and set a limit on the CPU and memory resources for each type of account. In this way, when one tenant requires excessive resources, it will only compromise its own efficiency but not affect other tenants. Resource tag-based isolation For data security under the parent-subsidiary company hierarchy, the user has set isolated resource groups for the subsidiaries. Data of each subsidiary is stored in its own resource group with three replicas, while data of the parent company is stored with four replicas: three in the parent company resource group and the other one in the subsidiary resource group. Thus, when an employee from a subsidiary requests data from the parent company, the query will only executed in the subsidiary resource group. Specifically, they take these steps: Workload group The resource tag-based isolation plan ensures isolation on a physical level, but as Apache Doris developers, we want to further optimize resource utilization and pursue more fine-grained resource isolation. For these purposes, we released the feature in . Workload Group Apache Doris 2.0 The Workload Group mechanism relates queries to workload groups, which limit the share of CPU and memory resources of the backend nodes that a query can use. When cluster resources are in short supply, the biggest queries will stop execution. On the contrary, when there are plenty of available cluster resources and a workload group requires more resources than the limit, it will get assigned with the idle resources proportionately. The user is actively planning their transition to the Workload Group plan and utilizing the task prioritizing mechanism and query queue feature to organize the execution order. Fine-grained user privilege management For regulation and compliance reasons, this payment service provider implements strict privilege control to make sure that everyone only has access to what they are supposed to access. This is how they do it: : System users of different subsidiaries or with different business needs are granted different data access privileges. User privilege setting : The mechanism of Apache Doris makes these operations easy. Privilege control over databases, tables, and rows ROW POLICY : This is done by creating views. Privilege control over columns Cluster stability guarantee : From time to time, system users might input faulty SQL, causing excessive resource consumption. A circuit-breaking mechanism is in place for that. It will promptly stop these resource-intensive queries and prevent interruption to the system. Circuit Breaking : The user has a frequent need to integrate historical data into their data platform. That involves a lot of data modification tasks and might stress the cluster. To solve that, they turn on the mode in the Unique Key model, enable and , and tune the data compaction parameters to control data ingestion concurrency. Data ingestion concurrency control Merge-on-Write Vertical Compaction Segment Compaction : Considering their two clusters in different cities, they employ Quality of Service (QoS) strategies tailored to different scenarios for precise network isolation and ensuring network quality and stability. Network traffic control : The user has integrated Doris with their internal monitoring and alerting platform, so any detected issues will be notified via their messaging software and email in real-time. Monitoring and alerting Cross-cluster replication Disaster recovery is crucial for the financial industry. The user leverages the Cross-Cluster Replication (CCR) capability and builds a dual-cluster solution. As the primary cluster undertakes all the queries, the major business data is also synchronized into the backup cluster and updated in real-time so that in the case of service downtime in the primary cluster, the backup cluster will take over swiftly and ensure business continuity. Conclusion We appreciate the users for their active with us along the way and are glad to see so many Apache Doris features fit their needs. They are also planning on exploring federated query, compute-storage separation, and auto maintenance with Apache Doris. We look forward to more best practices and feedback from them. communication Also published . here

Integrate Apache Doris Into Your Data Architecture: Real-time Data Warehousing

**GRAND CAYMAN, Cayman islands, January 25th, 2024/Chainwire/--**Gaming, Ecommerce and DeFi applications are leveraging Sui’s singular social authentication to eliminate the biggest hurdle blocking mainstream audiences from web3. , the rapidly ascending Layer-1 blockchain created by the leaders of Facebook's Diem project, today announced that over 10 decentralized applications (dApps) in the Sui ecosystem have integrated zkLogin, the first-of-its-kind primitive that makes Web3 login as simple as signing in with familiar web credentials such as Google or Twitch. Sui Sui continues to advance zkLogin, adding new providers and additional features such as multi-sig capability and more. Thanks to these recent additions, it’s estimated that thousands of users will now be able to use zkLogin and experience one of the world’s first one-click onboarding into crypto. “zkLogin is a native utility unique to Sui that empowers developers to offer users the ability to use the same social logins they use everywhere else to authenticate with web3 applications,” said Kostas Chalkias, Co-Founder & Chief Cryptographer of Mysten Labs, the a16z and Circle-backed company that created the Sui Network. “As we enter the next phase of adoption, the existence of tools like zkLogin will become a prerequisite to onboard mainstream users into the decentralized web.” Among the wallets that now support zkLogin are Chrome-based wallets such as Ethos Wallet and Sui Wallet. zkLogin is now also integrated into mobile applications (both on Play Store and AppStore) by using Suiet and Surf Wallet. Similarly, web3 gaming experiences such as Suilette and Quantum Temple are using zkLogin, to give their users an easier, more intuitive onboarding experience. Lastly, DeFi applications on Sui are also taking advantage of this unique strength of Sui. Well-known protocols like DegenHive, Blockbolt, Aftermath, Navi, and BlueFin have integrated the technology, allowing users to manage their digital assets and participate in financial activities without the hassle of managing private keys. “zkLogin is a game-changer in Web3 and particularly in DeFi,” said Zabi Mohebzada, Co-Founder at Bluefin. “With zkLogin, users only need a Google account to sign up and trade. No wallets are required. This enables us to unlock the security benefits of decentralized trading for a much broader universe of investors.” Launched in 2023 as one of the first industry solutions for Web2-based authentication for Web3, zkLogin makes it possible for users to join the Sui ecosystem without having to install separate wallets or memorize and manage seed phrases. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

10 Decentralized Applications Integrate Sui’s Groundbreaking zkLogin Google Authentication

Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples A Integrated Error Taxonomy – Details In this section, we describe the Integrated Error Taxonomy as proposed by Higashinaka et al. (2021). In principle, they differentiate between form violation and content violation. The form violation usually represents errors that oppose some kind of meta criteria, e.g., the form of language or the ignorance of social norms. In contrast, content violations refer to, e.g., inconsistent or redundant utterances, or other things that might cause a dialog breakdown. Content violation is hereinafter abbreviated as CV (form violation as FV). Furthermore, they generally refer to utterances, while we refer to system utterance, as this is evident from their examples and simplifies understanding (from our perspective). A.1 Utterance-Level Utterance-level errors typically expose languagegeneration deficiencies of the system. • Uninterpretable (I1), FV – The system’s utterance does not contain recognizable words or is just a text fragment, e.g., the utterance just consists of the words of yours. • Grammatical Error (I2), FV – The system’s utterance is grammatical incorrect, e.g., he will attend yesterday. • Semantic Error (I3), CV – The combination of predicates and arguments does not result in any meaning, e.g., I feel painting. • Wrong Information (I4), CV – The given information is clearly wrong, e.g., Meat Loaf was the songwriter of Jim Steinman (it was vice versa). A.2 Response-Level Response-level errors describe errors in system utterances, e.g., in responses to user questions or requests. • Ignore Question (I5), FV – The system utterance ignores the user’s question, e.g., U: What will the weather be like tomorrow? S: Look at these beautiful flowers... • Ignore Request (I6), FV – The system ignores the user’s request to do something, e.g., U: Please book me a room in the west part of town. S: I like sports. • Ignore Proposal (I7), FV – The system ignores the user’s proposal to do something, e.g., U: Let’s talk about movies. S: I like pizza. • Ignore Greeting (I8), FV – The system ignores the user’s greeting, e.g., U: Hello, my friend! S: What do you want from me? • Ignore Expectation (I9), CV – The system utterance does not have the expected content but the user’s intention has been successfully conveyed, e.g., U: Do you have a favorite football team? S: Yes. (the system should tell the user the name of his favorite football team). A.3 Context-Level Context-level errors refer not only to the local context, i.e., adjacent pairs of user utterance and system utterance, but to a broader (sometimes global) context. • Unclear Intention (I10), FV – The system utterance is on the current topic, but the intention is unclear, e.g., U: What is your favorite movie? S: I like Daddy’s Home 2. U: Oh, yes! Mel Gibson is so good in this role... S: The Evil Dead is so bloody... • Topic Transition Error (I11), FV – The system suddenly switches to another topic without explanation, e.g., U: I really enjoyed Feynman’s physics lecture. So enlightening! S: Well, what is your favorite food? • Lack of Information (I12), FV – The system utterance misses important information, such as subject, object, or modifier, e.g., U: It’s a beautiful day, isn’t it? S: I hate. (object is missing). • Self-Contradiction (I13), CV – The system utterance contradicts what it said before, e.g., S: I’m from Boston, Massachusetts. U: Oh, wow! I’m from Bartlesville, Oklahoma! S: Really? I’m from Seattle, Washington! • Contradiction (I14), CV – The system utterance contradicts what has been said by the user, e.g., U: I like strawberries. S: Oh, why don’t you like strawberries? • Repetition (I15), CV – The system repeats something it has already said before (this includes repeated questioning), e.g., S: I look forward to the concert. U: Oh, which concert? S: I look forward to the concert. A.4 Society-Level Society-level errors refer to errors that indicate that the system lacks etiquette or common sense. • Lack of Sociality (I16), FV – The system utterance lacks consideration towards the user and contains hate speech or is in general disrespectful, e.g., U: Yesterday I went to the federal employment agency. S: Okay. Are you incapable of finding a job on your own? • Lack of Common Sense (I17), CV – The system utterance contains content that differs from the opinion of the majority, e.g., S: I like to eat chicken raw. U: Oh, dear... You have to cook it first! This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Integrated Error Taxonomy: Form Violation vs Content Violation

Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples 7 Evaluation and Experiments In this section, we use the manually annotated dialogs from in a human evaluation to assess the impact of our modifications to the Integrated Error Taxonomy (Higashinaka et al., 2021). We also use these dialogs to investigate the performance impact of errors in system utterances and subsequent user responses as additional input signals for response generation in three SOTA language generation models, including GPT-2 (Radford et al., 2019), LLAMA (Touvron et al., 2023), and Flan-T5 (Chung et al., 2022). Section 6 7.1 Integrated Error Taxonomy – Evaluation To evaluate the impact of our modifications to reduce ambiguity and address missing error types in the Integrated Error Taxonomy (Higashinaka et al., 2021), we perform a human evaluation. We asked nine experts with NLP background and sound English skills to annotate 600 dialogs from those that were manually annotated by us in Section 6 (300 from both the filtered and random dialogs, 50 per dataset) with error and user response types using our modified Integrated Error Taxonomy (Section 4.1) and proposed user response type taxonomy (Section 4.2) 6 . Each of the dialogs was then assigned to two of these experts and thus annotated three times in total (including our own initial annotation). For comparison, we mapped all annotations back to the original Integrated Error Taxonomy. For merged error types, we asked the annotators for a second assessment using the original taxonomy. Table 10 shows the inter-annotator agreement (IAA) calculated using Krippendorff’s Alpha (Krippendorff, 2004) 7 and summarized by human-human and human-bot dialogs. In the case of human-human dialogs, the overall agreement is rather low. This also applies to the user response types (what was to be expected, as they directly depend on the error type annotations). During our analysis in Section 6, we found that in human-human dialogs, participants tend to suggest disagreement in a friendly manner, which complicates the identification of errors. This is different for human-bot dialogs, where participants tend to provide direct and clear feedback, such as You’re wrong. We attribute the low agreement for the annotations in human-human dialogs to this observation. Nonetheless, using our modified Integrated Error Taxonomy improves IAA over the original one in all cases. This is most obvious in the case of the human-human filtered dialogs, where it improves IAA by 0.14 points. A detailed analysis revealed that this is mainly due to (1) the condensed number of abstract error types, e.g., we merged ambiguous error types such as Ignore Proposal and Ignore Request, and (2) the newly added error types, such as Factually Incorrect, which were not covered in the original taxonomy. 7.2 Impact in Response Generation In the following, we investigate the performance impact of including errors in system utterances and the subsequent user responses as additional input signals in response generation. For this experiment, we consider three different SOTA language generation models: GPT-2 (Radford et al., 2019), LLAMA (Touvron et al., 2023), and FlanT5 (Chung et al., 2022). For GPT-2 and Flan-T5, we use the large variants. For LLAMA, we use the 7B variant8 . We use the dialogs annotated in Section 6 for this experiment. In a first step, we use the 967 dialogs without error and user response type annotations to train baselines models in the task of response generation. Next, we train the baseline models using the 188 error and user response type annotated dialogs and include the annotated data as additional input signals. For error types, we include the respective system utterance (Error Text). For user responses, we include the respective user utterance (User Response). As evaluation metrics, we use word-overlapping F1- Score (following Xu et al. (2023) with FITS) and BLEU (Papineni et al., 2002). We provide more details, including the baseline results, in Appendix I. Experimental Setup Table 11 shows the results9 . We find a large performance gap between Flan-T5 (Chung et al., 2022) and the other models. A detailed analysis revealed that both GPT-2 (Radford et al., 2019) and LLAMA (Touvron et al., 2023) generate reasonable and fluent responses, but mostly deviate from the target sequence. That aside, including user responses as an additional input signal improves the results over the other configurations, including None, for both Flan-T5 and GPT-2. For LLAMA, additionally using the error text improves the results over the other configurations. Results Overall, our results support the findings from recent works on learning from free-text human feedback in that including user responses to errors in system utterances is beneficial (Xu et al., 2023; Ung et al., 2022). However, we also show that including the error itself can have a positive impact. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Human Assessment of Enhanced Integrated Error Taxonomy

Within the ever-shifting sphere of technology, corporations are constantly hunting for revolutionary methods to gain an advantageous stance. Artificial Intelligence (AI) has become a metamorphic vigor, revolutionizing how associations function. No longer is AI merely a futuristic ideology; it is here, and firms that welcome AI as a worker instead of a mechanism are poised to reach success in the virtual age. : Understanding the Shift In the past, AI has typically been perceived as a means to simplify processes or . Yet, a fundamental change is occurring, pushing companies to regard AI as a collaborative partner. This transition from viewing AI as a mere tool to perceiving it as an associate is essential for companies hoping to remain ahead in the rapidly evolving business environment. Integrating AI into the workforce can bring about a multitude of advantages, particularly when it comes to honing decision-making acumen. In contrast to traditional utilities, AI offers the potential to quickly process large volumes of data and make reliable predictions based on such information. With the application of machine learning models, firms have access to knowledge that would normally require unorthodox amounts of time to generate if done by humans. For example, , market fluctuations, and former information to anticipate future developments, aiding enterprises in making enlightened determinations concerning commodity formation, marketing plans, and resource assignment. Employing AI for automating tedious and regularly occurring tasks enables human workers to concentrate on more intricate and imaginative parts of their jobs. Utilizing machines to fulfill routine chores can ultimately lead to higher productivity, avert blunders, and free up human labor to be involved in responsibilities that involve , reflective thought, and innovation. In customer service, AI-powered chatbots may attend to basic queries, leaving human representatives to take on the more sophisticated matters that necessitate an appreciation of the customer's requirement, together with an empathetic approach. The continual learning capacity of AI provides a unique advantage to businesses. As opposed to human workers, artificial intelligence workers do not suffer from exhaustion and their capabilities develop consistently. Consequently, the incorporation of AI as staff can allow firms to gain from machines that continually adapt to varying contexts. In situations where staying abreast of the latest tendencies and cutting-edge technologies is deemed pivotal, this dynamism is especially highly valued. In the world of finance, Artificial Intelligence (AI) can keep a perpetual watch over market trends, tweak trading techniques, and evaluate hazards quickly, offering a degree of malleability that is hard to obtain with traditional methods. It is not true that AI impedes creative thought; when properly implemented, it can amplify human creative output. By quickly sorting through large amounts of data and noticing unobvious patterns, AI can provide ideas that would never have occurred to a person. This collaborative interplay between man and machine promotes the joint production of original solutions. About content generation, Artificial Intelligence (AI) technology can , prevailing patterns, and past information to give content creators invaluable direction, provoking them to compose more appealing and pertinent content. Integrating AI within a workforce can bring a host of benefits to employees. From automating tedious jobs to providing personalized learning experiences, AI can play an important part in creating an improved work experience. By freeing people of tedious tasks, they will have the opportunity to focus on roles that bring more value to the organization. Additionally, this technology may be used to assist in upscaling and re-training staff, enabling them to keep up with the ever-changing needs of the job market. In Human Resources, technology can expedite the recruitment process, pinpoint the best candidates, and even foretell employee attrition, thereby enabling preemptive initiatives to keep prized personnel. : increase productivity AI can analyze consumer behavior emotional intelligence analyze audience likes and dislikes artificial intelligence-driven Conclusion The adoption of AI into the workforce signals a sea change in the manner firms function. Rather than remaining confined to an auxiliary post, AI is now a fundamental element of reasoned judgment, augmenting human aptitude, and resulting in a more productive and adaptive work environment. Organizations that adopt the change and perceive AI as a partner instead of a simple implement are on course to excel in the digital age. The days ahead are for those who realize the significance of jointly utilizing the advantages of human intelligence with the agility and effectiveness of machine intelligence. It is not about displacing people, but instead authorizing them to achieve feats of creativity and prosperity they hadn't imagined. As we progress down the AI-powered path, those who optimize its total possibility are the ones who dictate the contour of corporate progress

How to Integrate Artificial Intelligence as an Integral Member of Your Team

You've likely come across WordPress and Salesforce in your professional journey. Perhaps you've even had the chance to directly engage with these platforms. But, have you ever thought about combining the strengths of both through WordPress Salesforce Integration? If not, you're in the right place. Salesforce stands out as one of the most widely embraced Customer Relationship Management (CRM) platforms. It serves as a central hub for companies to streamline their workflows by integrating various applications and services. Its primary goal is to empower enterprises in building robust relationships with their customers. On the flip side, WordPress takes the lead as the go-to Content Management System (CMS) for website creation and hosting. The magic happens when you bring these two powerhouses together. Salesforce WordPress Integration can enhance your marketing workflows and elevate your customers' browsing experience. This integration allows you to seamlessly connect your WordPress forms with Salesforce, enabling your team to automatically add leads, customers, and other contacts to your CRM. In the following discussion, we'll delve into the world of WordPress and Salesforce integration. As we progress, you'll gain insights into the steps involved in setting up Salesforce WordPress Integration. Let's explore the synergies between these platforms and unlock new possibilities for your business. What is Salesforce to WordPress Integration Salesforce to WordPress Integration is the strategic alignment of Salesforce, a prominent Customer Relationship Management (CRM) platform, with WordPress, a leading Content Management System (CMS). This integration seamlessly connects the robust CRM functionalities of Salesforce with the dynamic content management capabilities of WordPress. It enables businesses to synchronize and leverage customer data, ensuring real-time updates on their websites. By automating the transfer of data between Salesforce and WordPress, manual data entry is eliminated, saving time and reducing errors. This integration not only centralizes customer information but also facilitates personalized content delivery, enhancing marketing efforts and fostering a more engaging user experience on WordPress-powered sites. It represents a powerful synergy between CRM and web presence, optimizing overall digital strategies for businesses. Benefits of Salesforce to WordPress Integration 1. Enhanced Marketing Efficiency: Salesforce to WordPress Integration empowers businesses to leverage the robust CRM data from Salesforce in crafting personalized and targeted content on their WordPress websites. The seamless connection allows for real-time updates, ensuring that marketing strategies are based on the latest customer interactions and preferences. 2. Streamlined Lead Management: This integration facilitates the smooth capture of leads from WordPress websites, directing them seamlessly into the Salesforce CRM system. By consolidating lead data in a centralized system, businesses can adopt a unified approach to lead nurturing, tracking, and conversion. 3. Data Consistency and Accuracy: Automatic synchronization between Salesforce and WordPress significantly reduces the risk of discrepancies or outdated information. The integration promotes data accuracy, providing reliable, real-time insights that enhance decision-making processes for strategic planning and execution. 4. Efficient Workflow Automation: Salesforce to WordPress Integration automates data transfer processes, eliminating the need for manual entry and saving valuable time for marketing and sales teams. Triggering workflows in Salesforce based on user interactions on WordPress ensures timely and relevant responses to customer actions. 5. Improved Customer Engagement: The integration fosters a cohesive approach to customer relationship management by seamlessly connecting CRM insights with online interactions on WordPress. Personalized content delivery enhances the overall user experience, strengthening connections with site visitors and potential customers. 6. Optimized Data Utilization: Unifying customer data from both platforms allows businesses to leverage a comprehensive view of customer interactions and behaviors. This optimization of CRM data enhances marketing strategies and facilitates a deeper understanding of customer preferences. 7. Cost and Time Savings: Reduction in manual data entry minimizes the likelihood of errors, resulting in cost savings by eliminating the need for extensive data correction efforts. Automation of processes streamlines operations, freeing up time for teams to focus on strategic initiatives and high-impact tasks. 8. Scalability and Flexibility: Salesforce to WordPress Integration provides a scalable solution that can evolve with the growing needs of businesses. This flexibility accommodates changes in marketing strategies, ensuring the integration remains effective as the customer base expands and data volumes increase. A Step-by-Step Manual for Integrating Salesforce with WordPress If you're eager to blend the power of Salesforce with the versatility of WordPress, here's a straightforward guide to kickstart the process: Prepare Your Salesforce Account: Begin by ensuring your Salesforce account is ready for integration. Create a custom object to house your WordPress leads and establish a web-to-lead form for efficiently gathering data from your WordPress website. Install Essential WordPress Plugins: To seamlessly integrate Salesforce with WordPress, install essential WordPress plugins. Options like Salesforce WP-to-Lead, Gravity Forms, Salesforce Add-on, and Salesforce Anywhere are popular choices. These plugins come with pre-built features, easily customizable to suit your specific business requirements. Connect Salesforce to WordPress: After installing the necessary plugins, link up Salesforce with your WordPress platform. Typically, this involves inputting your Salesforce API credentials into the WordPress plugin settings. Some plugins might need additional configuration for optimal functionality. Configure Integration Settings: With Salesforce connected to WordPress, tailor the integration settings to your needs. This might involve mapping WordPress fields to Salesforce fields, establishing lead assignment rules, and configuring other integration preferences. Most of these adjustments can be made within the WordPress plugin settings. Test the Integration: Once the integration settings are in place, conduct thorough testing to confirm its proper functioning. Submit a test lead from your WordPress website and verify that it seamlessly syncs with your Salesforce account. Additionally, test other integration features like marketing automation and lead nurturing to ensure their smooth operation. By following these steps, you'll successfully merge Salesforce with WordPress, streamlining your lead management and marketing automation processes. Keep in mind the importance of regular monitoring, making necessary updates or configurations to adapt to the evolving needs of your business. This integration can be a game-changer, enhancing your ability to manage leads efficiently and automate key marketing tasks. Best Practices for Salesforce to WordPress Integration By adhering to these best practices, you can navigate the Salesforce to WordPress integration process effectively, fostering a streamlined, secure, and efficient connection between these two powerful platforms. Clearly Define Integration Objectives Begin by outlining specific integration goals. Clearly define what data needs to be synced between Salesforce and WordPress and how it aligns with your overall business objectives. Choose Reliable Integration Tools Select reputable and well-supported integration tools or plugins that cater to Salesforce to WordPress Integration. Ensure they are reliable, secure, and compatible with the versions of both platforms. Thoroughly Plan Data Mapping Invest time in mapping data fields between Salesforce and WordPress. This meticulous planning ensures seamless data flow, preventing discrepancies and data inaccuracies during integration. Implement Real-Time Synchronization Enable real-time synchronization to keep data consistently updated across Salesforce and WordPress. This helps maintain accuracy and ensures that both platforms operate with the latest information. Regularly Test Integration: Conduct thorough testing during and after the integration process. Test various scenarios, including lead submissions and data updates, to verify the integration's functionality and identify and address any potential issues. Optimize Workflow Automation: Leverage workflow automation capabilities to streamline processes. Set up automation for routine tasks, such as lead assignment and follow-ups, to enhance efficiency and reduce manual intervention. Prioritize Data Security and Permissions: Implement robust security measures to safeguard sensitive data during integration. Configure user permissions meticulously, ensuring that only authorized personnel have access to specific information. Provide Training for Users: Train your team on using the integrated system effectively. This includes understanding how to navigate the integrated Salesforce features, input data, and utilize the enhanced capabilities for better Salesforce development productivity. Monitor Performance Metrics: Establish key performance indicators (KPIs) for the integration and regularly monitor them. This practice helps you gauge the impact on workflow, identify areas for improvement, and ensure the integration aligns with business objectives. Document and Maintain: Maintain comprehensive documentation of the Salesforce customizations made, integration setup, and configurations. Regularly update this documentation to ensure clarity for future maintenance and troubleshooting. Conclusion If you're thinking about connecting Salesforce with WordPress, invest time in finding the best integration method. Plan your integration strategy meticulously and consider teaming up with a reliable Salesforce integration Partner if needed. By adopting the right approach and following best practices, integrating Salesforce with WordPress has the potential to elevate your business to new heights.

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