Addressableis in the MarketingTech Industry

Addressable

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Addressable, a Web3 growth suite, has announced a partnership with AppsFlyer, a renowned mobile analytics platform. This partnership signifies a significant advancement in the mobile marketing sector, particularly for Web3 mobile applications, this connection enables mobile developers to use on-chain data to optimise their ad campaigns, an industry-first endeavour that promises to significantly improve marketing campaign efficacy. Addressable and AppsFlyer mobile advertising campaigns in accordance with On-Chain Lifetime Value (LTV) is a revolutionary concept that is presented by Flyer's partnership. As a result of this approach, the emphasis switches from cost-per-install analytics to the enhancement of on-chain conversions, which disclose the real spending and transaction histories of Web3 customers. Whales are users who are acknowledged for their big transactions inside the Web3 ecosystem and their high net value. Marketers, for example, may now concentrate on "whales." For the purpose of enabling this targeting, dependable on-chain wallet data is used. This makes the process of categorising and targeting certain groups of users more precise and effective. The shifting scenario was brought to light by Alex Yip, Director of Product Discovery for AppsFlyer, who said, "We've witnessed a surge in mobile adoption across the Web3 industry that encompasses new data needs and capabilities." Through the incorporation of Addressable into the AppsFlyer marketplace, Web3 advertisers are able to reap the benefits of a comprehensive picture of mobile analytics, which, when paired with accurate on-chain conversion data, allows them to improve the efficiency of their advertising campaigns. Prior to a recent period of time, mobile marketing solutions focused mostly on tracking and improving app installations as well as user conversions inside the app itself. On the other hand, this outdated strategy often ignored the vast amount of data that is available on the blockchain. The infrastructure that Addressable provides allows marketers to modify their marketing tactics by making use of on-chain conversion data. This data includes deposit events into wallets and exchanges. This makes it possible to have a more in-depth understanding of user behaviour and spending patterns, which is essential for effectively targeting and engaging significant customers. Mobile Marketing Ecosystem As a technology expert monitoring these developments, I believe the combination between Addressable and AppsFlyer is a game-changing move that has the potential to alter mobile marketing techniques across several sectors. This collaboration exemplifies the shifting environment in which data intelligence and technical improvements combine to provide highly targeted and successful marketing solutions. The future of mobile marketing, especially in the Web3 realm, is bright. Marketers should anticipate improved campaign success, greater conversion rates, and, ultimately, increased user engagement as their on-chain tracking and analysis tools improve. This initiative is not solely focused on technological integration; it aims to create a more transparent, efficient, and user-centric marketing environment. Addressable and AppsFlyer's strategic alliance is more than simply a technology improvement; it's a bold step towards establishing a more connected and data-driven marketing environment that meets the demands of both contemporary consumers and marketers. Moving forward, this partnership will likely serve as a model for others in the sector, signaling a shift towards more integrated and intelligent marketing techniques in the digital age. 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. Addressable , a Web3 growth suite, has announced a partnership with AppsFlyer , a renowned mobile analytics platform. This partnership signifies a significant advancement in the mobile marketing sector, particularly for Web3 mobile applications, this connection enables mobile developers to use on-chain data to optimise their ad campaigns, an industry-first endeavour that promises to significantly improve marketing campaign efficacy. Addressable AppsFlyer Addressable and AppsFlyer mobile advertising campaigns in accordance with On-Chain Lifetime Value (LTV) is a revolutionary concept that is presented by Flyer's partnership. As a result of this approach, the emphasis switches from cost-per-install analytics to the enhancement of on-chain conversions, which disclose the real spending and transaction histories of Web3 customers. Whales are users who are acknowledged for their big transactions inside the Web3 ecosystem and their high net value. Marketers, for example, may now concentrate on "whales." For the purpose of enabling this targeting, dependable on-chain wallet data is used. This makes the process of categorising and targeting certain groups of users more precise and effective. Addressable AppsFlyer The shifting scenario was brought to light by Alex Yip, Director of Product Discovery for AppsFlyer, who said, AppsFlyer, "We've witnessed a surge in mobile adoption across the Web3 industry that encompasses new data needs and capabilities." "We've witnessed a surge in mobile adoption across the Web3 industry that encompasses new data needs and capabilities." Through the incorporation of Addressable into the AppsFlyer marketplace, Web3 advertisers are able to reap the benefits of a comprehensive picture of mobile analytics, which, when paired with accurate on-chain conversion data, allows them to improve the efficiency of their advertising campaigns. Prior to a recent period of time, mobile marketing solutions focused mostly on tracking and improving app installations as well as user conversions inside the app itself. On the other hand, this outdated strategy often ignored the vast amount of data that is available on the blockchain. The infrastructure that Addressable provides allows marketers to modify their marketing tactics by making use of on-chain conversion data. This data includes deposit events into wallets and exchanges. This makes it possible to have a more in-depth understanding of user behaviour and spending patterns, which is essential for effectively targeting and engaging significant customers. Mobile Marketing Ecosystem As a technology expert monitoring these developments, I believe the combination between Addressable and AppsFlyer is a game-changing move that has the potential to alter mobile marketing techniques across several sectors. This collaboration exemplifies the shifting environment in which data intelligence and technical improvements combine to provide highly targeted and successful marketing solutions. The future of mobile marketing, especially in the Web3 realm, is bright. Marketers should anticipate improved campaign success, greater conversion rates, and, ultimately, increased user engagement as their on-chain tracking and analysis tools improve. This initiative is not solely focused on technological integration; it aims to create a more transparent, efficient, and user-centric marketing environment. Addressable and AppsFlyer's strategic alliance is more than simply a technology improvement; it's a bold step towards establishing a more connected and data-driven marketing environment that meets the demands of both contemporary consumers and marketers. Moving forward, this partnership will likely serve as a model for others in the sector, signaling a shift towards more integrated and intelligent marketing techniques in the digital age. 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

Addressable and AppsFlyer Revolutionize Mobile Marketing with Blockchain Integration

Addressable এবং AppsFlyer Blockchain ইন্টিগ্রেশনের সাথে মোবাইল মার্কেটিংকে বিপ্লব করে

Addressable und AppsFlyer revolutionieren Mobile Marketing mit Blockchain-Integration

Addressable y AppsFlyer revolucionan el marketing móvil con la integración de Blockchain

Addressable et AppsFlyer révolutionnent le marketing mobile avec l'intégration de la blockchain

एड्रेसेबल और एप्सफ्लायर ने ब्लॉकचेन एकीकरण के साथ मोबाइल मार्केटिंग में क्रांति ला दी

Addressable と AppsFlyer がブロックチェーン統合でモバイル マーケティングに革命を起こす

어드레서블(Addressable)과 앱스플라이어(AppsFlyer)는 블록체인 통합으로 모바일 마케팅에 혁신을 가져왔습니다.

Addressable e AppsFlyer revolucionam o marketing móvel com integração Blockchain

Addressable и AppsFlyer революционизируют мобильный маркетинг благодаря интеграции блокчейна

Adreslenebilir ve AppsFlyer, Blockchain Entegrasyonu ile Mobil Pazarlamada Devrim Yaratıyor

Địa chỉ và AppsFlyer Cách mạng hóa hoạt động tiếp thị trên thiết bị di động bằng tích hợp chuỗi khối

Addressable 和 AppsFlyer 通过区块链集成彻底改变移动营销

Welcome to another insightful edition of "Behind the Startup." Today, we delve into the fascinating world of Web3 marketing with our special guest, Tomer Sharoni, CEO of Addressable. Following their remarkable $13.5M raise led by BITKRAFT, Addressable stands at the forefront of Web3 growth marketing. We'll explore the intricacies of their journey, the innovative strategies behind their success, and their vision for the future of Web3 marketing. Addressable's Founders Reveal the Secrets Behind Their $13.5M Success in Exclusive Interview Ishan Pandey: Hi, Tomer Sharoni, great to have you here for our "Behind the Startup" series. To start with, congratulations on Addressable's successful funding round. Can you walk us through the journey that led to this achievement and how this capital will fuel your future endeavors in Web3 growth marketing? Addressable was founded two years ago by 3 friends, Asaf Nadler, Tomer Shlomo, and myself. Our goal was to address the specific needs of Web3 businesses leveraging our expertise in and . We engaged with over 100 companies through extensive market research, uncovering a common challenge - user acquisition and retention within the Web3 space. In Web3, user interactions are decentralized and anonymous, challenging communication. Traditional communication channels like social media and email remained prevalent across web3 communities, and businesses tried to communicate with new audiences organically in their social channels. Tomer Sharoni: big data AI Once they wanted to acquire new users outside the community or retain users who were drifting away from their community, they faced major communication challenges. Recognizing this wide need in the market, we devised a solution to bridge the gap between Web3 audiences and social media through data integration. Our platform now allows businesses to build and measure advertising campaigns based on users' Web3 activity. This innovation enables the creation of precise and measurable marketing strategies, a much-needed tool for Web3 businesses. The recent funding round has propelled us forward. We're expanding to new social networks and ad networks, broadening our blockchain integrations, and enhancing our analytics capabilities to include mobile and social network data. This investment will accelerate our growth and help us become a vital part of the Web3 ecosystem. Ishan Pandey: Reflecting on Addressable's fundraising journey, what were the key challenges you faced as a founder in convincing investors about the potential of Web3 marketing? How did you address these challenges? Convincing investors posed several challenges during our fundraising journey. Firstly, some investors questioned whether Web3 marketing was a distinct category, as it appeared to be an extension of Web2 marketing. They argued that we were still advertising on traditional platforms like social networks and . Additionally, the issue of measurement came up. Investors wondered if we were merely a subcategory of marketing, with the same metrics applied. Tomer Sharoni: Google However, what we emphasized, and what eventually resonated with many investors, was that Web3 marketing is unique. It bridges the gap between Web3, where all the commercial activity happens, and Web2 social networks, where all the communication happens. In Web2 marketing, there was no integration of public data from Web3 – about where people spend, what they do, their preferences, and more. This wealth of Web3 data remained unconnected to traditional marketing practices. This disconnect was a missed opportunity for companies looking to target and understand Web3 audiences better. Web3 data is extensive and highly accurate, offering insights into audiences' spending habits, preferences, and interests, which can significantly benefit businesses when utilized correctly. A second challenge we faced was the preference of many Web3 investors for native Web3 solutions. Addressable, however, takes a unique approach. We work with Web3 companies that want to target Web3 users but do so through Web2 platforms like social media. Some Web3 investors initially viewed this as contradictory to the Web3 ethos. They questioned why we were operating in Web2 channels when our focus was on Web3 users. Our response was that this approach aligns with how businesses currently operate. To grow their user base and their community, they seek out potential users on social media platforms like Twitter and Reddit, among others, where most potential users hang out. What we provide is a way to go outside the boundaries of the community, and have a precise and sustainable way for businesses to grow, by leveraging Web3 data for targeting. We explained that by helping these companies navigate Web2 channels effectively, we contribute to the growth of the entire Web3 industry. Ishan Pandey: Addressable's capability to attribute crypto wallet owners across numerous websites is groundbreaking. How does this technology work, and what sets it apart in the crowded field of digital marketing? Addressable's technology targets a specific group of internet users - crypto wallet owners, which number in the tens of millions among billions of internet users. Our unique approach involves matching audiences in Web3 and audiences on social networks such as Twitter and Reddit, as well as audiences on programmatic ad networks in the open web and mobile apps. We achieve this by analyzing behavioral patterns, and matching on-chain activities of web3 audience segments to online activities of social networks’ audience segments. Tomer Sharoni: This allows us to connect small, particular segments of Web3 audiences with their counterparts on social media. For example, if you want to target users who trade on a particular decentralized exchange, our technology can help you reach them accurately as a group, while strictly preserving the privacy of individuals. What sets us apart is our ability to bridge Web3 and Web2 data effectively, enabling advertisers to reach their desired audience with precision. This approach is unique in the crowded digital marketing landscape, as it empowers businesses to tap into the valuable world of Web3 users while leveraging familiar marketing channels. Ishan Pandey: Addressable's expansion into new ad networks and blockchains suggests a complex technical infrastructure. Can you elaborate on the challenges and solutions in scaling your technology stack to accommodate this growth? Expanding into new ad networks and blockchains means handling vast amounts of data and complex audience-building queries. To address these challenges, we've built a cutting-edge infrastructure, leveraging AWS and databases designed for large-scale analytics. We've also implemented efficient data lakes and indexing mechanisms. Tomer Sharoni: Our approach includes edge engineering and AI analytics for new contracts and activities on-chain. This enables us to scale seamlessly as we incorporate more blockchains, new contracts, and social networks. We use AI to identify patterns and automatically label new types of contracts and activities, making our entire stack highly scalable. Ishan Pandey: The integration of AI and machine learning is pivotal in modern marketing technologies. Can you discuss how Addressable incorporates these technologies to enhance campaign effectiveness and user targeting accuracy? Addressable leverages AI and machine learning in two crucial ways. First, we use behavioral matching AI to identify patterns in audience activity across social networks and blockchains. This helps us understand how Addressable's target audiences relate to blockchain activity, enhancing user targeting accuracy. Tomer Sharoni: Second, we utilize several Gen AI models to automatically identify and label every contract, activity, and business on-chain. This automation streamlines our scale and supports adding new businesses and blockchains almost automatically. Lastly, we employ AI algorithms for ongoing campaign optimization. By analyzing audience response and conversions, we continuously adjust the campaign, removing underperforming segments and adding lookalike segments that resemble high-performing ones. This ensures that our campaigns are consistently effective and efficient. Ishan Pandey: In the rapidly evolving landscape of Web3, how does Addressable ensure compliance with emerging regulations and privacy concerns, especially when dealing with sensitive user data like crypto wallet information? Addressable places a high priority on user privacy, aligning with the privacy-conscious culture of Web3 in 2024. We ensure compliance with regulations like GDPR and SOC2, by never identifying individuals. Tomer Sharoni: Instead, we create and match segments of audiences on-chain and on social media, avoiding the collection of any personally identifiable data. This approach not only meets GDPR requirements but also adheres to strict social network privacy rules, safeguarding user privacy while leveraging the data of Web3. Ishan Pandey: Addressable has managed a significant number of campaigns. From a technical standpoint, what have been the most challenging aspects of executing these campaigns at scale, and how have you resolved them? Tomer Sharoni: Managing hundreds of campaigns simultaneously presents several technical challenges. Firstly, integrating with multiple ad networks and standardizing data across various platforms and APIs can be complex. We've tackled this by ensuring seamless data ingestion and standardization while maintaining low latency. Another challenge is meeting the demand of companies looking to expand beyond the networks we initially supported. To address this, we had to scale our team and operations faster than originally planned, as the demand for growing businesses in Web3 and deploying marketing budgets across more channels continues to rise. Ishan Pandey: Lastly, looking towards the future, what emerging technologies or trends in the Web3 space are you most excited about, and how is Addressable preparing to leverage these for further growth and innovation? We're excited about emerging technologies in wallet-to-wallet communication, the rise of Web3 social media, and the development of native Web3 ad units. These advancements open up new avenues for advertising and attribution, as well as helping smaller networks, websites and creators to benefit from ads in a scalable way. We're also keen on the potential of social logins on Web3 websites, which can enhance our services and eventually lead us towards a Web3-centric advertising ecosystem that's more user-friendly and appealing to our customers. Addressable is poised to adapt and innovate in these evolving Web3 landscapes. Tomer Sharoni: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Inside the Web3 Marketing Revolution: Addressable's $13.5M Milestone with Tomer Sharoni

Web3 মার্কেটিং বিপ্লবের ভিতরে: Tomer Sharoni এর সাথে Addressable এর $13.5M মাইলফলক

Einblicke in die Web3-Marketing-Revolution: Der 13,5-Millionen-Dollar-Meilenstein von Addressable mit Tomer Sharoni

Dentro de la revolución del marketing Web3: el hito de 13,5 millones de dólares de Addressable con Tomer Sharoni

À l'intérieur de la révolution marketing Web3 : le jalon de 13,5 millions de dollars d'Addressable avec Tomer Sharoni

वेब3 मार्केटिंग क्रांति के अंदर: तोमर शारोनी के साथ एड्रेसेबल का $13.5M मील का पत्थर

Web3 マーケティング革命の内部: Tomer Sharoni による Addressable の 1,350 万ドルのマイルストーン

Web3 마케팅 혁명 속으로: Tomer Sharoni와 함께하는 Addressable의 1,350만 달러 이정표

Por dentro da revolução do marketing da Web3: marco de US$ 13,5 milhões da Addressable com Tomer Sharoni

Внутри маркетинговой революции Web3: веха Addressable в 13,5 миллионов долларов с Томером Шарони

Web3 Pazarlama Devriminin İçinde: Tomer Sharoni ile Adreslenebilir'in 13,5 Milyon Dolarlık Dönüm Noktası

Bên trong cuộc cách mạng tiếp thị Web3: Cột mốc 13,5 triệu đô la của addressable với Tomer Sharoni

Web3 营销革命的内部：Addressable 与 Tomer Sharoni 一起实现 1350 万美元的里程碑

TEL AVIV, Israel, December 21st, 2023/Chainwire/--Addressable, Web3 growth marketing leader, announced today the successful closure of its second funding round this year, reaching a total of $13.5 million so far. The infusion of capital will enable Addressable to supercharge its Web3 Growth Suite and expand its service to new ad networks and blockchains, solidifying its status as the Web3 growth marketing category leader. This significant milestone is based on Addressable’s strong momentum in its journey toward solving the Web3 Growth problem for businesses. The funding round was led by BITKRAFT Ventures, with participation from strategic investor Karatage, joining existing investors Viola Ventures, Fabric Ventures, Mensch Capital Partners, North Island Ventures, and several other strategic investors. Addressable’s capabilities have expanded significantly, enabling the attribution of crypto wallet owners across 400,000 websites, including Bloomberg, The New York Times, and Yahoo Finance. Addressable now spans three new ad networks, including Unity, Pubmatic, and Magnite, in addition to the initial coverage of the Twitter ad network. This expansion enables Addressable to provide more comprehensive and targeted paid ad campaigns across social networks, websites, and mobile apps. Additionally, Addressable's 1-Click SDK Installation, approved by Google and available on Google Tag Manager, simplifies the integration process for marketers, allowing for effortless tracking of website conversions, wallet connects, and blockchain conversions. Integrations with the Twitter Pixel, DSP pixels, Mixpanel, and Google Analytics further streamline conversion event tracking. “Addressable's commitment to revolutionizing Web3 Growth remains steadfast, and it becomes even more important in the current market,” stated Tomer Sharoni, co-founder and CEO at Addressable. “We're thrilled to lead Addressable's new investment round. As the gaming industry continues to evolve and expand its digital footprint, it's evident that Web3 technologies will play a crucial role in shaping the future of user engagement. Addressable's innovative Web3 growth suite stands out as a game-changer, offering marketers powerful tools to navigate this dynamic landscape and create profitable user growth at scale”. “We're excited to support Addressable's mission to power marketers and look forward to accelerating the impact their platform already has in the gaming industry and beyond. The Addressable team distinguishes itself through its battle-tested magic in data and product development, along with a world-class track record in execution,” says Malte Barth, founding general partner at BITKRAFT Ventures. Addressable currently serves over 50 customers and has flawlessly executed over 750 campaigns, and is excited to unveil enhancements that include tailored playbooks for Gaming, DeFi, Blockchain Infrastructure, Services, and Exchanges. “In the early stages of engaging Web3 gamers, targeted marketing has been key to our success. By leveraging insights from both traditional gamers and Web3 gamers, we've refined our approach to user acquisition. Addressable's Web3 Growth Suite has delivered early promising results in enhancing our marketing strategies, particularly in the precise targeting of our gaming audience. The platform's role in our marketing efforts, especially in the transition from traditional to Web3 gamer engagement, has been noteworthy. Addressable's ability to identify and connect with potential gaming customers aligns well with our objectives, making it a valuable tool for our future gaming campaigns”, said Michael Migliero, CMO of Big Time Studios. Founded in June 2022 by Tomer Sharoni, Tomer Shlomo, and Dr. Asaf Nadler, Addressable is a testament to their profound expertise in data analytics and AI. Together, they have over 20 publications spanning blockchain, machine learning, and big data. The Addressable team comprises 24 employees, distributed across their offices in Tel Aviv, New York, London and Madrid. About Addressable Founded in June 2022 by Tomer Sharoni, Tomer Shlomo, and Dr. Asaf Nadler, Addressable is the Web3 growth marketing industry leader. Addressable allows marketers across the space to target and market directly to Web3 audiences by associating blockchain and social data, analyzing on-chain and off-chain behavior, and using big data for precise performance tracking. Contact COO Asaf Nadler Addressable anadler@addressable.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Web3 Marketing Leader, Addressable, Completes $13.5M Funding Round Led by BITKRAFT

The popularity of is surging despite the pandemic, demonstrating high streaming viewership and ad spend expected to total by the end of 2020 and increase up to in 2021. However, while CTV offers advanced targeting, audience reach, and flexibility in running campaigns, not everything runs as smoothly when it comes to measuring the effectiveness of ad campaigns. connected TV advertising $8.11B $11.36B So far, several ad measurement limitations are keeping brands away from embracing CTV advertising fully. A lack of unified metrics makes it challenging for advertisers to pick highly relevant ad creatives and leads to higher prices to pay: CPMs for CTV may range between . Besides, as one streaming account is often shared between several family members and friends who can also watch video content and ads together, advertisers face one more CTV impression counting and measurement issue. On one hand, co-viewing looks promising as this trend seems to guarantee advertisers double reach. However, being mistakenly assumed to be static, co-viewing is actually dynamic, making it hard to separate target audiences and identify which content genre and time of day corresponds to which particular viewer category. $20 and $40 Other than that, there are more measurement and verification issues related to a lack of access to numerous device platforms and the (SSAI) in CTV ad serving, such as TheViewPoint. Put briefly, the first issue creates difficulties for 3rd-party verification vendors who can't access client-side data in many cases. In turn, the nuances of using SSAI include complications around invalid traffic detection. increasing usage of Server-Side Ad Insertion Yet, there's hope, as the industry has already been working on treatments to deliver better measurement support for CTV ad platforms. What progress has been made in bringing clarity and measurement unification so far? Much work needs to be done to bring common standards into the measurement system of ad campaigns efficiency, audience identification, attribution details, and data ownership. So far, the ad tech market players have started embracing first initiatives to improve standardization across the industry and provide more clarity and genuine insights. One way to do that is to use the pixels compatible with leading CTV/OTT streaming platforms like Apple TV, Amazon Fire TV, or Chromecast. Ad tech vendors use such pixels to get precise data about ad creatives viewability, tracking from as minimum as 1 second of ad display. Another approach to better measurement relates to the already mentioned SSAI, ad-stitching technology that seamlessly integrates ad video into the streamed content and should identify and pass information about the device as well as where content is consumed. This technology, however, doesn't work accurately sometimes. The solution to address this issue comes in establishing direct partnerships between advertisers and publishers. Specifically, publishers can help buy-side understand and correct unrecognised SSAI data signals. The essence distilled from direct partnerships can be especially concentrated if different publishers put their data together and hence provide their advertisers with the opportunity to impact campaigns' performance and get ultimate results. When it comes to measurement unification, it's also worth taking a closer look at Automatic Content Recognition (ACR) technology. By leveraging ACR, with video fingerprinting technology in its core, advertisers can collect data about content displayed across the TV screens right from the viewer's smart TV devices or installed TV and mobile apps. Hence, this technology helps advertisers measure their CTV ad performance, get insights into viewing behavior, and do that across CTV, mobile, and desktop screens. What else brings better standardization to the industry is solutions provided by Nielsen and other data analytics companies. With their reports, advertisers can better understand user behavior patterns and interests, segment the audience, and so sharpen their targeting. Though, that's not the only initiative Nielsen takes to bring the era of great addressable TV measurement closer. A new hope with Nielsen, IAB initiatives, and joining forces across the industry Just recently Nielsen that it will start measuring CTV campaigns in Digital Ad Ratings and Total Ad Ratings. First, this will specifically work for YouTube and YouTube TV apps on CTV to provide cross-platform metrics. Yet, we'll see more CTV apps and streaming platforms embracing addressable Nielsen TV measurement soon, as they expand their Advanced TV product suite with measuring ad impressions and audience reporting across all platforms. has announced We'll also witness better approaches to coping with co-viewing measurement limitations that can be combatted through 3rd-party measurement vendors as well as more empirical research and the implementation of industry norms. Other encouraging initiatives belong to IAB's technological advancements. We're talking about Open Measurement that was originally created to provide a common mobile SDK for the good of publishers and verification vendors. Based on the same concept, IAB works on Open Measurement for CTV support. This technology will provide 3rd-party verification vendors with shared API and SDKs to be integrated into every streaming platform. Along with the initiatives mentioned above, it's essential to bring unification to the whole industry which is nearly impossible without shaking the walled gardens built by the Big Four (Google, Amazon, Facebook, and Apple). Hence, the whole ad tech ecosystem needs to come together and convince the Big Four to start a dialogue about taking steps towards common solutions. Paving the way to the future of addressable TV ads measurement With all the promising steps and hopes, there's still untapped potential around addressable TV measurement. To help advertisers fully embrace CTV/OTT in 2021, ad tech vendors should keep on bracing the technology aspect. By working together on standard criteria, solutions focused on measuring streaming TV inventory, and activities aimed at drawing the attention of industry titans, ad tech as a whole can reach better control, transparency, and standardization of addressable TV ad measurement.

Addressable TV Ad Measurement: Will We See Groundbreaking Solutions In 2021?

Feel free to contribute on GitHub 💚 Introduction Database Systems usually either are disk-oriented systems or memory-centric targeting a two-level storage hierarchy. The advent of Byte-Addressable Non-Volatile Memory as Intel Optane (3D XPoint), however, is orders of magnitudes faster than traditional spinning disks or even traditional NAND based SSDs. Furthermore, fast random access on a byte-addressable level drops the requirement for excessive clustering. Disk oriented systems Disk-oriented systems use a buffer pool to cache frequently used pages in fast, volatile memory. If the page is not in the buffer pool, the storage layer has to fetch it from slow, non-volatile memory. The assumption usually is that random reads are slow (spinning disks), and writes have to be sequential. Thus, and to provide consistency, they have to introduce a write-ahead-log. First, these systems flush modified pages to the log doing sequential writes. Then, a background thread is checkpointing the changes into the storage files periodically. In-memory centric systems In-memory centric systems, on the contrary, are designed for systems, which have a lot of volatile memory. The system reads the data entirely into memory. They can overcome the need for a heavyweight buffer-pool manager component and concurrency control component. However, they still have to perform recovery management in the case of a power outage. Persistent, durable index-tree (tree of keyed tries) is a temporal document store. It retains the full history of each revision stored in database resources. Data is never modified in-place and only ever appended. Thus, SirixDB stores an ever-increasing state as a result of fine granular modifications. The following figure illustrates this: SirixDB Evolution of state through fine grained modifications. SirixDB indexes a document by using a tree of keyed trie structures. The index is similar to hash-array based tries, known from functional programming languages. It maps the persistence characteristics to durable storage. The following figure shows the page-structure of one revision: The always form a trie-structure. The trie itself might consist of several levels, whereas a level is added on demand if the current level can’t address the stored data anymore. The tries either index revisions or user-defined, AVL tree indexes (underneath a PathPage, a CASPage or a NamePage — depending on the kind of index). IndirectPages The whole tree-structure is a huge persistent tree. A persistent data structure is defined on Wikipedia as follows: In computing, a persistent data structure is a data structure that always preserves the previous version of itself when it is modified. Such data structures are effectively immutable, as their operations do not (visibly) update the structure in-place, but instead, always yield a new updated structure. The term was introduced in Driscoll, Sarnak, Sleator, and Tarjans’ 1986 article.” SirixDB shares unchanged structures between revisions. The following picture shows the page structure of two revisions after a transaction commit of the second revision: SirixDB stores an in-memory transaction intent log, which is only serialized on memory-pressure. On transaction-commit, SirixDB applies this log during a postorder-traversal. The , which is the main entry point into the resource-storage, is written last. UberPage Versioning for Record Pages The database pages in SirixDB, in general, are of variable length and don’t have to be padded to fit into a predefined block-storage size. RecordPages store the application data. Currently, a native layer for XML, as well as JSON documents, exists. Fine-granular, fast random reads due to byte-addressable non-volatile memory allows the versioning of these RecordPages as well. SirixDB implements several versioning strategies best known from backup systems for copy-on-write operations of record-pages. Namely, it either copies the full record-page that is any record in the page ( ) full only the changed records in a record-page regarding the former version ( ) incremental only the changed records in a record-page since a full-page dump ( ) differential Full Dump Versioning Storing the whole database page, although only a few records might have changed, on the one hand, requires way too much storage space. On the other hand, reading this page to fully reconstruct it in-memory instead of having to read scattered deltas is the most efficient way. Incremental Versioning Incremental-versioning is one extreme. Write performance is best, as it stores the optimum (only changed records). On the other hand, reconstructing a page needs intermittent full snapshots of pages. Otherwise, performance deteriorates with each new revision of the page as the number of increments increases with each new version. Differential Versioning Differential-versioning tries to balance reads and writes a bit better, but is still not optimal. A system implementing a differential versioning strategy has to write all changed records since a past full dump of the page. Thus, only ever two revisions of the page-fragment have to be read to reconstruct a record-page. However, write-performance also deteriorates with each new revision of the page. Drawbacks of the three state-of-the-art approaches Write peaks occur both during incremental versioning, due to the requirement of intermittent full dumps of the page. Differential versioning also suffers from a similar problem. Without an intermittent full dump, a system using differential versioning has to duplicate vast amounts of data during each new write. Marc Kramis came up with the idea of a novel sliding snapshot algorithm, which balances read/write-performance to circumvent any write-peaks. Sliding Snapshot Versioning The algorithm, as suggested by its name, makes use of a sliding window. First, the system has to write any changed record during a commit. Second, any record, which is older than a predefined length N of the window and which has not been changed during these N-revisions must be written, too. Only these N-revisions at max have to be read. The system might fetch the page-fragments in parallel or strictly linear. In the latter case, the page fragments are read, starting with the most recent revision. The algorithm stops once the full-page is reconstructed. You can find the best high-level overview of the algorithm in Marc’s Thesis . Evolutionary Tree-Structured Storage: Concepts, Interfaces, and Applications No write-ahead log needed for consistency SirixDB does not need a write-ahead log. During a transaction-commit the is swapped in an atomic operation. Thus, the data is always consistent without the need of a WAL, which is basically a second durable storage and completely unnecessary due to SirixDBs design. UberPage Lightweight buffer management SirixDB bridges the gap between in-memory and disk-oriented database systems. Instead of a heavyweight buffer and concurrency manager, it uses a lightweight buffer manager and stores direct pointers to in-memory data structures, during a fetch of a page from persistent storage. If the buffer manager has to evict a page, it sets the in-memory pointer of the parent page to . null Next, we’ll describe some of the most important design goals, while engineering SirixDB. Design Goals Some of the most important core principles are: Minimize Storage Overhead SirixDB shares unchanged data pages as well as records between revisions, depending on a chosen versioning algorithm during the initial bootstrapping of a resources. SirixDB aims to balance read and writer performance in its default configuration. It uses a huge persistent (in the functional programming language sense), durable index structure for the main document storage as well as versioned secondary indexes Concurrent SirixDB contains very few locks and aims to be as suitable for multithreaded systems as possible Asynchronous Operations can happen independently; each transaction is bound to a specific revision and only one read/write-transaction on a resource is permitted concurrently to N read-only-transactions Versioning/Revision history SirixDB stores a revision history of every resource in the database without imposing extra overhead. It uses a huge persistent, durable page-tree for indexing revisions and data Data integrity SirixDB, like ZFS, stores full checksums of the pages in the parent pages. That means that almost all data corruption can be detected upon reading in the future, we aim to partition and replicate databases in the future Copy-on-write semantics Similarly to the file systems Btrfs and ZFS, SirixDB uses CoW semantics, meaning that SirixDB never overwrites data. Instead, database-page fragments are copied/written to a new location Per revision and per page versioning SirixDB does not only version on a per revision, but also on a per page-base. Thus, whenever we change a potentially small fraction of records in a data-page, it does not have to copy the whole page and write it to a new location on a disk or flash drive. Instead, we can specify one of several versioning strategies known from backup systems or a novel sliding snapshot algorithm during the creation of a database resource. The versioning-type we specify is used by SirixDB to version data-pages Guaranteed atomicity and consistency (without a WAL) The system will never enter an inconsistent state (unless there is hardware failure), meaning that unexpected power-off won’t ever damage the system. This is accomplished without the overhead of a write-ahead-log (WAL) Log-structured and making the most out of byte-addressable NVM friendly SirixDB batches writes and syncs everything sequentially to persistent storage during commits. It never overwrites committed data and only clusters revisions. Database page fragments are of variable size and may be scattered. Thus, SirixDB benefits from fast random access and byte-addressable NVM devicesConclusion The advent of fast non-volatile, byte-addressable memory renders most of the assumptions of today’s database systems invalid. Random reads (and even writes) are fast, thus clustering index structures densely on non-volatile storage is not required anymore. Furthermore, byte-addressable storage upends the key design for block storage devices. Fixed-size blocks are not needed anymore. Instead, reads and writes can be done on a much more fine-granular level. I/O can mostly be done in parallel without a slow head moving around as it’s the case for spinning disks. As writes are slower than reads on NVM and to save storage space for its evolutionary store, are versioned. NVM eliminates the need to store all unchanged records of a page, even if a user only modifies a small number of records or even a single record. Thus, are stored in fragments and scattered on persistent storage. Their size is variable, and they might be scattered on persistent storage. RecordPages RecordPages The storage device underlying SirixDB should support fine granular byte-addressability or small block-sizes and fast random reads with possibly slower writes. Pages are never modified in-place. Thus “traditional” SSDs are a good fit as well. SirixDB batches writes and syncs these to persistent storage during a commit. It doesn’t need a WAL due to the atomic swapping of an , which is the main entry point into the storage of a resource. A resource is the equivalent to a table in relational database systems (currently either XML or JSON, but graphs or tables might be added in a future release). UberPage Help If you like the ideas presented in this article visit the and the . Give it a try and spread the word :-) Website Github Page My highest goal is to stabilize the storage engine, add some stuff needed for the front-end (for instance a ) as well as some JSON layer transactional primitives to copy subtrees into another resource. JSON diff-format I’m also working on a web together with another enthusiastic software engineer . We currently use , (some JavaScript) and I probably want to use in the future. However, I’m no front-end engineer and I’m just learning, Nuxt.js (Vue.js), TypeScript and struggle a lot of times. front-end Moshe Uminer Nuxt.js TypeScript D3.js Any help would be awesome. I’m envisioning a front-end with which we’re able to analyze the differences between revisions, for interacting with the storage and for executing time-travel queries to analyze for instance products or do audits. I have plenty of ideas to move forward, as for instance storing the log in Apache Kafka for horizontal scaling / sharding, adding cost-based query optimizations, building special non-blocking HTTP-Clients, adding Web-Sockets, a proper Kotlin DSL… Contributions are more than welcome 👩‍💻👨‍💻 Have fun and happy holidays Johannes

How I'm Engineering a Versioned Database Storage Engine for Byte-Addressable NVM

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DePIN Explained: What Are Decentralized Physical Infrastructure Networks?

In 2022, Fortune 500 companies in North America unlocked a business value of $1 trillion by adopting cloud technologies. 86% of these organizations utilized multiple cloud providers for their operations. With a Total Addressable Market (TAM) of $8.6 billion in 2023, the multi-cloud market size is projected to expand to $50 billion (28.6% CAGR) by 2030. Multi-cloud is becoming increasingly popular among large enterprises and Managed Service Providers (MSPs). It enables organizations to optimize costs, bolster regulatory compliance, improve the performance of cloud applications, and speed up innovation. Organizations can leverage the best features and pricing of different cloud providers to increase flexibility and reduce costs. By selecting technologies that are widely supported across providers, they can develop vendor-neutral applications. By adhering to security best practices and selecting suitable deployment regions, organizations can strengthen regulatory compliance, enhance resilience, and reduce the latency of their applications. Finally, by lowering technological, cost, and security barriers, organizations can accelerate engineering innovation. If you are a network engineer, cloud architect, or business leader devising a multi-cloud strategy for your organization, this article introduces you to high-level options to interconnect your multi-cloud. Interconnecting cloud deployments is complicated. Multi-cloud environments typically involve a mix of public and hybrid clouds customized to an organization's particular needs and goals. Achieving low latency, high performance, and cost-effective connectivity between applications, databases, and clients across different clouds can be a considerable challenge. What complexity arises, you ask? Consider a scenario where a database is hosted on one cloud platform while a streaming application operates on another. The challenge is establishing a reliable, secure connection between the database and the application servers without any data loss. Each public cloud offers unique networking capabilities and topologies, which complicates centralized management and orchestration. Cloud providers lack standardized models for inter-cloud connectivity, possibly to discourage customers from using multiple providers, as improved connectivity might reduce customer loyalty. Nevertheless, methods do exist to facilitate interconnection between different clouds. There are multiple ways to interconnect cloud environments. You can use your enterprise’s data center as a hub. An enterprise's data center functions as a hub routing traffic between public clouds or forwarding traffic from on-prem to the cloud. You have two options. Use a VPN tunnel between the cloud endpoints and the data center to send traffic over the internet securely. Alternatively, you can use dedicated connections- like AWS Direct Connect, Azure ExpressRoute, or GCP Dedicated Interconnect- between the cloud and the enterprise’s data center. Since colocation providers -such as Equinix, Digital Reality, and CoreSite interconnect the cloud to the Enterprise’s on-prem DC through their data centers, the process for creating these connections is time-consuming and expensive. Use a carrier-managed MPLS circuit for multi-cloud and hybrid-cloud connectivity. Public cloud providers like AWS, GCP, and Azure partner with internet service providers (ISPs) like Verizon, AT&T, and Orange to provide MPLS connectivity, allowing enterprises to link their cloud environments through these carriers. While specific steps vary between cloud providers, the process consists of two parts. First, enterprises create a dedicated connection to a colocation provider like EQUNIX. Next, they peer the connection with the partner's customer edge route using eBGP. After peering with eBGP, the inter-cloud traffic flows over the partner’s MPLS circuit. Use a virtual cloud router. Network as a Service Provider (NaaS) provides connectivity between clouds and the on-prem DC. They abstract the underlying Public cloud provider-specific connectivity features, making the orchestration, management, and monitoring of connections seamless. An enterprise creates a virtual router using the NaaS provider’s console and adds its dedicated cloud connections and datacenter gateway to the virtual router. This allows the router to route traffic between clouds and the enterprise’s data center. Which connectivity option should you choose? Each option has its advantages and disadvantages. Internet connectivity to the cloud is most practical for low-bandwidth scenarios. For legacy cloud applications that don't demand high availability, VPN tunnels are a sensible choice. They are simple to manage and cost-effective. Private connectivity is often essential for business-critical applications. The enhanced security, network performance, and reliability justify the additional expense for systems like your internal ERP or CRM, which require secure private access for compliance purposes. MPLS circuits, known for their reliability and predictability, are generally reserved for mission-critical applications. However, due to their costly and inflexible nature, marked by long-term contracts and extensive setup and removal periods, private connectivity offers a more flexible alternative. NaaS providers provide pricing flexibility via Pay-Per-User rates and are easy to manage. If you are considering reaping the many benefits of a multi-cloud strategy, choose the connectivity model appropriate for your business objectives. Links to blogs used for research: Insights from CTOs on cloud strategies | McKinseyMulti-Cloud
Management Market Size, Share | Report [2030] (fortunebusinessinsights.com) aws-network-connectivity-over-mpls.pdf (awsstatic.com) Who is selling NaaS, and what do you get? | Network World Multicloud Connectivity: A Complete Guide | Megaport Disclaimer: I am not affiliated with any of the businesses mentioned in this blog, nor do I represent them. The blog is written to educate the audience about the benefits of multi-cloud and networking options. It does not promote any specific company or vendor. In 2022, Fortune 500 companies in North America unlocked a business value of $1 trillion by adopting cloud technologies. 86% of these organizations utilized multiple cloud providers for their operations. With a Total Addressable Market (TAM) of $8.6 billion in 2023 , the multi-cloud market size is projected to expand to $50 billion (28.6% CAGR) by 2030. Multi-cloud is becoming increasingly popular among large enterprises and Managed Service Providers (MSPs). It enables organizations to optimize costs, bolster regulatory compliance, improve the performance of cloud applications, and speed up innovation. Organizations can leverage the best features and pricing of different cloud providers to increase flexibility and reduce costs. By selecting technologies that are widely supported across providers, they can develop vendor-neutral applications. By adhering to security best practices and selecting suitable deployment regions, organizations can strengthen regulatory compliance, enhance resilience, and reduce the latency of their applications. Finally, by lowering technological, cost, and security barriers, organizations can accelerate engineering innovation. Total Addressable Market (TAM) of $8.6 billion in 2023 If you are a network engineer, cloud architect, or business leader devising a multi-cloud strategy for your organization, this article introduces you to high-level options to interconnect your multi-cloud. Interconnecting cloud deployments is complicated. Multi-cloud environments typically involve a mix of public and hybrid clouds customized to an organization's particular needs and goals. Achieving low latency, high performance, and cost-effective connectivity between applications, databases, and clients across different clouds can be a considerable challenge. What complexity arises, you ask? Consider a scenario where a database is hosted on one cloud platform while a streaming application operates on another. The challenge is establishing a reliable, secure connection between the database and the application servers without any data loss. Each public cloud offers unique networking capabilities and topologies, which complicates centralized management and orchestration. Cloud providers lack standardized models for inter-cloud connectivity, possibly to discourage customers from using multiple providers, as improved connectivity might reduce customer loyalty. Nevertheless, methods do exist to facilitate interconnection between different clouds. There are multiple ways to interconnect cloud environments. You can use your enterprise’s data center as a hub. An enterprise's data center functions as a hub routing traffic between public clouds or forwarding traffic from on-prem to the cloud. You have two options. You can use your enterprise’s data center as a hub. Use a VPN tunnel between the cloud endpoints and the data center to send traffic over the internet securely. Alternatively, you can use dedicated connections- like AWS Direct Connect, Azure ExpressRoute, or GCP Dedicated Interconnect- between the cloud and the enterprise’s data center. Since colocation providers -such as Equinix, Digital Reality, and CoreSite interconnect the cloud to the Enterprise’s on-prem DC through their data centers, the process for creating these connections is time-consuming and expensive. Use a carrier-managed MPLS circuit for multi-cloud and hybrid-cloud connectivity. Public cloud providers like AWS, GCP, and Azure partner with internet service providers (ISPs) like Verizon, AT&T, and Orange to provide MPLS connectivity, allowing enterprises to link their cloud environments through these carriers. While specific steps vary between cloud providers, the process consists of two parts. First, enterprises create a dedicated connection to a colocation provider like EQUNIX. Next, they peer the connection with the partner's customer edge route using eBGP. After peering with eBGP, the inter-cloud traffic flows over the partner’s MPLS circuit. Use a carrier-managed MPLS circuit for multi-cloud and hybrid-cloud connectivity. Use a virtual cloud router. Network as a Service Provider (NaaS) provides connectivity between clouds and the on-prem DC. They abstract the underlying Public cloud provider-specific connectivity features, making the orchestration, management, and monitoring of connections seamless. An enterprise creates a virtual router using the NaaS provider’s console and adds its dedicated cloud connections and datacenter gateway to the virtual router. This allows the router to route traffic between clouds and the enterprise’s data center. Use a virtual cloud router. Which connectivity option should you choose? Each option has its advantages and disadvantages. Internet connectivity to the cloud is most practical for low-bandwidth scenarios. For legacy cloud applications that don't demand high availability, VPN tunnels are a sensible choice. They are simple to manage and cost-effective. Private connectivity is often essential for business-critical applications. The enhanced security, network performance, and reliability justify the additional expense for systems like your internal ERP or CRM, which require secure private access for compliance purposes. MPLS circuits, known for their reliability and predictability, are generally reserved for mission-critical applications. However, due to their costly and inflexible nature, marked by long-term contracts and extensive setup and removal periods, private connectivity offers a more flexible alternative. NaaS providers provide pricing flexibility via Pay-Per-User rates and are easy to manage. If you are considering reaping the many benefits of a multi-cloud strategy, choose the connectivity model appropriate for your business objectives. Links to blogs used for research: Insights from CTOs on cloud strategies | McKinsey Multi-Cloud
Management Market Size, Share | Report [2030] (fortunebusinessinsights.com) Insights from CTOs on cloud strategies | McKinsey Multi-Cloud
Management Market Size, Share | Report [2030] (fortunebusinessinsights.com) aws-network-connectivity-over-mpls.pdf (awsstatic.com) aws-network-connectivity-over-mpls.pdf (awsstatic.com) Who is selling NaaS, and what do you get? | Network World Who is selling NaaS, and what do you get? | Network World Multicloud Connectivity: A Complete Guide | Megaport Multicloud Connectivity: A Complete Guide | Megaport Disclaimer: I am not affiliated with any of the businesses mentioned in this blog, nor do I represent them. The blog is written to educate the audience about the benefits of multi-cloud and networking options. It does not promote any specific company or vendor.

Maximizing Business Value with Strategic Multi-cloud Connectivity.

SINGAPORE, Singapore, April 3rd, 2024/Chainwire/-- Bitlayer Labs, the first Bitcoin Layer 2 solution based on BitVM, announced a seed round of $5M led by Framework Ventures and ABCDE Capital, participation by StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital and many more. The announcement comes as Bitlayer prepares the launch of its open-incentive program Ready Player One, inviting developers worldwide to augment, enhance, and build on the protocol. Bitlayer Labs, the developer of Bitcoin Layer 2 protocol , announced today the close of a $5 million seed funding round at an $80 million valuation, driving a vision to become the computation layer of Bitcoin. Bitlayer Investors & Ecosystem Partners Blockchain-based venture firms Framework Ventures and ABCDE Capital led the round, with participation from StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital, Kenetic Capital, Kestrel, Global Coin Research, Pivot Global, and Web3Port. Many other incredible investors added value to the seed round. Prominent angel investors including Messari CEO Ryan Selkis, Messari co-founder Dan McArdle, Asymmetric Capital founder Dan Held, Hacken CEO Dyma, Sky Mavis CEO Trung and CTO Andy, and Kyber Network founder Loi Luu, among others, joined in capitalizing the round. Bitlayer’s seed round was delivered soaring value-add support from ecosystem partners including Hacken, AWS Cloud, Ankr, Polyhedra, Babylon, Particle Network, Meson, Nubit, BitSmiley, TokenPocket, Xverse, Flash Protocol, Umoja.xyz, RunesTerminal, and more. The Bitlayer Network Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing completeness. The protocol aims to build a more scalable and interconnected Bitcoin ecosystem. The securing of new capital pushes Bitlayer's lead in paving the way for more Bitcoin Layer 2 use cases, a race that will propel Bitcoin to prevail over other blockchains in size, scalability, and security. The fund will also enable Bitlayer to grow its team by hiring across business development and engineering faculties to support global expansion efforts. Investment firms are increasingly optimistic about the Bitcoin ecosystem as Bitlayer is rapidly gaining prominence, capturing the industry's attention. Investor Remarks Roy Learner, partner at Framework Ventures, spoke on the augmentation of Bitlayer with BitVM: “We are excited to back Bitlayer as they launch the first Bitcoin Layer 2 network based on BitVM. Looking at the trade-off space, BitVM struck us as the most practical approach to bring scalability and expressiveness into the BTC ecosystem without sacrificing security.With over $1T of global wealth looking to productively utilize their BTC, Bitlayer opens up the design space to introduce novel DeFi use cases and applications. Led by a team of OGs, we've been impressed by Kevin and Charlie's blend of deep technical background and rich crypto experience and are excited to partner with them as they deliver on their vision of making Bitcoin a more productive asset.” Qiao Wang, co-founder of Alliance DAO, emphasized the future of Bitlayer and Bitcoin L2s in the emerging era: “I spent the vast majority of my intellectual, social, and financial capital in the Ethereum ecosystem in the last decade, but deep down I've always been a Bitcoiner. The possibility to enhance Bitcoin's expressiveness and scalability via L2s such as Bitlayer is the most exciting moment in the history of Bitcoin. That is because there's over $1T of wealth stored on Bitcoin, and the holders would want to do something productive with it. Greater expressiveness and scalability enable this. Bitlayer is one of the most experienced, passionate, and technically capable teams to tackle this problem.” In the midst of building Bitlayer, co-founder Charlie Hu shared his thoughts on the protocol’s milestone: "This support signifies investors’ and builders’ trust in Bitlayer and the team, and demonstrates a robust backing for the Bitcoin ecosystem and the future of Bitcoin DeFi at large." Bitlayer ventures into the future of Bitcoin with newly supplied capital, anticipating a rush of development activity and investor interest in the Bitcoin Layer 2 space. "Bitlayer is dedicated to bringing super scalability into the Bitcoin ecosystem, with hundreds of millions of potential users as an addressable market," said Kevin He, co-founder of Bitlayer. "We’ve created the best, easiest-to-use scalability product in order to give users better fee rates and faster actions, all based on Bitcoin’s security foundation." With the approach of the Bitcoin halving event, the need for a robust Bitcoin ecosystem becomes more important than ever. Bitlayer’s exacting design and rigorous security auditing in preparation for public mainnet launch primes the protocol for a flourishing builder ecosystem. Mainnet launches on April 5th, with more milestones arriving soon on the project’s development pipeline. Ready Player One– Ecosystem Incentive Program. To accelerate ecosystem development and incentivize projects to deploy on the Bitlayer mainnet, Bitlayer is launching a series of ecosystem incentive programs– starting with the first event, Ready Player One. The campaign is an open incentive program designed to distribute token rewards valued at over $50M to protocols and teams that deploy to the Bitlayer mainnet and demonstrate exceptional performance. Bitlayer’s growth is accelerating, urging strong projects to join its ecosystem to grow the protocol in exponential ways. Bitlayer commits to providing comprehensive ecosystem support for all projects, offering the following resources to builders: Potential investment from the foundation and institutions Initial liquidity support Comprehensive product development resources Mentorship and pitch opportunities from top-tier incubators Support from the Bitcoin community and OGs Ecosystem collaboration and co-creation Users can find the full article for more information. here About Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing-completeness. Bitlayer is committed to becoming the computation layer for Bitcoin. It introduces ultra-scalability and inherits Bitcoin’s L1 security, providing users with high throughput and a low-cost transaction experience. Bitlayer Github Contact Digital Marketing Manager Sky Harris Bitlayer skyler@bitlayer.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Bitlayer, a Leading Bitcoin L2, Nets $5M In Funding, Unveils $50M 'Ready Player One' Program

**NEW YORK, NY, March 20th, 2024/Chainwire/--**Oraichain, an innovator in the integration of artificial intelligence with blockchain technology, has announced the highly anticipated Beta launch of its OraiBTC Subnet on March 19. The development marks the first time Bitcoin (BTC) will be integrated into the Oraichain ecosystem, offering a decentralized bridge for seamless BTC deposits and withdrawals. The Subnet is designed to facilitate the easy transfer of Bitcoin into and out of the Oraichain network, with users able to directly swap the asset for ORAI tokens and bridge BTC between the Oraichain and Bitcoin networks in both directions. In the future, it will also enable the transfer of BTC via IBC to many protocols throughout the Cosmos Ecosystem. An emphasis has been placed on ease of use, with the aforementioned functionalities made accessible via the website and OWallet browser. The integration will empower developers to build faster and more powerful dApp experiences, particularly for holders of the world’s best-known and most valuable cryptocurrency. OraiDEX The OraiBTC Subnet Beta launch provides an opportunity for users to actively participate in refining the platform’s features ahead of its full public release. In appreciation of their involvement, participants may also receive a special surprise, adding an extra element of excitement to the launch. Built on the robust foundation of Nomic’s design, OraiBTC leverages advanced Bitcoin features such as Taproot and Schnorr signatures, as well as a dedicated validator set, all of which ensures the utmost safety and integrity of bridged assets. The launch is a major step in Oraichain’s mission to become the go-to Layer-1 platform for AI-powered decentralized applications (dApps). By bringing Bitcoin into the Oraichain toolkit, the platform significantly expands the addressable market for AI dApp builders and offers greater versatility and potential for innovation. is a key component of Oraichain’s Mainnet 3.0 upgrade, which has implemented major changes to enhance speed and interoperability. Oraichain has recently reduced its block time to approximately 1 second, positioning it as one of the fastest networks in the Cosmos ecosystem and beyond. The introduction of OraiBTC The Oraichain team is currently focused on expanding its ecosystem, including through the development of GPU Staking. In addition to making significant investments in GPUs to support the AI applications running on the Oraichain mainnet, the Oraichain Foundation envisions GPU Staking as a novel approach to ensure that the value generated from increasing AI service demands directly benefits holders of ORAI tokens. About Oraichain is a permissionless Layer 1 for AI-powered dApps, developed to provide multidimensional Trustworthy Proofs of AI and data reliability. Oraichain With its AI Oracle at the core, Oraichain is designed to provide a decentralized system for the delivery of AI-generated data to smart contracts, maximizing transparency for developers and consumers. | | | | | | | Website X Telegram Discord GitHub Blog Coinmarketcap Coingecko Contact Tyree Robinson Tyree@orai.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Oraichain Announces Beta Launch Of OraiBTC Subnet For Seamless Bitcoin Integration Into Ecosystem

Oraichain ইকোসিস্টেমে নির্বিঘ্ন বিটকয়েন ইন্টিগ্রেশনের জন্য OraiBTC সাবনেটের বিটা লঞ্চ ঘোষণা করেছে

Oraichain kündigt Beta-Start des OraiBTC-Subnetzes für eine nahtlose Bitcoin-Integration in das Ökosystem an

Oraichain anuncia el lanzamiento beta de la subred OraiBTC para una perfecta integración de Bitcoin en el ecosistema

Oraichain annonce le lancement bêta du sous-réseau OraiBTC pour une intégration transparente du Bitcoin dans l'écosystème

ओराइचैन ने पारिस्थितिकी तंत्र में निर्बाध बिटकॉइन एकीकरण के लिए ओराईबीटीसी सबनेट के बीटा लॉन्च की घोषणा की

Oraichain がビットコインをエコシステムにシームレスに統合するための OraiBTC サブネットのベータ版の開始を発表

Oraichain, 생태계에 원활한 비트코인 통합을 위한 OraiBTC 서브넷 베타 출시 발표

Oraichain anuncia lançamento beta da sub-rede OraiBTC para integração perfeita de Bitcoin no ecossistema

Oraichain объявляет о бета-запуске подсети OraiBTC для плавной интеграции биткойнов в экосистему

Oraichain, Bitcoin'in Ekosistemle Sorunsuz Entegrasyonu İçin OraiBTC Alt Ağının Beta Lansmanını Duyurdu

Oraichain công bố ra mắt bản beta của mạng con OraiBTC để tích hợp Bitcoin liền mạch vào hệ sinh thái

Oraichain 宣布推出 OraiBTC 子网测试版，以将比特币无缝集成到生态系统中

After months of wait, Frax Finance is finally launching Fraxtal, their own chain. from an algorithmic stablecoin protocol, Frax has reinvented itself after the crash of Luna, leading to the creation of new primitives, including Liquid Staking (frxETH), bonds, Real World Assets, and more. This is proof of their resilience and vision to revolutionize DeFi: a modular blockchain with a unique scaling roadmap and incentives to incentivize user growth and development. Fraxtal is the culmination of their vision: Fraxtal is an EVM-compatible roll-up built on the OP Bedrock stack, guaranteeing full EVM equivalence, Ethereum alignment, and security, and facilitating the deployment of dApps. The particularity of Fractals is being infinitely complex, with patterns that seemingly repeat themselves forever. Furthermore, every part of the fractal, regardless of how zoomed in, looks very similar to the whole image. This concept is reflected in the strategic vision for Fraxtal. As Sam has mentioned repeatedly, Fraxtal is a tech thesis-driven chain and is not intended to be a standalone app-chain. Instead, Fraxtal wishes to be a modular L2 rollup incentivizing others to build on it. Currently, there are two main currents of thought with regard to the future architectural developments on blockchains: monolithic versus modular. To sum it up, the modular view sees the main components of the blockchain stack (validation, execution, Data Availability, etc.) as something that can be separated or combined, to tailor to specific use cases and improve the efficiency of blockchain networks. This short piece introduces Fraxtal, the way they incentivize users to build on their L2, and the role of FXS within this new ecosystem. Introducing Fraxtal Fraxtal benefits from the backing of Frax Finance, one of the most resilient DeFi protocols, with tentacles in almost all primitives. This provides Fraxtal with a , with the presence of several primitives that users can take advantage of. strong network and utility from the beginning The purpose of Fraxtal can therefore be intended as a general compute chain, with a roadmap of plenty of future integrations with Data Availability protocols, more L1, and other blockchain components. One such integration regards Eclipse, a protocol using a Solana Virtual Machina but settling on Ethereum. As a modular rollup, Fraxtal will feature several infrastructural and middleware components, “for other chains and networks to use, connect to, deploy L3s, and build on top of.” As an example, Fraxtal uses an developed by the Frax Team. in-house data availability module From its launch, expected in the 2nd week of March, Fraxtal will have a buffer period to allow developers to launch dApps, reflecting their paced-down approach, and making sure that everything is perfectly set. This is often the case for Frax, which has preferred to focus on long-term goals, rather than a short-term view. In the words of Sam, the founder of Frax, this approach consists in seeing “where the industry is going and make sure that you can create the most value there, with a long term grow oriented mindset.” This also means not jumping on trend following or catching, and maintaining a core focus on the long-term vision of the protocol. To achieve this purpose and incentivize development, Fraxtal has chosen a different path compared to many L2 launching these days. Instead of bootstrapping Fraxtal with the promises of an airdrop, the Frax team has come up with FLOX, a series of blockspace incentives for users and developers that are using the Fraxtal chain. FLOX is a system where users who transact on the chain and developers who deploy popular smart contracts get to earn FLOX points at every epoch. Epochs will be initially set with a weekly length, but their length can be further customized to avoid gaming of the mechanisms and have more granular time frames. Until now, gas rebates have been relatively inefficient in attracting users, as they often happen one-shot and not over a long period, and their money contribution is often negligible for projects. The same can be said for airdrops, which are a one-off stimuli that fail to contribute to user stickiness and retaining users. For this reason, Fraxtal has created FLOX points, which can be intended as a rewarding users for real usage. “perpetual airdrop” To avoid having mercenary capital or users gaming the system using their own contracts, Fraxtal introduces 3 mitigation mechanisms: : How much blockspace is this contract contributing to? How much fees is it creating for the Fraxtal network? Blockspace usage : This is similar to how Google ranks pages for domains, taking into account the popularity and ranking of the contract within the Fraxtal ecosystem. This ensures that a single user cannot game the system, making an average of how many users are effectively using this contract and how much each is depositing and spending. Contract Ranking Similar to how Data Availability Sampling works, Fraxtal uses a random block sampling to ensure the availability of assets at any point in time. Random Block Sampling: Combined, these mechanisms will reduce Sybill attacks, and prioritize organic sources of demand and a fair rewarding mechanism. This is much more powerful than “gas rebates” or “sequencer fee sharing” because projects earn much more rewards per $ of gas spent on their contracts than any fee sharing program can match." To sum it up, FLOX can be regarded as recurring incentives for usage and block space consumed. FLOX incentives will be represented by the ticker FXTL and will be“tokenized no later than 12 months after Fraxtal chain genesis.” Currently, the team has left open whether “FXTL points will be tokenized as a separate staking token for the chain (FXTL) or convert to FXS tokens at a specified ratio (or a combination of both).” The FLOX incentives will go live on March 13th, with an airdrop of FLOX points to veFXS stakers. Which brings us to the next question: what happens to FXS now? The Role of FXS Within the Fraxtal Ecosystem Dear FXS holders, fear not, for FXS will still have a central role within the Fraxtal ecosystem. In fact, FXS will assume the role of native token for Fraxtal, accruing value from the network and having the purpose of long-term staking tokens — with several flywheels as incentives. However, it is important to mention that gas fees on Fraxtal are paid in frxETH. Fraxtal also brings further utility to veFXS. Users can now stake veFXS on Fraxtal with unified balances with Ethereum Mainnet, which will serve to get FLOX points, vote on governance, and more. More on Fraxtal Fraxtal will also launch a Frax version of Bitcoin, named frxBTC. How does frxBTC get yield? Frax’s goal is to be the larger issue of the most important economic units of account and become a general-purpose chain. As such, one of the largest addressable markets, aside from the ones already captured by Frax (USDT, ETH), is Bitcoin. From launch, Fraxtal integrates Redstone Oracles: https://twitter.com/redstone_defi/status/1758506563416068121 Great explanation on FLOX by the one and only Flywheel team: https://twitter.com/FlywheelDeFi/status/1757845971751567617 Fraxtal launch thread by Frax Finance: I https://twitter.com/fraxfinance/status/1755386501926433087 The incentive systems, rewards, and FXTL points will start later in March, with the purpose of allowing partners & developers enough time to deploy their dApps and stake FXS to become eligible. The author of this post is not associated with the projects mentioned in this article in any way. All of the content above is not financial advice, and you are always encouraged to do your own research. Also published here

Frax Finance's New Layer 2: Everything You Need to Know About Fraxtal

Sitting down to digest the videos in Andrew Ng’s Generative AI course it is hard to ignore the prevalent use of the term data and how important large data sets are when building these large language models such as and Bard. Andrew Ng is Founder of Deeplearning.ai, he was a co-founder and head of Google Brain and Chief Scientist at Baidu. It is fair to say he knows a thing or two about the power of data. Chat GPT So, where do we get this data? Who controls the flow of this data? And how do companies find the inputs that will provide the greatest outputs when dealing with real-world use cases? These are questions that many practitioners in the world of blockchain today are asking and answering by providing access to streams of reliable, transparent data from verified sources. A seminal shift is underway when it comes to data-driven decision-making as AI and Blockchain technology emerge to put the power in the hands of the public. Breaking the barriers to entry and the age-old data monopoly held by larger corporations, blockchain technology, and decentralized data are democratizing information and transforming who and how companies of all sizes can use this information. The Historical Data Monopoly The phrase "knowledge is power" echoes through all discussions regarding access to data. As with the printing press empowering capitalist press owners, control over information governs us and shapes our destiny. However, the lessons from history underscore the need to ensure that power remains decentralized, preventing the concentration of control among a select few. For years, the manipulation and analysis of vast datasets were privileges held by the corporate giants with deep pockets. Smaller businesses, startups, and entrepreneurs found themselves on the sidelines, unable to compete and essentially working with data that had already been manipulated. This data monopoly created barriers to entry, stifling innovation and limiting opportunities for those without substantial resources. The rise of blockchain technology disrupts this narrative, offering decentralized access to data that redefines the point at which start-ups can get their hands on valuable data sets. No longer confined to the elite, data is becoming a democratized resource accessible to businesses of all sizes. This decentralization is not just about breaking down data silos; it's about dismantling the barriers that have kept smaller players from harnessing the full potential of information. Using decentralized data means that everyone has access but also that no single party is in control of the process. Nick Campion, Head of Marketing for Flare Network tells us that by avoiding single points of failure this type of data can help to prevent censorship, fraud, and downtime, which increases the security and integrity of the information being supplied. Campion outlines Flare’s ambitious goals, “Flare’s optimization is through the network’s architecture and incentivization structure, with the validators securing Flare’s proof-of-stake consensus mechanism also responsible for supporting the provision of decentralized data as data providers for the network’s native oracles. The goal is to grow the industry’s total addressable market, increasing what’s possible with blockchain by giving developers decentralized access to the broadest range of data and data proofs, provided at scale, low latency, and minimal cost.” Empowering Start-ups and SMEs One of the most significant impacts of decentralized data access is seen in how it empowers small and medium-sized enterprises (SMEs). Traditionally overshadowed by larger corporations, SMEs can now and decentralized data to make informed decisions. leverage blockchain Whether it's understanding market trends, consumer behavior, or optimizing operations, smaller businesses now have a seat at the data-driven table using Blockchain and Artificial Intelligence. Mat Yarger, Founder of Demia, a blockchain project securing data for carbon markets, notes that “Subject matter experts can leverage decentralized data for informed decisions by accessing high-integrity, real-time data. This empowers them to make data-driven choices, enhance research, and develop insights without compromising security or data quality.” Transparency, a cornerstone of responsible business practices, is no longer a luxury reserved for the big players. Blockchain's inherent transparency features allow businesses to showcase their ethical practices without the need for extensive resources. This transparency becomes a powerful tool for consumer trust, irrespective of the size of the business. Entrepreneurs and startups can now focus on creating value and innovative solutions rather than being bogged down by data access constraints. The democratization of information sparks a new era of creativity and problem-solving, where ideas, not resources, become the currency of progress. Addressing the Challenges While the shift towards decentralized data access brings about profound positive changes, it also poses challenges. Privacy concerns, security issues, and the need for robust regulatory frameworks are crucial considerations. The democratization of data also comes with a responsibility to actively participate in shaping how this data is used. Many of today’s oracle systems suffer from meaningful drawbacks: they are often hard to decentralize, expensive, slow, and difficult to scale. This is particularly relevant for emerging use cases such as machine learning and real-world assets, which have intensive data demands. Builders will realize that current systems are not able to handle this much data in a secure and decentralized fashion. These challenges present opportunities for collaborative efforts to establish responsible guidelines that ensure the ethical use of decentralized data. “Emerging applications require a highly scalable, low cost, decentralized, and low latency solution for data. New projects will be drawn to platforms with well-considered, native, decentralized data acquisition architecture - like Flare,” notes Campion. “Data silos, security concerns, inconsistent data quality, and complex access procedures. These hurdles hinder seamless data flow, slowing down innovation and decision-making processes. Decentralized data solutions, like Demia, aim to address these challenges, enabling frictionless data access and trust,” says Yarger. A Fairer Society Decentralized access to data is a catalyst for societal change. Dismantling the data monopolies means that more individuals and organizations can contribute to an equitable and responsible future. How practitioners empower small businesses to leverage this data is critical as the role of data in shaping AI models like Chat GPT and Bard becomes increasingly evident. Just last week, from the results of its released Gemini AI. Apologizing for the incorrect representation of the US Founding Fathers is one thing, but fixing a politically sensitive model that has been trained on enormous amounts of inaccurate data is a different beast to tackle. Google received an extreme level of negative backlash Furthermore, the crucial questions of data origin, control, and utilization resonate across diverse industries. The quest for access to transparent data sources becomes paramount as official bodies continue to explore the potential of these emerging technologies and foster more data-driven decision-making.

From Monopoly to Democracy: The Rise of Decentralized Data Empowerment

Quantum mechanics may make it possible to mine Bitcoin (the same Bitcoin) on two different planets without losing on time. It may even enable sending and receiving it! The following is a hypothetical analysis from the far future. The Challenge The cryptocurrency has a fixed block production time of 10 minutes. While this may be too slow for your coffee service, it is too fast considering the distances across planets. Bitcoin Consider the planet that keeps Elon Musk and many other people (I prefer Venus) hopeful about the future – Mars. Fast-forward to the future, many humans are living their best lives as Martians. That means they need Bitcoin. Mars is, on average, 140 million kilometers from Earth, a prohibitive distance to travel for rockets and radio signals. A rocket-powered mission carrying Bitcoin miners will still take seven months to reach the Red planet, while a signal propagating from Earth to Mars to instruct these miners to start mining takes . Mars rovers at least 5 minutes to arrive Five minutes is too long in the Bitcoin mining game because while everyone else is halfway through the race, the Martian miners will just be starting. The Solution One part of the solution is a quantum network to form a . We want to connect two cryptocurrencies that are both Bitcoin (how?)—one on Earth and one on Mars. multi-chain The other part is synchronized atomic clocks. Martians need not compete with Earth-people using some notion of absolute time.  As Einstein showed, that does not exist. A special form of relative time is the idea here. Synchronized to appear as if there is no barrier called outer space, 140 million kilometers across. The Martians should first have their own cryptocurrency, . But unlike those narratives, laying out a , we shall discuss here how to bridge these hash horizons using quantum mechanics. as many narratives have laid out law of hash horizons Let’s call the version of Bitcoin on Mars “Mars Bitcoin” or MBTC. MBTC could be built to behave just like Bitcoin at the time when MBTC was created. For example, say Bitcoin is going to be at block height 1,000,000 in 8 months and only 3.125 BTC are what Earth-people shall be mining then, that is exactly what the Bitcoin community on Mars should also be mining. 3.125 BTC. To set this up, we could copy all the critical Bitcoin core code at that time and add a unique MBTC Genesis block modeled to start at 3.125 BTC and count downwards. Now it might seem like there will be too little MBTC for the Martians to do business, but this shall be remedied with networked quantum computers and atomic clocks to allow bitcoins to flow from Earth to Mars and vice versa. Why atomic clocks? They are so precise . they lose one second after billions of years Why networked quantum computers? We need . perfect synchronization How to move Bitcoins from Earth to Mars without losing time This is a hypothetical idea, so let’s make more assumptions, like good scientists do. Many of these are currently impossible. Assume in our scenario we can keep two large entangled quantum states coherent for minutes and even hours, even while one of them is traversing the chaotic distance between Earth and Mars. Assume we have found a way to inject an entangled quantum state into a quantum computational system without it losing its coherence. We also assume we have found a way to seamlessly connect quantum computational systems to classical computational systems so that there is a process control flowing in either direction. Therefore, on Earth, we connect a quantum computer to the Bitcoin blockchain, to an atomic clock, and to a system for sending and receiving signals. The Martians also repeat the entire setup on Mars. Assume there is planetary and interplanetary unity to let all this happen, facilitated by hyperbitcoinization. Otherwise, it wouldn’t happen. Steps for Mining on Mars like you are on Earth Connect your MBTC miner to a source of electricity, e.g., a nuclear battery. Press “Synchronize with Earth miners”. This will make you wait for some minutes. Do wait the way Earth people have to wait for banks to process international payments. During this time, your atomic clock is adding a time delay while synchronizing with the Earth clock. This time delay takes into account the time taken for your system to query the Earth system, make it create two entangled states, send one to you, plug it into your system, and get on standby. Some time delay is maintained in the background to mimic the Earth clock exactly, which is 5+ minutes ahead. At the correct entry point, the two entangled states are measured by the Martian flipping a switch to start mining. A switch is also synchronously flipped on Bitcoin software on Earth machines. Remember that the time delay is canceled out by the atomic clocks. Steps for Sending Bitcoins from Earth to Mars You on Earth doing the sending should ask the Martian recipient to connect their MBTC node to a source of electricity like a nuclear battery. If they don’t, you will burn your coins somewhere in those 140 million kilometers of emptiness. Tell them to enter “Receive Mode”. Create a transaction and broadcast it to their node across space, using a packaged signal with an entangled state (for synchronicity with atomic clocks, as in mining above) and a non-entangled state of the signed transaction referencing their public MBTC wallet, which will bypass the quantum computers and go straight into the classical computer, then standby. As the two cryptocurrencies are multi-chained, they will behave as one blockchain. The Martian is notified, “Incoming transaction. Press to receive”. They press ENTER and voilà. The states are collapsed on both planets, and both the MBTC and the BTC blockchains record the transaction with a unified time. With the time delay removed. In reality, the bitcoins sent to Mars enter special wallet addresses on Earth’s blockchain, not addressable without a special MBTC–type key. The same goes for bitcoins from the Mars to Earth system. Futuristic multi-BTC-chain ish. Keep Building While a lot of the dynamics here are currently fictitious, they would enable Bitcoin to truly be a cosmic cryptocurrency without borders. Not even space would contain its magic. They would push its utility beyond merely a few years when mining of big coin rewards is done and over with to utility for thousands of years across entire star systems. This is because the biggest value would not be in mining coins; the value would be in exchanging value. Spreading unity and love from around the world to other worlds where humans like us live. Even more impressively, ideas from alternative cryptocurrencies and technologies might help make this possible in the fastest time ever. WAGMI Buidl. P.S.>> I created a game with classical and quantum-simulator code in which a bitcoin is sent from one quantum computer to another. Check it out ! here

Bitcoin Mining on Mars Might Be Possible 

121 Stories To Learn About Databases

You've launched your marketplace, it's gaining traction, but there's this nagging feeling in the back of your mind about your commission rates. Set in the early days of your startup, these rates were more of an educated guess than a calculated decision. Now, as you're steering through the complexities of marketplace dynamics, a question emerges – should you adjust your commission rates upwards? Imagine this: As a serial marketplace builder, I know that feeling. For the last 7 years, I have specialized in building marketplaces and digital products. In 2017, I became the co-founder & CTO of Radar, a driving schools marketplace in Eastern Europe, which was sold in 2020 to one of the local market leaders. From 2020 to 2023, I was also the Managing Director at itBoat, a global yachting marketplace with 10 years of history, 3x increased traffic, 10x increased partnership network and gained record-breaking sales through our platform. For many marketplace founders, the thought of increasing commissions represents a desirable change, but a really controversial one. On one hand, there's the undeniable need for financial sustainability; on the other, the fear of ruining the entire business model and relationships with partners. It's a dilemma that goes beyond just numbers; it goes into the core of value proposition, customer relationship, and your long-term strategy. In this series of articles, we're diving into the why's and how's of raising commission rates in your marketplace. In this article, we'll address the reasons to raise your commission and common fears associated with this process. And in the next article, we'll also look at a real-life case where doubling the commission paid off and exact strategies how to implement this change. So, whether you're contemplating this move or just curious about marketplace economics, let's unravel this together. Why Raise Commission Rate? When you first launched your marketplace, setting the commission rate probably felt like a shot in the dark. You wanted to be competitive, yet sustainable; attractive to users, yet profitable. But as any seasoned entrepreneur knows, the early days are about testing hypotheses, often with limited data on hand. Now, as your marketplace matures, it's time to revisit those early assumptions. Adjusting for Initial Estimates Early-stage marketplaces often set commission rates based on limited information. The rate you chose initially may have been a best guess under those circumstances. But as your platform evolves, so does your understanding of the market and your users' willingness to pay. Reassessing your commission structure with a wealth of data at hand means you can align your pricing more accurately with the value you provide. This isn't just about increasing profits; it's about ensuring your commission reflects the true worth of your services. That’s in my opinion the most important point here for early-stage marketplaces. Responding to Economic Changes The economic landscape is always changing, and a static pricing model may soon become outdated. Changes in market conditions, user expectations, and competitive environments necessitate a responsive approach to pricing. If your commission rates are frozen in time while everything else moves forward, you risk falling behind, both in terms of profitability and market relevance. Adapting your commission rates to the current economic reality is a crucial step in maintaining a healthy, thriving marketplace. Funding Platform Enhancements Higher commissions open up ways for reinvestment into your platform. This isn't just about padding the bottom line; it's about enhancing user experience, improving technology, and providing better services. Partners are more likely to accept a commission increase if they see tangible improvements in the platform – be it through more marketing features, more robust product features, or faster customer service. Preparing for Future Growth Finally, consider the long-term trajectory of your marketplace. As you scale, diversify, and tackle new challenges, you'll need a solid financial foundation. A well-thought-out commission increase can provide the necessary resources to fuel future growth, helping you to expand, innovate, and stay ahead of the curve in a competitive digital marketplace landscape. What if...? Addressing Fears Associated with Raising Commission Raising commission rates can be a nerve-wracking decision, primarily because of the potential impact on your sellers, who are the backbone of your marketplace. Let's address these fears and explore how to turn this challenge into an opportunity for strengthening partnerships. Fear of Losing Sellers The most immediate concern is the potential loss of sellers. They are, after all, your partners in business, and a hike in commission might initially seem off-putting. The key here is not just transparent communication about the increase, but also articulating the value they gain in return. Explain how the increased commission is reinvested into the marketplace to bring more buyers, enhance platform features, and ultimately boost their sales. When sellers understand that the increase is a move towards mutual growth, they are more likely to stay on board. The Dread of Initial Backlash Any significant change can provoke a strong reaction, and the fear of backlash from your current partners is valid. However, this can be mitigated with the right approach to communication. Set up meetings with your core partners to discuss these changes and gather feedback on the change. Be upfront and transparent about the reasons for the increase. More importantly, frame this change as an investment in the marketplace that benefits them directly. Highlight how this move will enable better services and a stronger platform, which in turn can lead to more business for them. In addition, this change presents an opportunity to deepen your relationship with your sellers. Engage in open dialogues, address their concerns, and gather their input. This not only helps in smoothing over any initial resistance but also makes your partnership more collaborative and productive. Sellers who feel heard and valued are more likely to be committed to your marketplace, even with a commission increase. Marketplaces are all about communication with your partners and users. Concerns Over Competitive Disadvantage With higher commissions, there's the worry that sellers might flock to competitors with lower fees. Counter this by highlighting what sets your marketplace apart. Emphasize the unique value you offer – whether it's a larger customer base, superior marketing support, or niche market access. Remember, it's not always about being the cheapest option; it’s about offering the best overall value. Your marketplace should strive to be indispensable by providing unmatched services and benefits that justify the commission. Transforming Challenge into Opportunity While the fear of a negative reaction is understandable, remember that this is also an opportunity to reinforce the value of your marketplace. A well-explained increase in commission, backed by visible improvements and benefits, can strengthen the trust and loyalty of your sellers. It's about reframing the narrative from a simple cost increase to a strategic move for collective progress and success. Conclusion As you can see, changing marketplace commissions is a really complex question, but it's an addressable one. Next time, we'll discuss more in-depth strategies on how to increase commission rates, along with a case study from my first marketplace, Radar, a marketplace for finding a driving school. For more insights into marketplaces, , startups, , and sales automatisation, feel free to follow and connect with me on or . ideas about AI innovations LinkedIn here at Hackernoon Your ideas, questions, and opportunities for collaboration are always welcome! The sculpture on the cover image, created by Oscar Estruga, is located in Vilanova i la Geltru, Spain.

Why You Shouldn't Be Scared to Increase Your Marketplace Commission

The chemistry of converting raw data into golden opportunities for demand generation is a mastery sought by many but perfected by few. The intersection of data and analytics with demand generation represents not merely a trend but a fundamental shift in how businesses approach market engagement and customer acquisition. With precision analytics, companies are not just passively observing market behaviors; they are actively predicting and shaping future demands. Recently, I interviewed , a Demand Gen Lead at . Steve Armenti Google Can you share a bit about yourself? I’m from New Hampshire and lived most of my life in New England. After college, I landed a sales job at an accounting software company outside of Boston. I was an SDR making 100 phone calls a day trying to book meetings with CFOs. This was 10 years ago before marketing and sales automation was as prevalent as it is today. However, I taught myself how to manually segment my territory using spreadsheets and designed my own A/B email experiments to get meetings. Eventually, I stopped making phone calls altogether and was producing the highest numbers on the team purely from emails. I didn’t know it but I was using ABM + personalization. Being in sales taught me about the human element of marketing and sales. At the end of every marketing campaign is an actual person who is trying to solve problems. In marketing, we often forget that as we sit in our office buildings (or home offices) designing campaigns. I try to remember that there are real people on the other end, just like us, and they can smell your bullsh*t from a mile away. How does data-driven decision-making impact B2B demand generation? In my opinion, it’s everything. I’ve built most of my career on using data to inform a variety of decisions: small and large about B2B demand generation and growth. Data can remove human error and opinion from the situation. For example, let’s say marketing and sales are feuding about the quality of MQLs. MQL quality can be very subjective. What matters is alignment between marketing and sales on what accounts you’re targeting, who in those accounts you’re acquiring, and what are their pain points. Who is the ideal customer profile? All of those things can be measured with data. You can pull data to show that an MQL came from a target account and that the persona is precisely the proper seniority, title, and job function. The data is used to establish a baseline of what is true and what is opinion. If you don’t have the data to prove what is true, go get it before you get into a shouting match. What specific data points are crucial for understanding B2B buyer behavior? I like to first look at a combination of demographics, firmographics, and psychographics. You can get these data points from 1P and 3P research, persona development, your website and marketing data, your CRM, surveys, and focus groups. This will give you a basic understanding of who is in your total addressable market. Second, I want to look at my data. How do customers behave? This data requires analyzing the person-level data against all of your marketing activities. The question you want to answer is “How does [persona] engage with [marketing campaign] and what is the behavior they are taking as a result” my Doing this exercise at scale will unlock a trove of insights about your customer that you can then organize and segment to fit the needs of your campaigns. What role does predictive analytics play in B2B demand generation? Predictive analytics can play a big role in any B2B demand gen program. It requires a solid data analytics infrastructure though first. Assuming there is a data warehouse and process in place to clean and ingest data, predictive analytics can help marketers do many things: Identify the highest propensity accounts, personas, and segments. Predict the timing of purchase intent helping your sellers reach out at the most opportune time Personalize messages or notifications based on user behavior Predict the effectiveness of a campaign or activity And now, with AI, this entire process is speeding up. AI will become better at collecting and processing large amounts of data to identify patterns and trends that marketers can take action on in real-time. As the prediction engine runs, and AI is feeding results back even faster than ever, marketers shift their role to the entire operation versus managing the hands-on creation process of marketing campaigns. directing How can businesses effectively leverage CRM data in demand-generation efforts? CRM data is a goldmine of information. It’s the source code to what makes your customers tick and what incentivizes them to buy. CRM data can be used to identify which accounts and personas have the highest propensity to buy. CRM data can be used to segment your audience into different groups based on their needs, interests, and purchase behavior. CRM data can be used to optimize your campaigns and improve performance. At the end of the day, all marketing should be measured on its ability to drive revenue – and your CRM is one of the most critical data sources to be able to do that. What are the common challenges in collecting and utilizing data for demand generation? Data silos. When data is stored in different systems and applications, it becomes really difficult to access and analyze. The accuracy and completeness of your data is important once you break down the silos. As they say, garbage in garbage out. So if you organized the data but it’s still crap quality, you aren’t even close. Collecting and utilizing data for demand generation can be a resource-intensive process often requiring skilled data scientists. This is a challenge in itself. Proper data infrastructure is essential to good B2B demand generation. That said, I think a big challenge the industry is facing is a lack of analytical skills for the average marketer. The marketer of the future needs to be as analytical as they are creative. How do you ensure data quality and accuracy in B2B demand generation campaigns? It starts with cleaning the data. Make sure you have a process to clean and join together the various data sources you want to use. You might find you are missing certain quality markers so you will need to look at enriching the data through other 1P sources or by licensing 3P data. Once you have a complete picture of your data (often called a data dictionary), you will want to validate that the data is accurate either through testing and QA or with an actual validation tool. Could you explain how data segmentation enhances B2B lead nurturing? Data segmentation is all about dividing your leads and contacts into groups based on certain characteristics, such as industry, company size, job title, location, or historical behavior. You want to use segmentation so you can tailor your marketing and sales messages to each group, increasing the chances they will take the action you want them to take. Using segmentation allows you to improve your targeting and make it more precise, increase the relevance and personalization of your email, and improve the efficiency of email as a channel overall. You send less messages that produce better results. What tools and technologies are indispensable for data-driven B2B demand generation? There are a million of them but you will want a demand platform, marketing automation platform, CRM, customer data platform, web/SEO/content analytics, social media software, and A/B testing software. I’m partial to , , , , , and . , , , , and are also great products. Demandbase Marketo Salesforce GrowthLoop Google Analytics SproutSocial HubSpot Kissmetrics Mailchimp Optimizely Buffer How does personalization based on data improve B2B lead conversion rates? At a high level, when you personalize your message, you show people that you understand their needs and interests. This can make them more likely to pay attention to your messages and grow an affinity toward your brand. This will likely increase the chances that people will engage with your marketing and do what you want them to do. Personalization is a game of compounding value. As your audience continues to feel like you understand them and can meet their needs, they go deeper into the funnel evaluating your product or service. Marketing continues to personalize until the person raises their hand and takes action or wants to talk to sales. The personalization doesn’t stop here. Onboarding, sales enablement, and product discovery all need to continue the experience the person felt during the evaluation process. How do you bridge the gap between data analytics and actionable demand-generation strategies? In my opinion, it’s a linear process that goes like this: Get your house (data warehouse) in order. Collect, clean, and ingest all the data you want into one source of truth Build the proper data pipelines to fuel your teams. Data needs to flow from the warehouse into dashboards, CDPs, reporting, etc. so teams can review that data in real-time Automate insights and reporting. Most of the regular, day-to-day reporting you need to do is simply looking at the same KPIs weekly and/or monthly. Standardize those KPIs and the reports. Set calendar invites for when you want your team to look at it. Pull your top insights and develop commentary about what changed from the last reporting period and why. Run optimization sprints. After you’ve collected insights, rank the top ones using some priority method [e.g. Reach, Interact, Convert, and Engage (RICE)] and go and A/B optimizations. Do this in a sprint fashion so you get quick feedback and quick data. That feedback and data flows back into your pipelines and the cycle becomes a perfect loop. What emerging trends in data and analytics are shaping the future of B2B demand generation? AI, of course. I’m stoked about what AI is going to do for B2B demand generation. On the data side, I’m personally exploring how AI can automate the collection and analysis of data, improve insights and decision-making, and identify patterns and trends in data that go unseen by humans to better understand customers and prospects. On the creative side, I’m excited about personalizing our marketing to each person. Right now I use a framework that identifies target accounts, ICP (personas), and then segments. The next level of this is down to the actual person. I’m also really interested in AI for the use of predictive performance and analysis in demand generation. Launching campaigns is a time-consuming process and when it fails, a lot of resources, budget, and time are wasted. With AI, predicting the success rate of campaigns is possible. Additionally, prediction around which accounts, personas, and people are most likely to buy is incredible. This plus the personalization is like Michael Jordan and Scottie Pippen in the 90s – unstoppable. Also published . here

What Google Can Teach Us About Effective B2B Demand Generation

Making new mistakes I’ve been a software engineer for about 15 years now. Throughout my career I learned a lot and applied these learnings to design and implement (and occasionally phase out or leave as is) many distributed systems. Along the way I made numerous mistakes and still keep making them. But as my main focus was reliability I’ve been looking back at my experience and the community to find ways to minimize the error frequency. My motto is: we must absolutely try making new mistakes (less obvious, more sophisticated). Making a mistake is fine - that is how we learn, repeating - is sad and discouraging. That’s probably what has always fascinated me about mathematics. Not only because it is elegant and concise, but because its logical rigor prevents mistakes. It forces you to think about your current context, what postulates and theorems you can rely on. Following these rules proves to be fruitful, you get the correct result. It is true, that computer science is a branch of mathematics. But what we usually practice is software engineering, a very distinct thing. We apply computer science achievements and discoveries to practice, accounting for time constraints and business needs. This blog is an attempt to apply semi-mathematical reasoning onto design and implementation of computer programs. We’ll put forward a model of different execution provides a framework to avoids many programming errors. regimes From humble beginnings When we learn to program and make our first tentative (or daring) steps we usually start with something simple: programming loops, doing basic arithmetic and printing the results in a terminal solving math problems, probably in some specialized environment as MathCAD or Mathematica We acquire muscle memory, learn language syntax and most importantly we change the way we think and reason. We learn to read the code, make assumptions about how it is being executed. We almost never start by reading a language standard and closely read through its “Memory model” section - because we are not yet equipped to fully appreciate and make use of those. We practice trial and error: we do introduce logical and arithmetic bugs in our first programs. These mistakes teach us to check our assumptions: is this loop invariant correct, can we compare array element’s index and length this way (where do you put this -1)? But if we don’t see some kind of errors, oftentimes implicitly we internalize some the system enforces and provides us. invariants Namely this one: Lines of code are always evaluation in the same order (serialized). This postulate allows us to assume that next propositions are true (we are not going to prove them): evaluation order does not change between executions function calls always return Mathematical axioms allow to derive and build larger structures on a solid basis. In mathematics, we have with 4+1 postulates. The last one says: Euclidean geometry parallel lines stay parallel, they do not intersect or diverge For millennia mathematicians tried to prove it, and derive it from the first four. Turns out that is not possible. We can replace this “parallel lines” postulate with alternatives and get different kinds of geometries (namely, hyperbolic and elliptic), which open up new prospects and turn out to be applicable and useful. After all our own planet’s surface is not flat and we have to account for that e.g. in GPS software and airplane routes. The need for change But before that let’s stop and ask the most engineering questions: why bother? If the program does its job, it is easy to support, maintain and evolve, why should we give up this cozy invariant of predictable sequential execution in the first place? I see two answers. First one is . If we can make our program run twice as fast or similarly - require half of the hardware - this is an engineering achievement. If using the same amount of computational resources we can grind through 2x (or 3, 4, 5, 10x) of data - it may open completely new applications of the same program. It may run on a mobile phone in your pocket instead of a server. Sometimes we can achieve speeds up by applying clever algorithms or rewriting in a more performant language. These are our first options to explore, yes. But they have a limit. Architecture almost always beats the implementation. Moor’s law has not been doing that well lately, a single CPU’s performance is growing slowly, RAM performance (latency, mainly) is lagging behind. So, naturally, engineers started to looking for other options. performance Second consideration is . Nature is chaotic, second law of thermodynamics constantly works against anything precise, sequential and repeatable. Bits flip, materials degrade, power browns out, wires get cut preventing our programs execution. Keeping sequential and repeatable abstraction becomes a hard job. If our programs could outlive software and hardware failures we could deliver services that have a competitive business advantage - that’s another engineering task we can start addressing. reliability Equipped with the goal, we can start experiments with non-serialized approaches. Threads of execution Let’s look at this chunk of pseudo code: ``` def fetch_coordinates(poi: str) -> Point: … def find_pois(center: Point, distance: int) -> List[str]: … def get_my_location() -> Point: … def fetch_coordinates(p) - Point: … def main(): me = get_my_location() for point in find_pois(me, 500): loc = fetch_coordinates(point) sys.stdout.write(f“Name: {point} is at x={loc.x} y={loc.y}”) We can read the code top-to-buttom and reasonably assume that `find_pois` function will be called `get_my_location`. And we’ll fetch and return coordinates of the first POI after fetching the next one. Those assumptions are correct and allow building a mental model, reason about the program. after Let’s imagine we can make our code to execute non-sequentially. There are many ways we can do that syntactically. We’ll skip experiments with statement reordering (that’s what modern compilers and CPUs do) and extend our language so that we’re able to express a new execution regime: or even with respect to other functions. Rephrasing, we need to introduce multiple of execution. Our program functions execute in a specific environment (crafted and maintained by OS), right now we’re interested in addressable virtual memory and a thread - a scheduling unit, something that can be executed by a CPU. function concurrently in parallel threads Threads come in different flavours: a POSIX thread, green thread,  coroutine, goroutine. Details differ greatly, but it boils down to something that can be executed. If several functions can run concurrently each needs its own scheduling unit. That is were multi-threading comes from, instead of one, we have several threads of execution. Some environments (MPI) and languages can create threads implicitly, but usually we have to do this explicitly using `pthread_create` in C, `threading` module classes in Python or a simple `go` statement in Go. With some precautions we can make the same code run mostly in-parallel: def fetch_coordinates(poi, results, idx) -> None:
  …
  results[idx] = poi


def main():
  me = get_my_location()
  points = find_pois(me, 500)
  results = [None] * len(points)  # Reserve space for each result
  threads = []
  for i, point in enumerate(find_pois(me, 500)):  # i - index for result
    thr = threading.Thread(target=fetch_coordinates, args=(poi, results, i))
    thr.start()
    threads.append(thr)
 for thr in threads:
   thr.wait()
for point, result in zip(points, results):
  sys.stdout.write(f“Name: {poi} is at x={loc.x} y={loc.y}”) our program can run on multiple CPUs and scale as the number of cores grows and finishes faster. Next engineering question we must ask: at what cost? We achieved our performance goal: We intentionally gave up on serialized and predictable execution. There is between a function + point in time and the data. At each point in time there is always a single mapping between a running function and its data: no bijection Next consequence is that one function can finish before another this time, next time it can be the other way. This new regime of execution leads to data races: when concurrent functions work with data it means that order of operations applied to the data is undefined. We start encountering data races and learn to deal with them using: critical sections: mutexes (and spinlocks) lock-free algorithms (the simplest form is in the snippet above) race detection tools etc At this point, we discover at least two things. First, there are multiple ways to access data. Some data is (e.g function scoped variables) and only we can see (and access it) and thus it is always in the state we’ve left it. However, some data is shared or . It still resides in our process memory, but we use special ways to access it and it might get out of sync. In some cases to work with it we copy it into our local memory to avoid data races - that’s why == ==is popular in Rust. local remote .clone() When we continue this line of reasoning, other techniques like thread-local storage come in naturally. We’ve just acquired a new gadget in our programming toolbelt, expanding what we can achieve by building software. However, there’s an invariant we still can rely on. When we reach out for shared (remote) data from a thread, we always get it. There is no situation when some memory chunk is not available. The OS will terminate (threads) by killing the process if the backing physical memory region malfunctions. The same applies to “our” thread if we locked a mutex, there is no way we might lose the lock and must stop what we’re doing immediately. We can rely on this invariant (enforced by the OS and modern hardware) that all participants are either dead or alive. All : if process (OOM), OS (kernel bug) or hardware encounters a problem - all our threads will cease to exist together without external leftover side-effects. all participants share the fate Inventing a process One important thing to note. How did we make this first step by introducing threads? We separated, forked. Instead of having one scheduling unit, we introduced multiple. Let’s keep applying this unsharing approach and see how it goes. This time we copy process virtual memory. That is called - a . We can run another instance of our program or start other existing utility. This is a great approach to: spawning process reuse other code with strict boundaries run untrusted code, isolating it from our own memory Almost all == ==work this way, so they are able to run untrusted Javascript executable code downloaded from Internet and terminate it reliably when you close a tab without bringing down the whole application. modern browsers This is yet another regime of execution we discovered by giving up the invariant and virtual memory and making a copy. Copies are not free: shared fate unsharing OS needs to manage memory related data structures (to maintain virtual -> physical mapping) Some bits could have been shared and thus processes consume additional memory Breaking free Why stop here? Let’s explore what else can we copy and our program among. But why go distributed in the first place? In many cases tasks at hand can be solved using a single machine. distribute We need to go distributed tenets so that our software stops depending on the inevitable problems that underlying layers encounter. to escape shared fate To name a few: OS upgrades: from time to time we need to reboot our machines Hardware failures: they happen more often than we’d like to External failures: power and network outages are a thing. If we copy an OS - we call that a and can run customers’ programs on a physical one and build a huge cloud business on it. If we take two or more computers and run our programs on each - our program can outlive even a hardware failure, providing 24/7 service and gain a competitive advantage. Large corporations long ago went even further and now Internet giants run copies in different datacenters and even continents, thus making a program resilient to a typhoon or a simple power outage. virtual machine But this independence comes at a price: the old invariants are not enforced, we’re on our own. No worries, we are not the first ones. There are a lot of techniques, tools and services to help us out. Takeaways We’ve just gained an ability to reason about systems and their respective execution regimes. Inside every large scale out system most parts are familiar sequential and stateless, many components are multi-threaded with memory types and hierarchies all held together by a mix of some truly distributed parts: The goal is to be able to distinguish where we’re currently, what invariants hold and act (modify/design) accordingly. We highlighted the basic reasoning, transforming “unknown unknowns” into “known unknowns”. Don’t take it lightly, this is a significant progress.

Breaking Axioms in Program Execution

প্রোগ্রাম এক্সিকিউশনে স্বতঃসিদ্ধ ব্রেকিং

Axiome bei der Programmausführung brechen

Rompiendo axiomas en la ejecución del programa

Briser les axiomes dans l'exécution du programme

कार्यक्रम निष्पादन में सिद्धांतों को तोड़ना

プログラム実行における公理の破壊

프로그램 실행에서 원칙 깨기

Quebrando Axiomas na Execução do Programa

Нарушение аксиом при выполнении программы

Program Uygulamasında Aksiyomları Çökmek

Phá vỡ các tiên đề trong việc thực thi chương trình

打破程序执行中的公理

Sometimes, it is interesting to ponder the relationship between seemingly independent technologies, as it can help us better understand each technology. This article will be this kind of article. A mere reflection on the similarities between distributed file systems, blockchain, and artificial intelligence. Distributed file systems like or are content-addressable storage systems. Both systems are based on a distributed database, where content can be accessed based on a given . When we want to access a file in such a system, we can do so with a 32-byte hash. IPFS Ethereum Swarm hash We submit this identifier to the system, which returns the content to us. This 32-byte identifier is generated based on the content and is unique for each piece of content. This means that every piece of content in the world can be assigned a unique 32-byte number. Think about how fascinating this is. For every video, song, text, book, or program that has ever been created or will be created, there is a unique 32-byte identifier. If this were not the case, IPFS or Swarm would not be able to function, as an identifier would be associated with two different pieces of content in the system, causing collisions. However, no one has ever found such a collision. Another example of the effectiveness of hashing is the Ethereum blockchain. Ethereum is a giant virtual computer. Users can send instructions (transactions) to the computer, which will then be executed by miners (who serve as the computer's processors), and they will modify the state of the computer. The state of the computer is stored in a structure called the , which is associative storage similar to IPFS, except that unlike with IPFS, the database is not distributed and every miner has a personal copy of the whole database. Patricia-Merkle tree When a block is created in the blockchain, only the hash of the virtual computer's state is stored, also in 32 bytes, and this is called the state root. This huge computer runs thousands of programs (smart contracts) and stores a massive amount of data, yet every possible state can be associated with a 32-byte number. Without this, the Ethereum network would not function. Although 32 bytes might not seem like much, it seems that in the world, we are able to compress almost everything into this size. And this is where artificial intelligence comes into play. As the hash is to the crypto world, embedding is to the world of AI. Embedding Embedding is when we map some information onto a vector. For easier understanding, we can imagine these vectors as points in a multi-dimensional space, but in reality, this is also a list of numbers just like in the case of hashing. Even a 32-byte hash can be imagined as a point in a 32-dimensional space. Therefore, embedding is a type of compression, similar to hashing. However, there is a major difference between hashing and embedding. Hash algorithms map the contents to completely random points in the multi-dimensional space, while in the case of embedding, it is important for similar contents to be close to each other. For example, in a facial recognition system, it is important for images of the same person to have vectors that are close to each other, as the system uses this information to determine if it is the same person or not. This is why hash algorithms use a relatively simple, fixed calculation, while embedding is a complex algorithm that is typically learned through training. Now that we understand the role of hash algorithms in the crypto world and have seen that 32 bytes can contain almost everything, we can better understand the functioning of systems such as LLMs (which are the base of systems like ChatGPT). When we ask a question to ChatGPT, a vector is created from our question, similar to when we create a hash for content. However, while a hash points to content in a distributed file system, in the case of ChatGPT, it points to a word that best fits the content. ChatGPT does nothing but predict the next word based on the given input, and then predicts the next word for the resulting content, and so on. (In reality, the process is a bit more complex, as it predicts tokens instead of words, and not just one token, but a list of the most likely tokens from which it randomly chooses.) ChatGPT is able to work efficiently because it has been trained on a massive text database and has some pre-made responses for almost every question. In this sense, we could compare it to a traditional large lexicon. What makes it unique is the continuity of its vectors. Even if we ask it a question that has never been asked before, the vector created from the question will be very close to the vectors of known questions, so the response given will likely be accurate. In the case of a distributed file system, we can access specific content with concrete hashes, while in the case of a large language model, we search for tokens that best match the embedding vector (which is some kind of a hash). In both cases, we are talking about a vast database, but in the first case, the database contains specific values associated with concrete keys, while in the second case, both the keys and values are located in a continuous space. It could be interesting to consider what kind of systems could emerge from the fusion of these two approaches. I can imagine, for example, a distributed vector database similar to IPFS, where everyone publishes their knowledge with embedding vectors. If I am looking for an answer to a question, the search is done with embedding vectors instead of content hashes in the DHT, and I receive the data that is relevant to my question. With the help of a language model, I can then construct a coherent response on my end. Instead of distributed file storage, the goal of the system would be to store distributed knowledge. The system could be the base of a distributed social network where individuals do not form connections. There are no friendships or followings. Instead, everyone posts important information on their wall, and if someone needs specific information, they specify their desired content, and the system gathers it from the network based on embedded vectors. In this way, members of the network can create a large collective mind, a collective superintelligence. Perhaps this is what the future of social networks could look like.

A Magical Relationship Between Distributed File Systems, Blockchain, and Artificial Intelligence

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