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Today, according to Enhance Ventures, there are along with thousands of accelerators and incubators - all offering unique experiences to entrepreneurs. In this article, we are set to explain how a startup - or a venture - studio can help bring your ideas to life. around 780 startup studios There is a common misbelief that simply having a solid idea is enough to start a business. Available funds on their own do not guarantee success, either. While some entrepreneurs lack experience or knowledge, others may need money or well-trained specialists, and certain people are not good at building internal procedures. To navigate a company to success, one needs a full package. This is when incubators and accelerators come to the rescue. There are in the world today. Among the most famous are Y Combinator, Techstars, and 500 Startups. There are numerous companies that have reached the exit stage in those organizations, including , Codeacademy, Udemy, and . no less than 7,000 accelerators and incubators Reddit Canva However, while accelerators and incubators offer many advantages to entrepreneurs, we are currently witnessing a boom of interest for startup studios. They present a different approach to launching startups and have their own funds and in-house experts. Startup studios go the extra mile from being just a mentor or investors - they become co-founders and partners. What is exactly a startup studio? People who work in startup studios know how to launch and scale a business. The examples of Zalando, Delivery Hero, Hello Fresh, Dots, bitly, Octiv, and others speak better than any words, about what a startup can become when entrepreneurs turn to studios. Startup studios bring together entrepreneurs, investors, and experienced specialists in all fields, and creates an ecosystem, essential to build a business. People, who work in a startup studio, are not just investors, but partners of the future company. They are focused on making the idea work and doing that quickly and with a profit. Facts and myths about startup studios Myth №1: “Startup studios are like mentors” Not everyone will engage with your business if you lack an MVP. Accelerators and incubators typically establish a predetermined timeframe for collaborating with startups, a term defined at the onset of cooperation. On the other hand, a startup studio aligns with the entrepreneur from the outset, shepherding the project through its entirety. The studio functions not only as an investor but also as a deeply involved co-founder, invested in the business's outcomes. Myth №2: “Startup studio will help with every project” Claiming to know everything often reveals a lack of true understanding. Seasoned professionals cultivate expertise and specialization in specific domains, nurturing these skills with pride. For instance, TechStars dedicates itself to tech startups, Buildit Accelerator hones in on IoT and hardware, and Startup Wise Guys embraces diverse niches, including FinTech and Cybersecurity. Certain enterprises also concentrate on specific geographic areas; Venture Catalysts, for instance, nurtures Indian startups, while Chinaccelerator, the largest accelerator in Asia, stands as a prime example. At SKL.vc, our approach involves meticulous evaluation of ideas before integration. We direct our attention towards ventures with global transformative potential, such as consumer apps, generative AI, BCI, and other groundbreaking concepts. Myth №3: “A startup studio does not care about the outcome” A venture studio is a co-founder of the future company, so it owns equity in it. Studying reports and listening to presentations about the work done is not something a studio does. The team is focused on building work quickly and efficiently and maintains a hands-on style so that every stage is optimized. The work at SKL.vc is structured as sprints - we set a target, and when we reach it, we analyze the results. This allows us to adjust the strategy on time. Every project is assigned a customized team of experts. They work until the startup is ready to work without the studio on its own. We select professionals with the most suitable set of skills and qualifications to make sure the project has the best human capital. SKL.vc also has vast experience of working with remote teams, that include top experts from all over the world. Myth №4: “People who work in a startup studio make mistakes, just like I, or other entrepreneurs, do” Entrepreneurs within startup studios avoid the trial-and-error route, foregoing learning solely from mistakes. This is due to the studio's adoption of a sequential launch strategy, leveraging past successful experiences to guide startups. Nevertheless, perfection remains elusive, as errors are inherent to all endeavors. Yet, by tapping into the expertise of top professionals in the industry, entrepreneurs can significantly reduce the probability of failure. At SKL.vc, we have curated an exceptional team of extraordinary individuals. This ensemble comprises not only studio founders and experts adept at shepherding startups but also advisors boasting extensive experience in their specialized domains. So, when can a startup studio help you? A studio does not guarantee success. Neither does an accelerator or an incubator. In the end, which has been in the US, has an exit rate of 33.61%. And Y Combinator, which is often named among the most famous accelerators in the world, has an exit rate of 11.15% (among the companies that reached this stage is, for instance, AirBnB) and ranks 16th in the Beta Boom study. There are many great ideas that will not hit the market, and there are numerous reasons for that. But we will address this next time. Angelpad ranked number one accelerator To understand, if you need a studio, consider the following: You have a clear idea and understanding of what you want A startup studio brings together unique experts. The team that works at SKL.vc has experience in venture capital, and marketing, knows how to scale projects, and has experience in other fields that are indispensable when building a business. We can set up internal procedures. We know what every stage must look like and how it must work. We get down to business very quickly without unnecessary meetings and long calls. We need not more than two meetings to make a decision. Sometimes entrepreneurs get so focused on their ideas, that they forget to take a step aside and look at the project from a fresh perspective. The studio, which is involved with every step, may provide such a perspective. You do not know how to build internal processes A venture studio does not focus on one startup but works on multiple startups in parallel. It means that its processes work in a smooth and flawless way. It can be adjusted to any business that the studio takes on board. When selecting to partner with a studio, an entrepreneur gets a professional with specific knowledge about how to build marketing strategy or financial planning, how to establish relationships with investors and make the product, and how to communicate with the stakeholders and others. You do not have a little black book When startups join us at SKL.vc, we always say that our network is your network now. We have access to human capital that is hard to get. And we also assign a superhero mentor to every project! You do not have funds We use our own capital to invest in the startups. When it comes to finding investment, entrepreneurs may take this task off their to-do list - we will do it ourselves. You do not have a team We can build the team from scratch and will make it tailored for the business. From advisors with specific experience relevant to your needs to key experts from different niches of the market that will help your company become a successful enterprise. A startup studio is one option available to entrepreneurs today, along with accelerators and incubators. However, the unique approach that studios offer has made them an attractive partner for those who want to build a successful business in an already crowded market.

Startup Studios: Why Founders Aspire to Join and How They Foster Company Building

Startup Studios: por qué los fundadores aspiran a unirse y cómo fomentan la creación de empresas

Startup Studios : pourquoi les fondateurs aspirent à rejoindre et comment ils favorisent la création d'entreprise

स्टार्टअप स्टूडियो: क्यों संस्थापक शामिल होने की इच्छा रखते हैं और वे कंपनी निर्माण को कैसे बढ़ावा देते हैं

スタートアップ スタジオ: 創業者が参加を熱望する理由と会社構築をどのように促進するか

Startup Studios: por que os fundadores desejam ingressar e como eles promovem a construção da empresa

Startup Studios: Tại sao các nhà sáng lập mong muốn tham gia và cách họ thúc đẩy việc xây dựng công ty

创业工作室：创始人为何渴望加入以及他们如何促进公司建设

High-Stakes Cash Games in Vegas When I was a younger and slightly dumber man, I was captivated by the allure of online poker and high-stakes No-limit Hold ‘em. Going to Vegas and battling it out for millions of dollars with all the high-rollers, was a big dream of mine. I would watch all the tutorials I could get my hands on, read whatever poker book was available to me, and binge every poker show out there. Now, this was a few years after the golden age of online poker which was kicked off by Chris, I shit you not, Moneymaker. An accountant from Nashville and hobby poker player who is said to be responsible for online poker exploding, after he won the WSOP Main Event, gaining entry based on a satellite tournament he played online. For the unfamiliar with online poker, a satellite tournament is basically a (often much) lower-priced tournament that gives the winner(s) entry to a higher-stakes tournament. I'm getting a bit sidetracked here, anyway. In this post-chris-money maker-winning-wsop-main-event environment, a lot of young people (i.e. nerdy white guys, try and guess why it was appealing to me) started printing money online. This laid the basis for a show called 2 Months 2 Million which was centered around this bunch of young poker pros who pooled their bankrolls (money) and gave themselves two months to make a net profit of 2 million dollars. They failed. Well, sort of failed, they made a bunch of money just not the two million dollars. I never made it into professional poker nor did I go to Las Vegas. I did, however, go into Software Engineering and happily forgot about poker and that show. That is until I came across this article about a guy who challenged himself to start from zero and, after one month, built a product that some stranger on the internet would pay money for. I loved the idea and was immediately reminded of that show a long time ago. The difference now being that I actually have acquired a skill set that would allow me to have a real shot at completing this challenge. Even better, I also have a business partner to do the challenge with me. So, from the 1st of February 2023, Rasmus and I will be attempting to start completely from scratch and after 30 days of work, have a product that will have made more than 1 dollar. A million seemed to bit too ambitious. Isn't This Just 12 Month 12 Start-ups but Shorter Maybe, yes, kind of, the chance that an idea is completely original is absolutely minuscule. I've tried to (mentally, at least) do the challenge made famous by Pieter Levels but never really got it kicked off. I started the preparations, loaded up on some ideas, and picked a date, but then, excuses got in the way (work, family, friends, take your pick), and it didn't go anywhere. Taking potentially 12 months off, or building 12 new projects on the side while working full-time just seems really exhaustive to me. But, dedicating one month to try and kick-start a project is way more manageable; both financially and risk-of-burn-out-wise. I used to build a lot of side-projects in my spare time when I just started as a software engineer, which was incredibly fun, but as I've gotten older, I just don't want to spend my time coding for 12+ hours a day. This limited-time scope seems to be a much better fit. Wait, But Why? Wantrepreneur: A person who aspires to be an entrepreneur, especially one who never realizes this ambition At the beginning of January, I wrote about Wantrepreneurship and how I've been wanting to break into this for quite some time now. I must have read every single book under the sun about entrepreneurship, creating micro-saas businesses and start-ups. Yet, I haven't even launched anything always holding back because I lack a good idea or because it's not the right time or insert-other-common-excuse (actually, it's not entirely true. I did launch something called ByGourmand intended to provide subscribers with a wide variety of recipes. If you're currently thinking "So, a shittier version of HelloFresh?", you've got the right idea). But, I seemed to have forgotten an important lesson; one I learned straight after uni having made the jump into software: you don't really learn anything until you take action. I.e., you need to do that thing you're consistently talking about and you will get better at it. I've spent long evenings on YouTube learning to program but equally long evenings building and writing software. And the last step, that's what actually makes the difference. One of my favorite Twitch streamers, theprimeagen, has a perfect acronym for this: TITS (time in the saddle, Elon Musk would love it - spend more time doing that thing. Having set an optimistic deadline while also making a public announcement is hopefully going to be the combination that really pushes this into gear. Additionally, teaming up with Rasmus for this project, I think it creates a situation that will push me to take a real stab at this. So, what's the plan? I'm glad you asked. The Plan This is quite an ambitious deadline, so we're going to be cutting quite a few corners wherever possible. Since we're starting from zero, we can't really have an already researched idea ready to go from day one; we need to start out doing some good old-fashioned brainstorming. No more than 2 days have been put aside for this, along with preliminary research into potential customers and the market. On the third day, we're taking a pick, putting maybe one or two ideas in reserve, and then starting to create a landing page and doing outreach. Based on the feedback, we will start building, do more outreach, and do more building. Rinse and repeat until we either run out of time or, make our first dollar. Honestly, it's a fairly simple approach, but given that this is mostly a learning experience, I would argue it's the ideal way forward. The thing about planning for an activity, that you are unfamiliar with in practice, is that it quickly turns into a big soup of postulates. In the famous words of Mark Hanna: Fugayzi, fugazi. It's a whazy. It's a woozie. It's fairy dust. It's not fucking real, but two people can quickly convince themselves about X hypothesis being very real because the world would be so much sweeter if it was. The plan can be summed up to be hypothesis-driven entrepreneurship if you will. We have a problem, think up potential experiments that could solve said problem, run them, and evaluate them. Continue what works, and stop what doesn't. It's worked for old Greeks; I'm sure it can work for a couple of Danish guys. Speaking for myself here, reaching out to strangers on the internet has always been an Achilles heel for me. I'm not sure why; the thought of it has always made me imagine myself as the human form of a spam email. I fucking hate spam; Escobar had burner phones, I have burner emails. But I've also slowly come to accept the fact that this is going to be a necessity if I ever want to move forward in this process. My first job at Uni was as a phone salesman where I made a sell in my very first phone call, and then never made one again. Hopefully, this month goes a bit better. Likely Outcome Most likely, this project will not make much money within this month. A firm rule is that we cannot beg friends and family to buy the product/service, so we, unfortunately, have to take the long route. Additionally, we could go the cash-grab route and do AI profile pictures, but that just seems boring at this point. Saas businesses have never been easy, and even though we might get lucky and strike gold; the takeaway here is hopefully going to be a lot of learnings that will push both of us into the next step in our entrepreneurial journey. Update You can read about the outcome here. High-Stakes Cash Games in Vegas When I was a younger and slightly dumber man, I was captivated by the allure of online poker and high-stakes No-limit Hold ‘em. Going to Vegas and battling it out for millions of dollars with all the high-rollers, was a big dream of mine. I would watch all the tutorials I could get my hands on, read whatever poker book was available to me, and binge every poker show out there. Now, this was a few years after the golden age of online poker which was kicked off by Chris, I shit you not, Moneymaker. An accountant from Nashville and hobby poker player who is said to be responsible for online poker exploding, after he won the WSOP Main Event, gaining entry based on a satellite tournament he played online. For the unfamiliar with online poker, a satellite tournament is basically a (often much) lower-priced tournament that gives the winner(s) entry to a higher-stakes tournament. I'm getting a bit sidetracked here, anyway. In this post-chris-money maker-winning-wsop-main-event environment, a lot of young people (i.e. nerdy white guys, try and guess why it was appealing to me) started printing money online. This laid the basis for a show called 2 Months 2 Million which was centered around this bunch of young poker pros who pooled their bankrolls (money) and gave themselves two months to make a net profit of 2 million dollars. They failed. Well, sort of failed, they made a bunch of money just not the two million dollars. I never made it into professional poker nor did I go to Las Vegas. I did, however, go into Software Engineering and happily forgot about poker and that show. That is until I came across this article about a guy who challenged himself to start from zero and, after one month, built a product that some stranger on the internet would pay money for. I loved the idea and was immediately reminded of that show a long time ago. The difference now being that I actually have acquired a skill set that would allow me to have a real shot at completing this challenge. Even better, I also have a business partner to do the challenge with me. So, from the 1st of February 2023, Rasmus and I will be attempting to start completely from scratch and after 30 days of work, have a product that will have made more than 1 dollar. A million seemed to bit too ambitious. Rasmus Isn't This Just 12 Month 12 Start-ups but Shorter Maybe, yes, kind of, the chance that an idea is completely original is absolutely minuscule. I've tried to (mentally, at least) do the challenge made famous by Pieter Levels but never really got it kicked off. I started the preparations, loaded up on some ideas, and picked a date, but then, excuses got in the way (work, family, friends, take your pick), and it didn't go anywhere. Pieter Levels Taking potentially 12 months off, or building 12 new projects on the side while working full-time just seems really exhaustive to me. But, dedicating one month to try and kick-start a project is way more manageable; both financially and risk-of-burn-out-wise. I used to build a lot of side-projects in my spare time when I just started as a software engineer, which was incredibly fun, but as I've gotten older, I just don't want to spend my time coding for 12+ hours a day. This limited-time scope seems to be a much better fit. Wait, But Why? Wantrepreneur: A person who aspires to be an entrepreneur, especially one who never realizes this ambition Wantrepreneur: A person who aspires to be an entrepreneur, especially one who never realizes this ambition At the beginning of January, I wrote about Wantrepreneurship and how I've been wanting to break into this for quite some time now. I must have read every single book under the sun about entrepreneurship, creating micro-saas businesses and start-ups. Wantrepreneurship Yet, I haven't even launched anything always holding back because I lack a good idea or because it's not the right time or insert-other-common-excuse (actually, it's not entirely true. I did launch something called ByGourmand intended to provide subscribers with a wide variety of recipes. If you're currently thinking "So, a shittier version of HelloFresh?", you've got the right idea). But, I seemed to have forgotten an important lesson; one I learned straight after uni having made the jump into software: you don't really learn anything until you take action. I.e., you need to do that thing you're consistently talking about and you will get better at it. I've spent long evenings on YouTube learning to program but equally long evenings building and writing software. And the last step, that's what actually makes the difference. One of my favorite Twitch streamers, theprimeagen , has a perfect acronym for this: TITS (time in the saddle, Elon Musk would love it - spend more time doing that thing. theprimeagen love it Having set an optimistic deadline while also making a public announcement is hopefully going to be the combination that really pushes this into gear. Additionally, teaming up with Rasmus for this project, I think it creates a situation that will push me to take a real stab at this. So, what's the plan? I'm glad you asked. The Plan This is quite an ambitious deadline, so we're going to be cutting quite a few corners wherever possible. Since we're starting from zero, we can't really have an already researched idea ready to go from day one; we need to start out doing some good old-fashioned brainstorming. No more than 2 days have been put aside for this, along with preliminary research into potential customers and the market. On the third day, we're taking a pick, putting maybe one or two ideas in reserve, and then starting to create a landing page and doing outreach. Based on the feedback, we will start building, do more outreach, and do more building. Rinse and repeat until we either run out of time or, make our first dollar. Honestly, it's a fairly simple approach, but given that this is mostly a learning experience, I would argue it's the ideal way forward. The thing about planning for an activity, that you are unfamiliar with in practice, is that it quickly turns into a big soup of postulates. In the famous words of Mark Hanna: Fugayzi, fugazi. It's a whazy. It's a woozie. It's fairy dust. Fugayzi, fugazi. It's a whazy. It's a woozie. It's fairy dust. It's not fucking real, but two people can quickly convince themselves about X hypothesis being very real because the world would be so much sweeter if it was. The plan can be summed up to be hypothesis-driven entrepreneurship if you will. We have a problem, think up potential experiments that could solve said problem, run them, and evaluate them. Continue what works, and stop what doesn't. It's worked for old Greeks; I'm sure it can work for a couple of Danish guys. Speaking for myself here, reaching out to strangers on the internet has always been an Achilles heel for me. I'm not sure why; the thought of it has always made me imagine myself as the human form of a spam email. I fucking hate spam; Escobar had burner phones, I have burner emails. But I've also slowly come to accept the fact that this is going to be a necessity if I ever want to move forward in this process. My first job at Uni was as a phone salesman where I made a sell in my very first phone call, and then never made one again. Hopefully, this month goes a bit better. Likely Outcome Most likely, this project will not make much money within this month. A firm rule is that we cannot beg friends and family to buy the product/service, so we, unfortunately, have to take the long route. Additionally, we could go the cash-grab route and do AI profile pictures, but that just seems boring at this point. Saas businesses have never been easy, and even though we might get lucky and strike gold; the takeaway here is hopefully going to be a lot of learnings that will push both of us into the next step in our entrepreneurial journey. Update You can read about the outcome here . here

One Month One Dollar - Part One: The Beginning

Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi, a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi , a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. Rahul Chaturvedi Rahul Chaturvedi With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. Rahul Chaturvedi's contributions Rahul Chaturvedi's contributions The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Chaturvedi's work Chaturvedi's work Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure : This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure

Mastering Real-Time Data: Rahul Chaturvedi's Strategies for Building Reliable Data Platforms

রিয়েল-টাইম ডেটা আয়ত্ত করা: নির্ভরযোগ্য ডেটা প্ল্যাটফর্ম তৈরির জন্য রাহুল চতুর্বেদীর কৌশলগুলি

Echtzeitdaten beherrschen: Rahul Chaturvedis Strategien zum Aufbau zuverlässiger Datenplattformen

Dominar los datos en tiempo real: estrategias de Rahul Chaturvedi para crear plataformas de datos confiables

Maîtriser les données en temps réel : les stratégies de Rahul Chaturvedi pour créer des plates-formes de données fiables

रियल-टाइम डेटा में महारत हासिल करना: विश्वसनीय डेटा प्लेटफ़ॉर्म बनाने के लिए राहुल चतुर्वेदी की रणनीतियाँ

リアルタイム データの習得: 信頼性の高いデータ プラットフォームを構築するための Rahul Chaturvedi の戦略

실시간 데이터 마스터하기: 안정적인 데이터 플랫폼 구축을 위한 Rahul Chaturvedi의 전략

Dominando dados em tempo real: estratégias de Rahul Chaturvedi para construir plataformas de dados confiáveis

Освоение данных в реальном времени: стратегии Рахула Чатурведи по созданию надежных платформ данных

Gerçek Zamanlı Verilerde Uzmanlaşmak: Rahul Chaturvedi'nin Güvenilir Veri Platformları Oluşturma Stratejileri

Làm chủ dữ liệu thời gian thực: Chiến lược xây dựng nền tảng dữ liệu đáng tin cậy của Rahul Chaturvedi

掌握实时数据：Rahul Chaturvedi 构建可靠数据平台的策略

DUBAI, UAE, July/9th,2024/Chainwire/LandRocker, a play-to-earn game set in an expansive universe of space exploration, has announced that its native token, LRT, will be listed on the centralized exchange BingX on July 9th. This development comes alongside the introduction of their novel dynamic roadmap, designed to offer transparency and engagement for the gaming community. Integral to the LandRocker gaming economy, the LRT token will be available on BingX, providing more access for gamers and investors. This listing follows the successful launch of LRT on MEXC and Uniswap, further expanding the token's reach and usability. The presale of LRT raised $2.8 million, demonstrating investor confidence in LandRocker’s potential. Additional listings on other tier-1 exchanges, including Bybit, KuCoin, and Gate.io, are anticipated soon. LandRocker's new dynamic roadmap is the first of its kind amongst Web3 projects, and is set to upend how gaming projects communicate progress. The feature integrates daily video updates and a live interactive map, allowing users to track the project's development in real time. The dynamic roadmap provides a level of transparency and engagement previously unseen in the crypto industry, fostering a stronger bond between the project and its community. "We are pioneering a new level of transparency and engagement in the gaming industry with our dynamic roadmap," said CEO Hamid Fathalian. "This approach sets a high standard for openness, strengthening the trust and involvement of our community and stakeholders, marking a first as no other project has implemented such a transparent and agile framework." The game’s approach helps control inflation and fosters a sustainable gaming economy and stable economic loop. Refined through billions of simulations over the past two years, LandRocker ensures ongoing rewards, economic stability, and continuous player engagement. LandRocker's mission is to develop a robust chart for its token that is as individual and powerful as possible, aiming to minimize its reaction to Bitcoin price drops. By building on a strong foundation, LandRocker aspires to establish an evergreen chart, ensuring long-term stability and growth for its token. Underscoring LandRocker’s commitment to creating a sustainable and resilient gaming economy, providing lasting value for its community and investors. The game’s Season Zero offers players the chance to explore a universe with 79 quintillion unique planets and compete for a combined prize pool of 20 million LRT. About LandRocker: LandRocker is a cutting-edge play-to-earn game set in a vast universe of space exploration and discovery. It offers players a unique blend of adventure, strategy, and multiplayer battles, along with opportunities to earn valuable crypto rewards. The game features innovative play-to-liberate, play-to-earn, and play-to-win modes, fostering a balanced and sustainable ecosystem. Players mine elements, craft equipment, and trade with others to maintain a stable economic loop. With a focus on quality gameplay and a resilient economy, LandRocker is poised to become a leading title in the P2E gaming space. Website | Telegram | Instagram | X | Discord Contact Homa Has homa@landrocker.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. DUBAI, UAE, July/9th,2024/Chainwire/LandRocker, a play-to-earn game set in an expansive universe of space exploration, has announced that its native token, LRT, will be listed on the centralized exchange BingX on July 9th. This development comes alongside the introduction of their novel dynamic roadmap, designed to offer transparency and engagement for the gaming community. Integral to the LandRocker gaming economy, the LRT token will be available on BingX, providing more access for gamers and investors. This listing follows the successful launch of LRT on MEXC and Uniswap, further expanding the token's reach and usability. The presale of LRT raised $2.8 million, demonstrating investor confidence in LandRocker’s potential. Additional listings on other tier-1 exchanges, including Bybit, KuCoin, and Gate.io, are anticipated soon. LandRocker's new dynamic roadmap is the first of its kind amongst Web3 projects, and is set to upend how gaming projects communicate progress. The feature integrates daily video updates and a live interactive map, allowing users to track the project's development in real time. new dynamic roadmap new dynamic roadmap The dynamic roadmap provides a level of transparency and engagement previously unseen in the crypto industry, fostering a stronger bond between the project and its community. "We are pioneering a new level of transparency and engagement in the gaming industry with our dynamic roadmap," said CEO Hamid Fathalian. "This approach sets a high standard for openness, strengthening the trust and involvement of our community and stakeholders, marking a first as no other project has implemented such a transparent and agile framework." "We are pioneering a new level of transparency and engagement in the gaming industry with our dynamic roadmap," said CEO Hamid Fathalian. "This approach sets a high standard for openness, strengthening the trust and involvement of our community and stakeholders, marking a first as no other project has implemented such a transparent and agile framework." The game’s approach helps control inflation and fosters a sustainable gaming economy and stable economic loop. Refined through billions of simulations over the past two years, LandRocker ensures ongoing rewards, economic stability, and continuous player engagement. LandRocker 's mission is to develop a robust chart for its token that is as individual and powerful as possible, aiming to minimize its reaction to Bitcoin price drops. By building on a strong foundation, LandRocker aspires to establish an evergreen chart, ensuring long-term stability and growth for its token. LandRocker LandRocker Underscoring LandRocker’s commitment to creating a sustainable and resilient gaming economy, providing lasting value for its community and investors. The game’s Season Zero offers players the chance to explore a universe with 79 quintillion unique planets and compete for a combined prize pool of 20 million LRT. About LandRocker: LandRocker is a cutting-edge play-to-earn game set in a vast universe of space exploration and discovery. It offers players a unique blend of adventure, strategy, and multiplayer battles, along with opportunities to earn valuable crypto rewards. LandRocker LandRocker The game features innovative play-to-liberate, play-to-earn, and play-to-win modes, fostering a balanced and sustainable ecosystem. Players mine elements, craft equipment, and trade with others to maintain a stable economic loop. With a focus on quality gameplay and a resilient economy, LandRocker is poised to become a leading title in the P2E gaming space. Website | Telegram | Instagram | X | Discord Website Website Telegram Telegram Instagram Instagram X X Discord Discord Contact Homa Has homa@landrocker.io 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

LandRocker’s LRT Token To Be Listed On BingX, Introducing Its Dynamic Roadmap

LandRocker এর LRT টোকেন BingX-এ তালিকাভুক্ত হবে, এর গতিশীল রোডম্যাপ প্রবর্তন করছে

LandRockers LRT-Token wird auf BingX gelistet und stellt seine dynamische Roadmap vor

El token LRT de LandRocker se incluirá en BingX, presentando su hoja de ruta dinámica

Le jeton LRT de LandRocker sera répertorié sur BingX, présentant sa feuille de route dynamique

लैंडरॉकर का LRT टोकन बिंगएक्स पर सूचीबद्ध होगा, इसका डायनामिक रोडमैप पेश किया जाएगा

LandRocker の LRT トークンが BingX に上場、ダイナミック ロードマップを発表

LandRocker의 LRT 토큰이 BingX에 상장되어 동적 로드맵을 소개합니다.

Token LRT da LandRocker será listado no BingX, apresentando seu roteiro dinâmico

Токен LandRocker LRT будет включен в листинг на BingX, представляя свою динамическую дорожную карту

LandRocker'ın LRT Tokeni BingX'te Listelenecek ve Dinamik Yol Haritası Tanıtılacak

Mã thông báo LRT của LandRocker sẽ được liệt kê trên BingX, giới thiệu lộ trình năng động của nó

LandRocker 的 LRT 代币将在 BingX 上市，并推出其动态路线图

Authors:
(1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu);
(2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com);
(3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Related Work Project Prerequisites Project Implementation Insight and Reflections Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Authors: Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Project Prerequisites Project Prerequisites Project Implementation Project Implementation Insight and Reflections Insight and Reflections Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Introducing High School Students to Version Control, Continuous Integration, and Quality Assurance

Effective Proxy Server Design and Implementation

Who’s Harry Potter? Approximate Unlearning in LLMs: Description of our technique

Survey and Analysis of Smart Contract Quality Assurance: Abstract and Introduction

MONACO CITY, Monaco, June 21st, 2024/Chainwire/--Memereum(MEME)sold over 1 million tokens within hours of its presale, reaching a total of over 24 million tokens sold in its current presale phase. The team views this rapid progress as an indication of strong market demand and confidence in Memereum. Innovative Features of Memereum Upon launch, Memereum will introduce MemeSwap, the first decentralized insured exchange. A beta version of MemeSwap is currently live and accessible on their website. This platform aims to provide a secure trading environment, leveraging decentralized insurance mechanisms to protect users' investments. MemeSwap aspires to enhance security and user confidence in the crypto exchange market. Staking Rewards Program Memereum includes an automatic staking system with a 183% annual percentage yield (APY). This yield aims to attract participants by providing notable rewards, positioning Memereum as an option for those interested in exploring new opportunities in the crypto space. Memereum Presale With the current presale price set at $0.04 and the launch price anticipated by the team to be $0.45, Memereum offers an opportunity for early investors to enter at a low price with the potential for returns. The presale structure includes a strategic price increase every 72 hours, aiming to encourage early participation. Market Presence Already listed on three exchanges ( BitVenus, Toobit, and Azbit), Memereum is poised to make a significant impact in the cryptocurrency market. Memereum's ability to sell over 1 million tokens within hours of its presale launch marks a significant achievement and highlights its potential in the cryptocurrency market. Conclusion As the cryptocurrency market continues to recover, Memereum is strategically positioned to benefit from this momentum. The next few months are crucial as Memereum executes its plans and builds on its early achievements. With a dedicated team, robust community support, and a clear vision, Memereum aims to make a lasting impact in decentralized finance. For more details on how to participate in Memereum’s ongoing presale, users can visit Memereum’s website. About Memereum (MEME) The strong interest in Memereum (MEME) can be attributed to its innovative approach in the blockchain sector and its growing community support. Memereum is the first blockchain insurance with an integrated DEX for supported token trading. Learn more about Memereum by clicking here. Contact Bessie Cooper Memereum support@memereum.net This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here, MONACO CITY, Monaco, June 21st, 2024/Chainwire/--Memereum(MEME)sold over 1 million tokens within hours of its presale, reaching a total of over 24 million tokens sold in its current presale phase. The team views this rapid progress as an indication of strong market demand and confidence in Memereum. Innovative Features of Memereum Upon launch, Memereum will introduce MemeSwap , the first decentralized insured exchange. A beta version of MemeSwap is currently live and accessible on their website. This platform aims to provide a secure trading environment, leveraging decentralized insurance mechanisms to protect users' investments. MemeSwap aspires to enhance security and user confidence in the crypto exchange market. MemeSwap MemeSwap decentralized insurance decentralized insurance Staking Rewards Program Memereum includes an automatic staking system with a 183% annual percentage yield (APY). This yield aims to attract participants by providing notable rewards, positioning Memereum as an option for those interested in exploring new opportunities in the crypto space. Memereum Memereum Memereum Presale With the current presale price set at $0.04 and the launch price anticipated by the team to be $0.45, Memereum offers an opportunity for early investors to enter at a low price with the potential for returns. The presale structure includes a strategic price increase every 72 hours, aiming to encourage early participation. presale presale Market Presence Already listed on three exchanges ( BitVenus , Toobit , and Azbit ), Memereum is poised to make a significant impact in the cryptocurrency market. Memereum's ability to sell over 1 million tokens within hours of its presale launch marks a significant achievement and highlights its potential in the cryptocurrency market. BitVenus BitVenus Toobit Toobit Azbit Azbit Conclusion As the cryptocurrency market continues to recover, Memereum is strategically positioned to benefit from this momentum. The next few months are crucial as Memereum executes its plans and builds on its early achievements. With a dedicated team, robust community support, and a clear vision, Memereum aims to make a lasting impact in decentralized finance. For more details on how to participate in Memereum’s ongoing presale, users can visit Memereum’s website . website website About Memereum (MEME) The strong interest in Memereum (MEME) can be attributed to its innovative approach in the blockchain sector and its growing community support. Memereum is the first blockchain insurance with an integrated DEX for supported token trading. Learn more about Memereum by clicking here. Memereum (MEME) Memereum (MEME) clicking here. clicking here. Contact Bessie Cooper Memereum support@memereum.net 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

Memereum Sells Over 1M Tokens Within Hours On Presale While Markets Rebound

Silicon Valley: With Swords & Shields

"When You Take VC Funding, the Clock Often Starts Ticking" Entrepreneur Jack Smith on Exiting Vungle

"আপনি যখন ভিসি ফান্ডিং নেন, ঘড়ি প্রায়শই টিক টিক শুরু করে" উদ্যোক্তা জ্যাক স্মিথ এক্সিটিং ভাংলে

„Wenn man VC-Finanzierung annimmt, beginnt die Uhr oft zu ticken“ – Unternehmer Jack Smith über den Ausstieg aus Vungle

"Cuando se obtiene financiación de capital de riesgo, el tiempo a menudo empieza a correr" El empresario Jack Smith habla sobre su salida de Vungle

"Lorsque vous prenez un financement en capital-risque, le temps commence souvent à tourner" L'entrepreneur Jack Smith à propos de sa sortie de Vungle

"जब आप वी.सी. फंडिंग लेते हैं, तो अक्सर समय की गति बढ़ने लगती है" उद्यमी जैक स्मिथ ने वुंगल से बाहर निकलने पर कहा

「VC から資金を調達すると、時間が刻々と動き始めることが多い」起業家ジャック・スミスが Vungle からの撤退について語る

"VC 자금 지원을 받으면 시계가 똑딱거리기 시작하는 경우가 많습니다." 기업가 Jack Smith, Vungle 종료

“Quando você recebe financiamento de capital de risco, o relógio geralmente começa a contar” Empreendedor Jack Smith sobre a saída do Vungle

«Когда вы берете венчурное финансирование, часы часто начинают тикать» Предприниматель Джек Смит о выходе из Vungle

Girişimci Jack Smith, Vungle'dan Çıkış Konusunda "VC Finansmanı Aldığınızda Zaman Sıklıkla İşlemeye Başlıyor"

"Khi bạn nhận tiền từ VC, đồng hồ thường bắt đầu tích tắc" Doanh nhân Jack Smith khi rời khỏi Vũngle

“当你接受风险投资时，时钟通常开始滴答作响”企业家杰克·史密斯谈退出 Vungle

Innovation seems to operate at a relentless pace and requires that today’s leaders have “agility on demand.”  Power Leader Jake Thompson’s "Lead Better Now" offers a toolkit for tech executives, or those who aspire to be leaders, better navigate a landscape where growth is essential. As a Keynote Speaker, Published Author, and Leadership Performance Coach who has worked with over 80,000 people, Thompson distills his extensive expertise into a guide that addresses the unique challenges faced by leaders in the tech industry. Drawing on personal experiences and the transformative journeys of leaders like his wife during the pandemic, the book equips tech leaders with the necessary tools to foster personal growth, build intentional connections, and master effective communication. This introduction to his principles sets the stage for tech leaders to enhance their influence and steer their teams toward groundbreaking innovations and sustained success in the fast-paced tech sector. Q1: What inspired you to write "Lead Better Now"...well, now? A1: I wanted to provide a practical guide to help new and seasoned leaders navigate the complexities of leadership, especially in challenging environments. My wife's struggles as an unprepared manager during the pandemic particularly motivated me to write this book and share the tools needed to succeed. Q2: You discuss the importance of competing with oneself. Can you elaborate on why this is crucial for effective leadership? A2: Competing with oneself is about striving to improve continuously. It’s crucial because true leadership begins with personal growth. By focusing on becoming a better version of ourselves, we set a positive example for our teams and create a culture of self-improvement. This mindset helps leaders stay motivated, resilient, and committed to their goals. Q3: How can leaders cultivate intentional connections within their teams? A3: Intentional connections are about building genuine relationships with team members. Leaders can cultivate these by actively listening, showing empathy, and being present in their interactions. It’s important to understand and value each team member’s contributions and challenges. Building trust and fostering open communication can significantly enhance team cohesion and performance. Q4: Communication is a major theme in your book. What are the key elements of effective communication for leaders? A4: Effective communication involves clarity, consistency, and courage. Leaders must clearly articulate their vision, goals, and expectations. Consistency in messaging helps build trust and reliability. Courageous communication means addressing difficult topics head-on and being transparent, even when it’s uncomfortable. These elements create an environment where team members feel informed, valued, and empowered. Q5: What common pitfalls do new leaders face, and how can they overcome them? A5: New leaders often struggle with imposter syndrome, inadequate training, and managing their ego. To overcome these pitfalls, they should seek continuous learning opportunities, find mentors, and develop self-awareness. It’s also important for leaders to focus on building strong relationships within their teams and fostering a supportive culture that encourages growth and innovation. Q6: Can you share a real-life example from your book that illustrates the impact of effective leadership? A6: One example is the story of my wife, Elena, who was thrust into a leadership role during the pandemic. Initially overwhelmed and unprepared, she learned to navigate her challenges through intentional learning and applying the principles I discuss in the book. Her journey from struggling manager to confident leader exemplifies how the right tools and mindset can transform leadership effectiveness. Q7: How can tech industry leaders specifically benefit from the principles in "Lead Better Now"? A7: The tech industry is fast-paced and constantly evolving, which makes effective leadership even more critical. By applying the principles in "Lead Better Now," tech leaders can better manage their teams, drive innovation, and navigate change. The focus on personal growth, intentional connection, and clear communication is particularly relevant in an industry where collaboration and adaptability are key. Q8: As a top keynote speaker on leadership, especially within tech companies, what trends or challenges are you currently seeing? A8: In my keynote speeches, particularly with tech companies, I observe a recurring theme of rapid change and the pressure to innovate constantly. This creates a unique set of challenges for tech leaders, such as managing remote teams, maintaining company culture in a hybrid environment, and fostering innovation while preventing burnout. Leaders in tech must be agile, empathetic, and strategic in their approach to overcome these hurdles. My talks often focus on equipping leaders with the tools to navigate these challenges, emphasizing the importance of continuous learning, adaptability, and fostering a supportive and innovative team culture. Innovation seems to operate at a relentless pace and requires that today’s leaders have “agility on demand.”  Power Leader Jake Thompson’s "Lead Better Now" offers a toolkit for tech executives, or those who aspire to be leaders, better navigate a landscape where growth is essential. As a Keynote Speaker, Published Author, and Leadership Performance Coach who has worked with over 80,000 people, Thompson distills his extensive expertise into a guide that addresses the unique challenges faced by leaders in the tech industry. Drawing on personal experiences and the transformative journeys of leaders like his wife during the pandemic, the book equips tech leaders with the necessary tools to foster personal growth, build intentional connections, and master effective communication. This introduction to his principles sets the stage for tech leaders to enhance their influence and steer their teams toward groundbreaking innovations and sustained success in the fast-paced tech sector. Jake Thompson’s "Lead Better Now" Q1: What inspired you to write "Lead Better Now"...well, now? Q1: What inspired you to write "Lead Better Now"...well, now? A1: I wanted to provide a practical guide to help new and seasoned leaders navigate the complexities of leadership, especially in challenging environments. My wife's struggles as an unprepared manager during the pandemic particularly motivated me to write this book and share the tools needed to succeed. A1: Q2: You discuss the importance of competing with oneself. Can you elaborate on why this is crucial for effective leadership? Q2: You discuss the importance of competing with oneself. Can you elaborate on why this is crucial for effective leadership? A2: Competing with oneself is about striving to improve continuously. It’s crucial because true leadership begins with personal growth. By focusing on becoming a better version of ourselves, we set a positive example for our teams and create a culture of self-improvement. This mindset helps leaders stay motivated, resilient, and committed to their goals. A2: Q3: How can leaders cultivate intentional connections within their teams? Q3: How can leaders cultivate intentional connections within their teams? A3: Intentional connections are about building genuine relationships with team members. Leaders can cultivate these by actively listening, showing empathy, and being present in their interactions. It’s important to understand and value each team member’s contributions and challenges. Building trust and fostering open communication can significantly enhance team cohesion and performance. A3: Q4: Communication is a major theme in your book. What are the key elements of effective communication for leaders? Q4: Communication is a major theme in your book. What are the key elements of effective communication for leaders? A4: Effective communication involves clarity, consistency, and courage. Leaders must clearly articulate their vision, goals, and expectations. Consistency in messaging helps build trust and reliability. Courageous communication means addressing difficult topics head-on and being transparent, even when it’s uncomfortable. These elements create an environment where team members feel informed, valued, and empowered. A4: Q5: What common pitfalls do new leaders face, and how can they overcome them? Q5: What common pitfalls do new leaders face, and how can they overcome them? A5: New leaders often struggle with imposter syndrome, inadequate training, and managing their ego. To overcome these pitfalls, they should seek continuous learning opportunities, find mentors, and develop self-awareness. It’s also important for leaders to focus on building strong relationships within their teams and fostering a supportive culture that encourages growth and innovation. A5: Q6: Can you share a real-life example from your book that illustrates the impact of effective leadership? Q6: Can you share a real-life example from your book that illustrates the impact of effective leadership? A6: One example is the story of my wife, Elena, who was thrust into a leadership role during the pandemic. Initially overwhelmed and unprepared, she learned to navigate her challenges through intentional learning and applying the principles I discuss in the book. Her journey from struggling manager to confident leader exemplifies how the right tools and mindset can transform leadership effectiveness. A6: Q7: How can tech industry leaders specifically benefit from the principles in "Lead Better Now"? Q7: How can tech industry leaders specifically benefit from the principles in "Lead Better Now"? A7: The tech industry is fast-paced and constantly evolving, which makes effective leadership even more critical. By applying the principles in "Lead Better Now," tech leaders can better manage their teams, drive innovation, and navigate change. The focus on personal growth, intentional connection, and clear communication is particularly relevant in an industry where collaboration and adaptability are key. A7: Q8: As a top keynote speaker on leadership, especially within tech companies, what trends or challenges are you currently seeing? Q8: As a top keynote speaker on leadership, especially within tech companies, what trends or challenges are you currently seeing? A8: In my keynote speeches, particularly with tech companies, I observe a recurring theme of rapid change and the pressure to innovate constantly. This creates a unique set of challenges for tech leaders, such as managing remote teams, maintaining company culture in a hybrid environment, and fostering innovation while preventing burnout. Leaders in tech must be agile, empathetic, and strategic in their approach to overcome these hurdles. My talks often focus on equipping leaders with the tools to navigate these challenges, emphasizing the importance of continuous learning, adaptability, and fostering a supportive and innovative team culture. A8:

Breaking the Cycle of Ineffective Leadership: 
Why Tech Needs to Lead Better Now

How Blockchain Is Igniting a New Era In Sports Fandom: Insights From Tenamint CEO Nadir Chaudhry

Authors:
(1) Arindam Mitra;
(2) Luciano Del Corro, work done while at Microsoft;
(3) Shweti Mahajan, work done while at Microsoft;
(4) Andres Codas, denote equal contributions;
(5) Clarisse Simoes, denote equal contributions;
(6) Sahaj Agarwal;
(7) Xuxi Chen, work done while at Microsoft;;
(8) Anastasia Razdaibiedina, work done while at Microsoft;
(9) Erik Jones, work done while at Microsoft;
(10) Kriti Aggarwal, work done while at Microsoft;
(11) Hamid Palangi;
(12) Guoqing Zheng
(13) Corby Rosset;
(14) Hamed Khanpour;
(15) Ahmed Awadall. Table of Links Abstract and Introduction Preliminaries Teaching Orca 2 to be a Cautious Reasoner Technical Details Experimental Setup Evaluation Results Limitations Conclusions and References A. AGIEval Subtask Metrics B. BigBench-Hard Subtask Metrics C. Evaluation of Grounding in Abstractive Summarization D. Evaluation of Safety E. Prompts used in Evaluation F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Output Abstract Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs’ reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. We contend that excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for different tasks, potentially different from the one used by the larger model. For example, while larger models might provide a direct answer to a complex task, smaller models may not have the same capacity. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). Moreover, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36K unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. We make Orca 2 weights publicly available at aka.ms/orca-lm to support research on the development, evaluation, and alignment of smaller LMs. 1 Introduction Large Language Models (LLMs) are enabling more natural and sophisticated interactions between humans and machines, enhancing user experience in existing applications like coding [3], web search [36], chatbots [45, 56], customer service and content creation. This transformation brought by LLMs is also paving the way for new innovative AI applications. Scaling LLMs like GPT-4 [44] and PaLM-2 [1] to ever more parameters led to emergent abilities [63] unseen in smaller models (less than ∼ 10B parameters), most notably the remarkable ability to reason zero-shot [23]. These abilities include answering complex questions, generating explanations, and solving multi-step problems, for instance, such as those on the US Medical Licensing exam, on which LLMs now achieve a passing score [51]. Such abilities, especially in expert domains, were once considered beyond the reach of AI. Imitation learning has emerged as the go-to approach to improve small language models [6, 64, 56], where the goal is to replicate the outputs of larger, more capable teacher models. While these models can produce content that matches the style of their teachers, they often fall short of their reasoning and comprehension skills [13]. While effective to some extent, imitation learning may limit the potential of smaller models, restricting them from utilizing the best solution strategies given the problem and the capacity of the model. In this work, we continue to pursue the question of how we can teach smaller LMs to reason. The objectives of Orca 2 are two-fold. Firstly, we aim to teach smaller models how to use a suite of reasoning techniques, such as step-by-step processing, recall-then-generate, recall-reason-generate, extract-generate, and direct-answer methods. Secondly, we aspire to help these models decide when to use the most effective reasoning strategy for the task at hand, allowing them to perform at their best, irrespective of their size. Like Orca 1, we utilize more capable LLMs to demonstrate various reasoning strategies across various tasks. However, in Orca 2, the reasoning strategies are carefully tailored to the task at hand, bearing in mind whether a student model is capable of the same behavior. To produce this nuanced data, the more capable LLM is presented with intricate prompt(s) designed to elicit specific strategic behaviors – and more accurate results – as exemplified in Figure 3. Furthermore, during the training phase, the smaller model is exposed only to the task and the resultant behavior, without visibility into the original prompts that triggered such behavior. This Prompt Erasure technique makes Orca 2 a Cautious Reasoner because it learns not only how to execute specific reasoning steps, but to strategize at a higher level how to approach a particular task. Rather than naively imitating powerful LLMs, we treat them as a reservoir of behaviors from which we carefully select those best suited for the task at hand. Some previous studies on training small models are limited in their evaluation protocol. They often rely on small number of tasks or on using other models for auto-evaluation by asking them to compare the outputs of two systems with a prompt like “given responses from system 1 (reference) and system 2 (target), which one is better?”. However, previous work [13, 42, 60, 67] has demonstrated that this approach has several drawbacks. In this work, we provide a comprehensive evaluation comparing Orca 2 to several other models. We use a total of 15 benchmarks (covering ∼100 tasks and over 36,000 unique prompts). The benchmarks cover variety of aspects including language understanding, common sense reasoning, multi-step reasoning, math problem solving, reading comprehension, summarization, groundedness, truthfulness and toxic content generation and identification. Our preliminary results indicate that Orca 2 significantly surpasses models of a similar size, even matching or exceeding those 5 to 10 times larger, especially on tasks that require reasoning. This highlights the potential of endowing smaller models with better reasoning capabilities. However Orca 2 is no exception to the phenomenon that all models are to some extent constrained by their underlying pre-trained model (while Orca 2 training could be applied any base LLM, we report results on LLaMA-2 7B and 13B in this report). Orca 2 models have not undergone RLHF training for safety. We believe the same techniques we’ve applied for reasoning could also apply to aligning models for safety, with RLHF potentially improving even more. This paper is available on arxiv under CC 4.0 license. Authors: (1) Arindam Mitra; (2) Luciano Del Corro, work done while at Microsoft; (3) Shweti Mahajan, work done while at Microsoft; (4) Andres Codas, denote equal contributions; (5) Clarisse Simoes, denote equal contributions; (6) Sahaj Agarwal; (7) Xuxi Chen, work done while at Microsoft;; (8) Anastasia Razdaibiedina, work done while at Microsoft; (9) Erik Jones, work done while at Microsoft; (10) Kriti Aggarwal, work done while at Microsoft; (11) Hamid Palangi; (12) Guoqing Zheng (13) Corby Rosset; (14) Hamed Khanpour; (15) Ahmed Awadall. Authors: Authors: (1) Arindam Mitra; (2) Luciano Del Corro, work done while at Microsoft; (3) Shweti Mahajan, work done while at Microsoft; (4) Andres Codas, denote equal contributions; (5) Clarisse Simoes, denote equal contributions; (6) Sahaj Agarwal; (7) Xuxi Chen, work done while at Microsoft;; (8) Anastasia Razdaibiedina, work done while at Microsoft; (9) Erik Jones, work done while at Microsoft; (10) Kriti Aggarwal, work done while at Microsoft; (11) Hamid Palangi; (12) Guoqing Zheng (13) Corby Rosset; (14) Hamed Khanpour; (15) Ahmed Awadall. Table of Links Abstract and Introduction Abstract and Introduction Preliminaries Preliminaries Teaching Orca 2 to be a Cautious Reasoner Teaching Orca 2 to be a Cautious Reasoner Technical Details Technical Details Experimental Setup Experimental Setup Evaluation Results Evaluation Results Limitations Limitations Conclusions and References Conclusions and References A. AGIEval Subtask Metrics A. AGIEval Subtask Metrics B. BigBench-Hard Subtask Metrics B. BigBench-Hard Subtask Metrics C. Evaluation of Grounding in Abstractive Summarization C. Evaluation of Grounding in Abstractive Summarization D. Evaluation of Safety D. Evaluation of Safety E. Prompts used in Evaluation E. Prompts used in Evaluation F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Output F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Output Abstract Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs’ reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. We contend that excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for different tasks, potentially different from the one used by the larger model. For example, while larger models might provide a direct answer to a complex task, smaller models may not have the same capacity. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). Moreover, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36K unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. We make Orca 2 weights publicly available at aka.ms/orca-lm to support research on the development, evaluation, and alignment of smaller LMs. 1 Introduction Large Language Models (LLMs) are enabling more natural and sophisticated interactions between humans and machines, enhancing user experience in existing applications like coding [3], web search [36], chatbots [45, 56], customer service and content creation. This transformation brought by LLMs is also paving the way for new innovative AI applications. Scaling LLMs like GPT-4 [44] and PaLM-2 [1] to ever more parameters led to emergent abilities [63] unseen in smaller models (less than ∼ 10B parameters), most notably the remarkable ability to reason zero-shot [23]. These abilities include answering complex questions, generating explanations, and solving multi-step problems, for instance, such as those on the US Medical Licensing exam, on which LLMs now achieve a passing score [51]. Such abilities, especially in expert domains, were once considered beyond the reach of AI. Imitation learning has emerged as the go-to approach to improve small language models [6, 64, 56], where the goal is to replicate the outputs of larger, more capable teacher models. While these models can produce content that matches the style of their teachers, they often fall short of their reasoning and comprehension skills [13]. While effective to some extent, imitation learning may limit the potential of smaller models, restricting them from utilizing the best solution strategies given the problem and the capacity of the model. In this work, we continue to pursue the question of how we can teach smaller LMs to reason. The objectives of Orca 2 are two-fold. Firstly, we aim to teach smaller models how to use a suite of reasoning techniques, such as step-by-step processing, recall-then-generate, recall-reason-generate, extract-generate, and direct-answer methods. Secondly, we aspire to help these models decide when to use the most effective reasoning strategy for the task at hand, allowing them to perform at their best, irrespective of their size. Like Orca 1, we utilize more capable LLMs to demonstrate various reasoning strategies across various tasks. However, in Orca 2, the reasoning strategies are carefully tailored to the task at hand, bearing in mind whether a student model is capable of the same behavior. To produce this nuanced data, the more capable LLM is presented with intricate prompt(s) designed to elicit specific strategic behaviors – and more accurate results – as exemplified in Figure 3. Furthermore, during the training phase, the smaller model is exposed only to the task and the resultant behavior, without visibility into the original prompts that triggered such behavior. This Prompt Erasure technique makes Orca 2 a Cautious Reasoner because it learns not only how to execute specific reasoning steps, but to strategize at a higher level how to approach a particular task. Rather than naively imitating powerful LLMs, we treat them as a reservoir of behaviors from which we carefully select those best suited for the task at hand. Some previous studies on training small models are limited in their evaluation protocol. They often rely on small number of tasks or on using other models for auto-evaluation by asking them to compare the outputs of two systems with a prompt like “given responses from system 1 (reference) and system 2 (target), which one is better?”. However, previous work [13, 42, 60, 67] has demonstrated that this approach has several drawbacks. In this work, we provide a comprehensive evaluation comparing Orca 2 to several other models. We use a total of 15 benchmarks (covering ∼100 tasks and over 36,000 unique prompts). The benchmarks cover variety of aspects including language understanding, common sense reasoning, multi-step reasoning, math problem solving, reading comprehension, summarization, groundedness, truthfulness and toxic content generation and identification. Our preliminary results indicate that Orca 2 significantly surpasses models of a similar size, even matching or exceeding those 5 to 10 times larger, especially on tasks that require reasoning. This highlights the potential of endowing smaller models with better reasoning capabilities. However Orca 2 is no exception to the phenomenon that all models are to some extent constrained by their underlying pre-trained model (while Orca 2 training could be applied any base LLM, we report results on LLaMA-2 7B and 13B in this report). Orca 2 models have not undergone RLHF training for safety. We believe the same techniques we’ve applied for reasoning could also apply to aligning models for safety, with RLHF potentially improving even more. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Orca 2: Enhancing Reasoning in Smaller Language Models - Abstract and Introduction

Unleash Your Potential: Weaponizing Active Learning with David Novak

Many people fail to grasp the wider meaning of the cryptocurrency movement and why it’s important to invest in projects that you find to be personally meaningful. When you use a currency, you are effectively supporting the country that the currency is designed to represent. This includes the legal, financial, and societal infrastructure that is in place, which is currently under gigantic strain in Western economies. This infrastructure has led to huge inequality, unaffordable rent, and an overall cost of living crisis - and it needs to be replaced. The most efficient method of replacement is a decentralized economy using cryptocurrencies as a payment mechanism. The Origins Of Cryptocurrency While many are aware of cryptocurrency now, few understand its prime purpose as a disruptive mechanism. The Bitcoin Genesis Block reads “The Times 03/Jan/2009 Chancellor on the brink of second bailout for banks,” a reference to the fact that banks were being paid off with taxpayer money. Meanwhile, taxpayers were often evicted from their own homes as a result of a bank-induced crisis. The mainstream media would spend more than 10 years in an outright assault against Bitcoin, calling it a Ponzi scheme and damaging to the environment. This is ironic, given that the banking system relies nearly exclusively on money printing to maintain its dominance. It has recently changed its tune somewhat. While initially trading for practically nothing, Bitcoin is now valued at ~$65,000 and could soar higher in 2025 and beyond. Ethereum, meanwhile, also has a strong price forecast according to industry and AI analysis, potentially hitting $3,500 in the near future. Such impressive valuations are no longer representative of arbitrary price increases but signal a changing lifestyle and investor sentiment. Bitcoin is increasingly accepted in smaller regions and is even an official legal tender for countries like El Salvador. BTC is further being used in many smaller regions in South Africa. At present, there are a plethora of cryptocurrencies to choose from, though the majority have next to zero utility value or long-term relevance. Choosing Sustainable Crypto Projects When selecting your cryptocurrency, you can approach it from a purely financial perspective, aiming to capitalize on technical analysis. But you can also approach it from a more humanitarian standpoint. It must be kept in mind that financial greed and the failure to hold politicians accountable are what has led to such a societal collapse - your investment makes a difference, so please make sure you allocate your funds to projects that make the best use of it for wider society. For instance, there are decentralized options involved in the renewable energy sector, such as PowerLedger. This ecosystem aspires to create a P2P energy economy without third parties overcharging for an efficient energy model. Clearly, it’s more efficient to send energy to a nearby household than to transfer it to a large national hub, where it is potentially sold and transferred to other countries. The media industry has come under fire from its financial sources and biases, which encourage outlets to represent large conglomerates and uphold certain narratives while failing to genuinely represent the views of ordinary people. Multiple projects are involved in the field of honest reporting and further provide alternative means of expression. This is what the meme industry is at its core. In spite of many degen coins and a bad reputation, they offer a means of expression for subcultural communities as an alternative form of expression. Veme offers a platform for decentralized community expression through video memes, which could be the next evolution of memes. Another sector is privacy, and there are plenty of privacy coins to choose from that are hard to trace. In particular, Monero which tends to have a stronger reputation than other coins. The Brave browser enables online privacy, so outside of investment, you can use these tools as a form of support. The list of projects and industries is too large to expand upon. But some projects are much more holistic than others and have better long-term potential. You can find those that match your ideological preferences while also having strong technical and financial architectures. Don’t invest in a well-meaning project if it has poor management or sloppy fundamentals - such an endeavor won’t help anyone at all. Crypto Integrity  - The Golden Investment Path Many people make the mistake of failing to invest in projects that are personally meaningful to them. This means that your money could be going towards an area that you don’t really believe in. Supporting an industry that you are against is actually a form of dishonesty against yourself! Do technical and personal research to align as many areas as possible for integrated, sustainable investment. This concept is also gaining traction in the Web2 industry with the rise of ESG investment. Align your personal values with your financial aspirations, and you might find it helpful not only to your bank balance - but to your overall well-being and peace of mind. Many people fail to grasp the wider meaning of the cryptocurrency movement and why it’s important to invest in projects that you find to be personally meaningful. Many people fail to grasp the wider meaning of the cryptocurrency movement and why it’s important to invest in projects that you find to be personally meaningful. When you use a currency, you are effectively supporting the country that the currency is designed to represent. This includes the legal, financial, and societal infrastructure that is in place, which is currently under gigantic strain in Western economies. This infrastructure has led to huge inequality, unaffordable rent, and an overall cost of living crisis - and it needs to be replaced. The most efficient method of replacement is a decentralized economy using cryptocurrencies as a payment mechanism. cost of living crisis The Origins Of Cryptocurrency While many are aware of cryptocurrency now, few understand its prime purpose as a disruptive mechanism. The Bitcoin Genesis Block reads “The Times 03/Jan/2009 Chancellor on the brink of second bailout for banks,” a reference to the fact that banks were being paid off with taxpayer money. Meanwhile, taxpayers were often evicted from their own homes as a result of a bank-induced crisis. The Bitcoin Genesis Block The Bitcoin Genesis Block “The Times 03/Jan/2009 Chancellor on the brink of second bailout for banks,” The mainstream media would spend more than 10 years in an outright assault against Bitcoin, calling it a Ponzi scheme and damaging to the environment. This is ironic, given that the banking system relies nearly exclusively on money printing to maintain its dominance. It has recently changed its tune somewhat. While initially trading for practically nothing, Bitcoin is now valued at ~ $65,000 and could soar higher in 2025 and beyond. Ethereum, meanwhile, also has a strong price forecast according to industry and AI analysis, potentially hitting $3,500 in the near future. S uch impressive valuations are no longer representative of arbitrary price increases but signal a changing lifestyle and investor sentiment. $65,000 strong price forecast strong price forecast $3,500 S Bitcoin is increasingly accepted in smaller regions and is even an official legal tender for countries like El Salvador. BTC is further being used in many smaller regions in South Africa . At present, there are a plethora of cryptocurrencies to choose from, though the majority have next to zero utility value or long-term relevance. smaller regions in South Africa Choosing Sustainable Crypto Projects When selecting your cryptocurrency, you can approach it from a purely financial perspective, aiming to capitalize on technical analysis. But you can also approach it from a more humanitarian standpoint. It must be kept in mind that financial greed and the failure to hold politicians accountable are what has led to such a societal collapse - your investment makes a difference, so please make sure you allocate your funds to projects that make the best use of it for wider society. For instance, there are decentralized options involved in the renewable energy sector, such as PowerLedger. This ecosystem aspires to create a P2P energy economy without third parties overcharging for an efficient energy model. Clearly, it’s more efficient to send energy to a nearby household than to transfer it to a large national hub, where it is potentially sold and transferred to other countries. The media industry has come under fire from its financial sources and biases, which encourage outlets to represent large conglomerates and uphold certain narratives while failing to genuinely represent the views of ordinary people. Multiple projects are involved in the field of honest reporting and further provide alternative means of expression. This is what the meme industry is at its core. In spite of many degen coins and a bad reputation, they offer a means of expression for subcultural communities as an alternative form of expression. Veme offers a platform for decentralized community expression through video memes, which could be the next evolution of memes. Another sector is privacy, and there are plenty of privacy coins to choose from that are hard to trace. In particular, Monero which tends to have a stronger reputation than other coins. The Brave browser enables online privacy, so outside of investment, you can use these tools as a form of support. The list of projects and industries is too large to expand upon. But some projects are much more holistic than others and have better long-term potential. You can find those that match your ideological preferences while also having strong technical and financial architectures. Don’t invest in a well-meaning project if it has poor management or sloppy fundamentals - such an endeavor won’t help anyone at all. Crypto Integrity  - The Golden Investment Path Many people make the mistake of failing to invest in projects that are personally meaningful to them. This means that your money could be going towards an area that you don’t really believe in. Supporting an industry that you are against is actually a form of dishonesty against yourself! Do technical and personal research to align as many areas as possible for integrated, sustainable investment. This concept is also gaining traction in the Web2 industry with the rise of ESG investment. Align your personal values with your financial aspirations, and you might find it helpful not only to your bank balance - but to your overall well-being and peace of mind. rise of ESG investment.

Integrity And Sustainability In Crypto Investment - Why It Matters More Than You Think

Why Self-Taught Skills Are the New MBA in Today's Economy

কেন স্ব-শিক্ষিত দক্ষতা আজকের অর্থনীতিতে নতুন এমবিএ

Warum autodidaktische Fähigkeiten in der heutigen Wirtschaft der neue MBA sind

Por qué las habilidades autodidactas son el nuevo MBA en la economía actual

Pourquoi les compétences autodidactes sont le nouveau MBA dans l'économie d'aujourd'hui

आज की अर्थव्यवस्था में स्व-शिक्षित कौशल क्यों नया एमबीए है?

今日の経済において独学のスキルが新しいMBAである理由

오늘날 경제에서 독학이 새로운 MBA인 이유

Por que as habilidades autodidatas são o novo MBA na economia atual

Почему навыки самообучения — это новый MBA в современной экономике

Neden Kendi Kendine Öğretilen Beceriler Günümüz Ekonomisinde Yeni MBA'dir?

Tại sao kỹ năng tự học là MBA mới trong nền kinh tế ngày nay

为什么自学技能是当今经济中的新 MBA

Metamorphosis Commenced: Network States v0.01

মেটামরফোসিস শুরু হয়েছে: নেটওয়ার্ক স্টেটস v0.01

Beginn der Metamorphose: Netzwerkzustände v0.01

Metamorfosis comenzada: Estados de la red v0.01

La métamorphose a commencé : États du réseau v0.01

मेटामोर्फोसिस शुरू हुआ: नेटवर्क स्टेट्स v0.01

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