The U.S. Department of Justice has announced that Health Net Federal Services (HNFS) and its parent company, Centene Corporation, have agreed to pay a Health Net Federal Services, a former administrator of the TRICARE military healthcare program, has agreed to settle allegations of cybersecurity ...

TRICARE Administrator Pays $11.23M Penalty to Resolve Cybersecurity-related FCA Claims

Health Net Federal Services (HNFS) and its parent company, Centene Corporation, have agreed to pay $11,253,400 to settle allegations that HNFS falsely certified compliance with cybersecurity requirements under its Defense Health Agency (DHA) TRICARE contract.

US healthcare org pays $11M settlement over alleged cybersecurity lapses

Whether you're a value, growth, or momentum investor, finding strong stocks becomes easier with the Zacks Style Scores, a top feature of the Zacks Premium research service.

Here's Why Centene (CNC) is a Strong Growth Stock

Albany Med Health System is leading the way in providing comprehensive breast health services that support and guide patients and their families along every step of their journey.

Columbia Memorial Health’s Center for Breast Health Supports Patients Through the Healthcare Journey

Gov. Tate Reeves’ top campaign contributors netted $1.4 billion in state contracts or grants from agencies the governor oversees.

Gov. Tate Reeves’ top political donors received $1.4 billion in state contracts from his agencies

It then lost two appeals and a legislative effort to hand it a contract. House Speaker Cameron Sexton, R-Crossville, sponsored the bill to give Rhythm, a subsidiary of Centene Corp., a fourth state contract with TennCare even though it lost out in the bidding.

Tennessee House Republicans push for delay of TennCare’s main contracts

Mutual of Omaha, a highly rated Fortune 400 organization offering a variety of insurance and financial products for individuals, businesses and groups throughout the United States, today announced it has formed a strategic alliance with Wellcare,

Mutual of Omaha and Wellcare to Offer Co-Branded Medicare Advantage Plans for 2024 Plan Year

The Medicare Advantage plans, which include no premium and low premium options, will be available in select markets as part of this fall's Annual Enrollment Period, which began Oct. 15 and runs through Dec. 7.

Tennessee’s House Speaker Cameron Sexton opposes extension of TennCare contracts as part of an effort to give a contract to a firm that lost a bid.

House Republicans push for delay of TennCare’s main contracts

Here is a selection of recent executive hires, promotions and layoffs occurring across the healthcare industry.

Healthcare Moves: A Monthly Summary of Hires and Layoffs

Centene

centene

Mr. Chennupati is a name that resonates profoundly within the dynamic world of cloud computing. Known for his strategic acumen and technical prowess, he has carved out an impressive career characterized by transformative cloud architecture, migration, and optimization projects. Mr. Chennupati brings over 14 years of extensive experience in collaborating with enterprise-level firms. His journey spans multiple sectors, including data storage, healthcare, security, SaaS, Cloud, AI, and IT consulting. As a forward-thinking leader, Mr. Chennupati has successfully guided numerous enterprises in harnessing cloud computing to meet their goals. His expertise lies particularly in cloud architecture, migration, and optimization, which he tailors to align with business objectives while staying ahead of industry trends. His work in cloud migration has been recognized with accolades such as the Oracle Innovation Award, underscoring his innovative contributions to the field. Early Career and Foundational Experiences In his lead roles at Coupa Software, Centene Corporation, and Veritas Technologies, Mr. Chennupati played a pivotal part in executing complex cloud migrations, each presenting unique challenges. At Coupa, he led the migration of travel applications to Coupa SaaS Platform, ensuring zero disruption by conducting staged migrations and thorough functionality checks. This meticulous approach guaranteed a seamless transition, maintaining operational continuity. At Centene, Mr. Chennupati effectively addressed the challenge of migrating 45 million enrollment member records from RedHat OpenShift to AWS Elastic Kubernetes Service (EKS). He created the architecture design for this large-scale migration, ensuring a smooth and efficient transition. He emphasized the importance of compliance and implementing stringent encryption and robust access controls to meet HIPAA standards. Similarly, at Veritas, he oversaw the migration of critical applications for 80,000 customers, leveraging advanced real-time data replication and rigorous pre-migration testing to transition to Oracle Cloud smoothly. These strategic efforts not only minimized risks and ensured regulatory compliance but also enhanced scalability, driving business growth and technological advancement. Planning and Executing Large-scale Cloud Migrations Mr. Chennupati’s approach to cloud migration can be best described as both meticulous and strategic. Leading the migration from OpenShift to AWS EKS at Centene Corporation, he focused on efficiency, security, and compliance. His approach began with a thorough assessment of applications and dependencies, followed by a phased migration plan. "I configured a parallel AWS environment, replicating our existing setup, and set up VPCs, IAM roles, and security groups," he explains. Critical applications were containerized with Docker, ensuring seamless deployment. Post-migration, security assessments using AWS CloudWatch, Prometheus, Grafana and ELK stack to ensure everything functioned correctly. Ensuring HIPAA compliance was a top priority, with optimized data security measures integrated throughout the process. This strategic migration significantly enhanced operational agility, reduced costs, and improved both scalability and security, showcasing Mr. Chennupati’s expertise in managing large-scale cloud migrations with minimal disruption. Key Strategies for Success Drawing from extensive experience in cloud migrations, Mr. Chennupati has developed a set of key strategies crucial for companies looking to move significant operations to the cloud. He recommends starting with a thorough initial assessment to evaluate the current IT setup, determining what can move to the cloud and what needs updating. "Define your performance, security, and compliance goals," he advises. Creating a phased migration plan focusing on critical applications can minimize disruptions. Selecting the appropriate cloud model and service model based on specific needs is essential for a successful transition. Mr. Chennupati also stresses the importance of implementing robust security measures like encryption and firewalls and ensuring regulatory compliance. "Utilize cloud provider migration tools, train your IT staff, and manage change effectively," he adds. Post-migration, continuous optimization of the cloud setup, and developing comprehensive disaster recovery strategies are crucial for sustained efficiency and security. These steps collectively ensure a smooth transition and a robust cloud environment. The Oracle Award project The Oracle Award for Innovation was bestowed upon Mr. Chennupati for his exemplary work in migrating Veritas Technologies' extensive data centers to Oracle Cloud Infrastructure (OCI). This project involved the seamless transition of critical systems, including WebLogic, SOA, and Oracle ERP applications for over 80,000 customers. "We successfully transitioned key systems, ensuring no disruption to daily operations," Mr. Chennupati explains. Key strategies included a custom migration plan with tailored tools and seamless data synchronization using Oracle’s replication technology. Additionally, optimized load management through OCI’s load balancing and robust security with advanced encryption and access controls were crucial. Post-migration, efficiency was enhanced, and comprehensive monitoring was established to maintain stability and reliability. These innovations set a new standard for large-scale cloud migrations, showcasing Mr. Chennupati’s ability to drive significant technological advancements. Optimization and Innovation in Cloud Solutions The approach to optimizing cloud solutions is both methodical and innovative in Mr. Chennupati’s work. "We leveraged AWS Auto Scaling and Azure Autoscale to dynamically fine-tune resources, seamlessly aligning with fluctuating demand while optimizing expenditures," Mr. Chennupati explains. He also implements real-time data replication and load management, significantly improving user experience and system robustness. By employing Amazon CloudFront and Azure CDN, he drastically cuts latency and boosts performance. “Tools like AWS Cost Explorer and Azure Cost Management were instrumental in reshaping our financial strategies, eliminating wasteful spending by pinpointing underutilized resources,” he adds. Through continuous monitoring and intelligent data stewardship, their cloud infrastructure now exemplifies operational excellence and fiscal prudence. Addressing Misconceptions and Pitfalls in Cloud Migrations Cloud migration often involves numerous misconceptions, particularly regarding its complexity and cost management. To address these issues, Mr. Chennupati conducts thorough pre-migration assessments to ensure applications are cloud-compatible and educates teams on proactive resource monitoring to control expenses. "I integrate specific security measures and compliance protocols right from the start," he explains, emphasizing a custom approach to security concerns. Additionally, comprehensive training and change management are essential to ease staff transition. Post-migration, Mr. Chennupati focuses on continuous optimization and monitoring to ensure the cloud setup aligns with organizational goals. This holistic strategy not only prevents common pitfalls but also maximizes the benefits of cloud migration. Evolution and Future of Cloud Architecture Reflecting on the evolution of cloud architecture, Mr. Chennupati notes the significant transformations from basic storage and SaaS applications to robust IaaS and PaaS capabilities within complex hybrid and multi-cloud systems. "This growth has been fueled by advances in serverless computing, containerization, and microservices," he observes. These innovations have dramatically improved scalability and management efficiency, fundamentally changing the landscape of cloud computing. Looking to the future, Mr. Chennupati predicts that cloud architecture will increasingly prioritize sustainability, aiming to minimize the environmental impact of data center operations. The integration of quantum computing is expected to bring extraordinary processing power, revolutionizing data handling and processing. Multi-cloud environments will become more prevalent, demanding improved interoperability across platforms. Furthermore, AI's role will expand into more complex system automation, real-time analytics, and sophisticated security protocols to counter advanced cyber threats. "In the next five years, these innovations will propel cloud solutions to be more automated, secure, and transformative," Mr. Chennupati asserts. Mr. Chennupati’s expertise in cloud computing has consistently driven significant advancements and innovations in the field. His ability to lead large-scale migrations and optimize cloud solutions has transformed operations for numerous organizations, setting new benchmarks for efficiency and security. From integrating businesses at Coupa Software to pioneering migration strategies recognized by Oracle, Mr. Chennupati’s work reflects a deep commitment to excellence and continuous improvement. As cloud architecture continues to evolve, his forward-thinking approach and dedication to sustainability and automation position him as a key figure in shaping the future of cloud solutions. His journey serves as an inspiring example of how strategic vision and technical skill can create impactful, lasting change in the technology landscape. Mr. Chennupati is a name that resonates profoundly within the dynamic world of cloud computing. Known for his strategic acumen and technical prowess, he has carved out an impressive career characterized by transformative cloud architecture, migration, and optimization projects. Mr. Chennupati brings over 14 years of extensive experience in collaborating with enterprise-level firms. His journey spans multiple sectors, including data storage, healthcare, security, SaaS, Cloud, AI, and IT consulting. As a forward-thinking leader, Mr. Chennupati has successfully guided numerous enterprises in harnessing cloud computing to meet their goals. His expertise lies particularly in cloud architecture, migration, and optimization, which he tailors to align with business objectives while staying ahead of industry trends. His work in cloud migration has been recognized with accolades such as the Oracle Innovation Award, underscoring his innovative contributions to the field. Early Career and Foundational Experiences In his lead roles at Coupa Software, Centene Corporation, and Veritas Technologies, Mr. Chennupati played a pivotal part in executing complex cloud migrations, each presenting unique challenges. At Coupa, he led the migration of travel applications to Coupa SaaS Platform, ensuring zero disruption by conducting staged migrations and thorough functionality checks. This meticulous approach guaranteed a seamless transition, maintaining operational continuity. At Centene, Mr. Chennupati effectively addressed the challenge of migrating 45 million enrollment member records from RedHat OpenShift to AWS Elastic Kubernetes Service (EKS). He created the architecture design for this large-scale migration, ensuring a smooth and efficient transition. He emphasized the importance of compliance and implementing stringent encryption and robust access controls to meet HIPAA standards. Similarly, at Veritas, he oversaw the migration of critical applications for 80,000 customers, leveraging advanced real-time data replication and rigorous pre-migration testing to transition to Oracle Cloud smoothly. These strategic efforts not only minimized risks and ensured regulatory compliance but also enhanced scalability, driving business growth and technological advancement. Planning and Executing Large-scale Cloud Migrations Mr. Chennupati’s approach to cloud migration can be best described as both meticulous and strategic. Leading the migration from OpenShift to AWS EKS at Centene Corporation, he focused on efficiency, security, and compliance. His approach began with a thorough assessment of applications and dependencies, followed by a phased migration plan. "I configured a parallel AWS environment, replicating our existing setup, and set up VPCs, IAM roles, and security groups," he explains. Critical applications were containerized with Docker, ensuring seamless deployment. Post-migration, security assessments using AWS CloudWatch, Prometheus, Grafana and ELK stack to ensure everything functioned correctly. Ensuring HIPAA compliance was a top priority, with optimized data security measures integrated throughout the process. This strategic migration significantly enhanced operational agility, reduced costs, and improved both scalability and security, showcasing Mr. Chennupati’s expertise in managing large-scale cloud migrations with minimal disruption. Key Strategies for Success Drawing from extensive experience in cloud migrations, Mr. Chennupati has developed a set of key strategies crucial for companies looking to move significant operations to the cloud. He recommends starting with a thorough initial assessment to evaluate the current IT setup, determining what can move to the cloud and what needs updating. "Define your performance, security, and compliance goals," he advises. Creating a phased migration plan focusing on critical applications can minimize disruptions. Selecting the appropriate cloud model and service model based on specific needs is essential for a successful transition. Mr. Chennupati also stresses the importance of implementing robust security measures like encryption and firewalls and ensuring regulatory compliance. "Utilize cloud provider migration tools, train your IT staff, and manage change effectively," he adds. Post-migration, continuous optimization of the cloud setup, and developing comprehensive disaster recovery strategies are crucial for sustained efficiency and security. These steps collectively ensure a smooth transition and a robust cloud environment. The Oracle Award project The Oracle Award for Innovation was bestowed upon Mr. Chennupati for his exemplary work in migrating Veritas Technologies' extensive data centers to Oracle Cloud Infrastructure (OCI). This project involved the seamless transition of critical systems, including WebLogic, SOA, and Oracle ERP applications for over 80,000 customers. "We successfully transitioned key systems, ensuring no disruption to daily operations," Mr. Chennupati explains. Key strategies included a custom migration plan with tailored tools and seamless data synchronization using Oracle’s replication technology. Additionally, optimized load management through OCI’s load balancing and robust security with advanced encryption and access controls were crucial. Post-migration, efficiency was enhanced, and comprehensive monitoring was established to maintain stability and reliability. These innovations set a new standard for large-scale cloud migrations, showcasing Mr. Chennupati’s ability to drive significant technological advancements. Optimization and Innovation in Cloud Solutions The approach to optimizing cloud solutions is both methodical and innovative in Mr. Chennupati’s work. "We leveraged AWS Auto Scaling and Azure Autoscale to dynamically fine-tune resources, seamlessly aligning with fluctuating demand while optimizing expenditures," Mr. Chennupati explains. He also implements real-time data replication and load management, significantly improving user experience and system robustness. By employing Amazon CloudFront and Azure CDN, he drastically cuts latency and boosts performance. “Tools like AWS Cost Explorer and Azure Cost Management were instrumental in reshaping our financial strategies, eliminating wasteful spending by pinpointing underutilized resources,” he adds. Through continuous monitoring and intelligent data stewardship, their cloud infrastructure now exemplifies operational excellence and fiscal prudence. Addressing Misconceptions and Pitfalls in Cloud Migrations Cloud migration often involves numerous misconceptions, particularly regarding its complexity and cost management. To address these issues, Mr. Chennupati conducts thorough pre-migration assessments to ensure applications are cloud-compatible and educates teams on proactive resource monitoring to control expenses. "I integrate specific security measures and compliance protocols right from the start," he explains, emphasizing a custom approach to security concerns. Additionally, comprehensive training and change management are essential to ease staff transition. Post-migration, Mr. Chennupati focuses on continuous optimization and monitoring to ensure the cloud setup aligns with organizational goals. This holistic strategy not only prevents common pitfalls but also maximizes the benefits of cloud migration. Evolution and Future of Cloud Architecture Reflecting on the evolution of cloud architecture, Mr. Chennupati notes the significant transformations from basic storage and SaaS applications to robust IaaS and PaaS capabilities within complex hybrid and multi-cloud systems. "This growth has been fueled by advances in serverless computing, containerization, and microservices," he observes. These innovations have dramatically improved scalability and management efficiency, fundamentally changing the landscape of cloud computing. Looking to the future, Mr. Chennupati predicts that cloud architecture will increasingly prioritize sustainability, aiming to minimize the environmental impact of data center operations. The integration of quantum computing is expected to bring extraordinary processing power, revolutionizing data handling and processing. Multi-cloud environments will become more prevalent, demanding improved interoperability across platforms. Furthermore, AI's role will expand into more complex system automation, real-time analytics, and sophisticated security protocols to counter advanced cyber threats. "In the next five years, these innovations will propel cloud solutions to be more automated, secure, and transformative," Mr. Chennupati asserts. Mr. Chennupati’s expertise in cloud computing has consistently driven significant advancements and innovations in the field. His ability to lead large-scale migrations and optimize cloud solutions has transformed operations for numerous organizations, setting new benchmarks for efficiency and security. From integrating businesses at Coupa Software to pioneering migration strategies recognized by Oracle, Mr. Chennupati’s work reflects a deep commitment to excellence and continuous improvement. As cloud architecture continues to evolve, his forward-thinking approach and dedication to sustainability and automation position him as a key figure in shaping the future of cloud solutions. His journey serves as an inspiring example of how strategic vision and technical skill can create impactful, lasting change in the technology landscape.

Cloud Architecture and Migration: The Expertise and Achievements of Anand Kumar Chennupati

Cloudová architektura a migrace: Odbornost a úspěchy Ananda Kumara Chennupatiho

Skyarkitektur og migration: Anand Kumar Chennupatis ekspertise og resultater

Cloud-Architektur und -Migration: Das Fachwissen und die Erfolge von Anand Kumar Chennupati

Arquitectura y migración a la nube: la experiencia y los logros de Anand Kumar Chennupati

クラウド アーキテクチャと移行: Anand Kumar Chennupati の専門知識と実績

Architecture ya mapata mpe migration: Expertise mpe ba réussites ya Anand Kumar Chennupati

Seni Bina Awan dan Migrasi: Kepakaran dan Pencapaian Anand Kumar Chennupati

د کلاوډ معمارۍ او مهاجرت: د آنند کمار چینوپتي تجربه او لاسته راوړنې

Bulut arhitekturasy we göç: Anand Kumar Chennupatiniň tejribesi we üstünlikleri

Хмарна архітектура та міграція: досвід і досягнення Ананда Кумара Ченнупаті

کلاؤڈ آرکیٹیکچر اور ہجرت: آنند کمار چینوپتی کی مہارت اور کامیابیاں

Kiến trúc đám mây và di chuyển: Chuyên môn và thành tựu của Anand Kumar Chennupati

My name is Jason Charney, and this is my first post here on . (Or should it be Nooners? There's some UNIX newsletter I subscribed to in the past year where the writer calls everybody Nixers. How about Nooninites!) Hackernoon Assuming there are a few features here that aren't on Medium, I'd like to start moving some of my tech-writing here. (Assuming that that's what Dave has allowed us to do here?) I am a Linux geek as well as a math nerd and radio enthusiast. I am a 2007 undergraduate from in St. Louis who has a Bachelors of Science in Computer Science. Maryville University I've run since 2003, however sometime around 2016, I started using . I've been spending a lot of time on lately. I've got a few projects going on . my own website Medium Codepen Github St. Louis is home to a lot of technology and business.  From startup incubators like , , , , , and , to corporate powerhouses like , , , , , , , , , Microsoft, , MasterCard, Bayer (nee Monsanto), , Boeing, and of course Anheuser-Busch, to government agencies like the and and organizations like the . T-REX Nebula Cortex TechArtista Covo Rise Worldwide Technology Graybar Emerson Electric Centene Panera Bread Watlow Express Scripts Square Pfizer Riot Games Nestle Purina National Geospacial-Intelligence Agency St. Louis Federal Reserve Danforth Plant Science Center There's been a lot of amazing stuff happening here lately. The St. Louis Blues won the Stanley Cup for the first time back in June. The Cardinals (at the time of this writing) are back on top in the National League Central Division. The Gateway Arch finished a multi-million dollar renovation. And Google is planning on stopping by at the end of the month to hold one of their events which I hope to be part of so I can tell everybody about it. Google Sandbox I know most of that doesn't sound that exciting or tech related. I thought maybe a couple of local icebreakers might add a little bit of content. I plant to write mostly about coding and how-to stuff as well as some things I've been doing lately. I'm sure other people might have things to post that are light-years ahead of what I've been fiddling around with lately, but nobody goes through high school without going through kindergarten, elementary school, and middle school/junior high first. So maybe there will be some easy stuff here or more complicated stuff.  It all depends really. Maybe I should write stuff such that I don't have 900+ tabs always open in Chrome which are slowly killing my 2016 Dell XPS 13. I'd really like to burn an Arch Linux distro onto a USB drive. So if anyone else is interested in learning how to do that, I'm hoping to post something like that here in the very near future. At any rate, welcome to post #1. It's great to meet you! Editor's Note: Meet more Hackernooners on the "Please Introduce Yourself" Thread

Greetings, Hackernooners!

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Survey with Industry Professionals 2 Survey with Industry Professionals 3 RQ1: Real-World use cases that necessitate output constraints 3 RQ1: Real-World use cases that necessitate output constraints 4 RQ2: Benefits of Applying Constraints to LLM Outputs and 4.1 Increasing Prompt-based development Efficiency 4 RQ2: Benefits of Applying Constraints to LLM Outputs and 4.1 Increasing Prompt-based development Efficiency 4.2 Integrating with Downstream Processes and Workflows 4.2 Integrating with Downstream Processes and Workflows 4.3 Satisfying UI and Product Requirements and 4.4 Improving User Experience, Trust, and Adoption 4.3 Satisfying UI and Product Requirements and 4.4 Improving User Experience, Trust, and Adoption 5 How to Articulate output constraints to LLMS and 5.1 The case for GUI: A Quick, Reliable, and Flexible Way of Prototyping Constraints 5 How to Articulate output constraints to LLMS and 5.1 The case for GUI: A Quick, Reliable, and Flexible Way of Prototyping Constraints 5.2 The Case for NL: More Intuitive and Expressive for Complex Constraints 5.2 The Case for NL: More Intuitive and Expressive for Complex Constraints 6 The Constraint maker Tool and 6.1 Iterative Design and User Feedback 6 The Constraint maker Tool and 6.1 Iterative Design and User Feedback 7 Conclusion and References 7 Conclusion and References A. The Survey Instrument A. The Survey Instrument ABSTRACT Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool. 1 INTRODUCTION Over the past few years, we have witnessed the extraordinary capability of Large Language Models (LLMs) to generate responses that are not only creative and diverse but also highly adaptable to various user needs [5, 7, 18, 21, 22, 29, 31, 32]. For example, researchers can prompt ChatGPT [25] to condense long articles into concise summaries for fast digestion; while video game developers can generate detailed character profiles with rich personality traits, backstories, and unique abilities on demand, simply by dynamically prompting an LLM with the game context and players’ preferences. As much as end-users appreciate the unbounded creativity of LLMs, recent field studies examining the development of LLMpowered applications have repeatedly demonstrated the necessity to impose constraints on LLM outputs [10, 30]. For instance, a user might require a summary of an article to be “strictly less than 20 words” to meet length constraints, or a generated video game character profile to be “a valid JSON that can be parsed by Python” for a development pipeline. However, as evidenced by many recent NLP benchmarks and evaluations [16, 36, 42, 43], current state-of-the-art LLMs still lack the ability to guarantee that the generated output will invariably conform to user-defined constraints in the prompt (sometimes referred to as controllability). Although researchers have proposed various methods to improve controllability, such as supervised finetuning with specialized datasets [35] or controlled decoding strategies [1, 4, 24, 40], they tend to only focus on addressing a narrow range of constraints without taking into consideration the diverse usage scenarios and rationale that real-world developers and end-users encounter when prototyping and building practical LLM-powered functionalities and applications [12–14, 23]. In this work, we took the first step to systematically investigate the scenarios and motivations for applying output constraints from a user-centered perspective. Specifically, we sought to understand: • RQ1: What real-world use cases would necessitate or benefit from being able to constrain LLM outputs? RQ1 • RQ2: What are the benefits and impacts of being able to apply constraints to LLM outputs? RQ2 • RQ3: How would users like to articulate their intended constraints to LLMs? RQ3 We investigated these research questions by distributing a survey to a broad population of industry professionals (software engineers, researchers, designers, project managers, etc.) who have experience building LLM-powered applications. Our analysis identified six primary categories of output constraints that users desire, each supported by detailed usage scenarios and illustrative examples, summarized in Table 1. In a nutshell, users not only need low-level constraints, which mandate the output to conform to a structured format and an appropriate length, but also desire high-level constraints, which involve semantic and stylistic guidelines that users would like the model output to adhere to without hallucinating. Notably, developers often have to write complex code to handle ill-formed LLM outputs, a chore that could be simplified or eliminated if LLMs could strictly follow output constraints. In addition, the ability to apply constraints could help ease the integration of LLMs with existing pipelines, meet UI and product specifications, and enhance user trust and experience with LLM-powered features. Moreover, we discovered that describing constraints in natural language (NL) within prompts is not always the preferred method of control for LLM users. Instead, they seek alternative mechanisms, such as using graphical user interfaces, or GUIs, to define and test constraints, which could offer greater flexibility and a heightened sense of assurance that the constraints will be strictly followed. Informed by these results, we present an early design of ConstraintMaker, a prototype tool that enables LLM users to experiment, test, and apply constraints on the format of LLM outputs (see Figure 2 for more details), along with feedback and insights from preliminary user tests. Overall, this paper contributes: • a comprehensive taxonomy summarizing both low-level and high-level output constraints desired by LLM users (Table 1), derived from 134 real-world use cases reported by our survey respondents (RQ1), • an overview of both developer and user-facing benefits of being able to impose constraints on LLM outputs (RQ2), • an exploration of LLM users’ preferences for expressing constraints, whether via GUIs or natural language (RQ3), • a initial design of the tool ConstraintMaker, which enables users to visually prototype LLM output constraints, accompanied by a discussion of preliminary user feedback. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors:
(1) Michael Xieyang Liu, Google Research, Pittsburgh, PA, USA (lxieyang@google.com);
(2) Frederick Liu, Google Research, Seattle, Washington, USA (frederickliu@google.com);
(3) Alexander J. Fiannaca, Google Research, Seattle, Washington, USA (afiannaca@google.com);
(4) Terry Koo, Google, Indiana, USA (terrykoo@google.com);
(5) Lucas Dixon, Google Research, Paris, France (ldixon@google.com);
(6) Michael Terry, Google Research, Cambridge, Massachusetts, USA (michaelterry@google.com);
(7) Carrie J. Cai, Google Research, Mountain View, California, USA (cjcai@google.com). Authors: Authors: (1) Michael Xieyang Liu, Google Research, Pittsburgh, PA, USA (lxieyang@google.com); (2) Frederick Liu, Google Research, Seattle, Washington, USA (frederickliu@google.com); (3) Alexander J. Fiannaca, Google Research, Seattle, Washington, USA (afiannaca@google.com); (4) Terry Koo, Google, Indiana, USA (terrykoo@google.com); (5) Lucas Dixon, Google Research, Paris, France (ldixon@google.com); (6) Michael Terry, Google Research, Cambridge, Massachusetts, USA (michaelterry@google.com); (7) Carrie J. Cai, Google Research, Mountain View, California, USA (cjcai@google.com).

"We Need Structured Output": Towards User-Centered Constraints on Large Language Model Output

Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes. Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes.

Design and Data Science: From a Human-in-the-Loop Approach to Human-Centered Design

The TechBeat: Mapping CX/UX Research Competencies to Stay Human-Centered in Product Development (10/22/2024)

The TechBeat: Mapping CX/UX Research Competencies to Stay Human-Centered in Product Development (10/20/2024)

The TechBeat: Mapping CX/UX Research Competencies to Stay Human-Centered in Product Development (10/16/2024)

Mapping CX/UX Research Competencies to Stay Human-Centered in Product Development

The virtual and alternative universe that is full of 3D spaces where we can have real-life-like experiences such as attending virtual seminars, shopping, purchasing digital properties mapped with real-world addresses, and many more, is called Metaverse. The Metaverse exists on the internet and it is going to entirely change the way we interact with each other on the internet. Web 3 and blockchain technology are strong foundational technology. I have already written multiple articles on Hackernoon mentioning so many aspects like the architecture of Metaverse, DeFi on the Metaverse, real-life use cases, Metaverse gaming, and many more. I hope you will love reading these two articles: DeFi in the Metaverse: Possibilities, Pitfalls, and Promises Metaverse Entertainment HackerNoon Prompt: So, let’s start! First, tell us a bit about yourself. For example, name, profession, and personal interests. I’m Vision NP and I’m a software developer, tech writer, and young physicist. Quantum Mechanics is my favorite branch of physics where I studied the behaviors of matter on an atomic and subatomic scale with corresponding mathematical treatments in-depth. Later, I started to explore the possibilities to automate science to solve so many complex mathematical problems in a concise duration. Some researchers had already discovered the possibilities of creating advanced “ ” getting inspired by quantum mechanical phenomena like superposition, interference, and entanglement. This is the turning point, I got attracted by blockchain technology where I have seen endless possibilities without centralized control. As I have a strong technical and mathematical background, my experiences are amazing in this field of tech. Quantum Computing How would you explain the Metaverse to your Mum? Hmm, interesting question! As my mum has a non-technical background, I should apply some tricky methods to make the Metaverse concept understandable. Well, she regrets seeing me playing games on PC every time so gaming is not the way to start with😝. I have to spot her area of interest. She loves purchasing Green Saree so, I would start this way. If you have to purchase an attractive Saree, you can test it virtually on the internet before reaching the shopping mall which will dramatically save you time and effort to pick the best suit, Green Saree, for you. This virtual world is called Metaverse. Her response would be then. aH-Ha Are you currently working on building a metaverse? If yes, shill it below ;) Not now but we will initiate building the Metaverse project shortly. Currently, we are working very hard to create the most ambitious and next-generation AI-enabled blockchain technology, and dApps on the Everscale blockchain. We need the best blockchain network with the following features to build the top-class Metaverse project that is as follows: High throughput, Highly scalable, Low latency, Unlimited bandwidth sharing, Better storage scaling, AI-enabled functionalities, and many more. What interests you the most about the possibilities of the Metaverse? Working on the Metaverse has endless possibilities, not a single one to mention here, to be honest, but if you wish me to express one, any sort of industry would get involved in the Metaverse sooner or later.  The projects like Sandbox, Decentraland, etc. have proved, the future of Metaverse is brightest than we have believed so far. Governments of different nations and private sector companies are flowing the cash to create the best-suit Metaverse because they have understood the possible values that the Metaverse could bring to this world. Recently, the social media giant Facebook was rebranded to Meta so it is the hottest topic to discuss on the internet now. If you are a developer, it could be an attractive full-time job working on the Metaverse because gaming, FinTech, and online E-commerce platforms are searching for the transition to the upgraded version of tech to meet the growing demands of users. Alright, let’s check the projected worldwide metaverse market size by which is estimated to reach over 1607 B by 2030. Emergen Research Here I have some remarkable applications of Metaverse to mention that are as follows: Metaverse could help scientific research like astronomy and the biomedical field could get an extra opportunity to conduct several types of research effectively in a short duration. It could be the digital destination for any sort of user in the near future. Most of the Metaverse would be open-source so developers would get the freedom to develop the best suit products and services. Easy-to-Use interface of Metaverse could easily captivate users. What do you hate the most about the current crop of centralized Gaming/VR industry? First, I utterly hate the centralized control over users’ data and privacy. No matter whether centralized gaming platforms are good or bad, if malicious attackers managed to hack the platforms, the sensitive data of users could be misused. Here are a few others to mention pointwise: Users have no full control over their in-app properties. High latency issue. Scalability issue is problematic because, during high traffic time, system down or server instability occurs. Implementation of VR gaming devices is very expensive and they are also responsible for radiation exposure. No effective Play-to-Earn economy to be worth playing time and no NFT creation to have indisputable ownership of the in-game assets. Thanks for your time! Any final words? Thank you too. Well, scientists are working hard to find the best semiconductor materials that will be extremely helpful to build advanced IoT sensors and IC chips as MIT researchers have recently better than the current semiconductor materials. Discoveries would help to invent powerful computers and devices to form the best Metaverse in the future. Another good news is, to develop very powerful quantum computers. If quantum computers eventually come to mainstream adoption with enhanced computing power, let’s imagine the future virtual universe aka Metaverse. BAs) far discovered Cubic Boron Arsenide (c- there are significant signs of progress

Metaverse to Reframe Human-centered Experiences

Social tokens are invaluable for creating and marketing user-centered economies in Web3. Here is how they work and why they have great moonshot potential. Last year, Nasdaq named social tokens the . next massive crypto trend This prediction has proved true. Social token popularity continues to grow rapidly, with more use cases and new designs constantly coming out. What are social tokens, and what’s their value? I’ll get you covered in this article on all these questions and more. By the end of its reading, you will fully understand the concept, the existing designs, and perspectives. Let’s get right into it. What are social tokens? Social tokens are cryptos that represent the value of a project, brand, community, or unique creator. In other words, social tokens are currencies that’re agreed to be used within a particular environment or micro-economy as a means of exchange for or experiences it provides. services Despite “social tokens” being a relatively new term, the idea behind this concept is not that fresh. We can think of them as upgraded token incentives adjusted to the current hot Web3 trends: disintermediated digital ownership, and community-focused framework of doing business. creator economy How are social tokens different from token incentives? In fact, they are not so different. The reason why we haven’t heard about social tokens until 2020 is that the Web3 ideology has not been firmly formulated yet. Just a few years ago, there was not much clarity about what Web3 is, and many would think of it as yet another hype rebranding of crypto. However, things have tremendously progressed since then, both on ideological and practical levels. The theoretical framework of Web3 and its foundational principles have been shaped. On a practical level, we got a whole ecosystem of platforms and projects making this vision real. As the crypto space evolves and transforms into the Web3 movement, so do some of its concepts. The same happened to token incentives. The old well-known concept was rethought and adjusted to the Web3 vision. And finally, we got social tokens. How are social tokens used? Depending on goals, social tokens can be used in many different ways. Only the sky and law are the limits. In most scenarios, they apply as marketing tools that help: bootstrap a project. Through social token distribution, projects can engage more users and coordinate them for the project mission; strengthen community loyalty and dedication; attract investment and workforce; and increase brand value and visibility. The long-term goal of social token implementation can be building unique ecosystems that are far more user-oriented and rewarding than currently existing business models. For their holders, social tokens can provide different kinds of value: VIP access to information or events, discounts, governance rights, free NFTs or other digital goods, merch, and the like. Below, I will walk you through particular types of social tokens and cover in more detail what they can grant to a user. Stick with me. How many social tokens are there? Let me answer with data (constantly changes): Coingecko What are social tokens examples? So far, there are loads of social token variations out there. Depending on who stands behind them, all social tokens can be broadly grouped into four categories: Personal or creator tokens Brand tokens Community tokens, and Social platform tokens. Here is the excellent infographic by Forefront that can help you better understand the social token landscape. Now in more detail about each type. Personal or creator tokens These tokens are issued by a unique creator or a group of creators (an influencer, an artist, an actor, a band - actually anyone who has something to offer) to capitalize on their creativity and market it. Depending on a creator's goals and value proposition, personal tokens can come in many different designs: earnable/buyable, granting access, discounts, swappable to NFTs, and the like. Essentially you can wrap literally anything your fans may be interested in into your personal token to the extent it's legally permitted. The examples of personal tokens are given in the Creators Map above. I would also like to mention ( ) - a Grammy Award-winning pop-rock band that is considered one of the most successful cases for fan-focused token launching. Portugal. The Man. $PTM Brand tokens Any brand can issue brand tokens, whether a crypto native or not (like, for example, Coca-Cola), to boost sales, create new revenue streams, and strengthen customer loyalty. For instance, in the traditional retail industry, the most native scenario of tokenization would be tying customer loyalty programs to retailer brand tokens. study on brand loyalty has shown that if tokens are proven to be simple to use, 79% of brand followers will use them. A KPMG Let’s take another non-crypto example - a sports club. Spanish soccer giant FC Barcelona ( ) and attracted USD 1.3 in less than two hours! had successfully sold out its social token BAR Community tokens Community tokens are based around particular communities and intended to provide their members exclusive access to information, expertise, human talent, events, etc. Examples of community tokens: token - represents the Ethereum investment community around the subreddit r/ethtrader. Members can earn Donuts by actively participating and contributing content to the subreddit. Donuts FWB token - represents - a community of artists, creators, thinkers, and people leading Web3 transformation. FWB token incentivizes community members to participate in Discord, attend calls, and host events. Friends with benefits $CLUB - a community token of - a social token incubator. $CLUB holders gain access to Discord, events, educational content, contribution opportunities, and governance. Seedclub Social platform tokens These tokens represent some interest in a platform that facilitates social token issuance and exchange. For example, they can provide their holders with membership perks (VIP access to events, discounts, etc.) and other special features like staking and participation in the platform's governance. Examples: , , , and . Roll Rally Chiliz Fyooz Is an NFT a social token? Originally, NFTs were not designed as social tokens. They are digital collectibles, providing their holders with a kind of exclusive IP rights to the unique content they represent. However, with time use cases of NFTs have evolved, and now many brands and individual creators use them as non-fungible social tokens. Examples: , , etc. IreneDAO VeeFriends The purposes are the same: self-marketing, user engagement, and capitalizing on that. So despite many saying that there is a whole different world between NFTs and social tokens, I would argue with that. NFTs can play the role of social tokens when used for the purposes described above. It’s worth noting that some creators apply NFTs in tandem with fungible social tokens to boost engagement. For instance, a creator can use NFTs to monetize their artwork while also releasing a social token that gives the holder access to personal Q&A sessions or art lessons. Meme social tokens Meme social tokens are hybrids of silly memes and reward tokens. They are a relatively new design of social money, which has all chances to explode. Here is why. First, meme social tokens preserve all qualities of meme coins, which are cryptos inspired by a joke or some funny narrative. Basically, pure meme coins are not supposed to represent any value but fun or to have any meaningful cases of application. However, despite their unserious nature, some meme coins like Dodge or Shiba Inu achieved enormous user adoption, and their prices saw meteoric growth at times. Second, meme social tokens are , meaning their holders can benefit from them in more ways than just price speculation. reward tokens One of the pioneers in meme social tokens design is the , backed by . DUCKIES platform Yellow Network DUCKIES platform aims to accelerate Yellow Network’s community growth and reward its participants (Yellowians) for their dedication and commitment to the project. Essentially, the DUCKIES Platform offers Yellowians a reward meme token $DUCKIES, that can be swapped for particular NFTs, Yellow Tokens, and other digital assets. $DUCKIES also enable their holders to participate in the community's governance and access its events. $DUCKIES are tokens, which means you can't buy them on the market. earnable New community participants can get some DUCKIES for free as a welcome bonus. Those who want to increase their DUCKIES’ holdings can do it by participating in DUCKIES learn-to-earn activities, contests, community initiatives, and the referral program. Is DUCKIES the next meme coin? Chances are, it can be. At least now, DUCKIES proves to have a great moonshot potential. Despite the crypto winter, DUCKIES crossed 2000% growth and tens of thousands of users in just one day upon its release. By the second week since the launch date, there are almost 150K active DUCKIES holders. Here is the proof: If the DUCKIES keep walking at this pace, they are well on their way to becoming the next meme coin. Combining meme and social tokens’ traits can be a huge step forward in social token design. Conclusion With the crypto space getting more impacted by the Web3 vision and creator economy, it's no surprise that social tokens are becoming the next big thing. For businesses and individuals, they can be great marketing shortcuts to build dedicated communities and create new revenue streams. For fans or customers, social tokens can provide participation in the economies they like - either as owners, investors, or a workforce. Sounds like win-with IMHO. By for Yellow Network. Julie Plavnik If you want to learn more about DUCKIES and Yellow Network, don’t be shy to ask your questions in . Yellow Telegram

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