NEXXT Journeys

In the digital era, the convergence of sustainability and offers a potent synergy. Picture an online shopping journey where each selection mirrors your eco-conscious ethos and recommendations cater to your preferences while considering environmental impact. This encapsulates the concept of "Sustainability and E-commerce Personalization." e-commerce personalization Furthermore, that 77% of consumers have actively favored, endorsed, or invested more in brands delivering personalized services or experiences. Forrester reports Sustainability in E-commerce Sustainability in e-commerce means conducting online business in a way that's eco-friendly, socially responsible, and economically viable. It's about balancing customer needs, profits, and our duty to the planet and society. E-commerce poses unique sustainability challenges: Excessive Packaging: One-click shopping often leads to overpackaging, increasing waste. Transportation Emissions: Fast shipping options contribute to carbon emissions. Product Returns: E-commerce sees more returns, which can be logistically and environmentally challenging. Energy Consumption: Data centers and warehouses powering e-commerce consume vast energy. Consumers can drive e-commerce towards sustainability: Demand : Expressing a preference for sustainable products encourages businesses to prioritize them. Eco-friendly Products Choose Slower Shipping: Opting for longer delivery times reduces the carbon footprint. Minimize Returns: Thoughtful purchases and awareness of return policies can reduce returns. Support Sustainable Brands: Patronizing e-commerce brands committed to sustainability sends a clear message about consumer values. Personalization in E-commerce At its essence, e-commerce personalization crafts a unique journey based on each customer's preferences and actions, departing from the one-size-fits-all approach. This extends beyond products to include every interaction, even the packaging. online shopping For example, customized cardboard box packaging, tailored to a customer's likes, can elevate the unboxing experience, leaving a memorable impression. can further enhance the personalization process. Different Types of Packaging The enchantment of e-commerce personalization lies in advanced algorithms and techniques. These tools sift through extensive data, from browsing history to buying patterns, to grasp each customer's desires. Recommendation engines, a prevalent technique, leverage this data to propose products that resonate with a customer's interests. Ever wonder how Amazon always seems to predict your next purchase? That's personalization in action. For businesses, it's a route to increased sales and customer loyalty. By showcasing what customers want when they want it, businesses can boost conversion rates and customer contentment. The outcome? Elevated revenue and fortified brand allegiance. Conversely, consumers enjoy time-saving and more gratifying shopping experiences. No more wading through irrelevant product pages. Instead, they encounter options aligning with their preferences and needs, enhancing the shopping journey's smoothness and gratification. The Intersection of Sustainability and E-commerce Personalization Picture a shopping experience tailored to your values and environmental concerns. Personalization in e-commerce makes this a reality. By analyzing your past choices, online retailers can suggest eco-friendly products. This means you're more likely to see sustainable items, reducing the temptation for less eco-friendly options. Personalization also encourages eco-friendly shopping. When customers see sustainable choices that match their preferences, they tend to make green purchases, benefiting both the environment and sustainable practices. E-commerce platforms are adopting eco-friendly personalization: Product Recommendations: Algorithms suggest items with lower carbon footprints. Delivery Options: Personalized shipping choices promote greener methods. Sustainable Packaging: You can choose minimal or eco-friendly packaging. Reward Systems: Loyalty programs reward sustainable choices. However, this fusion of sustainability and personalization raises challenges. Privacy must balance tailored recommendations. Ethical concerns arise about influencing choices for sustainability. Transparency is crucial to inform customers. Customizing Eco-Friendly Shopping Journeys Picture stepping into an online store where, instead of overwhelming product choices, you're met with a personalized selection tailored to your sustainability preferences. Whether you're passionate about reducing plastic waste, supporting fair trade, or buying locally sourced products, e-commerce platforms now use data-driven insights to curate shopping experiences that align with your eco-conscious values. Innovative include algorithms that consider a product's environmental impact, such as its carbon footprint, materials, and ethical sourcing. e-commerce developments When you search for items like shoes or kitchen appliances, you'll receive recommendations that not only match your style but also support your commitment to eco-friendly choices. But the eco-friendly journey doesn't stop at product selection. Sustainable e-commerce takes it a step further by offering eco-conscious delivery and packaging options, like carbon-neutral shipping or minimal packaging to reduce waste. Some platforms even let you consolidate orders into one shipment, cutting down transportation emissions. Personalization goes beyond product suggestions; it now encourages eco-friendly actions. E-commerce websites reward customers for sustainable choices, like opting for slower, eco-friendly shipping, and offering loyalty points or discounts on future purchases. These incentives motivate shoppers to make environmentally responsible decisions while they shop. Conclusion In the dynamic world of e-commerce, the fusion of sustainability and personalization is paving the way for eco-conscious shopping journeys. As consumers increasingly prioritize eco-friendly choices, the marriage of tailored experiences, environmentally-minded recommendations, sustainable delivery, and incentives for green behavior hold the promise of a more eco-friendly shopping landscape. In essence, it's a win-win scenario where consumers can shop guilt-free, and businesses can thrive by aligning with the values of a greener, more sustainable future.

Sustainability and E-commerce Personalization: Customizing Eco-Friendly Shopping Journeys

Welcome back to our 'Behind the Startup' series where we dive into the journeys of innovative companies reshaping industries. Today, we're revisiting Travala.com through a conversation with Juan Otero, who last joined us in May 2020. At that time, Juan forecasted a recovery in the travel sector which largely rang true. As we catch up with him today, we'll learn how Travala.com has navigated the evolving landscape of the travel industry, especially in light of the ongoing shifts towards digital and decentralized technologies. Join us as Juan shares insights on the company's growth, the integration of web3 technologies, and how these strategies are positioning Travala.com for future challenges and opportunities in the travel sector. Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Juan Otero: Thanks Ishan, great to be speaking with you again. So much has happened at Travala.com in the last four years as the travel industry rebounded and the web3 industry continued to swell. In that time, we’ve significantly expanded our offering through partnerships with some of the biggest names in travel, including Expedia and KAYAK. Our accommodation selection multiplied to over 2.2 million properties, and we introduced flights with 600+ airlines as well as 410,000+ activities, with a total of over 3 million travel products now available on Travala.com. Our team has also more than doubled in size to accommodate our growth, with hires across all departments from the management team to security, developers, marketing, and customer support. All of these developments have culminated in tremendous growth in customers, bookings, and revenue, with our gross revenue growing from less than $1 million in Q1 2020 to over $22 million in Q1 2024. Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Juan Otero: From the rise of domestic travel to the influx of digital nomads across the globe, the pandemic has prompted the emergence of many new trends that look likely to continue well into the future. Where we once might’ve promoted a weekend away, we’re now encouraging longer, more immersive experiences through marketing, exclusive mobile-only deals on stays, and by offering an average of 16% savings on hotels after booking a flight. One of the often overlooked developments in recent years has been the rapid emergence of metasearch engines. With shifting behaviours in how savvy travellers search for, compare, and book travel, we recognised the importance of these metasearch engines—which have quickly gained market share when it comes to the discovery phase of travel—and became the first crypto-friendly online travel agency to be listed on leading metasearch engines KAYAK and Google Hotels, alongside incumbents in the travel industry. Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Juan Otero: We’ve honed our web3 strategy by partnering with projects to promote cross-community growth and leverage the power of highly engaged web3 audiences. As a result, we now have over 100 cryptocurrency payment options and frequently conduct campaigns with partners to promote the ability to travel with crypto on Travala.com. From leading web3 events like TOKEN2049 and Paris Blockchain Week to traditional brands interested in expanding their audience—such as Marriott International and the Catalan Tourist Board—we’re leveraging our position as the bridge between the world of traditional travel and the web3 space to continue underscoring our reputation as the leading crypto-native travel booking service. We’ve also introduced Bitcoin rewards for Platinum and Diamond members of the AVA Smart Program, the travel rewards program available on Travala.com in partnership with the AVA Foundation. These two membership groups can receive up to 10% back in Bitcoin on bookings made via Travala.com, which can be put towards future bookings or transferred out of the platform. Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Juan Otero: As consumer behaviours continue evolving and products become increasingly tailored to the millennial and Gen Z audience that now make up the majority of the travel market, the key advantage that we have over more established players in the travel industry is our agility as a team, coupled with our innovation-first approach. We predict that crypto payments will become more commonplace as the web3 industry continues to mature, to the point where it will no longer be a point of differentiation. Other web3 elements (and how successfully they can be incorporated into the travel experience) will therefore be the main area of differentiation for Travala.com. From areas like web3-oriented loyalty programs to digital collectibles, we’re continuously exploring new ways that enhance the experience and make it more rewarding, pushing the boundaries of what travel is in today’s age. Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com's market dynamics and user behavior? Juan Otero: The positive market action driven by the Bitcoin halving saw the cryptocurrency reach an all-time high in mid-March earlier this year and resulted in our bookings soaring, leading to the strongest quarter in Travala.com’s history. As approximately 75% of our bookings are purchased with crypto, we experience a strong correlation between a crypto bull market and high booking volume on our platform, given the additional value available for travellers to derive from their crypto assets in dollar-value terms.
With the fourth Bitcoin halving now behind us, we expect the crypto market to continue performing well in the next 12–18 months, based on historic trends following previous Bitcoin halving events. Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Juan Otero: We see Travala.com as becoming a hub for crypto travellers to create genuine connections with the world, themselves, and each other. Web3 is a community movement, and our community is constantly brimming with discussions about travel tips, destinations to explore, the broader web3 space, and suggestions for Travala.com. We want Travala.com to be more than just a platform where travellers go to book their travel; in the long term, we envision Travala.com solidifying its position as the world's leading web3 travel ecosystem, where knowledge, creativity, and of course the act of travel itself is rewarded. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Welcome back to our 'Behind the Startup' series where we dive into the journeys of innovative companies reshaping industries. Today, we're revisiting Travala.com through a conversation with Juan Otero, who last joined us in May 2020. At that time, Juan forecasted a recovery in the travel sector which largely rang true. As we catch up with him today, we'll learn how Travala.com has navigated the evolving landscape of the travel industry, especially in light of the ongoing shifts towards digital and decentralized technologies. Join us as Juan shares insights on the company's growth, the integration of web3 technologies, and how these strategies are positioning Travala.com for future challenges and opportunities in the travel sector. Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Ishan Pandey: Welcome back, Juan; it's a pleasure to have you in our 'Behind the Startup' series. Last time we spoke in May 2020, you correctly predicted about a recovery starting in Q3, particularly for domestic travel post Covid-19, with a full recovery including international travel by Q4. As we continue our 'Behind the Startup' series, could you update us on how Travala.com has evolved since then, especially considering the ongoing shifts in the travel industry? Travala.com Juan Otero: Thanks Ishan, great to be speaking with you again. So much has happened at Travala.com in the last four years as the travel industry rebounded and the web3 industry continued to swell. In that time, we’ve significantly expanded our offering through partnerships with some of the biggest names in travel, including Expedia and KAYAK. Juan Otero: Travala.com Our accommodation selection multiplied to over 2.2 million properties, and we introduced flights with 600+ airlines as well as 410,000+ activities, with a total of over 3 million travel products now available on Travala.com . Travala.com Our team has also more than doubled in size to accommodate our growth, with hires across all departments from the management team to security, developers, marketing, and customer support. All of these developments have culminated in tremendous growth in customers, bookings, and revenue, with our gross revenue growing from less than $1 million in Q1 2020 to over $22 million in Q1 2024. Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Ishan Pandey: During our last discussion, you highlighted some pivotal adjustments made at Travala.com in response to the pandemic. Looking at the current times, how has your business model or strategy changed to align with the new dynamics of the travel industry? Travala.com Juan Otero: From the rise of domestic travel to the influx of digital nomads across the globe, the pandemic has prompted the emergence of many new trends that look likely to continue well into the future. Where we once might’ve promoted a weekend away, we’re now encouraging longer, more immersive experiences through marketing, exclusive mobile-only deals on stays, and by offering an average of 16% savings on hotels after booking a flight. Juan Otero: One of the often overlooked developments in recent years has been the rapid emergence of metasearch engines. With shifting behaviours in how savvy travellers search for, compare, and book travel, we recognised the importance of these metasearch engines—which have quickly gained market share when it comes to the discovery phase of travel—and became the first crypto-friendly online travel agency to be listed on leading metasearch engines KAYAK and Google Hotels, alongside incumbents in the travel industry. Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Ishan Pandey: Cryptocurrencies have seen substantial growth in value and popularity since we last talked. How is Travala.com planning to capitalise on this trend? Are there new strategies or technologies you're implementing? Travala.com Juan Otero: We’ve honed our web3 strategy by partnering with projects to promote cross-community growth and leverage the power of highly engaged web3 audiences. Juan Otero: As a result, we now have over 100 cryptocurrency payment options and frequently conduct campaigns with partners to promote the ability to travel with crypto on Travala.com . From leading web3 events like TOKEN2049 and Paris Blockchain Week to traditional brands interested in expanding their audience—such as Marriott International and the Catalan Tourist Board—we’re leveraging our position as the bridge between the world of traditional travel and the web3 space to continue underscoring our reputation as the leading crypto-native travel booking service. Travala.com We’ve also introduced Bitcoin rewards for Platinum and Diamond members of the AVA Smart Program, the travel rewards program available on Travala.com in partnership with the AVA Foundation. These two membership groups can receive up to 10% back in Bitcoin on bookings made via Travala.com , which can be put towards future bookings or transferred out of the platform. Travala.com Travala.com Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Ishan Pandey: With rising competition and shifting consumer behaviors, how does Travala.com intend to differentiate itself in the travel industry? Travala.com Juan Otero: As consumer behaviours continue evolving and products become increasingly tailored to the millennial and Gen Z audience that now make up the majority of the travel market, the key advantage that we have over more established players in the travel industry is our agility as a team, coupled with our innovation-first approach. Juan Otero: We predict that crypto payments will become more commonplace as the web3 industry continues to mature, to the point where it will no longer be a point of differentiation. Other web3 elements (and how successfully they can be incorporated into the travel experience) will therefore be the main area of differentiation for Travala.com . From areas like web3-oriented loyalty programs to digital collectibles, we’re continuously exploring new ways that enhance the experience and make it more rewarding, pushing the boundaries of what travel is in today’s age. Travala.com Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com's market dynamics and user behavior? Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com's market dynamics and user behavior? Ishan Pandey: Previously, you mentioned the impact of Bitcoin halving on your operations. Can you expand on how recent cryptocurrency events have influenced Travala.com 's market dynamics and user behavior? Travala.com Juan Otero: The positive market action driven by the Bitcoin halving saw the cryptocurrency reach an all-time high in mid-March earlier this year and resulted in our bookings soaring, leading to the strongest quarter in Travala.com ’s history. Juan Otero: Travala.com As approximately 75% of our bookings are purchased with crypto, we experience a strong correlation between a crypto bull market and high booking volume on our platform, given the additional value available for travellers to derive from their crypto assets in dollar-value terms.
With the fourth Bitcoin halving now behind us, we expect the crypto market to continue performing well in the next 12–18 months, based on historic trends following previous Bitcoin halving events. Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Ishan Pandey: Finally, considering all the challenges and opportunities that have emerged, what is your long-term vision for Travala.com in this new era of travel and technology? Travala.com Juan Otero: We see Travala.com as becoming a hub for crypto travellers to create genuine connections with the world, themselves, and each other. Web3 is a community movement, and our community is constantly brimming with discussions about travel tips, destinations to explore, the broader web3 space, and suggestions for Travala.com . Juan Otero: Travala.com Travala.com We want Travala.com to be more than just a platform where travellers go to book their travel; in the long term, we envision Travala.com solidifying its position as the world's leading web3 travel ecosystem, where knowledge, creativity, and of course the act of travel itself is rewarded. Travala.com Travala.com Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

How Travala.com Skyrocketed to $22 Million in Revenue: Lessons for the Travel Industry

কিভাবে Travala.com স্কাইরোকেট $22 মিলিয়ন আয়ে পৌঁছেছে: ভ্রমণ শিল্পের জন্য পাঠ

Wie Travala.com seinen Umsatz auf 22 Millionen Dollar steigerte: Lehren für die Reisebranche

Cómo Travala.com se disparó hasta alcanzar los 22 millones de dólares en ingresos: lecciones para la industria de viajes

Comment Travala.com a grimpé à 22 millions de dollars de revenus : leçons pour l'industrie du voyage

कैसे Travala.com का राजस्व 22 मिलियन डॉलर तक पहुंचा: यात्रा उद्योग के लिए सबक

Travala.com が収益 2,200 万ドルに急上昇した方法: 旅行業界への教訓

Travala.com이 2,200만 달러의 매출로 급상승한 방법: 여행 산업을 위한 교훈

Como Travala.com disparou para US$ 22 milhões em receita: lições para a indústria de viagens

Как выручка Travala.com взлетела до $22 миллионов: уроки для туристической индустрии

Travala.com'un Geliri Nasıl 22 Milyon Dolara Yükseldi: Seyahat Sektörü İçin Dersler

Làm thế nào Travala.com tăng vọt doanh thu lên 22 triệu USD: Bài học cho ngành du lịch

Travala.com 如何实现收入 2200 万美元：旅游业的教训

Decoding Success: Case Studies of 3 Successful Tech Enterprises

Web3 Gaming is developing fast, but limitations such as isolated in-game economies and single-use NFTs restrict its full potential. Eldarune — a multi-faceted gaming ecosystem with average monthly transactions of over 100,000 — seems to break down these barriers and promote a more all-encompassing experience. Eldarune is the biggest Turkish Web3 gaming project that has the most advanced technology in the Web3 gaming market to date. The studio was conceived in Seedify, a Web3 gaming-focused incubator and launchpad, and has garnered the support of BNB Chain, Bybit, Decubate, Hacken, Immutable, Layer One X, and other The team behind Eldarune is based in Turkey and consists of 28 individuals, each bringing their expertise to the table.Can Picak, the CEO and project spokesperson, is a prominent industry leader with a background in Web3 gaming and blockchain marketing. He has practical experience in integrating gaming with blockchain and leveraging AI and IoT in startups. Building a Gaming Studio Powerhouse: The DIGA Labs Legacy Eldarune is a creation of DIGA Labs, a cutting-edge blockchain gaming solutions provider. Founded in 2021 by a team of passionate gamers and blockchain enthusiasts, DIGA Labs has a proven gaming industry track record and brings in software development expertise. This expertise shows that, technologically speaking, Eldarune titles are state-of-the-art. The company breaks the mold of traditional Web3 gaming, letting the players use their digital assets across multiple games. ELDA tokens and NFTs are like universal game currency, working in all their titles. This creates a connected ecosystem where all in-game efforts have real value and can be applied to different gaming experiences. ELDA Token The ELDA token is the lifeblood of the Eldarune ecosystem, powering a diverse range of in-game experiences. Players can use ELDA to access exclusive features and content, and also NFT purchases and rentals to expand their arsenals. ELDA unlocks participation in competitive activities like arena battles and tournaments – a juicy chance to win even more tokens and prestigious rewards. Players can also stake their ELDA for passive income through farming and burning mechanisms or directly invest it in powerful in-game items and character upgrades. For those eager to join the Eldarune adventure, ELDA is readily available for purchase on popular DEXs and CEXs like Bybit, MEXC, and PancakeSwap. A Universe of Games: Eldarune's Multi-Faceted Approach Eldarune is building a sprawling gaming universe that is not confined to a single blockchain. This multi-chain approach leverages the strengths of BNB Chain, Polygon, and Ethereum, creating a versatile and expansive player experience. On the 8th of April, Eldarune will also integrate Avalanche, and later, Solana will join the party. So, what is the multi-chain approach all about? Imagine playing different games where your cool sword or rare character skin follows you from one adventure to the next. That's the magic of Layer One X tech – Eldarune’s partner. It lets you trade your digital assets (tokens, NFTs, and customization items) seamlessly between their games, even if they’re on different blockchains. Simply put, you can jump between games without losing your hard-earned loot, creating a more connected experience and opening doors to potentially sweet rewards. So far, Eldarune’s universe includes four titles. World of Elymnias (WoE), an open beta idle game, lets players build empires and collect loot with minimal effort, perfect for casual sessions. Strategic minds can test their skills in the closed beta of Heroes of Eldarune (HoE), a TCG where they can build their ultimate deck and outwit opponents. Journey (open Beta) offers a captivating narrative adventure through text-based choices, allowing players to craft their own story. For the competitive crowd, ELDA RUSH, a closed alpha MOBA, is on the waitlist, promising fast-paced brawls. Eldarune's Diverse Gaming Universe: World of Elymnias (WoE) This idle game attracts over 10,000 daily active users. WoE has PvP modes where you can test your strategic prowess against other players in the arena. It is currently in open Beta, and players can download the game’s APK through the Eldarune Discord channel. Heroes of Eldarune (HoE) HoE is a TCG (Trading Card Game) with simple mechanics and user-friendly graphics. The game’s continuous development and new features are decided through collaboration with the Eldarune community and DAO. HoE is currently in the Beta stage, expected to launch in Q3 2024. Journey It is a text-based adventure game in which players explore a narrative-driven world, with over 50,000 users actively engaging in the storyline. You'll send your troops on dangerous journeys, following the narrative's twists and turns as you make choices that shape your path. Completing quests and progressing through the story unlocks valuable rewards. Eldarune Journey is in closed Beta. ELDA Rush It is a multiplayer online battle arena (MOBA) game akin to popular titles like Dota 2 and League of Legends. Assemble your team of powerful heroes, each with distinct abilities, and dive into the battlefield. ELDA Rush is currently in a closed Alpha stage with a limited player base. Players can join Eldarune's live streams to experience the game firsthand and potentially participate in the testing phase. Interoperability, Multi-Chain Dominance – and More Eldarune's true innovation lies in its interoperable NFTs and multi-chain functionality, setting it apart in the crowded Web3 gaming space. As of 2024, it delivers the most advanced technology in the market: Interoperable NFTs Players can use their acquired NFTs across all Eldarune titles. In-game NFTs in Eldarune transcend individual games, boosting their value and utility. The ability to rent or sell these NFTs on marketplaces creates a highly liquid in-game economy, allowing players to earn real-world value from their assets. Multi-Chain Gameplay Eldarune is currently operational on BNB, Polygon, and Ethereum, which is a great chance for players from different ecosystems to connect and compete. This creates a more expansive and inclusive gaming environment, attracting a wider player base. AI-powered Eldarune elevates gameplay through its AI that tailors experiences to individual players. By learning from your choices and actions, the AI personalizes the in-game world and adjusts difficulty for continued engagement. Players can train the AI and make NPCs fight alongside them in battles; AI-powered NPCs can create unique missions, and trained AI champions can be sold as NFTs. Community-focused Eldarune creates a space where players can contribute to the game's evolution. Further boosting engagement, a system of 21 seasons keeps players hooked, contributing to a sustainable and dynamic in-game economy. Gameplay Versatility Each title offers multiple modes, from conquering campaigns and exploring dungeons to battling clan bosses and testing skills in PvP arenas. This diversity ensures there's something for everyone. Accessibility is paramount, with all Eldarune games playable across browsers, mobile devices, PCs, and even consoles, allowing players to jump in anytime, anywhere. Deep Submersion Eldarune goes beyond captivating gameplay. Each title has its own immersive soundtrack album, setting a new standard for atmospheric experiences. Web3 Gaming Ecosystem That Constantly Evolves With over 20,000 unique NFT holders and the ELDA token reaching an all-time high of $0.062 in January 2024, the Eldarune ecosystem appears to be thriving. The project’s roadmap outlines a continuous development process with exciting prospects – Eldarune gears up for a busy March and April. Public Beta Testnets for both Heroes of Eldarune and Journey kick off in March, followed by exciting Public Beta Testnet Tournaments for both games in March and April. 2024 brings even more action with a free mint event on Avalanche (April 15th), Avalanche integration, L1X integration, and an Avalanche HoE Tournament. Eldarune's commitment to interoperability, multi-chain functionality, and a diverse gaming portfolio positions it as a strong contender in the Web3 gaming landscape. Even though the project is still under development, with Elda Rush and HoE yet to be fully launched, the initial games offer a glimpse into Eldarune's potential. The long-term success will hinge on the execution of the ambitious roadmap and the continuous delivery of engaging and vanward gaming experiences. This story was distributed under HackerNoon’s Brand As An Author Program. Learn more about the program here. Web3 Gaming is developing fast, but limitations such as isolated in-game economies and single-use NFTs restrict its full potential. Eldarune — a multi-faceted gaming ecosystem with average monthly transactions of over 100,000 — seems to break down these barriers and promote a more all-encompassing experience. Eldarune Eldarune Eldarune is the biggest Turkish Web3 gaming project that has the most advanced technology in the Web3 gaming market to date. The studio was conceived in Seedify , a Web3 gaming-focused incubator and launchpad, and has garnered the support of BNB Chain, Bybit, Decubate, Hacken, Immutable, Layer One X, and other Web3 gaming Seedify Seedify The team behind Eldarune is based in Turkey and consists of 28 individuals, each bringing their expertise to the table. Can Picak , the CEO and project spokesperson, is a prominent industry leader with a background in Web3 gaming and blockchain marketing. He has practical experience in integrating gaming with blockchain and leveraging AI and IoT in startups. Can Picak Can Picak Building a Gaming Studio Powerhouse: The DIGA Labs Legacy Eldarune is a creation of DIGA Labs , a cutting-edge blockchain gaming solutions provider. Founded in 2021 by a team of passionate gamers and blockchain enthusiasts, DIGA Labs has a proven gaming industry track record and brings in software development expertise. DIGA Labs DIGA Labs This expertise shows that, technologically speaking, Eldarune titles are state-of-the-art. The company breaks the mold of traditional Web3 gaming, letting the players use their digital assets across multiple games. ELDA tokens and NFTs are like universal game currency, working in all their titles. This creates a connected ecosystem where all in-game efforts have real value and can be applied to different gaming experiences. ELDA Token The ELDA token is the lifeblood of the Eldarune ecosystem, powering a diverse range of in-game experiences. Players can use ELDA to access exclusive features and content, and also NFT purchases and rentals to expand their arsenals. ELDA token ELDA token ELDA unlocks participation in competitive activities like arena battles and tournaments – a juicy chance to win even more tokens and prestigious rewards. Players can also stake their ELDA for passive income through farming and burning mechanisms or directly invest it in powerful in-game items and character upgrades. For those eager to join the Eldarune adventure, ELDA is readily available for purchase on popular DEXs and CEXs like Bybit , MEXC , and PancakeSwap . Bybit Bybit MEXC MEXC PancakeSwap PancakeSwap A Universe of Games: Eldarune's Multi-Faceted Approach Eldarune is building a sprawling gaming universe that is not confined to a single blockchain. This multi-chain approach leverages the strengths of BNB Chain, Polygon, and Ethereum, creating a versatile and expansive player experience. On the 8th of April, Eldarune will also integrate Avalanche, and later, Solana will join the party. So, what is the multi-chain approach all about? Imagine playing different games where your cool sword or rare character skin follows you from one adventure to the next. That's the magic of Layer One X tech – Eldarune’s partner . It lets you trade your digital assets (tokens, NFTs, and customization items) seamlessly between their games, even if they’re on different blockchains. Simply put, you can jump between games without losing your hard-earned loot, creating a more connected experience and opening doors to potentially sweet rewards. partner partner So far, Eldarune’s universe includes four titles. World of Elymnias (WoE), an open beta idle game, lets players build empires and collect loot with minimal effort, perfect for casual sessions. Strategic minds can test their skills in the closed beta of Heroes of Eldarune (HoE), a TCG where they can build their ultimate deck and outwit opponents. Journey (open Beta) offers a captivating narrative adventure through text-based choices, allowing players to craft their own story. For the competitive crowd, ELDA RUSH, a closed alpha MOBA, is on the waitlist, promising fast-paced brawls. Eldarune's Diverse Gaming Universe: World of Elymnias (WoE) World of Elymnias (WoE) This idle game attracts over 10,000 daily active users. WoE has PvP modes where you can test your strategic prowess against other players in the arena. It is currently in open Beta, and players can download the game’s APK through the Eldarune Discord channel . Eldarune Discord channel Eldarune Discord channel Heroes of Eldarune (HoE) Heroes of Eldarune (HoE) HoE is a TCG (Trading Card Game) with simple mechanics and user-friendly graphics. The game’s continuous development and new features are decided through collaboration with the Eldarune community and DAO. HoE is currently in the Beta stage, expected to launch in Q3 2024. Journey Journey It is a text-based adventure game in which players explore a narrative-driven world, with over 50,000 users actively engaging in the storyline. You'll send your troops on dangerous journeys, following the narrative's twists and turns as you make choices that shape your path. Completing quests and progressing through the story unlocks valuable rewards. Eldarune Journey is in closed Beta. ELDA Rush ELDA Rush It is a multiplayer online battle arena (MOBA) game akin to popular titles like Dota 2 and League of Legends. Assemble your team of powerful heroes, each with distinct abilities, and dive into the battlefield. ELDA Rush is currently in a closed Alpha stage with a limited player base. Players can join Eldarune's live streams to experience the game firsthand and potentially participate in the testing phase. Interoperability, Multi-Chain Dominance – and More Eldarune's true innovation lies in its interoperable NFTs and multi-chain functionality, setting it apart in the crowded Web3 gaming space. As of 2024, it delivers the most advanced technology in the market: Interoperable NFTs Interoperable NFTs Players can use their acquired NFTs across all Eldarune titles. In-game NFTs in Eldarune transcend individual games, boosting their value and utility. The ability to rent or sell these NFTs on marketplaces creates a highly liquid in-game economy, allowing players to earn real-world value from their assets. Multi-Chain Gameplay Multi-Chain Gameplay Eldarune is currently operational on BNB, Polygon, and Ethereum, which is a great chance for players from different ecosystems to connect and compete. This creates a more expansive and inclusive gaming environment, attracting a wider player base. AI-powered AI-powered Eldarune elevates gameplay through its AI that tailors experiences to individual players. By learning from your choices and actions, the AI personalizes the in-game world and adjusts difficulty for continued engagement. Players can train the AI and make NPCs fight alongside them in battles; AI-powered NPCs can create unique missions, and trained AI champions can be sold as NFTs. Community-focused Community-focused Eldarune creates a space where players can contribute to the game's evolution. Further boosting engagement, a system of 21 seasons keeps players hooked, contributing to a sustainable and dynamic in-game economy. Gameplay Versatility Gameplay Versatility Each title offers multiple modes, from conquering campaigns and exploring dungeons to battling clan bosses and testing skills in PvP arenas. This diversity ensures there's something for everyone. Accessibility is paramount, with all Eldarune games playable across browsers, mobile devices, PCs, and even consoles, allowing players to jump in anytime, anywhere. Deep Submersion Deep Submersion Eldarune goes beyond captivating gameplay. Each title has its own immersive soundtrack album, setting a new standard for atmospheric experiences. Web3 Gaming Ecosystem That Constantly Evolves With over 20,000 unique NFT holders and the ELDA token reaching an all-time high of $0.062 in January 2024, the Eldarune ecosystem appears to be thriving. reaching an all-time high reaching an all-time high The project’s roadmap outlines a continuous development process with exciting prospects – Eldarune gears up for a busy March and April. Public Beta Testnets for both Heroes of Eldarune and Journey kick off in March, followed by exciting Public Beta Testnet Tournaments for both games in March and April. 2024 brings even more action with a free mint event on Avalanche (April 15th), Avalanche integration, L1X integration, and an Avalanche HoE Tournament. Eldarune's commitment to interoperability, multi-chain functionality, and a diverse gaming portfolio positions it as a strong contender in the Web3 gaming landscape. Even though the project is still under development, with Elda Rush and HoE yet to be fully launched, the initial games offer a glimpse into Eldarune's potential. The long-term success will hinge on the execution of the ambitious roadmap and the continuous delivery of engaging and vanward gaming experiences. This story was distributed under HackerNoon’s Brand As An Author Program. Learn more about the program here. This story was distributed under HackerNoon’s Brand As An Author Program. Learn more about the program here . here

Eldarune: Revolutionizing Web3 Gaming with Interoperability and Multi-Chain Dominance

Eldarune: ইন্টারঅপারেবিলিটি এবং মাল্টি-চেইন আধিপত্য সহ ওয়েব3 গেমিংকে বিপ্লবী করা

Eldarune: Revolutionierung des Web3-Gamings durch Interoperabilität und Multi-Chain-Dominanz

Eldarune: revolucionando los juegos Web3 con interoperabilidad y dominio de múltiples cadenas

Eldarune : révolutionner le jeu Web3 avec l'interopérabilité et la domination multi-chaînes

एल्डारून: इंटरऑपरेबिलिटी और मल्टी-चेन प्रभुत्व के साथ वेब3 गेमिंग में क्रांति लाना

Eldarune: 相互運用性とマルチチェーン優位性で Web3 ゲームに革命を起こす

Eldarune: 상호 운용성과 다중 체인 지배력을 통해 Web3 게임에 혁명을 일으키다

Eldarune: Revolucionando os jogos Web3 com interoperabilidade e domínio multi-chain

Eldarune: революция в Web3-играх благодаря совместимости и доминированию нескольких цепочек

Eldarune: Birlikte Çalışabilirlik ve Çoklu Zincir Hakimiyeti ile Web3 Oyunlarında Devrim Yaratıyor

Eldarune: Cách mạng hóa trò chơi Web3 với khả năng tương tác và thống trị đa chuỗi

Eldarune：通过互操作性和多链主导性彻底改变 Web3 游戏

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Experimental Datasets And Models MovieLens Dataset The MovieLens100K1 (Harper and Konstan 2015) dataset contains 100k ratings of N=1682 films from among M=943 users. The original user ratings run from 0 to 5. Following Geffner and Domke (2022), we binarise the ratings into just likes/dislikes, by taking user-ratings of {0, 1, 2, 3} as a binarised-rating of 0 (dislikes) and user-ratings of {4, 5} as a binarised-rating of 1 (likes). We assume binarised ratings of 0 for films which users have not previously rated. The probabilistic graphical model is given in Fig 3. We use n to index films, and m to index users. Each film has a known feature vector, xn, while each user has a latent weight-vector, zm, of the same length, describing whether or not they like any given feature. There are 18 features, indicating which genre tags the film has (Action, Adventure, Animation, Childrens,...). Each film may have more than one tag. The probability of the user liking a film is given by taking the dot-product of the film’s feature vector with the latent weight-vector, and applying a sigmoid (σ(·)) (final line) Additionally, we have latent vectors for the global mean, µ, and variance, ψ, of the weight vectors. We use a random subset of N = 20 films and M = 450 users for our experiment to ensure high levels of uncertainty. We use equally sized but disjoint subset of users held aside for calculation of the predictive log-likelihood. In Fig. 1c,d we consider the variance and MSE of estimators of zm. Bus Delay Datase In our second experiment, we model the length of delays to New York school bus journeys,2 working with a dataset supplied by the City of New York (DOE 2023). Our goal is to predict the length of a delay, based on the year, y, borough, b, bus company, c and journey type, j. Specifically, our data includes the years 2015 − 2022 inclusive and covers the five New York boroughs (Brooklyn, Manhatten, The Bronx, Queens, Staten Island) as well as some surrounding areas (Nassau County, New Jersey, Connecticut, Rockland County, Westchester). There are 57 bus companies, and 6 different journey types (e.g. pre-K/elementary school route, general education AM/PM route etc.) We take I = 60 delayed buses in each borough and year, and take Y = 3 years and B = 3 boroughs. We then split along the I dimension to get two equally sized train and test sets. Thus, each delay is uniquely identified by the year, y, the borough, b, and the index, i, giving delayybi The delays are recorded as an integer number of minutes and we discard any entries greater than 130 minutes. We have a hierarchical latent variable model describing the impact of each of the features (year, borough, bus company and journey type) on the length of delays. Specifically, the integer delay is modelled with a Negative Binomial distribution, with fixed total count of 130, as we discard entries with delays longer than this. The expected delay length is controlled by a logits latent variable, logitsybi, with one logits for each delayed bus. The logits is a sum of three terms: one for the borough and year jointly, one for the bus company and one for the journey type. Each of these three terms is themselves a latent variable that must be inferred. First, we have a term for the year and borough, YearBoroughWeightyb, which has a hierarchical prior. Specifically, we begin by sampling a global mean and variance, GlobalMean and GlobalVariance. Then for each year, we use GlobalMean and GlobalVariance to sample a mean for each year, YearMeany. Additionally, we sample a variance for each borough, BoroughVarianceb. Then we sample a YearBoroughWeightyb from a Gaussian distribution with a year-dependent mean, YearMeany, and a borough-dependent variance, BoroughVarianceb. Next, the weights for the bus company and journey type are very similar. Specifically, we have one latent weight for each bus company, CompanyWeightc, with c ∈ {1, . . . , 57}, and for each journey type, JourneyTypeWeightj, with j ∈ {1, . . . , 6}. We have a table identifying the bus company, bybi, and journey type, jybi, for each delayed bus journey (remember that a particular delayed bus journey is uniquely identified by the year, y, borough, b and index i). In logitsybi we use these tables to pick out the right company and journey type weight for that particular delayed bus journey, CompanyWeightcybi and JourneyTypeWeight cybi. The final generative model is, and the graphical model is given in Fig. 4 Experiment Details We ran all experiments on an 80GB NVIDIA A100 GPU and averaged the results over three different dataset splits generated using different random seeds. On each dataset split, the MovieLens experiments required a maximum 26.9 GB of memory at any one time. For the NYC Bus Breakdown experiments this value was 0.9 GB. For each experiment using a single dataset  split, we used global IS, MP IS, VI and HMC methods to obtain (for each value of K in the IS methods, or each iteration of VI/HMC) 1000 values of the ELBO, predictive log-likelihood (on a test set that was disjoint from the training set), and posterior mean estimates for each latent variable in the model. These 1000 values were averaged for the ELBO and predictive log-likelihood, whilst the 1000 mean estimates were used to calculate mean squared error or variance per variable, based on whether the observed data had been sampled from the model itself—in which case the true variable values were known—or not. We present error bars in the top rows of Fig. 1 and Fig. 2 representing the standard deviation over the three dataset splits. For VI, we optimize our approximate posterior using Adam with learning rates of 10−2, 10−3 and 10−4 (we found that learning rates faster than 10−2 were unstable), and ran 100 optimization steps for both for the MovieLens and Bus Breakdown models. For HMC, we ran the No U-Turn Sampler on our GPU using JAX (Bradbury et al. 2018) to obtain 10 consecutive samples after a single step with PyMC’s (Salvatier, Wiecki, and Fonnesbeck 2016) pre-tuning and with all other parameters set to their default values. We present results of the first 5 samples (not including the tuning sample) in order to align as best we can with the small time-scale of the IS methods. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Experimental Datasets And Models MovieLens Dataset The MovieLens100K1 (Harper and Konstan 2015) dataset contains 100k ratings of N=1682 films from among M=943 users. The original user ratings run from 0 to 5. Following Geffner and Domke (2022), we binarise the ratings into just likes/dislikes, by taking user-ratings of {0, 1, 2, 3} as a binarised-rating of 0 (dislikes) and user-ratings of {4, 5} as a binarised-rating of 1 (likes). We assume binarised ratings of 0 for films which users have not previously rated. The probabilistic graphical model is given in Fig 3. We use n to index films, and m to index users. Each film has a known feature vector, xn, while each user has a latent weight-vector, zm, of the same length, describing whether or not they like any given feature. There are 18 features, indicating which genre tags the film has (Action, Adventure, Animation, Childrens,...). Each film may have more than one tag. The probability of the user liking a film is given by taking the dot-product of the film’s feature vector with the latent weight-vector, and applying a sigmoid (σ(·)) (final line) Additionally, we have latent vectors for the global mean, µ, and variance, ψ, of the weight vectors. We use a random subset of N = 20 films and M = 450 users for our experiment to ensure high levels of uncertainty. We use equally sized but disjoint subset of users held aside for calculation of the predictive log-likelihood. In Fig. 1c,d we consider the variance and MSE of estimators of zm. Bus Delay Datase In our second experiment, we model the length of delays to New York school bus journeys,2 working with a dataset supplied by the City of New York (DOE 2023). Our goal is to predict the length of a delay, based on the year, y, borough, b, bus company, c and journey type, j. Specifically, our data includes the years 2015 − 2022 inclusive and covers the five New York boroughs (Brooklyn, Manhatten, The Bronx, Queens, Staten Island) as well as some surrounding areas (Nassau County, New Jersey, Connecticut, Rockland County, Westchester). There are 57 bus companies, and 6 different journey types (e.g. pre-K/elementary school route, general education AM/PM route etc.) We take I = 60 delayed buses in each borough and year, and take Y = 3 years and B = 3 boroughs. We then split along the I dimension to get two equally sized train and test sets. Thus, each delay is uniquely identified by the year, y, the borough, b, and the index, i, giving delayybi The delays are recorded as an integer number of minutes and we discard any entries greater than 130 minutes. We have a hierarchical latent variable model describing the impact of each of the features (year, borough, bus company and journey type) on the length of delays. Specifically, the integer delay is modelled with a Negative Binomial distribution, with fixed total count of 130, as we discard entries with delays longer than this. The expected delay length is controlled by a logits latent variable, logitsybi, with one logits for each delayed bus. The logits is a sum of three terms: one for the borough and year jointly, one for the bus company and one for the journey type. Each of these three terms is themselves a latent variable that must be inferred. First, we have a term for the year and borough, YearBoroughWeightyb, which has a hierarchical prior. Specifically, we begin by sampling a global mean and variance, GlobalMean and GlobalVariance. Then for each year, we use GlobalMean and GlobalVariance to sample a mean for each year, YearMeany. Additionally, we sample a variance for each borough, BoroughVarianceb. Then we sample a YearBoroughWeightyb from a Gaussian distribution with a year-dependent mean, YearMeany, and a borough-dependent variance, BoroughVarianceb. Next, the weights for the bus company and journey type are very similar. Specifically, we have one latent weight for each bus company, CompanyWeightc, with c ∈ {1, . . . , 57}, and for each journey type, JourneyTypeWeightj, with j ∈ {1, . . . , 6}. We have a table identifying the bus company, bybi, and journey type, jybi, for each delayed bus journey (remember that a particular delayed bus journey is uniquely identified by the year, y, borough, b and index i). In logitsybi we use these tables to pick out the right company and journey type weight for that particular delayed bus journey, CompanyWeightcybi and JourneyTypeWeight cybi. The final generative model is, and the graphical model is given in Fig. 4 Experiment Details We ran all experiments on an 80GB NVIDIA A100 GPU and averaged the results over three different dataset splits generated using different random seeds. On each dataset split, the MovieLens experiments required a maximum 26.9 GB of memory at any one time. For the NYC Bus Breakdown experiments this value was 0.9 GB. For each experiment using a single dataset  split, we used global IS, MP IS, VI and HMC methods to obtain (for each value of K in the IS methods, or each iteration of VI/HMC) 1000 values of the ELBO, predictive log-likelihood (on a test set that was disjoint from the training set), and posterior mean estimates for each latent variable in the model. These 1000 values were averaged for the ELBO and predictive log-likelihood, whilst the 1000 mean estimates were used to calculate mean squared error or variance per variable, based on whether the observed data had been sampled from the model itself—in which case the true variable values were known—or not. We present error bars in the top rows of Fig. 1 and Fig. 2 representing the standard deviation over the three dataset splits. For VI, we optimize our approximate posterior using Adam with learning rates of 10−2, 10−3 and 10−4 (we found that learning rates faster than 10−2 were unstable), and ran 100 optimization steps for both for the MovieLens and Bus Breakdown models. For HMC, we ran the No U-Turn Sampler on our GPU using JAX (Bradbury et al. 2018) to obtain 10 consecutive samples after a single step with PyMC’s (Salvatier, Wiecki, and Fonnesbeck 2016) pre-tuning and with all other parameters set to their default values. We present results of the first 5 samples (not including the tuning sample) in order to align as best we can with the small time-scale of the IS methods.

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Experimental Datasets and Model

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Experiment We provide empirical results comparing the global and massively parallel importance weighting/sampling methods, as well as VI and HMC baselines. We considered two datasets: MovieLens100K (Fig. 1) and NYC Bus Breakdown (Fig. 2). MovieLens100K (Harper and Konstan 2015) contains 100k ratings of N=1682 films from among M=943 users. NYC Bus Breakdown describes the length of around 150,000 delays to New York school bus journeys, segregated by year, borough, bus company and journey type. We use hierarchical probabilistic graphical models for these datasets, which are described in more detail in Appendix section Experimental Datasets And Models. For global and massively parallel importance sampling, we use the prior as a proposal. For VI, we used a factorised approximate posterior and optimized using Adam with learning rates ranging from 10−2 to 10−4  (learning rates faster than 10−2 were unstable). For HMC, we used the NUTS (Hoffman, Gelman et al. 2014) implementation from PyMC (Salvatier, Wiecki, and Fonnesbeck 2016). We consider four quantities in Fig. 1 and Fig. 2. We begin by looking at the ELBO (Fig. 1a,2a). While massively parallel estimates of the ELBO can be computed using previous methods (e.g. Heap and Laurence 2023), it is nonetheless useful as it is a good measure of the quality of our approximate posteriors (as the ELBO can be written as the sum of the marginal likelihood and the KL-divergence between the true and approximate posterior Jordan et al. 1999; Kingma, Welling et al. 2019). Next, we plot the predictive log-likelihood, based on posterior samples obtained using the approach in the Methods section. In both of these plots, we see dramatic improvements against standard “global” importance sampling for a fixed K (top row), and for a fixed time against other baselines such as VI and HMC (bottom row). Finally, we consider the quality of the posterior moment estimates computed using the approach in the Methods section. In particular we consider two measures of the quality of our posterior mean estimator. First, we consider the variance of the estimator (Fig. 1c,2c); in the ideal case, when we have exactly computed the posterior mean, this variance should be zero. While this variance can be zero in other cases (e.g. when the mean estimator always returns a constant), it does provide a lower-bound on the expected squared error between the estimator and the true value. Second, we directly considered the MSE between our posterior mean estimator and the true latent variable (Fig. 1d,2d). However, this quantity requires us to know the true value for the latent variable, which requires us to generate data from the model (we only generated data for d, columns a–c use real data). Again, in both of these cases, we see dramatic improvements for massively parallel against standard “global” importance sampling for a fixed K (top row), and for a fixed time against other methods such as VI and HMC (bottom row). Note the extremely poor performance of VI and HMC is because these are iterative methods that take many steps, and hence a long time to reach good performance. HMC is especially problematic on these short timescales, because it needs many gradient steps before it returns even a single posterior sample (and this is ignoring burn-in and adaptation phases). In contrast, importance sampling gets a reasonable answer much more quickly, as it just a single, non-iterative computation. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Experiment We provide empirical results comparing the global and massively parallel importance weighting/sampling methods, as well as VI and HMC baselines. We considered two datasets: MovieLens100K (Fig. 1) and NYC Bus Breakdown (Fig. 2). MovieLens100K (Harper and Konstan 2015) contains 100k ratings of N=1682 films from among M=943 users. NYC Bus Breakdown describes the length of around 150,000 delays to New York school bus journeys, segregated by year, borough, bus company and journey type. We use hierarchical probabilistic graphical models for these datasets, which are described in more detail in Appendix section Experimental Datasets And Models. For global and massively parallel importance sampling, we use the prior as a proposal. For VI, we used a factorised approximate posterior and optimized using Adam with learning rates ranging from 10−2 to 10−4  (learning rates faster than 10−2 were unstable). For HMC, we used the NUTS (Hoffman, Gelman et al. 2014) implementation from PyMC (Salvatier, Wiecki, and Fonnesbeck 2016). We consider four quantities in Fig. 1 and Fig. 2. We begin by looking at the ELBO (Fig. 1a,2a). While massively parallel estimates of the ELBO can be computed using previous methods (e.g. Heap and Laurence 2023), it is nonetheless useful as it is a good measure of the quality of our approximate posteriors (as the ELBO can be written as the sum of the marginal likelihood and the KL-divergence between the true and approximate posterior Jordan et al. 1999; Kingma, Welling et al. 2019). Next, we plot the predictive log-likelihood, based on posterior samples obtained using the approach in the Methods section. In both of these plots, we see dramatic improvements against standard “global” importance sampling for a fixed K (top row), and for a fixed time against other baselines such as VI and HMC (bottom row). Finally, we consider the quality of the posterior moment estimates computed using the approach in the Methods section. In particular we consider two measures of the quality of our posterior mean estimator. First, we consider the variance of the estimator (Fig. 1c,2c); in the ideal case, when we have exactly computed the posterior mean, this variance should be zero. While this variance can be zero in other cases (e.g. when the mean estimator always returns a constant), it does provide a lower-bound on the expected squared error between the estimator and the true value. Second, we directly considered the MSE between our posterior mean estimator and the true latent variable (Fig. 1d,2d). However, this quantity requires us to know the true value for the latent variable, which requires us to generate data from the model (we only generated data for d, columns a–c use real data). Again, in both of these cases, we see dramatic improvements for massively parallel against standard “global” importance sampling for a fixed K (top row), and for a fixed time against other methods such as VI and HMC (bottom row). Note the extremely poor performance of VI and HMC is because these are iterative methods that take many steps, and hence a long time to reach good performance. HMC is especially problematic on these short timescales, because it needs many gradient steps before it returns even a single posterior sample (and this is ignoring burn-in and adaptation phases). In contrast, importance sampling gets a reasonable answer much more quickly, as it just a single, non-iterative computation.

Using Autodiff to Estimate Posterior Moments, Marginals and Samples: Experiments

You think in binary: 0 / 1. You share results when the work is done. But marketing is “storytelling.” . For that moment, when it's all done, you launch it, and people say, WOW. But I'm sorry, nobody will bother to react. You heroically code 100h/week Imagine watching Titanic with just one scene - Jack’s death. Boring, right?
It’s the story and plot that make it interesting. in fact, the outcome isn't even important after all. So, apply this to your work. Turn your journey into a captivating tale, like a TV series or social media drama, with episodes. People are less interested in what you built but more in how you did it and the rollercoaster ride along the way. Here are some tips: → Talk about your idea and how you came up with it → Share your brainstorming process → Show off any mockups you’ve made → Discuss why you chose certain tools or frameworks → Regularly update on your progress, including both successes and failures(hide nothing) → Post small demos daily/weekly → Be real and honest. Don’t look perfect. → Write like you’re chatting with your best friend. → Engage with others, sharing their journeys. → Summarize each stage of your project. → Publish "how-tos" every week based on your project Remember: People love stories more than products! The more raw and unfiltered the story is, which again relates back to our key notion here, people will engage more because it serves them as an imported experience, learning from other people's hacks and mistakes." What if your target audience doesn't care about the process? They aren't developers? It doesn't matter; the goal here is to master storytelling. It's easier to master it in the known space first. Once you mastered it, slowly start switching to the actual target audience. You can use X for devpreneur audience and Linkedin or reddit for your target audience. Your next step? If you never done anytihng from above, do it now, and reply with your post here. I'll read it and give you direct feedback on quality and what can be improved. Let's go. Also published . here

My Advice For Devpreneurs As A Coder Who Learned Marketing

Metaphysics and Mathematics 2: The Complex Space of Prime Numbers

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Since n.d.

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