MHA has announced the appointment of Rowan Porter as its new transaction services director, who strengthens the firm’s north-west corporate finance team. Based out o ...

MHA appoints Liverpool corporate finance director | Accountancy Today

GXO Logistics is working with financial advisers to explore a potential sale, Bloomberg’s Crystal Tse, Aaron Kirchfeld, and Kiel Porter report. According to people familiar with the matter, the company started reviewing its options after receiving interest from possible buyers.

GXO Logistics exploring possible sale, Bloomberg reports

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

Raptors Jontay Porter Receives Lifetime Ban for Gambling Violations | THE 
ODD COUPLE

The NBA banned Toronto Raptors&#x27; Jontay Porter after finding that he “violated league rules&quot; by &quot;betting on NBA games” and &quot;disclosing confidential information to sports bettors.&quot;

Toronto Raptors’ Jontay Porter NBA betting ban is predictable

It’s nearly impossible to pull off a betting scheme like former Raptor Jontay Porter tried in a world of legal, regulated and monitored gambling.

Athletes beware: Jontay Porter NBA betting scheme is a lesson in stupidity

Toronto Raptors two-way player Jontay Porter was banned from the NBA on Wednesday after a league probe found he disclosed confidential information to sports bettors and wagered on games, even betting on the Raptors to lose.

Raptors' Jontay Porter banned from NBA for betting on games | CBC Sports

The NBA banned Toronto Raptors forward Jontay Porter for allegedly betting on games, passing on information to gamblers and claiming illness to influence a wager, the league said Wednesday.

Jontay Porter banned from NBA for gambling on games, giving info to bettors and limiting play for betting purposes

An NBA investigation found Toronto Raptors forward Jontay Porter disclosed confidential information to bettors, limited his participation in games for betting purposes and wagered on games.

NBA bans Jontay Porter for violating league’s gambling rules - The Washington Post

Raptors center Jontay Porter has been banned from the NBA for violating gambling rules, the league announced Wednesday.

NBA bans Raptors' Jontay Porter for gambling violations - ESPN

Jontay Porter has been under investigation since March.

Jontay Porter receives lifetime ban from NBA for violating gambling rules - Yahoo Sports

Porter

porter

Heroku's been taking it on the chin lately for their response to what looks to be a pretty-damn-serious security incident—and for good reason because about the whole event has been . their communication abysmal Personally, this is a pretty big letdown, because I've been riding the for quite some time. As a software developer with a history of working for teeny-tiny startups (in addition to a fair amount of freelancing), Heroku's Platform-as-a-Service Solution(PaaS) —has been a lifesaver for all the cool kids out there. Heroku train When you're crunched for time and resources, offloading things like server maintenance, security, database management, and all the other that accompany running a software company is incredibly valuable; and Heroku's add-on marketplace is just icing on the cake. tasks Trust me, there's nothing more satisfying than spinning up all the internal and external resources you need within a single pane of glass. As a freelancer, this sort of organization makes billing clients (and eventually transferring application ownership) a snap. Heroku Alternatives But... all of that is gone now. It has been outweighed by a complete and total lack of trust in a company that I've historically put a of trust in. As a result, I've been exploring a few Heroku alternatives that are all new to the PaaS space (or, at least, relatively new to ). lot relatively me To be clear, I'm skipping the most well-known solutions like 's App Platform and 's App Engine, because I think most people are already aware of them, so flogging that particular horse is probably just a waste of bits and bytes. DigitalOcean Google Cloud That said, I've stumbled across a handful of solutions that are both unique pretty "indie" feeling—a great combination in the "tools by developers, for developers" category if you ask me. awesome and Fly.io I've heard of a few times over the years, but it wasn't until just the last few days that I've really taken the opportunity to try it out; and, I've got to say, it's pretty damn awesome. Fly.io While not nearly as "feature robust" as Heroku (read: no "marketplace"), it is a Docker-driven solution with an generous free tier, and one of the most impressive commitments to technical writing I've ever seen (which, if I'm being honest, is only outshone by DigitalOcean's approach). incredibly As the founder of , I personally own and manage any of the integrations and websites that we rely on, and have chosen Fly.io as our new destination. The transition from Heroku to Fly.io was about as painless as you could imagine, and the terminal-driven management has been a great experience. The MUD Coders Guild If you are on the fence and don't have a huge application footprint, you definitely can't go wrong with Fly. Render is a new solution for me, but it most closely resembles Heroku as far as features and functionality go (although that isn't to imply that it is no better than Heroku). A winner in the 2019 TechCrunch Startup Battlefield (an event I will forever associate with Pied Piper and Middle-Out Encryption thanks to HBO's ), Render offers all the bells and whistles that we've come to expect in a PaaS platform: Render Silicon Valley Autoscaling Managed databases Preview environments Docker deployments And cron support actual I have yet to dig deeply into it, but will likely be moving some of my more complicated freelance projects over to Render when the opportunity presents itself (and, just as an aside, their free tier is about as generous as you can ask for). too Porter I've examined in the past as part of an roundup for TechTarget and liked what I saw. Porter Open Source PaaS really What sets Porter apart from the other solutions on this list is that it is an open-source self-hosted option. While they do offer a managed cloud solution that relies on your backend infrastructure, the self-hosted option is super compelling as it gives you total control over your infrastructure. and own While I am personally not in the "manage all my things" game, this is an excellent solution for people who are looking to make the leap from hosting PaaS to managing their own infrastructure. Conclusion Obviously, there are a hell of a lot more platform-as-a-service providers out there than just these three. From big to small, proprietary to open source, it's a pretty big space. That said, these three are the more compelling ones in my mind, and I will be watching them as they continue to grow. If these don't quite fit your needs and are looking for more options to try, take a look at this . There are some great solutions available. Awesome-PaaS list Also published at . flower.codes

Try these 3 Alternatives to Heroku

Decoding the algorithms of company success has been a central focus of business research. If market success were exact and replicable, startups and tech giants could transform it from an anomaly into a consistent outcome. In the absence of a deterministic path, researchers have sought heuristics to enhance the likelihood of success. One such heuristic can be found in the different types of strategies a business should follow to achieve success. Michael Porter, the key reference in strategy, classifies strategies into Cost Leadership, Differentiation, and Focus. However, there is also a very popular theory developed by Michael Treacy and Fred Wiersema, that identifies Customer Intimacy, Product Leadership, and Operational Excellence as the main paths to be followed. Except for a few details, a considerable level of correspondence can be found between these schools of thought. It is pretty reasonable to say that Cost Leadership corresponds to Operational Excellence, and Differentiation corresponds to Product Leadership. In Porter's framework, there is also the angle of Focus, but this alone is not a source of competitive advantage. In reality, Focus is a dimension that should be added to the Differentiation and Cost strategies to clarify that a broader or more focused market was chosen. Customer Intimacy Moreover, we have the kind of strategy that Treacy and Wiersema call Customer Intimacy. For them, Customer Intimacy holds a special place among all strategies. Customer Intimacy is all about understanding customers, tailoring offerings, and fostering strong relationships, with the end goal of maximizing customer lifetime value (LTV) in a practical, measurable way. Given these insights, it's no wonder the theory was adopted worldwide and is still widely used in companies and taught in business schools to this day. It is important to recognize that Treacy and Wiersema introduced valuable customer concepts ten years before the inaugural book on Net Promoter Score, at a time when customer lifetime value was an obscure concept lacking mature definitions in both the market and academia. We will examine each main pillar of the Customer Intimacy discipline to understand its overall framework and determine if it still makes sense to follow its rationale given the significant advancements in customer analysis. 1. Having a Deep Knowledge of the Customers Despite decades of theories and frameworks, such as Customer Intimacy and Net Promoter Score, most companies remain product-centric, often unable to identify specific customers behind each transaction. This limits their view to anonymous data points, making true customer-centricity a challenge. Gathering essential customer information across interactions provides valuable insights without needing exhaustive detail. Key practices, like integrating financial, product, and support data, can yield significant benefits. For those seeking deeper insights, social network data and advanced analytics are options, but this depth often depends more on resources and scalability than strictly on strategy. 2. Tailoring Products to Individual Needs Tech companies are increasingly focused on last-mile customization, using machine learning and algorithms to deliver tailored experiences at scale. For SaaS and consumer apps, investing in technologies that respond to user behavior is essential. Platforms like Netflix and e-commerce apps leverage predictive analytics and recommendation engines to keep experiences highly personalized. Balancing individual personalization with scalable product development requires prioritizing high-value customer needs without compromising the product’s direction. Some decisions must still be made at the product level to ensure customization aligns with the overall roadmap. This approach maximizes user value while keeping the platform's broader goals intact. 3. Building Strong and Lasting Relationships In today’s SaaS landscape, maintaining strong customer relationships extends well beyond the initial sale. Retention strategies use advanced analytics and machine learning to anticipate churn and identify at-risk users for proactive interventions. Personalization also includes engagement-driven communication, ensuring interactions stay relevant. CRM systems and data warehousing allow companies to keep a complete view of each customer’s journey, making each touchpoint more impactful. Maximizing LTV (Lifetime Value) is essential for tech companies focused on recurring revenue. By combining Customer Intimacy principles with real-time data, companies adapt to customer needs dynamically. This approach enables SaaS providers to deliver personalized experiences that retain high-value users, driving long-term growth in competitive markets. Enter Customer Centricity In many aspects, the retirement of Customer Intimacy implies the triumph of this idea. Analyzing its main pillars, we saw ideas that should be applied everywhere, not just to companies that have chosen this type of strategy. Using the same nomenclature proposed by the authors of the theory, we argue that the companies who opted for Operational Excellence and Product Leadership should have some activities that were proposed for the Customer Intimacy value discipline. Customer Intimacy did touch upon the idea of customer lifetime value (LTV), though it wasn’t central to the theory. At that time, this concept was still emerging. Today, we’ve moved beyond Customer Intimacy to the model of Customer Centricity, where strategies and actions are focused on identifying valuable clients and maximizing their long-term value. This shift allows us to approach customer relationships more analytically and profitably, leveraging data to drive growth. Not Just Semantics Without further thought, the idea that is being proposed here can look like just some semantic discussion without much importance, but this is not the case. In 1992, when this theory was proposed, we were in the early stages of personal computing. CRM systems were still in their infancy. Organizing and using client data was such a significant challenge that it was fair to say those companies had 'Customer Intimacy'. A few more sophisticated companies would know how much each client spent in a given year and create some internal procedures based on this information. Now, we have accessible computer systems and popular statistical models that allow small companies to go much farther than the most sophisticated companies of that time. Finally, there is another important reason for this change. Strategy authors all agree that companies shouldn't be stuck in the middle, they should choose one type of strategy. With Customer Intimacy as a distinct value discipline, companies may believe they don’t need to organize their strategy and operations around the value of their clients. But in reality, companies with any kind of strategy need to understand and try to maximize their client's value. Conclusion One option is to maintain the current framework — recognizing the higher standard for customer understanding across all companies — and reserve the category of Customer Intimacy for those that truly excel in this area. However, we believe this approach would cause more harm than good. While some companies might fit this classification, they would be so few that it wouldn't justify having a distinct category for them. Also, they would necessarily have a strategy that could be classified somewhere on the differentiation and cost spectrum. Adding a further dimension to the traditional cost and quality value spectrum increases complexity without substantial benefit. There are microeconomic reasons why companies should stick with just these two dimensions to identify their optimal strategy. As with everything a company does, the decision of which specific customer-centric activities to develop depends on its overall strategy and available resources. Careful analyses need to be conducted to determine which activities should be performed in a way that maximizes return for the business. Now, clients must be at the center of your strategy, with all customer-centered activities geared toward maximizing business return, regardless of your broader strategy or specific characteristics. Decoding the algorithms of company success has been a central focus of business research. If market success were exact and replicable, startups and tech giants could transform it from an anomaly into a consistent outcome. In the absence of a deterministic path, researchers have sought heuristics to enhance the likelihood of success. One such heuristic can be found in the different types of strategies a business should follow to achieve success. Michael Porter, the key reference in strategy , classifies strategies into Cost Leadership, Differentiation, and Focus. However, there is also a very popular theory developed by Michael Treacy and Fred Wiersema, that identifies Customer Intimacy, Product Leadership, and Operational Excellence as the main paths to be followed. Michael Porter, the key reference in strategy theory developed by Michael Treacy and Fred Wiersema , that identifies Customer Intimacy , Product Leadership , and Operational Excellence as the main paths to be followed. Michael Treacy and Fred Wiersema Customer Intimacy Product Leadership Operational Excellence Except for a few details, a considerable level of correspondence can be found between these schools of thought. It is pretty reasonable to say that Cost Leadership corresponds to Operational Excellence, and Differentiation corresponds to Product Leadership. In Porter's framework, there is also the angle of Focus, but this alone is not a source of competitive advantage. In reality, Focus is a dimension that should be added to the Differentiation and Cost strategies to clarify that a broader or more focused market was chosen. Customer Intimacy Moreover, we have the kind of strategy that Treacy and Wiersema call Customer Intimacy. For them, Customer Intimacy holds a special place among all strategies. For them, Customer Intimacy holds a special place among all strategies. Customer Intimacy is all about understanding customers, tailoring offerings, and fostering strong relationships, with the end goal of maximizing customer lifetime value (LTV) in a practical, measurable way. Given these insights, it's no wonder the theory was adopted worldwide and is still widely used in companies and taught in business schools to this day . It is important to recognize that Treacy and Wiersema introduced valuable customer concepts ten years before the inaugural book on Net Promoter Score, at a time when customer lifetime value was an obscure concept lacking mature definitions in both the market and academia. used in companies and taught in business schools to this day It is important to recognize that Treacy and Wiersema introduced valuable customer concepts ten years before the inaugural book on Net Promoter Score, at a time when customer lifetime value was an obscure concept lacking mature definitions in both the market and academia. We will examine each main pillar of the Customer Intimacy discipline to understand its overall framework and determine if it still makes sense to follow its rationale given the significant advancements in customer analysis. 1. Having a Deep Knowledge of the Customers Despite decades of theories and frameworks, such as Customer Intimacy and Net Promoter Score, most companies remain product-centric, often unable to identify specific customers behind each transaction. This limits their view to anonymous data points, making true customer-centricity a challenge. Gathering essential customer information across interactions provides valuable insights without needing exhaustive detail. Key practices, like integrating financial, product, and support data, can yield significant benefits. For those seeking deeper insights, social network data and advanced analytics are options, but this depth often depends more on resources and scalability than strictly on strategy. Gathering essential customer information across interactions provides valuable insights without needing exhaustive detail. 2. Tailoring Products to Individual Needs Tech companies are increasingly focused on last-mile customization, using machine learning and algorithms to deliver tailored experiences at scale. For SaaS and consumer apps, investing in technologies that respond to user behavior is essential. Platforms like Netflix and e-commerce apps leverage predictive analytics and recommendation engines to keep experiences highly personalized. Balancing individual personalization with scalable product development requires prioritizing high-value customer needs without compromising the product’s direction. Some decisions must still be made at the product level to ensure customization aligns with the overall roadmap. This approach maximizes user value while keeping the platform's broader goals intact. Some decisions must still be made at the product level to ensure customization aligns with the overall roadmap. 3. Building Strong and Lasting Relationships In today’s SaaS landscape, maintaining strong customer relationships extends well beyond the initial sale. Retention strategies use advanced analytics and machine learning to anticipate churn and identify at-risk users for proactive interventions. Personalization also includes engagement-driven communication, ensuring interactions stay relevant. CRM systems and data warehousing allow companies to keep a complete view of each customer’s journey, making each touchpoint more impactful. Maximizing LTV (Lifetime Value) is essential for tech companies focused on recurring revenue. By combining Customer Intimacy principles with real-time data, companies adapt to customer needs dynamically. This approach enables SaaS providers to deliver personalized experiences that retain high-value users, driving long-term growth in competitive markets. Maximizing LTV (Lifetime Value) is essential for tech companies focused on recurring revenue. Enter Customer Centricity In many aspects, the retirement of Customer Intimacy implies the triumph of this idea. Analyzing its main pillars, we saw ideas that should be applied everywhere, not just to companies that have chosen this type of strategy. Using the same nomenclature proposed by the authors of the theory, we argue that the companies who opted for Operational Excellence and Product Leadership should have some activities that were proposed for the Customer Intimacy value discipline. Customer Intimacy did touch upon the idea of customer lifetime value (LTV), though it wasn’t central to the theory. At that time, this concept was still emerging. Today, we’ve moved beyond Customer Intimacy to the model of Customer Centricity, where strategies and actions are focused on identifying valuable clients and maximizing their long-term value. This shift allows us to approach customer relationships more analytically and profitably, leveraging data to drive growth. Customer Intimacy did touch upon the idea of customer lifetime value (LTV), though it wasn’t central to the theory. At that time, this concept was still emerging. Not Just Semantics Without further thought, the idea that is being proposed here can look like just some semantic discussion without much importance, but this is not the case. In 1992, when this theory was proposed, we were in the early stages of personal computing. CRM systems were still in their infancy. Organizing and using client data was such a significant challenge that it was fair to say those companies had 'Customer Intimacy'. A few more sophisticated companies would know how much each client spent in a given year and create some internal procedures based on this information. Now, we have accessible computer systems and popular statistical models that allow small companies to go much farther than the most sophisticated companies of that time. Finally, there is another important reason for this change. Strategy authors all agree that companies shouldn't be stuck in the middle, they should choose one type of strategy. Finally, there is another important reason for this change. Strategy authors all agree that companies shouldn't be stuck in the middle, they should choose one type of strategy. With Customer Intimacy as a distinct value discipline, companies may believe they don’t need to organize their strategy and operations around the value of their clients. But in reality, companies with any kind of strategy need to understand and try to maximize their client's value. Conclusion One option is to maintain the current framework — recognizing the higher standard for customer understanding across all companies — and reserve the category of Customer Intimacy for those that truly excel in this area. However, we believe this approach would cause more harm than good. While some companies might fit this classification, they would be so few that it wouldn't justify having a distinct category for them. Also, they would necessarily have a strategy that could be classified somewhere on the differentiation and cost spectrum. While some companies might fit this classification, they would be so few that it wouldn't justify having a distinct category for them. Also, they would necessarily have a strategy that could be classified somewhere on the differentiation and cost spectrum. Adding a further dimension to the traditional cost and quality value spectrum increases complexity without substantial benefit. There are microeconomic reasons why companies should stick with just these two dimensions to identify their optimal strategy. As with everything a company does, the decision of which specific customer-centric activities to develop depends on its overall strategy and available resources. As with everything a company does, the decision of which specific customer-centric activities to develop depends on its overall strategy and available resources. Careful analyses need to be conducted to determine which activities should be performed in a way that maximizes return for the business. Now, clients must be at the center of your strategy, with all customer-centered activities geared toward maximizing business return, regardless of your broader strategy or specific characteristics.

It's Time to Retire the Customer Intimacy Value Discipline

AIR-Bench: A New Benchmark for Large Audio-Language Models

Thermodynamic Limits Around M-dwarf Stars: Acknowledgements

LOS ANGELES, United States, July 30th, 2024/Chainwire/--DSCVR, the largest web3 social media and community-building platform, announces the launch of Canvas, a groundbreaking framework that enables developers to effortlessly build and integrate custom applications directly into DSCVR’s social feeds. With just a few tweaks any web application can be made into a Canvas application. Combined with DSCVR’s API, Canvas provides an easy-to-build framework to launch and distribute embedded apps on the DSCVR platform. Canvas apps can have rich interfaces previously unseen in Web3 social that extend beyond the existing frame standards of buttons and textboxes to support games, interactive media, and more. Developers can find viral distribution for Canvases in feed, making them instantly accessible to the community and scaling them across hundreds of thousands of users. With Canvas, building, launching, and managing embedded apps has never been easier or more innovative, offering end users dynamic and rewarding experiences. A New Era of Web3 Embedded App Development DSCVR Canvas introduces a framework that makes app creation accessible to a breadth of developers, from seasoned engineers to students just learning Javascript. The platform offers a user-friendly interface that simplifies the development process, allowing anyone to build dynamic, interactive applications without advanced technical skills. Canvas is also built to support existing Frames and Solana Blinks so developers in these ecosystems can easily share their apps on DSCVR. “We’re excited to introduce Canvas to our developer community,” said Rick Porter, CEO of DSCVR. “Our goal is to empower developers with the framework they need to create and scale apps within the DSCVR ecosystem for the benefit of our users. Canvas is a significant step towards realizing that vision.” Key Features of DSCVR Canvas: Transactions: Canvas applications can interact directly with user wallets which are already paired with DSCVR accounts, creating new utility for NFTs and fungible totokens. Seamless Integration: Apps built on Canvas integrate effortlessly with the DSCVR platform, leveraging robust APIs and the speed of the platform for smooth performance. Enhanced Performance: Designed for scalability, Canvas supports everything from simple ways to earn crypto, tipping, to games and other complex applications. Rich Media Support: Users can incorporate images, videos, and interactive elements to create engaging and immersive apps. Driving Innovation in Web3 Social MeMedia With more canvases being built on DSCVR, users will enjoy a wide range of fun and engaging experiences directly within their social feed. From interactive games and trading platforms to token minting, DSCVR Canvas Embedded Apps will enrich the user experience, making social interactions more dynamic and rewarding. These apps will allow users to easily monetize their activities, participate in immersive gaming and trade digital assets, transforming the way they interact on the platform. At the same time, this is an opportunity for developers to gain distribution for their applications to an audience of hundreds of thousands of users. Launch Event and Community Engagement To celebrate the launch, DSCVR will be organizing a global hackathon on August 5th hosted by Dorahacks to encourage experimentation and creativity among its developer community. Sponsors include Metaplex, Matrica, Helius, Spiderswap and Tweed. Notable judges include Austen Federa, Jon Wong, and Pedro Miranda from the Solana Foundation as well as Josh Finer from Solana Ventures. “We’re committed to supporting our developer community and providing them with the support and tools they need to succeed,” added Rick Porter. “DSCVR Canvas is a testament to our dedication to innovation in the Web3 social ecosystem.” About DSCVR With over 500k monthly unique visitors, DSCVR (pronounced “Discover”) is a Web3 social network combining the usability of a Web2-style social app with crypto technology that unlocks ownership, monetization and distribution for users, creators, and projects. Through its native crypto functionality, DSCVR rewards users and supercharges community-building, while its API allows developers and partners to build applications that target through the social graph, distribute through the feed, and monetize in posts. DSCVR’s seed investors include Polychain Capital, Upfront Ventures, BDMI, Shima Capital, Tomahawk VC, and Fyrfly Venture Partners. Users can follow DSCVR on Twitter (X) for more updates. Contact Press Inquiries Cher Park DSCVR press@dscvr.one This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. LOS ANGELES, United States, July 30th, 2024/Chainwire/--DSCVR, the largest web3 social media and community-building platform, announces the launch of Canvas, a groundbreaking framework that enables developers to effortlessly build and integrate custom applications directly into DSCVR’s social feeds. With just a few tweaks any web application can be made into a Canvas application. Combined with DSCVR’s API, Canvas provides an easy-to-build framework to launch and distribute embedded apps on the DSCVR platform. Canvas apps can have rich interfaces previously unseen in Web3 social that extend beyond the existing frame standards of buttons and textboxes to support games, interactive media, and more. Developers can find viral distribution for Canvases in feed, making them instantly accessible to the community and scaling them across hundreds of thousands of users. With Canvas, building, launching, and managing embedded apps has never been easier or more innovative, offering end users dynamic and rewarding experiences. A New Era of Web3 Embedded App Development DSCVR Canvas introduces a framework that makes app creation accessible to a breadth of developers, from seasoned engineers to students just learning Javascript. The platform offers a user-friendly interface that simplifies the development process, allowing anyone to build dynamic, interactive applications without advanced technical skills. Canvas is also built to support existing Frames and Solana Blinks so developers in these ecosystems can easily share their apps on DSCVR. “We’re excited to introduce Canvas to our developer community,” said Rick Porter, CEO of DSCVR. “Our goal is to empower developers with the framework they need to create and scale apps within the DSCVR ecosystem for the benefit of our users. Canvas is a significant step towards realizing that vision.” “We’re excited to introduce Canvas to our developer community,” said Rick Porter, CEO of DSCVR. “Our goal is to empower developers with the framework they need to create and scale apps within the DSCVR ecosystem for the benefit of our users. Canvas is a significant step towards realizing that vision.” Key Features of DSCVR Canvas: Transactions: Canvas applications can interact directly with user wallets which are already paired with DSCVR accounts, creating new utility for NFTs and fungible totokens. Transactions: Seamless Integration: Apps built on Canvas integrate effortlessly with the DSCVR platform, leveraging robust APIs and the speed of the platform for smooth performance. Enhanced Performance: Designed for scalability, Canvas supports everything from simple ways to earn crypto, tipping, to games and other complex applications. Rich Media Support: Users can incorporate images, videos, and interactive elements to create engaging and immersive apps. Driving Innovation in Web3 Social MeMedia With more canvases being built on DSCVR, users will enjoy a wide range of fun and engaging experiences directly within their social feed. From interactive games and trading platforms to token minting, DSCVR Canvas Embedded Apps will enrich the user experience, making social interactions more dynamic and rewarding. These apps will allow users to easily monetize their activities, participate in immersive gaming and trade digital assets, transforming the way they interact on the platform. At the same time, this is an opportunity for developers to gain distribution for their applications to an audience of hundreds of thousands of users. Launch Event and Community Engagement To celebrate the launch, DSCVR will be organizing a global hackathon on August 5th hosted by Dorahacks to encourage experimentation and creativity among its developer community. Sponsors include Metaplex, Matrica, Helius, Spiderswap and Tweed. Notable judges include Austen Federa, Jon Wong, and Pedro Miranda from the Solana Foundation as well as Josh Finer from Solana Ventures. “We’re committed to supporting our developer community and providing them with the support and tools they need to succeed,” added Rick Porter. “DSCVR Canvas is a testament to our dedication to innovation in the Web3 social ecosystem.” “We’re committed to supporting our developer community and providing them with the support and tools they need to succeed,” added Rick Porter. “DSCVR Canvas is a testament to our dedication to innovation in the Web3 social ecosystem.” About DSCVR With over 500k monthly unique visitors, DSCVR (pronounced “Discover”) is a Web3 social network combining the usability of a Web2-style social app with crypto technology that unlocks ownership, monetization and distribution for users, creators, and projects. DSCVR Through its native crypto functionality, DSCVR rewards users and supercharges community-building, while its API allows developers and partners to build applications that target through the social graph, distribute through the feed, and monetize in posts. DSCVR’s seed investors include Polychain Capital, Upfront Ventures, BDMI, Shima Capital, Tomahawk VC, and Fyrfly Venture Partners. Users can follow DSCVR on Twitter (X) for more updates. Twitter Contact Press Inquiries Cher Park DSCVR press@dscvr.one This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

DSCVR Launches Canvas: A Massive Leap For Web3 Social Embedded Apps

DSCVR ক্যানভাস চালু করেছে: Web3 সোশ্যাল এমবেডেড অ্যাপের জন্য একটি বিশাল ঝাঁপ

DSCVR bringt Canvas auf den Markt: Ein gewaltiger Sprung für eingebettete soziale Web3-Apps

DSCVR lanza Canvas: un gran salto para las aplicaciones sociales integradas en Web3

DSCVR lance Canvas : un grand pas en avant pour les applications sociales Web3 intégrées

डीएससीवीआर ने कैनवास लॉन्च किया: वेब3 सोशल एम्बेडेड ऐप्स के लिए एक बड़ी छलांग

DSCVR が Canvas をリリース: Web3 ソーシャル埋め込みアプリの大きな飛躍

DSCVR, 캔버스 출시: Web3 소셜 임베디드 앱의 대대적인 도약

DSCVR lança Canvas: um grande salto para aplicativos sociais incorporados na Web3

DSCVR запускает Canvas: огромный скачок для встраиваемых приложений Web3 Social

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Illustration created with NightCafe As we’ve followed the fascinating twists and turns of numerous works of fiction, whose plots are in one way or another connected with robots, how many of us thought about the fact that we are already living in the very "robotic future" that is often presented in science fiction books, movies, and TV series? It's not as if someone drew a line and said: "By crossing this line, we are stepping into a new era." It's pretty obvious that even the fastest adoption of technology is still gradual. And that's fortunate, as it gives us the opportunity to adapt to the major socio-economic changes that are associated with technological advances. And there is no doubt that robotization entails these major changes. Let's take a look at the current level of robotics development and implementation. Statistics, as usual, will most accurately present the current state of affairs. And the most eloquent statistics of all are financial statistics. And it says that in 2022, the volume of the robotics market amounted to $34 billion. A rather modest, in our opinion, forecast says that by 2028, the market will grow to $45 billion a year. Interestingly, the service robot market is developing most actively. In 2022, it will account for 76% of total market share. This, of course, does not mean that industrial robots have ceased to be of interest. It's just that businesses realized their benefits and the opportunities they provide much earlier. In addition, the previous generation of robotics technology was simply unable to bring even marginally satisfactory models of service robots to market. Today, the state of affairs has changed. Several service and collaborative robots (cobots) have emerged from their diapers and begun to perform their duties in a wide variety of spheres. We are sure that among the readers of this text, there are those who, at home or in the office, use a robot vacuum cleaner and/or robotic carts to deliver various items. But these are by no means the most interesting examples of robotics technology. They are already being replaced by more advanced examples. An example of the next iteration is the Aeo service robot, which is capable of performing various actions using its manipulators. Another is Optimus, a series of humanoid robots from Tesla. It is expected that in the very near future - by 2025 - the market for cobots alone (i.e., robots that interact with people in the workspace) will exceed $12 billion. The current generation of service robots and cobots are already capable of taking over a number of routine, low-skill tasks. For example, they can perform the functions of nurses, security personnel, cleaners, porters, packers, waiters, pickers, "serve and fetch" assistants, and so on and so forth. As a rule, they are equipped with AI that is smart enough to execute voice commands that are given in any form. A separate topic is robotic transportation. According to forecasts, by 2030, 10% of all vehicles on the planet will be self-driving. And this figure is also rather modest in our opinion, and is not because of a lack of technical capability, but instead will result from a combination of price and psychological factors. As far as industrial robots are concerned, today, the creation of fully automated plants and factories is in full swing. In developed countries like South Korea, about 1,000 robots per 10,000 workers are being used in the manufacturing sector. This is a very approximate figure, but in this case, it is not the exact ratio of people to robots that matters. The salient issue is the complexity of these industrial robots. The idea of the factory of the future as an analog of traditional  "human-powered" production facilities in which human beings have been replaced by humanoid robots is fundamentally wrong. Anyone who has been to a robotized production facility knows that an ordinary industrial robot is, in fact, an automatic machine tool. Or an entire assembly line. You will agree that this kind of production unit cannot be considered simply as an improved (from the point of view of factory production) analog of a human being. Each such robot replaces entire teams and workshops. Illustration created with NightCafe The logical development of this concept is the creation of fully automated production facilities. In other words, plants, factories, and other production facilities will each, in fact, be a large and incredibly complex robot. By the way, there is a place for humanoid robots in these production environments, too. However, they should not be considered industrial robots; instead, they should be identified as a new level of service and collaborative robots designed to interact with other robots and maintain their workspaces. What will it look like? Note that we consider most predictions for robotics adoption and development that are statistically based to be too conservative. Of course, solid researchers simply must proceed with caution. But we will allow ourselves to draw on a history in which technological progress has often raced ahead of predictions based on statistical analysis. Think of the rapid evolution of cell phones into smartphones or the first personal computers into light, sleek laptops. So, let's try to imagine a robotic life ten years from now based on a similar rapidity in development. Industrial and factory production will be fully roboticized. Mass-produced goods will be produced by robotic factories. Service robots and cobots will maintain and repair these production facilities; self-directed transportation will deliver raw materials to them and export their finished products. Agriculture will also become extremely automated. This is particularly true with the development of technologies for growing meat products. Traditional livestock farms will mostly disappear, which, it should be noted, will have a positive impact on the environment. Service robots will take over most of the work in commerce and the service industry, which hasn’t transitioned into virtual reality. They will also take care of household chores, repairs, construction, cleaning, street beautification, and the like. Freight transportation and public transport will be completely taken over by self-driving trains, planes, airships, copters, buses, cabs, air cabs, and so on. The share of self-driven private transportation will gradually increase. This trend will be accelerated under the pressure of increasingly stringent safety requirements for drivers. All of the above, combined with the development of artificial intelligence and augmented reality, will rid people almost completely of the work being performed currently that is strenuous, boring, monotonous, or poses a risk to health (or is simply dangerous). In other words, people are not going to have to do most of the work that people have to do now. But what will mankind do? At least that part of it that will enjoy civilization’s benefits? This is the final chapter of our future fantasy. Robert McCall – Metropolis 2050 #4 The article was created in collaboration with Andriy Tkachenko Illustration created with NightCafe Illustration created with NightCafe Illustration created with NightCafe As we’ve followed the fascinating twists and turns of numerous works of fiction, whose plots are in one way or another connected with robots, how many of us thought about the fact that we are already living in the very "robotic future" that is often presented in science fiction books, movies, and TV series? It's not as if someone drew a line and said: "By crossing this line, we are stepping into a new era." It's pretty obvious that even the fastest adoption of technology is still gradual. And that's fortunate, as it gives us the opportunity to adapt to the major socio-economic changes that are associated with technological advances. And there is no doubt that robotization entails these major changes. Let's take a look at the current level of robotics development and implementation. Statistics, as usual, will most accurately present the current state of affairs. And the most eloquent statistics of all are financial statistics. And it says that in 2022, the volume of the robotics market amounted to $34 billion . A rather modest, in our opinion, forecast says that by 2028, the market will grow to $45 billion a year. $34 billion $34 billion Interestingly, the service robot market is developing most actively. In 2022, it will account for 76% of total market share . This, of course, does not mean that industrial robots have ceased to be of interest. It's just that businesses realized their benefits and the opportunities they provide much earlier. In addition, the previous generation of robotics technology was simply unable to bring even marginally satisfactory models of service robots to market. 76% of total market share 76% of total market share Today, the state of affairs has changed. Several service and collaborative robots (cobots) have emerged from their diapers and begun to perform their duties in a wide variety of spheres. We are sure that among the readers of this text, there are those who, at home or in the office, use a robot vacuum cleaner and/or robotic carts to deliver various items. But these are by no means the most interesting examples of robotics technology. They are already being replaced by more advanced examples. An example of the next iteration is the Aeo service robot, which is capable of performing various actions using its manipulators. Another is Optimus, a series of humanoid robots from Tesla . It is expected that in the very near future - by 2025 - the market for cobots alone (i.e., robots that interact with people in the workspace) will exceed $12 billion . Aeo Aeo Tesla Tesla $12 billion $12 billion The current generation of service robots and cobots are already capable of taking over a number of routine, low-skill tasks. For example, they can perform the functions of nurses, security personnel, cleaners, porters, packers, waiters, pickers, "serve and fetch" assistants, and so on and so forth. As a rule, they are equipped with AI that is smart enough to execute voice commands that are given in any form. A separate topic is robotic transportation. According to forecasts, by 2030, 10% of all vehicles on the planet will be self-driving. And this figure is also rather modest in our opinion, and is not because of a lack of technical capability, but instead will result from a combination of price and psychological factors. 10% of all vehicles 10% of all vehicles As far as industrial robots are concerned, today, the creation of fully automated plants and factories is in full swing. In developed countries like South Korea, about 1,000 robots per 10,000 workers are being used in the manufacturing sector. This is a very approximate figure, but in this case, it is not the exact ratio of people to robots that matters. The salient issue is the complexity of these industrial robots. The idea of the factory of the future as an analog of traditional  "human-powered" production facilities in which human beings have been replaced by humanoid robots is fundamentally wrong. Anyone who has been to a robotized production facility knows that an ordinary industrial robot is, in fact, an automatic machine tool. Or an entire assembly line. You will agree that this kind of production unit cannot be considered simply as an improved (from the point of view of factory production) analog of a human being. Each such robot replaces entire teams and workshops. about 1,000 robots per 10,000 workers about 1,000 robots per 10,000 workers Illustration created with NightCafe Illustration created with NightCafe The logical development of this concept is the creation of fully automated production facilities. In other words, plants, factories, and other production facilities will each, in fact, be a large and incredibly complex robot. By the way, there is a place for humanoid robots in these production environments, too. However, they should not be considered industrial robots; instead, they should be identified as a new level of service and collaborative robots designed to interact with other robots and maintain their workspaces. What will it look like? Note that we consider most predictions for robotics adoption and development that are statistically based to be too conservative. Of course, solid researchers simply must proceed with caution. But we will allow ourselves to draw on a history in which technological progress has often raced ahead of predictions based on statistical analysis. Think of the rapid evolution of cell phones into smartphones or the first personal computers into light, sleek laptops. So, let's try to imagine a robotic life ten years from now based on a similar rapidity in development. Industrial and factory production will be fully roboticized. Mass-produced goods will be produced by robotic factories. Service robots and cobots will maintain and repair these production facilities; self-directed transportation will deliver raw materials to them and export their finished products. Agriculture will also become extremely automated. This is particularly true with the development of technologies for growing meat products. Traditional livestock farms will mostly disappear, which, it should be noted, will have a positive impact on the environment. Service robots will take over most of the work in commerce and the service industry, which hasn’t transitioned into virtual reality. They will also take care of household chores, repairs, construction, cleaning, street beautification, and the like. Freight transportation and public transport will be completely taken over by self-driving trains, planes, airships, copters, buses, cabs, air cabs, and so on. The share of self-driven private transportation will gradually increase. This trend will be accelerated under the pressure of increasingly stringent safety requirements for drivers. All of the above, combined with the development of artificial intelligence and augmented reality, will rid people almost completely of the work being performed currently that is strenuous, boring, monotonous, or poses a risk to health (or is simply dangerous). In other words, people are not going to have to do most of the work that people have to do now. But what will mankind do? At least that part of it that will enjoy civilization’s benefits? This is the final chapter of our future fantasy. Robert McCall – Metropolis 2050 #4 Robert McCall – Metropolis 2050 #4 The article was created in collaboration with Andriy Tkachenko The article was created in collaboration with Andriy Tkachenko

What Will the Spring of Technology Bring? Part 3: Robotic Life

From Nuisance to News Sense: Conclusion, Limitations and Ethic, and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw}@cs.cmu.edu);
(2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}@andrew.cmu.edu)
(3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA. Table of Links Abstract and Introduction
Related Work
The NEWSSENSE Framework
Pilot Study
System Overview
Discussion
Conclusion, Limitations and Ethics, and References
A. Appendix: Prompt for Claim Extraction 5 System Overview Following our user study, we implemented the NEWSSENSE framework as a browser plugin, which adds augmentations to news articles encountered on the web. Figure 1 shows the final appearance of the browser plugin. Code for system and plugin can be accessed at github.com/jmilbauer/NewsSense, and a demo video can be viewed at youtu.be/2D5LYbsQJak. This section provides a description of the natural language processing system which powers NEWSSENSE. Figure 2 illustrates the four general steps of the pipeline: Collection, Selection, Filtering, and Linking. 5.1 Article Collection First, we must collect a cluster of news articles that are all about the same news event. Our implementation scrapes data from Google News Stories, a website that collects many articles about the same events across news venues. After collecting article URLs via Google News Stories, we then collect the content of each article. A typical story contains over 50 articles. 5.2 Claim Selection The next phase of the pipeline is to select the claims within each article cluster. We initially assumed a 1-to-1 mapping between sentences and claims, but quickly found that news articles often contain complex multi-clause sentences, which are not suitable for natural language inference. To address this issue, we few-shot prompting to generate a list of claims from sentences using a large language model (LLM). In our experiments, prompt exemplars are drawn from the PROPSEGMENT dataset (Chen et al., 2022), and the LLM used is OpenAI text-davinci-003. Full prompt details are provided in Appendix A. We also note that the authors of PROPSEGMENT report that T5-Large performs reasonably well on the task, suggesting the possibility for further pipeline improvements. 5.3 Claim Filtering Articles often contain over 30 sentences. For a cluster of 50 articles, a pairwise comparison of the full cartesian product of sentences has O((NL) 2 ), which is in practice well over 1,000,000 comparisons. Performing this level of computation at scale, even if we are pre-computing results for each article cluster, is simply not feasible. To address this, we perform an initial filtering step with leverages the fact that the vast majority of claims across any two articles are unrelated. We consider two approaches for claim filtering: Embedding Similarity filtering (ES) and Lexical Overlap filtering (LeO). For Embedding Similarity filtering, we encode each claim in each article using a Transformerbased sentence encoder. Then, for each claim we retain only the k most similar other claims for comparison. In our implementation, we use the Sentence Transformers (Reimers and Gurevych, 2019) all-Mini-LM-L6-v2. For Lexical Overlap filtering, we compare each sentence only with sentences that have overlapping words, as these sentences are likely to discuss similar topics. In our implementation, we process claims by first remove stopwords, then stemming using the NLTK (Loper and Bird, 2002) implementation of the Porter Stemmer (Porter, 1980), and compute overlap scores using the Jaccard Index. We evaluated each filtering method on the MNLI (Williams et al., 2017) validation data, treating pairs of randomly sampled sentences as negative examples, and labeled “entailment" and “contradiction" sentence pairs as positive examples. For ES, we set a threshold of 0.3 cosine similarity; for LeO, we set a threshold of 0.1 overlap. We note that all-Mini-LM-L6-v2 included MNLI in its training data. We include a summary of the results of these experiments in Table 1, which indicates that the ES method outperforms the LeO method. Of particular interest is the true negative rate, as this indicates the percentage of non-related sentences we expect to filter out. 5.4 Claim Linking Once claim pairs have been filtered, we classify each pair according to the Natural Language Inference (NLI) framework, as “entailment," “contradiction," or “neutral." We employ a pretrained language model, RoBERTa (Liu et al., 2019), which was then fine-tuned on MNLI (Williams et al., 2017), a popular dataset for NLI. We download this fine-tuned version of RoBERTa from the Hugging Face model library [11]. To avoid clutter, we keep fewer than 100 of the most confident predictions for each positive class (entailment or contradiction) within the article cluster. Claims are then assigned back to the sentences from which they were generated, and the sentence pairs are linked. [11] https://huggingface.co/roberta-large-mnli This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw}@cs.cmu.edu); (2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}@andrew.cmu.edu) (3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw}@cs.cmu.edu); (2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}@andrew.cmu.edu) (3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA. Table of Links Abstract and Introduction Related Work The NEWSSENSE Framework Pilot Study System Overview Discussion Conclusion, Limitations and Ethics, and References A. Appendix: Prompt for Claim Extraction Abstract and Introduction Abstract and Introduction Related Work Related Work The NEWSSENSE Framework The NEWSSENSE Framework Pilot Study Pilot Study System Overview System Overview Discussion Discussion Conclusion, Limitations and Ethics, and References Conclusion, Limitations and Ethics, and References A. Appendix: Prompt for Claim Extraction A. Appendix: Prompt for Claim Extraction 5 System Overview Following our user study, we implemented the NEWSSENSE framework as a browser plugin, which adds augmentations to news articles encountered on the web. Figure 1 shows the final appearance of the browser plugin. Code for system and plugin can be accessed at github.com/jmilbauer/NewsSense, and a demo video can be viewed at youtu.be/2D5LYbsQJak. This section provides a description of the natural language processing system which powers NEWSSENSE. Figure 2 illustrates the four general steps of the pipeline: Collection, Selection, Filtering, and Linking. 5.1 Article Collection First, we must collect a cluster of news articles that are all about the same news event. Our implementation scrapes data from Google News Stories, a website that collects many articles about the same events across news venues. After collecting article URLs via Google News Stories, we then collect the content of each article. A typical story contains over 50 articles. 5.2 Claim Selection The next phase of the pipeline is to select the claims within each article cluster. We initially assumed a 1-to-1 mapping between sentences and claims, but quickly found that news articles often contain complex multi-clause sentences, which are not suitable for natural language inference. To address this issue, we few-shot prompting to generate a list of claims from sentences using a large language model (LLM). In our experiments, prompt exemplars are drawn from the PROPSEGMENT dataset (Chen et al., 2022), and the LLM used is OpenAI text-davinci-003. Full prompt details are provided in Appendix A. We also note that the authors of PROPSEGMENT report that T5-Large performs reasonably well on the task, suggesting the possibility for further pipeline improvements. 5.3 Claim Filtering Articles often contain over 30 sentences. For a cluster of 50 articles, a pairwise comparison of the full cartesian product of sentences has O((NL) 2 ), which is in practice well over 1,000,000 comparisons. Performing this level of computation at scale, even if we are pre-computing results for each article cluster, is simply not feasible. To address this, we perform an initial filtering step with leverages the fact that the vast majority of claims across any two articles are unrelated. We consider two approaches for claim filtering: Embedding Similarity filtering (ES) and Lexical Overlap filtering (LeO). For Embedding Similarity filtering, we encode each claim in each article using a Transformerbased sentence encoder. Then, for each claim we retain only the k most similar other claims for comparison. In our implementation, we use the Sentence Transformers (Reimers and Gurevych, 2019) all-Mini-LM-L6-v2. k For Lexical Overlap filtering, we compare each sentence only with sentences that have overlapping words, as these sentences are likely to discuss similar topics. In our implementation, we process claims by first remove stopwords, then stemming using the NLTK (Loper and Bird, 2002) implementation of the Porter Stemmer (Porter, 1980), and compute overlap scores using the Jaccard Index. We evaluated each filtering method on the MNLI (Williams et al., 2017) validation data, treating pairs of randomly sampled sentences as negative examples, and labeled “entailment" and “contradiction" sentence pairs as positive examples. For ES, we set a threshold of 0.3 cosine similarity; for LeO, we set a threshold of 0.1 overlap. We note that all-Mini-LM-L6-v2 included MNLI in its training data. We include a summary of the results of these experiments in Table 1, which indicates that the ES method outperforms the LeO method. Of particular interest is the true negative rate, as this indicates the percentage of non-related sentences we expect to filter out. 5.4 Claim Linking Once claim pairs have been filtered, we classify each pair according to the Natural Language Inference (NLI) framework, as “entailment," “contradiction," or “neutral." We employ a pretrained language model, RoBERTa (Liu et al., 2019), which was then fine-tuned on MNLI (Williams et al., 2017), a popular dataset for NLI. We download this fine-tuned version of RoBERTa from the Hugging Face model library [11]. To avoid clutter, we keep fewer than 100 of the most confident predictions for each positive class (entailment or contradiction) within the article cluster. Claims are then assigned back to the sentences from which they were generated, and the sentence pairs are linked. [11] https://huggingface.co/roberta-large-mnli

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