The unprofitable rideshare company looks set to leave Australia after users and contractors were locked out of the service.

Uber rival Ola quits Australia - Drive

Rideshare market is concentrating after initial boon.

Uber competitor Ola shuts down in Australia | Information Age | ACS

One of the three ride-sharing companies will close its app on Friday, according to an email, after more than six years of operation.

Uber ride-share rival Ola hits the brakes in NZ, Australia

Rideshare company Ola is shutting down across Australia after sending an email to its customers and drivers its service will be unavailable from April 12.

Ola ride share shuts down across Australia | Daily Mail Online

Customers have been told not to get into an Ola car from April 12.

Confusion as Ola suddenly pulls Australian rideshare service

Ola, o﻿ne of the world&#x27;s largest rideshare platforms, looks set to stop operating in Perth in a matter of d...

Ola: Major rideshare platform set to exit Perth

One of Australia’s most popular rideshare companies will end operations in the State from Friday.

Ola Rideshare discontinues operations in WA without warning | PerthNow

While Rutgers spoiled Penn State’s visit to Piscataway, they have a tough test ahead. The Nittany Lions won three of their last five games. During the 2023 campaign, the Scarlet Knights struggled away from Piscataway and posted a 1-6-1 record.

Rutgers men's soccer to face Penn State in Quarterfinals of Big Ten Tournament

The shooting was the country’s 36th mass killing this year, according to a database maintained by The Associated Press and USA Today in partnership with Northeastern University. At least 188 people have died in those killings, which are defined as ...

List: Mass killings in the United States since January

USA Lacrosse has selected Henry Youth Lacrosse to host a Pick Up & Play youth lacrosse clinic as part of National Celebrate Lacrosse Week event, which is November 4-12.

Henry Youth Lacrosse to host free clinic for National Celebrate Lacrosse Week

Ola Usa

Ola: More Than Infrastructure - A New Gateway Connecting Web2 and Web3 Worlds

ওলা: অবকাঠামোর চেয়েও বেশি - ওয়েব2 এবং ওয়েব3 ওয়ার্ল্ডকে সংযুক্ত করার একটি নতুন গেটওয়ে

Ola: Mehr als Infrastruktur – Ein neues Gateway, das die Web2- und Web3-Welten verbindet

Ola: Más que infraestructura: una nueva puerta de enlace que conecta los mundos Web2 y Web3

Ola : Plus qu'une infrastructure - Une nouvelle passerelle reliant les mondes Web2 et Web3

ओला: बुनियादी ढांचे से कहीं अधिक - वेब2 और वेब3 दुनिया को जोड़ने वाला एक नया प्रवेशद्वार

Ola: 単なるインフラストラクチャ以上のもの - Web2 と Web3 の世界をつなぐ新しいゲートウェイ

Ola: 인프라 그 이상 - Web2와 Web3 세계를 연결하는 새로운 게이트웨이

Ola: Mais que infraestrutura – um novo gateway conectando os mundos Web2 e Web3

Ола: Больше, чем инфраструктура: новый шлюз, соединяющий миры Web2 и Web3

Ola: Altyapıdan Daha Fazlası - Web2 ve Web3 Dünyalarını Birleştiren Yeni Bir Ağ Geçidi

Ola: Hơn cả cơ sở hạ tầng - Một cổng mới kết nối thế giới Web2 và Web3

Ola：不仅仅是基础设施——连接 Web2 和 Web3 世界的新网关

If the complex and costly hardware required for traditional mining has been a deterrent for you, or if you’re weary of the relentless grind to boost transaction volumes just to earn fees, now there’s a refreshing alternative. Discover the brand-new mobile mining experience that ‘ ’ provides, where your smartphone becomes your portal to a rewarding ‘verify to earn’ journey. Massive Introduction We’re thrilled to introduce , our innovative mobile mining app, poised for launch alongside the upcoming at beginning of April. As the first project in the blockchain sector to leverage ‘ZKVM+DePIN+AI’ technology, Ola is at the forefront, pioneering a new, fair, open and user-centric approach to blockchain interaction with this initiative, Massive. Massive Ola Testnet Alpha Massive simplifies the blockchain experience by removing the need for expensive hardware or technical expertise. Our vision for Massive is direct and clear: to democratize web3 access by providing a platform where anyone with a smartphone can participate and earn rewards through simple verification tasks. Our commitment with Massive is to lower the entry barriers to blockchain technology, making it accessible and equitable for all users. By introducing the ‘verify to earn’ concept, we’re not only changing how people mine; we’re also transforming their role within the blockchain ecosystem from passive observers to active participants. Massive, as a crucial part of Ola’s development strategy, . Ola is set to open applications for invitation codes on March 18th, releasing the first set of 1,000 codes for node registration. This initial offering is limited to 1,000 nodes, with registrations being accepted on a first-come, first-served basis. Additionally, the first 1,000 nodes will have the privilege of generating invitation codes, allowing them to invite newcomers with these codes to earn rewards. plans to allocate about 10% of Ola’s token to verifiers & miners, with an initial 1% being used for Ola Testnet Alpha Join us as we embark on this exciting journey to make blockchain technology intuitive, inclusive, and rewarding for everyone, everywhere. Background The industry has tirelessly explored various strategies to engage traditional users (those outside the web3 ecosystem), including gaming, infrastructure, and incentivizing through airdrops. Notable efforts like StepN’s ‘move to earn’ and Starkware’s airdrops for non-Web3 developers momentarily captured high interest, hinting at potential breakthroughs. However, their inability to sustain long-term momentum and impact highlighted the critical need for innovative, enduring approaches that truly resonate with and retain users in the blockchain realm. “We recognized the need for a standout feature that resonates deeply with users; thus, Massive was conceived as our bold solution to this industry challenge,” - Simon Chen, co-founder and CEO of Ola Ola’s mission with Massive is to catalyze the widespread adoption of web3, advocating for a decentralized and fair digital ecosystem. By introducing the ‘verify to earn’ concept, where anyone with a smartphone can participate in mining, Massive aims to significantly lower these barriers. Additionally, users can engage with multiple projects over time on their smartphones, enhancing the longevity and diversity of their participation. This approach will foster a more inclusive environment, encouraging a broader demographic to engage with, understand, and contribute to the web3 space, while receiving long-term rewards. The introduction of Massive is a strategic move toward making blockchain more accessible and engaging for the general public, thus infusing the industry with new participants and fresh perspectives. Through this initiative, Ola envisions transforming mere observers into active contributors, nurturing a dynamic and sustainable blockchain community. Massive: Fair Opportunities for All In the realm of traditional mining, such as the Proof of Work (POW) mining mechanism, the requirement for costly hardware presents a significant barrier to ordinary users. When developing Massive, Ola focused on reducing entry barriers, enhancing user experience, and increasing user rewards, all while guaranteeing utmost fairness for EVERY user. Massive is a decentralized authentication network composed of various mobile elements. In terms of user-friendliness, Massive boasts several key features: User Access Threshold: : Users can easily access Massive by simply downloading the necessary app and wallet on their smartphones. Low Entry Barrier : Massive supports a broad range of mobile models, compatible with both Android and iOS (coming soon) systems. Minimal Device Requirements User Rewards (10% of Ola’s tokens): : Users receive rewards by completing verification tasks, providing a straightforward way to earn. Verify to Earn : In addition to earning verification rewards, users can also gain additional returns by inviting newcomers with a referral code and through staking activities. Multiple Reward Streams User Experience: : Users can join or leave the network at their convenience, offering the flexibility to suit individual needs. Flexible Access and Exit : Without the need for complex operations or maintaining high transaction volumes, Massive simplifies user participation. Ease of Use : Compared to distributing funds across multiple addresses, using a smartphone to participate presents a lower barrier for the average user. User-Friendly Enhanced Network Security and Intelligence: : Massive harnesses AI+zk technology to elevate network security and intelligence, utilizing AI for nuanced analysis of node service statuses. This results in refined task distribution and heightened network efficiency. AI + ZK Integration Platform Fairness and Sustainability: : Massive ensures fairness for all users, where everyone’s time and contributions are equally valued. Fair : Users can engage with multiple projects over time on their smartphones, enhancing the longevity and diversity of their participation. Sustainable Massive Changes the Game: Say Hi to Your New Web Roles At Massive, your role is fundamentally transformed. Gone are the days of having to “slave away” by artificially boosting transaction volumes and amounts to earn incentives. Now, all you need is a standard smartphone to connect. By simply staying active and verifying, you can consistently earn rewards. But your role evolves further; once you connect as an Ola node, you become an essential part of maintaining the network’s security. You might have felt the imbalance before, as web3 whales could distribute their funds across multiple addresses to amplify their earnings, a feat not so easily replicated by purchasing multiple smartphones. Hence, Massive levels the playing field, offering a balanced opportunity for both whales and retailers. On the Massive platform, everyone participates and contributes in the same manner, ensuring fairness. This approach incentivizes a broader spectrum of users to engage with the web3 ecosystem, lowering entry barriers and bolstering decentralization and equity within the system. With Massive, every participant has the chance to earn rewards fairly, fostering wider participation and a healthier network. Distinct from other staking or mining models where more staking could mean more chances to validate, Massive champions decentralization and equity. Every node stakes an equal value, and by merely staying online and completing verification tasks, rewards can be earned. Time is valued equally across all participants, ensuring that mining on Massive is inherently fair. This paradigm encourages broader and more equitable user engagement, reinforcing the network’s integrity and community inclusiveness. Looking Ahead: Shaping the Future of Blockchain and Web3 Mobile Experiences with ZK Technology As we gaze into the future, our expectations for the Massive are set even higher: Firstly, as a purely decentralized ZK verification network, Massive is poised to accelerate the modularization of blockchain design. Secondly, with an increasing influx of new users joining the blockchain, we anticipate the emergence of genuine Web3 mobile technology. This evolution promises not only to propel the advancement of blockchain technology and invigorate the ecosystem but also to offer users a more equitable, open, and convenient digital experience. Collectively, these efforts aim to facilitate the widespread adoption and development of blockchain technology, steering the industry toward a more prosperous future. Join the Venture! Ola is tackling blockchain’s biggest hurdles: fairness and accessibility, with its gamechanging ‘Massive’ app, leveraging “ZKVM+DePin+AI” technology. Massive simplify blockchain interactions with a ‘verify to earn’ model, opening the digital door for anyone with a smartphone. 🗓️ March 18 marks the launch of Ola’s first 1,000 node registration codes, with mining, new features, and greater rewards soon to follow. Early nodes gain the advantage of creating invite codes for collaborative reward earning. Join us on this venture and be among the first to benefit! About us is an open-source Hybrid ZK Rollup that delivers programmable scalability and privacy to blockchains, empowering individuals through data ownership. Incubated by cryptographers and engineers of Sin7y Labs in 2022, Ola offers a layer2 ZK Stack, featuring Ola-lang, OlaVM, OlaUI, OlaOS, and a series of developer-friendly tools, facilitating the development of any type of contracts. In 2023, Ola launched the Ambassador Program and OVP Points Rewards, now with 20+ global ambassadors. For 2024, Ola plans to roll out public testnet, mainnet, mining mechanism (PoW, ZKP, & Verifier), and a range of incentive programs steadily. If you are interested in joining or partnering with us to contribute to a fair, decentralized, and permissionless world, please contact us at . Ola contact@olavm.org | | | | | | | | | | Website Twitter Discord Telegram Whitepaper GitHub LinkedIn YouTube HackMD Medium HackerNoon

The First Ola Mobile Mining App: A New ‘Verify to Earn’ Paradigm with a 10% Token Airdrop Plan

প্রথম ওলা মোবাইল মাইনিং অ্যাপ: একটি 10% টোকেন এয়ারড্রপ প্ল্যান সহ একটি নতুন 'ভেরিফাই টু আর্ন' দৃষ্টান্ত

Die erste mobile Mining-App von Ola: Ein neues „Verify to Earn“-Paradigma mit einem 10 %-Token-Airdrop-Plan

La primera aplicación de minería móvil de Ola: un nuevo paradigma de "verificar para ganar" con un plan de lanzamiento aéreo de tokens del 10%

La première application Ola Mobile Mining : un nouveau paradigme « Vérifier pour gagner » avec un plan Airdrop de 10 % de jetons

पहला ओला मोबाइल माइनिंग ऐप: 10% टोकन एयरड्रॉप प्लान के साथ एक नया 'वेरिफाई टू अर्न' प्रतिमान

最初の Ola モバイル マイニング アプリ: 10% トークン エアドロップ プランを備えた新しい「検証して獲得」パラダイム

최초의 Ola 모바일 채굴 앱: 10% 토큰 에어드롭 계획을 통한 새로운 '수익 확인' 패러다임

O primeiro aplicativo de mineração móvel Ola: um novo paradigma de ‘verificar para ganhar’ com um plano de lançamento aéreo de token de 10%

Первое мобильное приложение для майнинга Ola: новая парадигма «подтверди, чтобы заработать» с планом Airdrop 10% токенов

İlk Ola Mobil Madencilik Uygulaması: %10 Token Airdrop Planıyla Yeni Bir "Kazanmak İçin Doğrula" Paradigması

Ứng dụng khai thác di động Ola đầu tiên: Mô hình 'Xác minh để kiếm tiền' mới với kế hoạch thả 10% mã thông báo

第一个 Ola 移动挖矿应用程序：新的“验证赚取”范式和 10% 代币空投计划

Welcoming the New Year, we're excited to announce that the Ola Incentivized Pre-Alpha Testnet is now live, inviting both developers and users to foster engagement and innovation. , as an open-source Hybrid ZK Rollup, enhances blockchain ecosystems with programmable scalability and enhanced data privacy. This inaugural testnet release includes features like Ola-lang 0.1.0, OlaOS 0.1.0, AA Deployment, ECDSA & Poseidon Hash, among others, facilitating the development of any type of contract. Ola Ola will conduct rounds of incentivized public testnets leading up to the Mainnet launch. A total of 10 million future Ola tokens, limited-edition NFTs, Olavm Points (OVPs), and mystery rewards will be allocated as incentives. can create innovative zk dApps in just 4 steps using the comprehensive Ola toolkits, which include Ola-lang 0.1.0, OlaVM, OlaUI, and OlaOS 0.1.0, with further advancements on the way. Developers are empowered to actively participate in Ola community governance. All OVP holders will have the opportunity to propose and vote on Ola's future direction, shaping the evolution. Users Join us on this transformative journey, where together we'll unlock Ola's immense potential and share in the rewards of our collective success. Ola Incentivized Pre-Alpha Testnet Overview Easy Building with Ola: A Developer's Guide 🛠️  Tools and Functions The Ola Incentivized Pre-Alpha Testnet provides developers with a minimum viable version of the Ola network. Developers can write smart contracts based on their business needs, deploy them on the Ola network, and use command-line tools and SDKs to send transactions and query status. More features and tools will be released in the near future. The features supported in this release include: : Ola-lang is a Turing-complete language that can be used to write provable programs. The syntax of Ola-lang is designed to be similar to Solidity, making it convenient for smart contract developers to get started. You can find the Ola-lang developer . Ola-lang 0.1.0 documentation here : OlaOS is the implementation of the Ola node. Users can submit transactions, query data, and check transaction status through the JSON-RPC interface to OlaOS. OlaOS 0.1.0 : Ola natively supports Account Abstraction(AA). Users can deploy custom AA contracts and handle signature and transaction execution logic through AA. AA Support : Ola provides support for ECDSA and supports the zk-friendly hash function, Poseidon hash. ECDSA and Poseidon Hash 🖥️  Deploy Your ZK dApp in 4 Steps The Ola DevTestnet offers a local development environment designed for quick and efficient building and testing of Ola smart contracts. The setup process – encompassing environment configuration, account creation, smart contract deployment, and interaction – is streamlined to be completed in under 20 minutes. For a detailed step-by-step guide, visit our tutorial at . Ola Developer Quick Start Setting Up the Environment To begin interacting with the Ola network and compiling Ola-lang code, the command-line tool Olatte needs to be installed. Properly configuring environment variables for Olatte simplifies command execution and improves readability. Installation instructions are tailored for different operating systems: For macOS: via Homebrew For Debian/Ubuntu: via APT For Red Hat/Fedora: via RPM Creating an Account Once Olatte is installed, you can proceed to create a signer. This involves creating a keystore file and registering a public key, essential steps for secure interactions on the network. Deploying a Smart Contract Writing and compiling Ola smart contracts is straightforward with Ola-lang. Use the following command to compile: Lua
luaCopy code
olatte compile path/to/MyContract.ola path/of/output/dir This will generate a .bin file for deployment and an .abi file for interface definitions. Deploy your contract with: Bash
bashCopy code
olatte deploy --network test path/to/MyContract_bin.json Interacting with a Smart Contract Olatte facilitates two primary methods for smart contract interactions: call: Executes read-only functions. invoke: Performs write operations that alter the contract's state. With these simple steps, you'll have the Ola testnet operational in just a few minutes, ready for development and testing! 🤔 Advantages and Opportunities Ola stands out in ZK dApp development, offering scalability, enhanced privacy (coming soon) through zero-knowledge proofs, and an intuitive toolkit for both new and seasoned developers. In addition, Ola supports innovation with incentives for promising projects, providing financial and resource backing. As the testnet progresses enthusiastically, we will collaborate with top global developer communities and conduct online workshops. Our core team members will lead developers to explore Ola, deploying a variety of interesting dapps on our testnet using Ola's toolkits. Developers can explore these opportunities in and apply for incentives at: Ola Builder Program https://forms.gle/AFM4h3Jc5Y23Xfr28 Fair Participation Prioritized: Toward a Decentralized Ola Community Hub 👥  Advancing Community Empowerment Ola is committed to enhancing data privacy and ownership, with a strong focus on community-driven growth. The launch of the and in 2023, featuring over 20 global ambassadors, fosters active participation in shaping Ola’s future. This strategy aims for balanced development, gradually transitioning towards a more decentralized ecosystem. OVP Program Ambassador Program Community members, including managers and content creators, play a growing role in decision-making processes, while network maintainers ensure stability and security. Strategic partnerships and adherence to regulatory standards are also crucial to Ola’s growth strategy. This collaborative and decentralized approach is expected to cement Ola’s position in the blockchain infrastructure, promoting long-term success through community engagement and technological reliability. Visit Ola community hub for more information: https://ola-2.gitbook.io/ola-community-hub/ 🗳 ️Coming Soon: Vote and Propose to Engage in Ola’s Future We’re issuing limited OVPs and empowering all #OVP holders to participate in governance and decision-making. At Ola, we value our community’s support and engagement, offering limited-quantity OVPs as appreciation. By empowering each OVP holder with the opportunity to actively shape our future, we’re fostering a strong community. In the coming weeks, we’ll invite all OVP holders to voice their opinions in our governance and decision-making processes. Stay tuned to make a significant impact on the Ola ecosystem. 🤑 Join Ola X Aspecta Galxe event and share a growing OVP prize pool With the launch of the Ola Pre-Alpha testnet, we are excited to launch Ola BuilderState on Aspecta and celebrate alongside Aspecta on Galxe. All developers and users are welcome to participate, share in a 500k OVP pool and obtain an Aspecta identity. Ola and Aspecta have established a long-term partnership to elevate the global developer community and extend ZK expertise. Looking ahead, we’re facilitating knowledge sharing and hosting a series of reward-given developer-centric events and campaigns to foster a dynamic, innovative, and cooperative ecosystem for everyone involved. Ola Future Ahead: Upcoming Features & Roadmap Our post-Devtestnet roadmap is geared towards testnet iteration, implementing crucial upgrades, and introducing a range of new features and functionalities to enhance Ola, ultimately culminating in a fully decentralized mainnet that offers optional privacy features. Upcoming features in the internal testing phase, expected to be part of the next stage, include: : Introduce mining mechanisms that secure the network and incentivize participants. PoW, ZKP, and Verifier Mining Mechanisms : To maintain network integrity and prevent DDoS attacks, each transaction will require gas. Gas Fee Model : We're implementing Verifier contracts on Ethereum for validating L2 state updates and proofs, enhancing security and trust. Ethereum Layer 1 Verifier : Seamless integration between L1 and Ola L2 will enable users to easily deposit assets from Ethereum to Ola L2. Layer 1 and Layer 2 Deposit and Withdrawal : Our Rollup's DA strategy will support diverse methods, including those offered by Ethereum and Celestia, for robust data availability. Enhanced Data Availability (DA) Layer Integration : Facilitating interoperability and asset transfers between different blockchain networks. Cross-Chain Bridge Functionality : Offering Prover proofs in both CPU and GPU modes, significantly speeding up the proof generation process. GPU-Accelerated Prover : Expanding our support to include advanced cryptographic primitives like EDDSA and BLS signatures. Extended Support for Account Abstraction Cryptographic Primitives : Introducing privacy options to enhance user discretion and security on the platform. Optional Privacy Features Step on the journey with Ola: Empower Individuals with Data Ownership Since November, Ola has received over 200 high-quality dApp deployment applications through our testnet whitelist activity, covering areas such as gaming, SocialFi, and DID. We are excited to witness these projects thrive within Ola's ecosystem. Looking ahead, Ola is dedicated to continuously enhancing testnet, integrating ZK proof privacy with blockchain technology, and setting the stage for our mainnet launch mid-year. We're excited to introduce a new mining mechanism (such as PoW, ZKP, Verifier etc.) and unfold our token economic model in the coming months. This will include detailed incentive rules tailored for our diverse community, including users, developers, miners, and other key stakeholders, marking a significant stride in our journey towards blockchain innovation. Join us in creating a fairer, more decentralized world with Ola. Play your part in shaping the future of Ola, and share in the rewards of our collective success. As we push the boundaries of innovation, we look forward to the remarkable milestones ahead. Thank you for being a vital part of Ola's journey. Let's seize this pivotal moment and embark on a memorable journey towards the Ola Mainnet! About us Ola is an open-source Hybrid ZK Rollup that delivers programmable scalability and privacy to blockchains, empowering individuals through data ownership. Incubated by cryptographers and engineers of Sin7y Labs in 2022, Ola offers a layer2 ZK Stack, featuring Ola-lang, OlaVM, OlaUI, OlaOS, and a series of developer-friendly tools, facilitating the development of any type of contracts. In 2023, Ola launched the Ambassador Program and OVP Points Rewards, now with 20+ global ambassadors. For 2024, Ola plans to roll out public testnet, mainnet, mining mechanism (PoW, ZKP, & Verifier), and a range of incentive programs steadily. If you are interested in joining or partnering with us to contribute to a fair, decentralized, and permissionless world, please contact us at . contact@olavm.org | | | | | | | | | | Website Twitter Discord Telegram Whitepaper GitHub LinkedIn YouTube HackMD Medium HackerNoon

Introducing Ola's Pre-Alpha Testnet: Empowering Data Ownership and ZK Smart Contract Innovation  

Ola-এর প্রি-আলফা টেস্টনেট চালু করা হচ্ছে: ডেটার মালিকানা এবং ZK স্মার্ট চুক্তি উদ্ভাবনের ক্ষমতায়ন

Wir stellen Olas Pre-Alpha-Testnetz vor: Stärkung des Dateneigentums und ZK-Smart-Contract-Innovation

Presentamos la Testnet Pre-Alpha de Ola: potenciando la propiedad de datos y la innovación de contratos inteligentes de ZK

Présentation du testnet pré-alpha d'Ola : renforcer la propriété des données et l'innovation des contrats intelligents ZK

ओला के प्री-अल्फा टेस्टनेट का परिचय: डेटा स्वामित्व और जेडके स्मार्ट कॉन्ट्रैक्ट इनोवेशन को सशक्त बनाना

Ola のプレアルファ テストネットの紹介: データ所有権の強化と ZK スマート コントラクト イノベーション

Ola의 사전 알파 테스트넷 소개: 데이터 소유권 강화 및 ZK 스마트 계약 혁신

Apresentando a rede de teste pré-alfa da Ola: capacitando a propriedade de dados e a inovação de contratos inteligentes ZK

Представляем пре-альфа-тестовую сеть Ola: расширение возможностей владения данными и инновации в смарт-контрактах ZK

Ola'nın Alfa Öncesi Test Ağıyla Tanışın: Veri Sahipliğini Güçlendirme ve ZK Akıllı Sözleşme Yeniliği

Giới thiệu Mạng thử nghiệm Pre-Alpha của Ola: Trao quyền sở hữu dữ liệu và đổi mới hợp đồng thông minh ZK

介绍 Ola 的 Pre-Alpha 测试网：赋能数据所有权和 ZK 智能合约创新

A comparative study of Aztec, Miden, and Ola In this article, we delve into the concept of “Hybrid Rollup,” examining how projects Aztec, Miden, and Ola approach this technology. We investigate their unique smart contract languages, explore state tree designs, and consider the trade-offs in privacy designs. Our objective is to provide a comprehensive overview of Hybrid Rollup technologies, helping you understand their key components and envision their future trajectory. What is Hybrid Rollup? We are delighted to see that our recent initiatives have been garnering an increasing amount of attention in the market. “Hybrid Rollup” is the most accurate summary of what we at Ola have been working on: : Rollup a. It operates at Layer 2, but it also has the flexibility to function at Layer 3, depending on the platform utilized for the verification contract deployment. b. It’s a scalability solution. c. It has programmability - “Rollup” doesn’t specifically indicate this feature; “Programmable Rollup” is more accurate. : Hybrid a. It supports public, private, and hybrid contract types. b. Developers can freely choose the contract type based on their needs. c. Users can freely choose the transaction type in hybrid contracts. The diagram below simply illustrates the positioning and functionalities of Hybrid Rollup. Currently, it’s known that three projects, namely , , and , are dedicated to building in this direction. Although the technical details of each vary, they share Aztec Miden Ola a common vision: to bring more possibilities, higher security performance, more real-world scenarios and more everyday users to the blockchain industry. Programmable Rollup As a (Programmable) Rollup, a smart contract (SC) language is needed to support developers in building DApps. However, since Privacy isn’t EVM (Ethereum Virtual Machine) compatible, Solidity cannot be used directly to develop Private DApps. Therefore, a custom SC language is required, one that can support both the writing of public contracts and private contracts. Of course, this also necessitates the adaptation of the VM module, as it needs to maintain different types of state trees. Currently, the SC languages for the Aztec, Miden, and Ola projects are as shown in the following table: Projects Type Language name Language type Turing-complete Status Aztec ZK-ZKDSL NOIR DSL No In Development Miden ZK-ZKVM Miden IR GPL Yes In Development Ola ZK-ZKVM Ola lang GPL Yes In Development The diagram below shows the different processing logic of Aztec and Ola (Miden is similar to Ola): The most significant difference lies in the fact that in Aztec (and also in Aleo), each function of a contract is viewed as a fixed computation. At the compilation stage, specific circuits and pk/vk (public keys/verification keys) are generated for these computations, and the vk is used to identify the function being called. This leads to the following implications: Function calls require special handling. The correct passing and returning of parameters are achieved in Aleo by introducing read-only registers, with the number of registers not being fixed. The logic of the function must be deterministic computation; otherwise, it cannot be represented as a deterministic circuit and pk/vk cannot be generated. Dynamic types are not supported, such as variables for the number of loops and dynamic array types. This is because these are unknown at the compilation stage and are only known during execution, thus it is impossible to compile the function into a circuit at the compiler stage. In the design of Ola, we are not treating each function as a specific computation, for which we generate and store a unique pk/vk (public key/verification key) within the contract. Instead, we have designed an ISA-based VM, OlaVM. It’s universal, Turing-complete, and zk (zero-knowledge) friendly, capable of supporting computations of arbitrary logic. Simultaneously, we’ve defined a universal constraint system for the instruction set to regulate the correct execution of programs. Based on these designs, Ola possesses the following capabilities: Turing Completeness: It supports computations of any logic, including dynamic types. ZK Friendliness: A simplified constraint system is employed to regulate the correct execution of any program. Savings on Computation and Storage: There is no need for additional computation and storage of pk/vk for each function. Hybrid Global State Tree Design Within the Hybrid Rollup, two types of states will be maintained separately. One is the public state tree, corresponding to the account type, and the other is the private state tree, corresponding to the UTXO (Unspent Transaction Output) type, which we will refer to as the Note type going forward. The table below indicates the types of trees adopted by different projects: Projects Public state tree Private note tree Private nullifier tree Aztec SMT Append-only Merkle tree Indexed Merkle tree Miden SMT Append-only Merkle tree SMT Ola - scheme-1 SMT Append-only Merkle tree Indexed Merkle tree Ola -scheme-2 SMT SMT - This article will not delve into the detailed design principles of different tree states. The design principles of different trees have already been provided in the table above. We express our respect for the work of the Aztec and Miden teams. Below, we will introduce the characteristics of different state trees: Public state tree The leaf nodes of the public state tree are the plaintext information of the accounts. It needs to support four functions: addition, deletion, modification, and search. Sparse Merkle Tree (SMT) is the best choice for this purpose, as in SMT: a. The hash of an empty node is fixed, which can be cached, and the nodes don’t need to store the full data of the tree. b. When calculating the root, if an empty value is encountered, it can be directly utilized, leading to a reduction in the number of hash operations. c. Even though the tree size is fixed, the upper limit is infinitely large, with a total of 2^256 leaf nodes. d. It supports non-membership proof. Private note tree The leaf nodes of the private state tree are the commitment information of the note, and do not contain plaintext information. Each privacy transaction will consume old notes and generate new ones. If the note tree is updatable, nodes or listeners can infer the note information involved in the current transaction based on the leaf status of the state tree, such as which note commitments have been spent, which new note commitments have been generated, but the plaintext information of the note will still not be revealed. In other words, the user information and transaction information of this privacy transaction will still not be exposed. Therefore, in order to implement the untraceability of privacy transactions, the private state tree can only be append-only, and leaf nodes can not be deleted or updated. Private nullifier tree The private nullifier tree is a tree maintained to assist the . Its main purposes are to: untraceability of privacy transactions a. Prevent the same note from being double-spent; b. Disconnect the link between the inputs of a privacy transaction and the outputs of previous transactions, achieving untraceability; c. As shown in the diagram below: Therefore, for the private nullifier tree, it is necessary to support leaf addition and non-membership proof. Hence, the Sparse Merkle Tree (SMT) is a relatively good choice. From an efficiency perspective, Aztec introduced the concept of an index merkle tree, which is an enhanced version of the SMT. Privacy Design Ola has been contemplating privacy for a long time. What kind of needs and desires do users have regarding privacy? a. Sometimes, users do not want their on-chain transactions to be monitored. b. Sometimes, users do not want their on-chain data to be used illegally or without compensation. c. The cost of private actions and public actions should be similar. The table below shows the privacy features that the three projects - Aztec, Miden, and Ola - can provide: Projects Traceability Data privacy User Privacy Compliance Aztec no yes yes nomal Miden no yes yes nomal Ola - scheme -1 no yes yes nomal Ola - scheme -2 yes yes yes better All the solutions in the table above can achieve the two points a and b mentioned earlier. If we adopt the design of three different state trees in the Tree design section, this would provide the highest level of security, namely, . However, this comes at the cost of a more complex implementation, transaction structure, circuit design, and higher transaction costs. untraceable privacy transactions Therefore, when Ola designed privacy, it faced a trade-off between the untraceability of privacy transactions and the cost of privacy transactions: a. Given that user transactions on the blockchain are already private, such as the implementation of Data privacy and User privacy, is there still a need to implement the untraceability feature, even if it leads to more complex designs and higher costs? b. If privacy transactions can be traced, will it completely destroy users’ privacy? We believe that traceability might be a desirable privacy protection scheme: a. It can still protect the privacy of user transactions, and achieve user data ownership. b. It has a simpler privacy architecture design. c. It requires less computational resources for proof, hence the transaction cost would be lower. d. It is regulator-friendly, making it easy to trace the flow of privacy objects (without revealing other information). Based on the considerations above, Ola will support two levels of privacy solutions, leaving the choice of privacy needs for developers. Over the next few months, Ola will gradually implement and verify the aforementioned solutions. Summary It can easily extend corresponding functionalities to platforms that do not have privacy and high-performance features while inheriting their network security, such as Ethereum, , etc. All that is needed is to deploy the corresponding verification contracts and bridge contracts on their chains. Ola is a ZK-ZKVM platform, whose primary purpose is to build a Layer2 infrastructure that combines optional privacy with high performance. BSC, Aptos, zkSync We will continue to monitor and report on the latest developments in this field. Should you have any queries or suggestions, we would be delighted to hear from you. Please feel free to reach out to us at . contact@sin7y.org Also published . here

Hybrid Rollup — What do Aztec, Miden, & Ola Hold for the Future of Next-Generation Infrastructure?

TL:DR Ola is the first Programmable Privacy platform based on ZKVM and ZK-ZKVM, committed to bringing controllable, optional privacy to Ethereum. Ola achieves better programmability by implementing a General-Purpose Smart Contract Language based on the Instruction Set Architecture (ISA) rather than the circuit or constraint language, i.e., DSL. Ola’s privacy design includes a robust key management system, a UTXO-like account structure, and a constraint logic other than proof of programmability. Developers and users can easily utilize Ola for various purposes, including contract deployment, data control, etc. The public testnet of Ola aims for a Q4, 2023 launch. The attributes of a public blockchain ledger are truly remarkable. It is transparent, decentralized, and immutable, revolutionizing the traditional way of establishing trust. Its completely decentralized consensus protocol ensures that the latest stored state is always correct, making it highly trustworthy. Meanwhile, Web3 is a protocol built on blockchain technology that aims to achieve decentralization while also empowering users to truly own their data (data ownership). This is a fundamental goal of Web3, as blockchain technology enables decentralization without the need for trust in third parties. Additionally, privacy can give users control over their data, which can address the common issue of data being used and profited from without their consent in both Web2 and blockchain fields. User privacy can be roughly divided into two parts: and . Identity Privacy Data Privacy Decentralized Identity projects ( ) aim to solve the problem of user identity privacy by allowing users to prove their identity without revealing any key information about themselves. DID Data privacy, on the other hand, mainly addresses the protection of data for users on blockchain. Ola, as the first Programmable Privacy platform based on ZKVM and ZK-ZKVM, is committed to bringing controllable, optional privacy to Ethereum, the largest public blockchain. A Programmable Privacy platform consists of two critical technical components: Programmability and Privacy. This article aims to introduce the design principles behind Ola from these two perspectives. Programmability Why ZKVM? There are currently two forms of Programmability: ZKDSL and ZKVM. ZKDSL forms, such as , , , and , define basic operators that correspond to a circuit unit. Syntax rules are also defined on top of these operators, allowing for the creation of programs. circom halo2 Noir Leo In contrast, ZKVM forms, such as Risc0, Miden, and OlaVM, implement a General-Purpose Smart Contract Language (GPSML) based on an Instruction Set Architecture (ISA). GPSML not only defines basic arithmetic instructions but also includes loops, calls, return instructions, memory, and storage read and write instructions, resulting in a higher level of language abstraction and programmability than DSL. The different processes between ZKDSL and ZKVM are shown in the following figure: Essentially, a DSL language is a circuit or constraint language. The main purposes of using DSL are: To avoid developers directly writing the underlying circuit constraints, reducing the threshold for writing circuits and zkDapps. To make it easier for other developers to understand the constraint logic of the circuit. DSL offers better readability than low-level circuits. This is also the reason why the team defined the language, which directly uses PIL to write micro-ops and then calls -libs to convert them into underlying constraints. Polygon Hermez PIL circom Therefore, the DSL language is more suitable for expressing simple calculation programs and cannot be used to write smart contracts. Smart contracts often involve relatively complex logic. To implement such logic, a higher abstraction language is required for better programmability. Thus, Ola chose to implement better programmability based on ZKVM, , and a custom smart contract language, , which enables developers to implement more complex dApps. OlaVM Ola-lang How to Achieve the Fastest ZKVM: Tips and Tricks Although ZKVM-based systems are a better choice for programmability, the performance of the prover is still a crucial aspect to consider. Poor prover performance can directly affect the TPS of the entire system, resulting in a poor user experience and negatively impacting the system’s overall construction. Therefore, it’s essential to build a full-featured, zk-friendly ZKVM to ensure optimal performance. Ola aims to become a high-performance ZK-ZKVM platform, and its key technical features are shown in Figure 3: How to achieve smaller execution paths: By utilizing a register-based VM design and incorporating support for features. non-deterministic How to design more concise & succinct constraints: By utilizing an Instruction Set Architecture to reduce the number of instruction sets and decrease the degree of the constraint system. Algebraic RISC How to implement and optimize the fastest ZK algorithm: By utilizing the Starky algorithm ( + ) and the fastest , among other techniques. STARK Goldilocks field blake3 hash Specific technical details can be found on . olavm/docs Privacy As for why the UTXO-like model is more suitable for privacy rather than the Account model, we won’t go into too much detail here. Please refer to the paper (page 11) to learn more. Ola will mainly adopt the following design in terms of privacy: Aleo Robust key management system. : To protect the sender’s address information, Ola employs a one-time signature design, where each transaction is signed using a unique public and private key pair. One-time signature : Ola also utilizes one-time addresses to enhance sender anonymity. The receiver’s address used to accept a transaction changes for every transaction. One-time address : The view key allows the sender to view the transaction information, which may be required for regulatory scrutiny or for personal data sales. To ensure privacy, Ola’s view key can be updated once it has been exposed. Updatable view key UTXO-like account structure — Note-based. Following the concept of Zcash, the input and output of each transaction is represented by a Note. This structure enables easy hiding of transaction sender and receiver information. Constraint logic other than proof of programmability. This requires: Ensuring computational integrity of the commitment Ensuring the effectiveness of the commitment Cost the computational integrity of the Note Nullifier Verifying the validity of the signature Ensuring computational integrity of one-time addresses Ensuring computational integrity of the generated note Proof of Programmability -This includes off-chain computation and proof of public function (Account-based) as well as off-chain computation and proof of private function (Note-based). Figure 4 shows a simple comparison of dApps-specific privacy and Programmable Privacy, which is broadly similar except for the circuit logic for state transitions: Benefits for Developers and Users Ola’s long-term vision is to bring Programmable Privacy through its ZK-ZKVM platform, which offers the following benefits to developers and users: For Developers Developers can freely choose to deploy public contracts (Account-based), privacy contracts (Note-based), and ordinary contracts (Account & Note-based). For public contracts, Ola is a ZKVM. For privacy contracts, Ola is a ZK-ZKVM. For ordinary contracts, Ola is a ZK-ZKVM or ZKVM, depending on the user’s transaction type Transfer of assets between public and private accounts Intra-contract, no bridge contract is required, supported by default. Cross-contract, a bridge contract is required. For Users For ordinary contract types, users can freely choose the transaction type. For public/private contract types, users can only execute transactions of the corresponding type. Users have a view key to disclose executed private transactions. Ola supports the update of the view key so that after the view key is exposed, the privacy transactions executed by the user in the future will always be parsed. Ola supports asset transfers between public and private accounts. Figure 5 depicts the infrastructure of Ola, assuming that the proof is not generated by the end user. It is important to note that ideally, privacy transactions are assembled into a transaction and sent out after the end user generates the proof. However, in reality, most end users have limited computing power, and therefore, it is necessary to delegate proof generation to a node with stronger computing power. While the transaction information may be exposed to the proxy node, it can not forge a successful transaction based on this private information due to the one-time signature design scheme. Looking Ahead Ola has completed the proof-of-concept (PoC) implementation of and for programmability and is currently undergoing continuous rapid iteration. Additionally, the design of the privacy module is also being internally promoted. OlaVM Ola-lang We plan to release the programmable privacy public testnet (ZK-ZKVM testnet) in Q4 2023. However, before that, there is still a lot of work to be done. Ola has already established a Discord community, and all developers and users are welcome to join and contribute, which will be rewarded with Ola points and multiple perks and privileges as the Ola ecosystem grows stronger. About This article aims to provide an update on the latest developments and news related to and , which has the potential to revolutionize the way we approach privacy and security in the digital age. Ola zero-knowledge cryptography We will continue to monitor and report on the latest developments in this field. Please write to < > if you’d like to join or partner with us. contact@sin7y.org Photo by on Jason Dent Unsplash

Ola: The Programmable Privacy Platform for Ethereum

Whenever I am rushing for an office meeting and have to take an , I press the elevator button multiple times, even after seeing it already ON. It is an act I do to alleviate my stress in the moment. I act irrationally, inspite of knowing that I cant speed up things. This happens to all of us in our daily lives, especially when we are in a hurry and can’t wait for things to happen on their own. elevator , the ride hailing service in India, has introduced a cool feature on their app recently, that asked the user to let them (Ola) keep looking for a ride for five more minutes, in case the user didn’t get one in the first go, in an attempt to make things easier for the users. Ola While I think this is a really good problem to solve that actually helps users from having to try multiple times to book a ride, I felt it could have been implemented in a slightly better way. Let me explain you why I feel so. Be it going to theatres to watch a movie, or heading to a railway station to catch that train to my hometown, whatever the situation is, I always get ready in the nick of time and then try to find a cab. Sometimes, I don’t get a cab when I try for the first time, but I keep trying. I count every second as I stare impatiently at the progress bar hoping to find a ride as soon as possible and whenever I feel it is taking longer, I cancel the ride and try to book again. Not that doing it this way gets me the cab faster but I feel I am in control of things by doing so. I reduce my anxiety by doing so. This is precisely why I felt this feature could have been implemented in a better way. As you can see in the screenshots below, whenever I choose the option of letting Ola look for a ride for five more minutes, I don’t see the progress bar anymore. I don’t see anything inching forward. Sure, I am notified that the app is going to search for a ride in five minutes. Sure, I have an option to cancel this attempt and place a request one more time. But, by not showing the progress, the app is not making me feel that I am in control. Showing the progress of an action that takes longer is a fundamental principle of user-interface design. References: Progress indicators make a slow system insufferable and reduce uncertainty as explained in the article . https://www.nngroup.com/articles/progress-indicators/ Here is another article from NewYork times. https://www.nytimes.com/2016/10/28/us/placebo-buttons-elevators-crosswalks.html

Does Ola need an elevator button?

With the latest update now Uber is showing trip destinations to drivers before they decide to accept a ride to enable them to make informed choices. You will likely not have to pay Uber trip cancellation fee and the driver is unlikely to cancel the trip either. The thing now from change from here, it has been always hard to predict if a driver would cancel a ride or not as the conversation of user-driver used to be on a phone call. Since the driver is now more informed about the pick and drop location of the user and is incentives separately for the distance traveled to pick up the user, . the focus now shifts from cancellation to driver accepting a ride Let’s understand from a Data Science (Machine Learning) perspective what happens behind the scene to match user requests to a driver whose probability to accept the ride is high and get your request fulfilled in the minimum time possible. In summary, to find the best cab drivers for you — within a few seconds; these ride-hailing companies (Uber, Lyft, Ola, Rapido, etc.) run a matching algorithm and also check a driver’s ride acceptance probability before pushing a request to them. In this Newsletter, we shall discuss how we can build a driver ride acceptance probabilistic model. Objective: Predict if a driver will accept ride request or not and find the probability of acceptance? Characteristics / Features Required In order to figure out the features required to solve this problem in a ride-hailing business, a data scientist must be well-versed with domain knowledge. Product thinking is always important for a data scientist. 1. Trip Drive to User (Client) Distance (Driver to Pickup Distance) Time of the Day (Morning / Afternoon / Evening / Late Night) Trip duration/distance (Pick up to Drop Distance) Payment method (some drivers prefer Cash Mode more than online payments) Destination of the client (drivers don’t like to go to a destination where they will have to struggle to get their next client) Ride Type — Pool request or Normal request 2. Driver a) Enroute or Available Whether the driver is available (no rides) If the driver is en route — the trip is about to end or in the middle of it b) Historic Features In the last one week/month avg. number of rides accepted rate Total trips completed throughout the day — If the driver has achieved his incentive target of the day or not Current days acceptance rate (acceptance rate = Number of requests accepted / Total requests received) 3. Vehicle Type Cab / Auto Rickshaw / Bike (based on vehicle type some drivers don’t accept client pickups in lonely areas — safety concerns) Many Bike / Auto Rickshaw drivers don’t prefer ride requests of > 10kms 4. Rider (Client) Rider Rating (driver doesn’t like to deal with clients below 4 ratings) Rider gender (some drivers don’t accept ride requests based on request time and client gender) Rider Image (interesting experiment someone did by replacing his image with a zombie image and got less driver acceptance; proving the image of the client to the driver also matters) Interesting read profile image matters on both (driver and rider) side 😂: ‘Zombie’ drivers are scamming people out of cash with horrible profile pictures 5. Traffic Based on request time — drivers sometimes don’t like to accept rides in heavy traffic zones (the duration of the trip to complete or to reach the client — depends on traffic in the region) 6. Special Events (occasional change) Weather — Rainy Day / Sunny Covid restrictions in that Area (Quarantine zone) Festival Day based on region — Holi, Diwali, Christmas, etc. Modeling We now have a rich feature set that can help us predict whether a driver will accept a client’s ride request or not. We use standard statistical machine learning supervised classification algorithms(with spot-checking): Logistic Regression (Linear Model) Decision Tree (Non-Linear Model) Bagging Classifier — Random Forest Classifier (Ensemble Model) Boosting Classifier — LightGBM, XGBoost, etc. : AUC-ROC, F-beta score (beta = 2; if Recall is twice important as Precision) Model Metrics Conclusion I hope you understood the business problem and can relate to the features we picked for modeling out the patterns. While there is no silver bullet solution and these problems are way more complex, our aim was to improve the user experience and minimize the user-driver matching time as even a millisecond of change in the driver-user matching algorithm can help save millions of dollars. According to a paper entitled The Cost of Latency in High-Frequency Trading, a 1-millisecond advantage in latency can be worth upwards of $100 million per year. I hope you learned something new from this post. If you liked it, hit 👏, subscribe to my newsletter, and share this with others. Stay tuned for the next one! Connect, Follow or Endorse me on if you found this read useful. LinkedIn I am open to Gigs or Consults you can reach out to me on LinkedIn: https://www.linkedin.com/in/shaurya-uppal/ Also published . here

Using Machine Learning to Build a Ride Acceptance Model for Uber

Massive is a decentralized ZK verification network service focused on achieving mass adoption for Web3. It aims to alleviate or even solve some of the key issues currently facing the industry. Here’s a breakdown: Mass Adoption: Massive could attract more users The sector is actively exploring ways to attract more traditional users and resources. Massive takes a novel approach by starting with mobile phones, a device most users are familiar with. By employing a “Verify to Earn” incentive mechanism and simplifying operations, Massive aims to draw more participants. This strategy helps newcomers gradually understand and join the Web3 industry, continuously injecting fresh blood into the sector. Web3 Highly Effective and Cheap: Massive could work as a settlement layer of blockchain Currently, all Rollup solutions based on ZK technology face delays in state confirmation on Layer1 (L1) due to cost and security concerns. This is typically addressed through proof aggregation technology and verification delays. introduces a solution that ensures Layer2 (L2)states can be decentralized and verified immediately after proof generation, significantly reducing the time for state transition confirmation. The verified state is then synchronized to L1, ensuring a swift and secure process. Massive Capital Efficiency: Massive could improve profits Nodes in the POS (Proof of Stake) network can join through staking and re-staking. If approach is reused, the POS consensus network can achieve Ethereum-level security. Massive supports staking and re-staking of various token types and offers flexible staking schemes tailored to different ecosystem types. Eigenlayer’s Modular Design: Massive could complete the modularity The emergence of Massive further advances the modular design of blockchain. It can be specifically divided into ZKRollup (off-chain computation), DA (Data Availability — off-chain transaction storage), ZKVaaS (Zero-Knowledge Verification as a Service — off-chain verification), and L1 (state updates and consensus). Keywords: Zero Knowledge, Mobile devices, Staking, Re-staking, Modularity, POS Introduction In essence, the blockchain industry is currently plagued by a crucial question: "How do we attract more traditional users to this sector?" Even though we witnessed a historical moment in January when a BTC spot ETF was approved, allowing some traditional investors to trade BTC in a compliant manner, the influx of new users and resources still falls far short of the industry's urgent need for fresh participants. Simply opening up investment opportunities can only bring in some investors; achieving mass adoption requires different strategies. Some believe gaming could be the key to reaching this goal, others see potential in social media, and still, others think AI could pave the way. Regardless of the method, it's clear that industry insiders are all striving for mass adoption of blockchain. A recent event that caught my attention was Starkware's STRK token airdrop, which highlighted a significant fact: some developers outside of the usual crypto circles began to explore and show interest in entering the industry. This interest was sparked because Starkware conducted an airdrop to contributors of certain GitHub repositories, developers who might not have known about blockchain previously but became interested due to this event. Another application that left a strong impression on me is StepN, an app that became popular in the last bull market for its "earn while you run" concept. Users simply needed to purchase a pair of virtual shoes and then could earn rewards by running a very straightforward proposition daily. This led to a surge in user numbers and effectively broke into new user demographics. These examples underscore applications or infrastructure projects capable of attracting new users to the industry. To draw new users, the following characteristics are necessary: Simple operation, easy for users to get started; Low hardware requirements; An incentive mechanism. Both examples satisfy the first criteria: GitHub is familiar to developers, and mobile phones are familiar to general users. They also meet the second criteria, requiring either a computer or a mobile phone. The third criteria is met as well, with one offering "contribute to earn" and the other "running to earn." However, to continuously attract new users, sustainable incentives are essential. For Starkware, continuous token airdrops may not be viable; likewise, for StepN, perpetual mining and selling are also unsustainable. Thus, for an application to sustainably attract new users and hold long-term value, it must inherently sustain its appeal. Massive serves as an infrastructure service aimed at sustainably attracting new users to achieve the goal of mass adoption. It can support all ZK chain services, whether ZK layer1, ZK layer2, or ZK layer3, introducing new users to these ZK chains, enhancing system security, reducing operational costs, and improving capital efficiency. Architecture Massive is a POS (Proof of Stake) consensus network that primarily utilizes mobile devices as its nodes. Users are required to download the official app on their mobile devices, which supports both iOS and Android, the two leading operating systems. The architectural framework is outlined as follows (Fig.1): To become a node, users can fall into one of the following two categories: New users without any cryptocurrency assets can initially join the network. Their accumulated rewards must reach a certain amount before they can start unlocking subsequent mining rewards. Users already possessing some cryptocurrency assets can stake these assets to become a node on the Massive network. Depending on the underlying L1 blockchain, users can stake different assets. For example, in a Massive network serving BTC L2/L3, users can stake BRC20 and BTC assets. For Ethereum-based L2/L3 services, users can stake ERC20 and ETH assets. The wallet will integrate several mainstream ZK technology stacks for verification, such as plonky2, plonky3, and boojum, all of which belong to the STARK family. Following market trends, support for mainstream algorithms from the SNARK family, such as Halo2 or Nova, will be iteratively introduced. Mobile nodes that have completed staking will form a POS network to provide Timely Settlement for all Layer 2s and Layer 3s. If there is an excessive number of participating users, sharding technology can be utilized to form multiple independent POS networks according to specific standards. Provide Timely Settlement As mentioned earlier, Massive is not solely focused on attracting users; it also aims to address some of the current issues in the industry. Among these, the confirmation time for ZK L2s/L3s is a frequently discussed issue. Before we delve into this, let's take a look at the main solutions for ZK L2s as illustrated in the following diagram (Fig.2): It's evident that in the design of most L2 solutions, there's a need to aggregate a sufficient number of proofs on L2 and then send them to Ethereum for verification. This approach is adopted to reduce costs. Even with this premise, the aggregated proofs sent to L1 must wait for a certain period before calling the Verify contract to alter the global state, a measure taken from a security standpoint, although this delay can be configured. Therefore, it can be concluded that a L2 transaction, to be finally confirmed, requires a waiting period of between 2 to 23 hours. Until then, the validity of the proofs is verified only by a centralized sequencer. The diagram below (Fig.3) illustrates the current situation where, after their generation, proofs will be verified. Therefore, the primary intention behind Massive's design is to ensure that, before a proof is verified on L1, it goes through a verification process by a decentralized network, so everyone can verify now. It's important to note that if the POS network used for verifying the proof is provided by Eigenlayer's AVS ( ) service, then this POS network possesses the same level of security as Ethereum. Ultimately, only the latest global state needs to be updated on Ethereum. This aspect also offers insights into the modular design evolution discussed in later chapters. Actively Validated Services After incorporating Massive, the entire process flow for L2s could be transformed as shown in the following diagram (Fig.4): By extracting the ZK verify function from L1 and placing trust in the security of the Massive network, then adding a challenge mechanism to reduce the probability of Massive acting maliciously, the entire system's security will depend on the security of the Massive network. If the ecosystem of these ZK L2s is more finance-oriented, Massive could be built on EigenLayer's AVS to ensure the cost of acting maliciously is sufficiently high. Conversely, if the ecosystem of these ZK L2s is related to gaming or social scenarios, then a basic Massive construction would suffice. Capital Efficiency Staking and re-staking have recently become very popular concepts, focusing on capital efficiency. Reutilizing staked assets to achieve compound interest is an attractive economic mechanism currently seen in platforms like Blast and Manta. Unlike these schemes, Massive offers a novel destination for native assets across various networks, as well as for staked assets, by allowing them to become nodes within the Massive network through staking and re-staking mechanisms. Since the Massive network can provide more users, lower costs, and improved security for ZK L2s/L3s, nodes within the Massive network will share in the network maintenance profits, embodying the "verify to earn" concept. Based on the actual circumstances of ZK L2s/L3s, different security levels of ZKVaaS can be chosen. The security level depends on the total amount staked and the number of nodes in the network. New Modular Design As mentioned earlier, Massive, from a certain perspective, further changes the process of blockchain modular design. Before Massive, modular design mainly consisted of execution modularization, with various Rollups solutions, and Data Availability (DA) modularization, with various DA solutions, leaving only verification, storage, and consensus functions on L1. After the introduction of Massive, modular design will evolve to include execution modularization, DA modularization, and verification modularization, with only storage and consensus remaining on L1. This is logical since verification itself is part of computation. Given that execution modularization has already moved computation off-chain, then only storage and consensus should remain on-chain. The specifics are illustrated in the following diagram (Fig.6): If verification remains on Layer 1 (L1), the specific consensus process involves each node executing, verifying proofs, and updating the state. However, if we extract the verification process and assign it to a separate network for consensus, the L1 consensus process would then only involve updates to storage. Under the latest modular design, existing L1 nodes will no longer undertake any computational tasks. They will only handle transactions related to the update of the global state and then execute the consensus process. There are two main reasons for this approach: As mentioned in previous sections, considering costs and security, the execution of verification can be significantly delayed. Therefore, extracting it and utilizing the Massive network for verification not only achieves rapid validation but also reduces the hardware requirements for verification nodes, making it easier for users to connect. The security of the separate Massive network is ensured by the amount of staked assets and the number of nodes. Compared to the original scheme, this might sacrifice a certain level of security (which depends on the value of the staked assets) but, in return, offers significant architectural advantages. The Ecosystem of Massive Leveraging the three core features of mobile phone mining, staking/re-staking, and Timely Settlement, Massive has the potential to become the largest network, providing safe, efficient, and low-cost services for a variety of ZK L2s/L3s. Additionally, due to its extremely low barrier to entry and profit effects, it will inject a significant number of real users into the industry. Massive's vision is to facilitate massive adoption within the industry. Mobile mining represents a highly exploratory practice. Through straightforward operations, lower thresholds, and continuous earnings, it aims to utilize the computational power of smartphones. According to statistics, the global sales of smartphones exceeded 1 billion units in 2023. Solana and Aptos have also successively launched their own Web3 phones. Through the Massive network, these devices will be leveraged. Reference Eigenlayer whitepaper: https://docs.eigenlayer.xyz/assets/files/EigenLayer_WhitePaper-88c47923ca0319870c611decd6e562ad.pdf Scroll explorer: https://scrollscan.com/batch/86321 Polygon explorer: https://zkevm.polygonscan.com/batches Zksync contract: https://etherscan.io/address/0xa0425d71cB1D6fb80E65a5361a04096E0672De03#readContract Starkware explorer: https://voyager.online/block/572773 Halo2: https://github.com/zcash/halo2 Nova: https://eprint.iacr.org/2021/370 Celestia: https://docs.celestia.org/learn/how-celestia-works/overview

Unveiling the Technical Vision Behind Massive: Decentralized, Cheap, Fair, and More

বিশালের পিছনে প্রযুক্তিগত দৃষ্টিভঙ্গি উন্মোচন করা: বিকেন্দ্রীভূত, সস্তা, ন্যায্য এবং আরও অনেক কিছু

Enthüllung der technischen Vision hinter Massive: Dezentralisiert, günstig, fair und mehr

Revelando la visión técnica detrás de Massive: descentralizado, barato, justo y más

Dévoiler la vision technique derrière Massive : décentralisé, bon marché, équitable et plus encore

बड़े पैमाने पर तकनीकी दृष्टि का अनावरण: विकेंद्रीकृत, सस्ता, निष्पक्ष और बहुत कुछ

Massive の背後にある技術的ビジョンを明らかにする: 分散化、安価、公正など

Massive의 기술적 비전 공개: 분산화, 저렴함, 공정함 등

Revelando a visão técnica por trás do Massive: descentralizado, barato, justo e muito mais

Раскрытие технического видения Massive: децентрализованное, дешевое, справедливое и многое другое

Massive'ın Arkasındaki Teknik Vizyonu Ortaya Çıkarıyoruz: Merkezi Olmayan, Ucuz, Adil ve Daha Fazlası

Tiết lộ tầm nhìn kỹ thuật đằng sau Massive: Phi tập trung, giá rẻ, công bằng và hơn thế nữa

揭示 Massive 背后的技术愿景：去中心化、廉价、公平等等

Migrating from one OLAP database to another is huge. Even if you're unhappy with your current and have found some promising candidates, you might still hesitate to do the big surgery on your data architecture because you're uncertain about how things are going to work. So you need experience shared by someone who has walked the path. data tool Luckily, a user of has written down their migration process from ClickHouse to Doris, including why they need the change, what needs to be taken care of, and how they compare the performance of the two databases in their environment. Apache Doris To decide whether you want to continue reading, check if you tick one of the following boxes: You need your join queries to be executed faster. You need flexible data updates. You need real-time data analysis. You need to minimize your components. If you do, this post might be of some help to you. Replacing Kylin, ClickHouse, and Druid with Apache Doris The user undergoing this change is an e-commerce SaaS provider. Its data system serves real-time and offline reporting, customer segmentation, and log analysis. Initially, they used different OLAP engines for these various purposes: : The system provides offline reporting services for over 5 million sellers. The big ones among them have more than 10 million registered members and 100,000 SKUs, and the detailed information is put into over 400 data cubes on the platform. Apache Kylin for offline reporting : This entails high-frequency updates, high QPS, and complicated SQL. ClickHouse for customer segmentation and Top-N log queries : Sellers extract data they need by combining different dimensions, and such real-time reporting requires quick data updates, quick query response, and strong stability of the system. Apache Druid for real-time reporting The three components have their own sore spots. runs well with a fixed table schema, but every time you want to add a dimension, you need to create a new data cube and refill the historical data in it. Apache Kylin is not designed for multi-table processing, so you might need an extra solution for federated queries and multi-table join queries. And in this case, it was below expectation in high-concurrency scenarios. ClickHouse implements idempotent writing, so it does not support data updating or deletion itself. That means when there is something wrong at the upstream, you will need a full data replacement. And such data fixing is a multi-step process if you think it all the way through because of all the data backups and movements. Plus, newly ingested data will not be accessible for queries until it is put in segments in Druid. That means a longer window such that data inconsistency between upstream and downstream. Apache Druid As they work together, this architecture might be too demanding to navigate because it requires knowledge of all these components in terms of development, monitoring, and maintenance. Also, every time the user scales a cluster, they must stop the current cluster and migrate all databases and tables, which is not only a big undertaking but also a huge interruption to business. fills these gaps. Apache Doris : Doris is good at high-concurrency queries and joins queries, and it is now equipped with an inverted index to speed up searches in logs. Query performance : The Unique Key model of Doris supports both large-volume update and high-frequency real-time writing, and the Duplicate Key model and Unique Key model supports partial column update. It also provides exactly-once guarantee in data writing and ensures consistency between base tables, materialized views, and replicas. Data update : Doris is MySQL-compatible. It supports easy scaling and light schema change. It comes with its own integration tools, such as Flink-Doris-Connector and Spark-Doris-Connector. Maintenance So they plan on the migration. The Replacement Surgery ClickHouse was the main performance bottleneck in the old data architecture, and why the user wanted the change in the first place, so they started with the ClickHouse. Changes in SQL statements Table creation statements The user-built their own SQL rewriting tool that can convert a ClickHouse table creation statement into a Doris table creation statement. The tool can automate the following changes: : It converts ClickHouse field types into the corresponding ones in Doris. For example, it converts String as a Key into Varchar and String as a partitioning field into Date V2. Mapping the field types : Some tables have historical partitions, and the number of partitions should be specified upon table creation in Doris. Otherwise, a "No Partition" error will be thrown. Setting the number of historical partitions in dynamic partitioning tables : It decides the number of buckets based on the data volume of historical partitions; for non-partitioned tables, it decides the bucketing configurations based on the historical data volume. Determining the number of buckets : It decides the time to live of partitions in dynamic partitioning tables. Determining TTL : For the Unique Key model of Doris, it can specify the data import order based on the Sequence column to ensure orderliness in data ingestion. Setting the import sequence Query statements Similarly, they have their own tool to transform the ClickHouse query statements into Doris query statements. This is to prepare for the comparison test between ClickHouse and Doris. The key considerations in the conversions include: : This is simple, given the mapping rules in table creation statements. Conversion of table names : For example, the function in ClickHouse is equivalent to , is equivalent to and , and and should be converted to window functions. Conversion of functions COUNTIF SUM(CASE WHEN_THEN 1 ELSE 0) Array Join Explode Lateral View ORDER BY GROUP BY : ClickHouse goes by its own protocol, while Doris is MySQL-compatible, so there needs to be an alias for subqueries. In this use case, subqueries are common in customer segmentation, so they use Difference in semantics sqlparse Changes in data ingestion methods Apache Doris provides broad options for data writing methods. For the real-time link, the user adopts Stream Load to ingest data from NSQ and Kafka. For the sizable offline data, the user tested different methods, and here are the takeouts: Insert Into Using Multi-Catalog to read external data sources and ingesting with Insert Into can serve most needs in this use case. Stream Load The Spark-Doris-Connector is a more general method. It can handle large data volumes and ensure writing stability. The key is to find the right writing pace and parallelism. The Spark-Doris-Connector also supports Bitmap. It allows you to move the computation workload of Bitmap data in Spark clusters. Both the Spark-Doris-Connector and the Flink-Doris-Connector rely on Stream Load. CSV is the recommended format choice. Tests on the user's billions of rows showed that CSV was 40% faster than JSON. Spark Load The Spark Load method utilizes Spark resources for data shuffling and ranking. The computation results are put in HDFS, and then Doris reads the files from HDFS directly (via Broker Load). This approach is ideal for huge data ingestion. The more data there is, the faster and more resource-efficient the ingestion is. Pressure Test The user compared the performance of the two components on their SQL and join query scenarios, and calculated the of Apache Doris. CPU and memory consumption SQL query performance Apache Doris outperformed ClickHouse in 10 of the 16 SQL queries, and the biggest performance gap was a ratio of almost 30. Overall, Apache Doris was 2~3 times faster than ClickHouse. Join query performance For join query tests, the user used different sizes of main tables and dimension tables. : user activity table (4 billion rows), user attribute table (25 billion rows), and user attribute table (96 billion rows) Primary tables : 1 million rows, 10 million rows, 50 million rows, 100 million rows, 500 million rows, 1 billion rows, and 2.5 billion rows. Dimension tables The tests include and . Full join queries join all rows of the primary table and dimension tables while filtering join queries retrieve data of a certain seller ID with a filter. The results are concluded as follows: full join queries, filtering join queries WHERE Primary table (4 billion rows): Full join queries: Doris outperforms ClickHouse in full join queries with all dimension tables. The performance gap widens as the dimension tables get larger. The largest difference is a ratio of 5. Filtering join queries: Based on the seller ID, the filter screened out 41 million rows from the primary table. With small dimension tables, Doris was 2~3 times faster than ClickHouse; with large dimension tables, Doris was over ten times faster; with dimension tables larger than 100 million rows, ClickHouse threw an OOM error, and Doris functions normally. Primary table (25 billion rows): Full join queries: Doris outperforms ClickHouse in full join queries with all dimension tables. ClickHouse produced an OOM error with dimension tables larger than 50 million rows. Filtering join queries: The filter screened out 570 million rows from the primary table. Doris responded within seconds, and ClickHouse finished within minutes and broke down when joining large dimension tables. Primary table (96 billion rows): Doris delivered relatively quick performance in all queries, and ClickHouse was unable to execute all of them. In terms of CPU and memory consumption, Apache Doris maintained stable cluster loads in all sizes of join queries. Future Directions As the migration goes on, the user works closely with the , and their feedback has contributed to the making of . We will continue assisting them in their migration from Kylin and Druid to Doris, and we look forward to seeing their Doris-based unified data platform come into being. Doris community Apache Doris 2.0.0 Also published . here

Migrating from ClickHouse to Apache Doris: Boosting OLAP Performance

Six months ago, I wrote about for our data management system. Back then, we were struggling with the auto-generation of SQL statements. As days pass, we have made progresses big enough to be references for you, so here I am again. why we replaced ClickHouse with Apache Doris as an OLAP engine We have adopted Large Language Models (LLM) to empower our Doris-based OLAP services. LLM + OLAP Our incentive was to save our internal staff from the steep learning curve of SQL writing. Thus, we used LLM as an intermediate. It transforms natural language questions into SQL statements and sends the SQLs to the OLAP engine for execution. Like every AI-related experience, we came across some friction: LLM does not understand data jargons, like "fields", "rows", "columns" and "tables". Instead, they can perfectly translate business terms like "corporate income" and "DAU", which are basically what the fields/rows/columns are about. That means it can work well only if the analysts use the exact right word to refer to the metric they need when typing their questions. The LLM we are using is slow in inference. It takes over 10 seconds to respond. As it charges fees by token, cost-effectiveness becomes a problem. Although the LLM is trained on a large collection of public datasets, it is under-informed of niche knowledge. In our case, the LLM is super unfamiliar with indie songs, so even if the songs are included in our database, the LLM will not able to identify them properly. Sometimes our input questions require adequate and latest legal, political, financial, and regulatory information, which is hard to be included in a training dataset or knowledge base. We need to connect the LLM to wider info bases in order to perform more diversified tasks. We knock these problems down one by one. 1. A semantic layer For problem No.1, we introduce a semantic layer between the LLM and the OLAP engine. This layer translates business terms into the corresponding data fields. It can identify data filtering conditions from the various natural language wordings, relate them to the metrics involved, and then generate SQL statements. Besides that, the semantic layer can optimize the computation logic. When analysts input a question that involves a complicated query, let's say, a multi-table join, the semantic layer can split that into multiple single-table queries to reduce semantic distortion. 2. LLM parsing rules To increase cost-effectiveness in using LLM, we evaluate the computation complexity of all scenarios, such as metric computation, detailed record retrieval, and user segmentation. Then, we create rules and dedicate the LLM-parsing step to only complicated tasks. That means for the simple computation tasks, it will skip the parsing. For example, when an analyst inputs "tell me the earnings of the major musical platforms", the LLM identifies that this question only entails several metrics or dimensions, so it will not further parse it but send it straight for SQL generation and execution. This can largely shorten query response time and reduce API expenses. 3. Schema Mapper and external knowledge base To empower the LLM with niche knowledge, we added a Schema Mapper upstream from the LLM. The Schema Mapper maps the input question to an external knowledge base, and then the LLM will do parsing. We are constantly testing and optimizing the Schema Mapper. We categorize and rate content in the external knowledge base, and do various levels of mapping (full-text mapping and fuzzy mapping) to enable better semantic parsing. 4. Plugins We used plugins to connect the LLM to more fields of information, and we have different integration methods for different types of plugins: : This is especially useful when we need to "teach" the LLM the latest regulatory policies, which are often text files. Firstly, the system vectorizes the local text file, executes semantic searches to find matching or similar terms in the local file, extracts the relevant contents and puts them into the LLM parsing window to generate output. Embedding local files : The marketplace is full of third-party plugins that are designed for all kinds of sectors. With them, the LLM is able to deal with wide-ranging topics. Each plugin has its own prompts and calling function. Once the input question hits a prompt, the relevant plugin will be called. Third-party plugins After we are done with above four optimizations, the SuperSonic framework comes into being. The SuperSonic framework Now let me walk you through this : framework An analyst inputs a question. The Schema Mapper maps the question to an external knowledge base. If there are matching fields in the external knowledge base, the question will not be parsed by the LLM. Instead, a metric computation formula will trigger the OLAP engine to start querying. If there is no matching field, the question will enter the LLM. Based on the pre-defined rules, the LLM rates the complexity level of the question. If it is a simple query, it will go directly to the OLAP engine; if it is a complicated query, it will be semantically parsed and converted to a DSL statement. At the Semantic Layer, the DSL statement will be split based on its query scenario. For example, if it is a multi-table join query, this layer will generate multiple single-table query SQL statements. If the question involves external knowledge, the LLM will call a third-party plugin. Example: To answer whether a certain song can be performed on variety shows, the system retrieves the OLAP data warehouse for details about the song, and presents it with results from the Commercial Use Query third-party plugin. OLAP Architecture As for the OLAP part of this framework, after several rounds of architectural evolution, this is what our current OLAP pipeline looks like. Raw data is sorted into tags and metrics, which are custom-defined by the analysts. The tags and metrics are under unified management in order to avoid inconsistent definitions. Then, they are combined into various tagsets and metricsets for various queries. We have drawn two main takeaways for you from our architectural optimization experience. 1. Streamline the links Before we adopted Apache Doris, we used to have ClickHouse to accelerate the computation of tags and metrics, and Elasticsearch to process dimensional data. That's two analytic engines and requires us to adapt the query statements to both of them. It was high-maintenance. Thus, we replaced ClickHouse with Apache Doris, and utilized the functionality to connect Elasticsearch data to Doris. In this way, we make Doris our unified query gateway. Elasticsearch Catalog 2. Split the flat tables In early versions of our OLAP architecture, we used to put data into flat tables, which made things tricky. For one thing, flat tables absorbed all the writing latency from upstreams, and that added up to considerable loss in data realtimeliness. For another, 50% of data in a flat table was dimensional data, which was rarely updated. With every new flat table came some bulky dimensional data that consumed lots of storage space. Therefore, we split the flat tables into metric tables and dimension tables. As they are updated in different paces, we put them into different data models. : We arrange metric data in the Aggregate Key model of Apache Doris, which means new data will be merged with the old data by way of SUM, MAX, MIN, etc. Metric tables : These tables are in the Unique Key model of Apache Doris, which means new data record will replace the old. This can greatly increase performance in our query scenarios. Dimension tables You might ask, does this cause trouble in queries, since most queries require data from both types of tables? Don't worry, we address that with the Rollup feature of Doris. On the basis of the base tables, we can select the dimensions we need to create Rollup views, which will automatically execute . This relieves us of the need to define tags for each Rollup view and largely speed up queries. GROUP BY Other Tricks In our experience with Apache Doris, we also find some other functionalities handy, so I list them here for you, too: 1. Materialized View A Materialized View is a pre-computed dataset. It is a way to accelerate queries when you frequently need to access data of certain dimensions. In these scenarios, we define derived tags and metrics based on the original ones. For example, we create a derived metric by combining Metric 1, Metric 2, and Metric 3: . Then, we can create a Materialized View for it. According to the Doris release schedule, version 2.1 will support multi-table Materialized Views, and we look forward to that. sum(m1+m2+m3) 2. Flink-Doris-Connector This is for Exactly-Once guarantee in data ingestion. The Flink-Doris-Connector implements a checkpoint mechanism and two-stage commit, and allows for auto data synchronization from relational databases to Doris. 3. Compaction When the number of aggregation tasks or data volume becomes overwhelming for Flink, there might be huge latency in data compaction. We solve that with Vertical Compaction and Segment Compaction. Vertical Compaction supports loading of only part of the columns, so it can reduce storage consumption when compacting flat tables. Segment Compaction can avoid generating too much segments during data writing, and allows for compaction while writing simultaneously. What's Next With an aim to reduce costs and increase service availability, we plan to test the newly released Storage-Compute Separation and Cross-Cluster Replication of Doris, and we embrace any ideas and inputs about the SuperSonic framework and the Apache Doris project.

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