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Kleer

kleer

Calling all Hackers!! We're thrilled to announce Klever, a game-changer in the world of crypto finance and blockchain solutions, and we were nominated for HackerNoon's prestigious Startup of the Year awards! Our headquarters are based in the vibrant city of São Paulo, Brazil. Vote for us now and show your support: Every vote counts! Click here! Are you also participating in SOTY 2023? If so, click here to fill out this interview. Let us introduce you to Klever, your ultimate gateway into the world of cryptocurrencies: Simplifying Crypto Management for All Are you a crypto newbie? Don't worry; we've got your back. At Klever, we believe that crypto should be accessible to everyone. That's why we've built a revolutionary platform with a user-friendly interface that makes managing and navigating the crypto world a breeze. With built-in features like our intuitive Swap tool, Web3 Browser, and round-the-clock support team, we've created an all-in-one crypto wallet app and ecosystem that takes the complexity out of crypto finance. Unleashing Creativity with Kleverchain But we didn't stop there. We've disrupted the industry with our very own blockchain, . It's a cutting-edge, low-code blockchain solution designed to empower entrepreneurs and developers in the crypto space. With Kleverchain, the path from idea to execution is drastically shortened. We offer pre-built dapps called Kapps, so you can transform your vision into reality faster than ever before. Kleverchain Standing Tall Among the Crowd What sets Klever apart from the sea of startups in the crypto finance space? It's our unwavering commitment to our users and the relentless pursuit of excellence. With over 3 million Klever Wallet downloads, we've built a thriving community that believes in our mission. Our blockchain ecosystem has already witnessed the success of numerous projects, thanks to the tireless efforts of our visionary team. And with a network available in over 30 languages, we provide unparalleled assistance to our users worldwide. global support 2023 and Beyond: Unlocking the Future of Crypto Finance While challenges and bearish sentiments persist, we remain optimistic. Regulatory landscapes are evolving, adoption rates are soaring, and market dynamics are shifting. At Klever, we're at the forefront of these transformations, anticipating every turn and paving the way for a brighter tomorrow. Why We Embarked on HackerNoon's Startup of the Year Awards Participating in is more than a chance to celebrate our achievements — it's an opportunity to inspire and be inspired. It's about standing shoulder-to-shoulder with the best, gaining recognition, and forging new connections. We believe in the power of collaboration and sharing our vision with the world. HackerNoon's Startup of the Year awards It's Time to Vote for Klever Now, it's your turn to make a difference. Join us in shaping the future of crypto finance by casting your vote for Klever in HackerNoon's Startup of the Year awards. Explore our innovative blockchain at and discover the simplicity of our wallet at . Stay updated with the latest trends and insights by following us on Twitter at klever.org klever.io klever_io. Klever is nominated as Startup of the Year in São Paulo, Brazil. Vote for us today!

Simplifying Crypto Management for All - Interview with Startups of the Year Nominee, Klever 

What is Happening? released its blockchain with prebuilt and ready-to-use smart contract functionalities for developers to build web3 applications. The KleverChain is secured through the Proof-of-Stake consensus mechanism composed of 21 validators that work on a randomised selection of master nodes dynamically changed every Epoch. Klever mainnet A user must first submit an application to be considered a contender for the position of a master node on KleverChain. The cost of 100,000 KLV is charged and destroyed during the application procedure for electing a masternode. Candidates for Klever Finance Blockchain master node must have 1,500,000 KLV in self-staking before being deemed active by KleverChain. Through self-staking and delegated-staking, master nodes must meet the barrier of 10,000,000 KLV before they can create blocks in the consensus. Masternode owners may indicate whether or not delegated staking is permitted and the maximum number of nodes that can be assigned to each entity. : Vested Interest Disclosure The author is an independent contributor publishing via our . 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 story for quality, but the claims hereon belong to the author. #DYOR brand-as-author program What is Klever Finance? Klever Finance is developing blockchain solutions and goods for millions of consumers across the globe. Klever has created products and services on top of the world's leading blockchain networks over the years and presently hosts 100+ blockchain nodes on top of 20+ key blockchain protocols to guarantee maximum uptime for our worldwide consumers' every crypto demand. With over 160 developers globally, Klever provides extensive knowledge of blockchain technology, cryptography, and immutability for supporting high performance, speed and security for web3 applications. crypto Understanding the Impact of Masternodes on the Consensus Mechanism of a Blockchain Masternodes are very beneficial for a blockchain startup building a decentralized blockchain network. Web3 applications working on top of a blockchain network use the master node as a full node to validate transactions. Due to the proof-of-stake consensus mechanism transactions are completed more efficiently and scalable on a blockchain network as compared to proof-of-work (PoW). The transaction is verified by each and every node on a blockchain network. If one node refuses to confirm the transaction, the specific transaction is orphaned by the blockchain network. It is critical to note that the master node network allows for scalable transactions if the number of master nodes is centralized. However, this leads to a high centralisation risk as it the risk of a single point of failure. Nevertheless, this can be tackled with new consensus algorithms that solve the 51% attack on PoW and PoS blockchains. Further, it is critical to think about the consensus algorithm to govern the master nodes as per the principles of decentralized governance. Klever uses Practical Byzantine Fault Tolerance (PBFT) as a method to evaluate and grant bonuses to master nodes as part of its decentralized governance framework. increases Why Proof-of-Stake is the Future for Decentralized Governance As per Cong. T Nguyen, et al, Internet-of-Vehicles networks are increasingly important as Internet-of-Things (IoT) and networking technologies develop at breakneck speed. This has forced the automotive industry to develop smart vehicles that are capable of sensing and communicating, necessitating an IoV blockchain-based platform. becoming Vehicles and RSUs in an IoV network might share information via a blockchain-based approach which operates on a decentralized network. To tackle the governance approach, it is beneficial to use a form of Proof of Stake called Delegated Proof of Stake (DPoS), where the stake is RSUs' reputation in the blockchain network. Under this model, it is necessary for an RSU to make a deposit, which will be forfeited if the RSU engages in a malevolent activity, and for the RSU's reputation rating to exceed a certain level. Block proposers are picked among the applicants through the round-robin selection procedure to propose blocks that include data sharing records and reputation ratings for each round. The new block is subsequently added to the chain by a vote of the other candidates. This is a novel way of governing network members in a decentralized governance principles-based manner. PoS and DPOS are the future of the consensus mechanisms when it comes to developing applications for the web3 and the IoT industry. Final Thoughts on Klever Blockchain Mainnet Release The Klever mainnet blockchain release is a sign that the web3 ecosystem is going ahead with full force! Building the infrastructure for developing web3 applications for different economic sectors will create the next foundations of the technological revolution. In my opinion, the majority of blockchain networks such as KleverChain and Ethereum using a PoS consensus mechanism instead of PoW indicates a trend toward less energy-consuming blockchains which are secure and scalable in nature. Don’t forget to share the story! Image credits: , Pontus Wellgraf , and Pierre Borthiry. Dmitry Demidko

Klever Releases Blockchain Mainnet to Support Building Scalable Web3 Applications 

HackerNoon Reporter: Please tell us briefly about your background. Starting out in the first cohort of data folks at Just Eat, my co-founder and I built their data org through to IPO before I moved onto doing the same role at Not On The High Street and most recently Zego, through to their unicorn status. What's your startup called? And in a sentence or two, what does it do? is the ‘third wave’ of data stack tooling. End-to-end data processing automation, without any engineering requirement. Kleene provides the fastest speed to insight for data analysts and business users, whilst establishing a robust, full-scalable infrastructure. kleene.ai What is the origin story? With Matt and I’ve been at the coalface of implementing data stacks across different businesses, we’ve seen the painful reality of building technical teams and still not delivering value to business users fast enough. We got our heads together to map what ‘great’ looks like and how future stacks could work FOR the business. kleene’s third wave approach was our output and it’s resonating excellently with those who are seeking a better future for their data. What do you love about your team, and why are you the ones to solve this problem? Of course, our fundamental experience in the space has set a strong platform for delivering a better way, but what’s most exciting is how our team of hungry, high-caliber colleagues has bought into the vision and continues to take it to new levels. We’re a growth mindset first hiring company, so we’re looking for everyone to come in, contribute and move our vision forward. If you weren’t building your startup, what would you be doing? More than likely repeating the same problems and challenges of my previous roles in a new Chief Data Officer position. Hopefully, I’ll never have to build a data stack from scratch again thanks to kleene! At the moment, how do you measure success? What are your core metrics? We’re all about building repeatable engines in every department at this point, so I’ll give you headline metrics: Revenue - New ARR CS - Net Revenue Retention What’s most exciting about your traction to date? Honestly, it’s repeatability. We reached a crux point and it’s consistently scaling from there Q on Q, which is ideally what we were looking to achieve from the outset. Equally, we’re very buoyant about the types of customers we’re bringing on board, as they’re the most exciting growth companies in their respective spaces! What technologies are you currently most excited about, and most worried about? And why? This might seem cliche from someone in data, but AI is the thing I’m believing most strongly in. We’ve seen some amazing projects like the ‘Deep Mind Folding Project’ really start to deliver meaningful value to the world. Long may it continue! What drew you to get published on HackerNoon? What do you like most about our platform? Having been a HackerNoon admirer for a while, it was brilliant for the team to be nominated as part of your ‘Startups of the Year’ Award and we wanted to ensure we’re feeding back into the community. We’re also always open to chatting with the community about data! What advice would you give to the 21-year-old version of yourself? You hear this a lot from founders - ‘starting my own thing earlier’. Until you’ve done it, you don’t realize how much can be learned so quickly! What is something surprising you've learned this year that your contemporaries would benefit from knowing? We’ve seen some incredible resilience and ambition from our clients and team throughout the challenging Covid period, so it’s that no matter what life throws at us, you can still build a great business by focussing on execution. kleene.ai is nominated as in HackerNoon’s Startups of the Year. one of the best startups in London

'At the Coalface of Implementing Data Stacks': kleene's Co-founder & CEO Andrew Thomas  

Acknowledgments: Special thanks to , , and for their feedback and contributions. Rein Wu Dylan Zhang Bill Shi tl’dr People have been predicting prediction markets to take off sooner or later, and ongoing UX improvements have primed this segment for takeoff But to scale to billions of users, we need “something new” beyond ongoing UX improvements, and that is AIs as the cog and the linchpin in the machine An AI quartet of content creators, event recommenders, liquidity allocators, and information aggregators can catalyze massive new activity in this space Integrating these AIs into the current prediction market framework can enable prediction markets at a microscopic scale, making them personally attractive and relevant The prediction market primitive paves the way for Tinder-like prediction market apps, embedding predictive trading experiences into our everyday digital existence Every decision starts with a prediction. Consider pondering over Bitcoin’s potential: “Will purchasing Bitcoin now yield a doubled investment by year’s end? If the “yes” prospect is judged even marginally more likely than “no,” it would be economically rational to decide to buy Bitcoin in the absence of superior alternatives. But why stop at Bitcoin? Imagine we could architect markets rooted in predictions about all kinds of events, such as who will be the next US president or which country will win the World Cup. Here, not assets but forecasts themselves are traded. Predictions shape markets; markets validate our predictions Prediction markets have been called the “holy grail of epistemic technology” by Vitalik. Vitalik has a knack for seeing big things before others. So he’s a good source for frontrunning narratives. He proposed the idea of an AMM on Ethereum seven years ago in a . “Another guy” named Hayden Adams took the call to action and started building it, on a $60K grant. Two years later, Uniswap was .' blog post born If Vitalik’s blog posts can initiate the creation of dollar industries, we should probably pay attention to them. For example, it happens that Vitalik was excited about using prediction markets in governance – a radical form of governance known as “futarchy” – and now we have doing just that, with big VC firms like Pantera . $100+ billion back in 2014 Meta DAO taking part in it But it’s his more around prediction markets + AI that we want to focus on, as we are starting to see the beginnings of something big here. recent discussions Prediction markets are primed to take flight The market leading prediction market right now is Polymarket, owing to its ongoing UX improvements and expansion of event categories and event offerings. The monthly volume recently hit all-time highs and is likely to go higher with the US presidential election in November of this year (Polymarket activity is US-centric). There is further precedent to believe that prediction markets could take off this year. Besides crypto markets reaching all-time highs in 2024, we also have one of the biggest election years in history this year. Eight of the world’s ten most populous nations, including the US, India, Russia, Mexico, Brazil, Bangladesh, Indonesia, and Pakistan, are also going to the polls. We also have the 2024 Summer Olympics upcoming in Paris. But given monthly volumes are still in the tens of millions when it could reach hundreds of millions, let’s consider some of the limitations of current prediction markets: Centralized control over event creation A lack of incentives for community content creators Insufficient personalization Predominantly US-centric, overlooking substantial international opportunities But we need “something new” We believe that thing is AI. We need AIs as players in the game. We expect that soon, it will be common to see AIs (bots) participating alongside human agents in prediction markets. We can already see live demos of this in and , among many others who will likely enter this scene. More on this later. Omen PredX AIs need AIs as arbiters of the game. Although relatively rare, there can be instances where dispute resolution is important and necessary in a prediction market. For example, in a presidential election, the results may be very close, and allegations of voting irregularities may surface. So, while the prediction market may close, favoring Candidate A, the official electoral commission may declare Candidate B as the winner. Those betting on Candidate A will argue against the outcome due to alleged voting irregularities while those betting on Candidate B will argue that the electoral commission decision reflects the “true” outcome. A lot of money may be on the line. Who’s right? Answering this question poses several challenges: Players may not trust human arbiters due to their biases Human arbitration can be slow and expensive DAO-based prediction resolutions are vulnerable to Sybil attacks To address this, prediction markets can use multi-round dispute systems a la and only humans are involved in rare cases where disputes reach a deadlock. Players can trust AIs to be impartial, as fabricating enough training data to bias them is unfeasible. Also, AI arbiters work faster and at a much lower cost. is building in this direction. Kleros, except using AIs instead of humans to solve disputes at earlier rounds, xMarkets AIs create desire For prediction markets to really take off, they need to be able to engage sufficient interest to push people past the psychological threshold of actually trading prediction assets. It may not take much to do this for general topics a lot of people care about, like who will win a presidential election or the Superbowl. However, including only general topics severely limits potential liquidity. Ideally, a prediction market could tap into the liquidity of specific events of high interest to niche audiences. This is how targeted advertising works, and we all know targeted advertising works. To achieve this, prediction markets need to solve four general challenges: Highly relevant event supply is key. To grab the attention of a niche yet dedicated audience, event creators must deeply understand their community’s interests to drive participation and volumes. Event Supply: Demand needs to be high within the particular targeted community, taking into account their demographic and psychographic idiosyncrasies. Event Demand: There’s enough opinion diversity and dynamics within the targeted community to drive sufficient liquidity to retain both parties and minimize slippage. Event Liquidity: Players should have easy access to enough information to make them confident to make a bet. This could include a background analysis, relevant historical data, and expert opinions. Information Aggregation: Now, let’s see how AI could address each of these challenges: Content creator AIs (“copilots”) assist in the creation of content beyond human capacities or motivation. AIs suggest timely and relevant event topics by analyzing trends from news, social media, and financial data. Content creators – whether human or AI – will be rewarded for generating engaging content that keeps their communities lively. Community feedback enhances the AI’s understanding of its communities, making it an iteratively improving content creation engine to bond content creators and their audiences. Content Creator AIs: Event recommender AIs tailor event suggestions to users based on their interests, trading history, and specific needs, focusing on recommending events ripe for debate and trading opportunities. It adapts to users’ behaviors across different regions, cultural contexts, and times. The end goal is a highly targeted feed of events, free from personally irrelevant content that clutters prediction market platforms today. Event Recommendation AIs: Liquidity allocator AIs tackle counterparty liquidity risk by optimizing liquidity injections to narrow the bid-ask spread. To minimize risk, AIs can implement the (LMSR) AMM model specifically designed for minimizing risk in low-liquidity prediction markets. They could also incorporate that dynamically adjust liquidity depth, protocol fees, and the bonding curve to further minimize risk. These AIs manage event liquidity from a general LP pool, rewarding contributions with accrued fee revenue or platform tokens as further incentives. All in all, this means preemptive adaptation to market changes, reduced slippage, and better price stability. Liquidity Allocator AIs: logarithmic market scoring rule reinforcement learning agents These AIs harness compute over a wide array of indicators (e.g., on-chain data, historical data, news, sentiment indicators) for players to comprehensively understand the event. From there, the information aggregation AIs can offer well-rounded projections, turning prediction markets into the go-to source for informed decision-making and alpha. Projects can choose to token-gate access to the insights gleaned by information aggregation AIs because in prediction markets, . Information Aggregation AIs: knowledge = money Now, let’s see what this looks like when you piece it together. Below, you can see the main components and workings of a prediction market without AIs (in black) and with AIs (in blue). In the non-AI model, content creators (usually the platform itself) arbitrarily create events, supply liquidity (initially subsidized by their treasuries), save the events to an event database and promote them in bulk to human players. This is how Polymarket currently works, and it's working quite well. But I think it can get a lot better. In the AI model, content creator copilot AIs support content creators in creating and promoting events inside targeted general or niche communities. Liquidity provision is supported by liquidity allocator AIs that optimize liquidity injections over time through learning player order books and using external data from oracles and other data vendors. Event recommendation AIs use stored events in the event database and wallet transaction history to optimize event recommendations tailored based on personal interests. Finally, information aggregation AIs collect information from data vendors to provide educational and contextual information to human players and to inform AI players about their prediction decisions. The end game? A fine-tuned prediction market system that enables prediction markets to work at a microscopic scale. Prediction markets at this scale would enable a different user experience, one that is more like Tinder or TikTok. As the events are highly targeted, they could be fed to you in a feed a la TikTok, and – even with today’s wallet and blockchain technology – players could place bets by swiping left or right a la Tinder. Imagine that: people making micro-bets on the events they personally care about while they’re commuting to work or school. Supercharging information aggregation Of the most notorious difficult outcomes to predict is asset prices, so let’s focus here to see how AIs perform when pushing at the edges of what is possible in prediction markets. Using AI to predict asset prices is actively being explored in academic circles. Machine learning (ML) techniques like linear models, random forests, and support vector machines have been to predict cryptocurrency prices with better accuracy than human judges. These models have discovered that behavioral indicators like Google search intensity explain price variance. shown IBM research artificial prediction markets for commodity price prediction, offering a compelling case study on integrating AI with prediction markets. Their research highlights the potential of artificial prediction markets to aggregate diverse and evolving real-time information sources for making better predictions even in complex real-world problems like predicting the prices of volatile commodities not traded on online exchanges (e.g., ethylene, hydrocarbons). The reason AI agents can outperform standard ML models here is that they learn over time by themselves – aka agency. explored Another study comparing random forest regression and LSTM to predict Bitcoin’s next-day price that the former performed better in terms of fewer prediction errors. It also showcased the power of AI in information aggregation breadth – far beyond ordinary human capacity – to model 47 variables across eight categories, including (a) Bitcoin price variables, (b) technical indicators of Bitcoin, (c) other token prices, (d) commodities; (e) market indices: (f) foreign exchange; (g) public attention); and (h) dummy variables of the week. The most important predictors varied over time from US stock market indices, oil price, and Ethereum price in 2015-2018 to Ethereum price and a Japanese stock market index in 2018-2022. It also found that for Bitcoin’s next-day price, the random forest regression performs best with a one-day lag. showed We can infer that in some popular prediction markets, there is simply too little time for a busy human to aggregate, analyze, and interpret sufficiently large amounts of data to make good predictions. Or the problems are simply too complex. But AIs can do this. AI token recommendation is building a decentralized foundational model of crypto, which has been applied in AI-generated token recommendations derived from on-chain behaviors. Currently, their large graph neural network (GNN) uses on-chain behavioral data to estimate the alpha probabilities of various tokens. GNNs are a class of AI models designed specifically to process data represented as graphs, making them useful where data is interconnected with a relational structure, such as the p2p transactional networks of blockchains. Pond Dither is another token recommendation AI with a token-gated Telegram alert bot that takes a time-series modeling approach to the token recommendation. Solving the thin markets problem One of the main challenges facing prediction markets is that the markets are too thin to attract enough players and volume. But there is a major difference between the prediction markets of the 2010s vs the 2020s, and that is the . As Vitalik points out: possibility of ubiquitous participation by AIs To add, it’s possible to the automated market maker (AMM) models underlying prediction markets. For example, an of over 2 million transactions on Polymarket identified problems with liquidity provisioning in converging prediction markets using the traditional constant product AMM (x*y=k), including: improve analysis As prediction markets converge (i.e., as the outcome becomes more certain), LPs are incentivized to remove their liquidity. because the risk of holding “losing” tokens increases. For example, in a market converging toward “yes,” the “no” tokens become less valuable (i.e., impermanent loss), posing a risk to LPs who might end up with worthless tokens if they don’t sell in advance. Convergence and liquidity removal. This is rational behavior This reduction in liquidity can lead to less accuracy and more bias as prediction markets converge. Specifically, in the volume-weighted price range of 0.2 to 0.8, ‘no’ tokens are often underpriced, and ‘yes’ tokens are often overpriced. Bias and inaccuracy. To address these issues, the authors propose a “ liquid market maker” (SLMM) model and demonstrate that it can increase volumes and accuracy in converging prediction markets. It does this by introducing a concentration function into the model (a la Uniswap v3) in which LPs provide a liquidity position that is only active for specific price intervals. The result is reduced risk exposure, ensuring that the number of valuable tokens (e.g., ‘yes’ tokens in the market converging to ‘yes’ outcome) held by LPs does not converge to zero as prices adjust, unlike in the constant product AMM. smooth LP-trader tradeoffs There is a balancing act that must be reached when choosing a concentrated liquidity AMM variant like the SLMM for converging prediction markets. While you’re trying to reduce risk for LPs, you end up disincentivizing some trading activity. Specifically, while concentrated liquidity can make it less likely that LPs lose out as the market converges on a sure outcome (thus reducing premature withdrawal), it may also reduce trading opportunities to profit on small price changes (e.g., like moving from $0.70 to $0.75) due to increased slippage, especially for large orders. The direct consequence is that traders’ potential profit margins are squeezed. For instance, if they expect a small price move from $0.70 to $0.75, the slippage may limit the capital they can effectively deplore to capture the expected upside. Looking forward, it will be important to trial various adjustments on the tradeoff term in these market-maker formulas to find the sweet spot. Conclusion The prediction market primitive is a powerful one. Of course, like any other crypto primitive, it faces challenges, but we are confident that they will be overcome. As they are gradually overcome, we can expect to see this primitive reused to answer all sorts of questions in a wide variety of digital contexts. With advancements in targeting and liquidity solutions, we can expect the development of niche prediction markets. For example, take X (formerly Twitter) users: Will X introduce a Premium++ or equivalent by the end of the year? Will X make the edit tweet feature available to all users by Q3? Will X report an increase in daily active users in the next quarterly report? Will X’s advertising revenue increase or decrease in the next quarter? Will X announce new major partnerships with content creators by the end of year? Will X release a blockchain or cryptocurrency-related feature by Q3? Interestingly, these questions don’t need to stay confined to standalone prediction market websites. They could be integrated directly into X or other platforms via browser extensions.  We may start to see micro-prediction markets pop up regularly in our everyday online experiences, enriching ordinary browsing with speculative trading opportunities. I intentionally wrote some of the questions above and asked ChatGPT to write the others. Which did I write, and which did the content creator AI write? If it’s hard to tell, that’s because ChatGPT’s content creator AI is already really good. So are the information aggregation AIs and recommendation engines built by other Big Tech (look at the ads Google and Instagram feed you). While matching the performance of these models will take work and time, they demonstrate the feasibility of these AI categories. The main open question lacking precedent is more in the direction of liquidity allocator AIs, AI players, and the development of self-improvement and goal-directedness in AIs – the evolution from basic machine learning to verifiable AI agents. If you’re building in these spaces or this post resonates with you, do ! reach out Relevant Reading Buterin, V. (2024). . The promise and challenges of crypto + AI applications Rein Y. Wu, et al., Intent-centric Prediction Market with AI and Web3 Technology, Blockchain - Pioneering the Web3 Infrastructure for an Intelligent Future, 2024. Kapp-Schwoerer, L. (2023). . Improved Liquidity for Prediction Markets Author Affiliation Hiroki Kotabe is the Research Principal at Inception Capital (formerly OP Crypto). Inception Capital is a first-check venture fund focusing on early-stage web3 startups and emerging fund managers. Since raising $50M for Inception Venture Fund I in September 2021, they have been at the forefront of the industry, investing at the earliest stage in over 30 projects, such as Scroll, Merit Circle, Avalanche, and Celestia. Their $30M Inception Fund of Funds has invested in web3 funds like Orange DAO, Escape Velocity, Alliance, Syncrasy, and Everyrealm.

The Prediction Market Primitive: Using AIs to Create Prediction Markets at Microscopic Scale

In the presentation below, Clément Lesaege, co-founder of , presented the attack costs of different oracle networks and how to calculate the potential profit from corrupting them. Kleros https://youtu.be/bzqZnUC5VkI?si=T2azT5qDPdzunW4Y&embedable=true Below is a glossary of key concepts mentioned during Clement’s talk, intended as a supplement to his video presentation. About Kleros is a decentralized protocol built on the Ethereum blockchain that leverages crowdsourcing and game theory to arbitrate disputes quickly, and economically. The protocol’s core product is the Kleros court, where disputes are settled through a majority vote by randomly selected jurors incentivized to participate using , the native token of the platform. Kleros $PNK 51% Attacks A 51% attack or a majority attack occurs when more than half of the power to submit information is controlled by a single person or group of people. Essentially, this group has the ability to submit whatever information that is beneficial to them. Due to the decentralized nature of blockchains and Oracle protocols, 51% attacks will always be a risk. Because of this, protocols must utilize methods to make attacks economically unfeasible. Check out for a deep dive into all things related to oracle manipulation. this article Arbitration Protocols Arbitration protocols are decentralized systems that operate on predefined rules and smart contracts and are designed to address on-chain disputes. When disputes arise, both parties submit their case details and evidence to a panel of randomly selected jurors who evaluate the information and vote on the verdict. These jurors are rewarded, using the native token, when they arbitrate honestly or may have their staked tokens slashed in case of misconduct. Examples of arbitration protocols include and . are some popular examples of on-chain arbitration. Kleros Aragon Court Here Forking Forking refers to the process of creating a new version of a blockchain’s protocol, resulting in the splitting of the original blockchain into two separate chains. Forks occur when there are fundamental disagreements or updates within the community about the direction or rules of the blockchain. There are two main types of forks, namely . hard forks and soft forks Re-Staking In a network, participants lock up a certain amount of their cryptocurrency as collateral to become validators or participate in the consensus process. Re-staking involves taking the rewards earned from staking, such as transaction fees or newly minted tokens, and adding them back to the initial stake, effectively compounding the amount of staked tokens. PoS This allows participants to benefit from both the initial staked amount and the additional rewards generated. MEV Maximal Extractable Value (MEV) refers to the profits that miners or validators can potentially make by reordering and including transactions in a specific order within a block. On the blockchain, transaction ordering can affect the final outcome and pricing of transactions. Profits from MEV are gained by miners who engage in practices such as front-running, back-running, and sandwich attacks. This involves prioritizing transactions that are likely to profit, such as executing trades just ahead of large transactions or exploiting arbitrage opportunities between different trading platforms. A prominent example of MEV is the on the decentralized finance (DeFi) protocol . flash loan attack bZx Learn more about Kleros here: Kleros Website Kleros Documentation Kleros Twitter Clément Lesaege Twitter The Blockchain Oracle Summit is the world’s only technical summit that dives deep into the use cases, limitations, and impacts of oracles on the wider blockchain ecosystem. Leading speakers worldwide gathered in Paris to share their work and experience building and using Oracle solutions. Article by . Michael Abiodun

How to Calculate The Attack Costs Of Different Oracle Networks: The Key Concepts

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Cách tính toán chi phí tấn công của các mạng Oracle khác nhau: Các khái niệm chính

如何计算不同预言机网络的攻击成本：关键概念

https://youtu.be/V2ciagItiqI?si=bCJSIS9lJwJSk04A&embedable=true In the presentation below, Sasa Milic, an independent researcher, presented on oracle hacks that have occurred in the past two years and the various conclusions that can be drawn from them. Below is a glossary of key concepts mentioned during Sasa’s talk, intended as a supplement to her video presentation. WHAT IS A BLOCKCHAIN ORACLE? Native blockchain data (e.g., ownership and transfer of tokens) are validated by every node in the network. This validation process makes the network tamper-proof (except in the case of a ). However, a consequence of this tamper resistance is the limited access to external data sources, since such data—and its transmission—can be easily tampered with. 51% attack To create complex blockchain applications—beyond simple token transfers—you need access to external data. For example, most decentralized finance (DeFi) applications require price data (e.g., to get a or keep a stablecoin pegged to 1 USD). Indeed, asset prices are still the most common type of data generated and processed by blockchain oracles. strike price WHAT ARE ORACLE HACKS? Oracle hacks occur when attackers exploit vulnerabilities in the design or implementation of Oracle networks. In such a hack, the attacker usually manipulates the oracle to feed incorrect data into a blockchain contract. This often leads to funds being erroneously transferred, typically to the hacker’s account. The core of these attacks lies in compromising the data integrity that smart contracts rely on, resulting in financial losses or other disruptions. At the beginning of her presentation, the speaker clarifies that most of the losses attributed to Oracle hacks are actually due to market manipulation attacks rather than a flaw in the Oracle design. Sasa divides Oracle hacks into two significant categories: statistical/data errors or errors in integration. CONFIDENCE INTERVALS Confidence Intervals refer to a range of values used to estimate the true quantity of a particular parameter. These values are accompanied by a percentage, typically ranging from 95% to 99.9%, that represents the degree of confidence in that interval. An example of a confidence interval is an estimate of the price of Bitcoin as [$29,504, $29,507] with a 99% confidence level which means that the publisher is 99% confident that the price of Bitcoin at that time will fall between the given values. Some oracle protocols, like , require price publishers to provide a confidence interval report to build transparency and confidence. Pyth OUTLIER DETECTION AND REMOVAL MECHANISMS Outlier detection, also known as anomaly detection, refers to the various techniques and processes used to identify and eliminate outliers in data. Outliers refer to data points that significantly deviate from the data pattern. For instance, if an oracle provides price data to be $100, $101, $200, $99, $102,$10, and so on, $200 and $10 should be flagged as outliers. Outliers are identified by applying statistical methods to remove data points that exceed pre-defined boundaries. Outlier detection mechanisms are crucial because they improve data integrity and reduce biases, though the speaker questions their effectiveness in the context of oracle networks. ORACLE AGGREGATION FUNCTION Oracle Aggregation Function refers to an algorithm that plays a crucial role in improving the quality of oracle data by combining multiple data inputs from different oracles into a single, aggregated value. Some of the most popular algorithms used are Median Price Value, Volume-Weighted Average Price (VWAP), and Time-weighted Average Price (TWAP). The last two are becoming increasingly popular because they are more manipulation-resistant. VWAP calculates an average price based on trading volume, where sources with larger trading volume weigh more than others, while TWAP calculates an average price based on time intervals. This on Chainlink’s blog details the differences between TWAP and VWAP price aggregation. article DISPUTE PERIODS Dispute periods are specific time frames during which participants can challenge the validity or accuracy of data provided by oracles. It is a natural step after data aggregation to resolve suspected technical errors, inconsistencies, or manipulations. These disputes are eventually resolved through voting or an arbitration protocol like . is an example of a two-tiered oracle network that has a second layer for dispute resolution. Kleros Chainlink 2.0 SANITY BOUNDS Sanity bounds, also known as sanity checks, are pre-defined thresholds or limits used to validate the data provided by oracles before it is accepted for aggregation. It is an example of outlier detection mechanisms utilized by protocols. These bounds ensure that the price data falls within reasonable and expected ranges. If the data provided by an oracle lies outside the sanity bounds, it is considered invalid and is not included in the aggregation process. This discusses the use of sanity bounds as a manipulation mitigation method for price oracles. article Connect with Sasa Millic: LinkedIn Twitter Medium Youtube The Blockchain Oracle Summit is the world’s only technical summit that dives deep into the use cases, limitations, and impacts of oracles on the wider blockchain ecosystem. Leading speakers worldwide gathered in Paris to share their work and experience building and using oracle solutions. Article by . Michael Abiodun Also published . here

Understanding Vulnerabilities and Safeguards in Blockchain Oracles

ডিকোডিং ওরাকল হ্যাকস: ব্লকচেইন ওরাকলে দুর্বলতা এবং সুরক্ষা বোঝা

Oracle-Hacks entschlüsseln: Schwachstellen und Schutzmaßnahmen in Blockchain-Oracles verstehen

Decodificando trucos de Oracle: comprensión de las vulnerabilidades y salvaguardas en los oráculos Blockchain

Décoder les hacks Oracle : comprendre les vulnérabilités et les garanties dans les Oracles Blockchain

डिकोडिंग ओरेकल हैक्स: ब्लॉकचेन ओरेकल में कमजोरियों और सुरक्षा उपायों को समझना

Oracle ハックの解読: ブロックチェーン オラクルの脆弱性と保護策を理解する

Oracle 해킹 디코딩: 블록체인 오라클의 취약점 및 보호 장치 이해

Decodificando Oracle Hacks: Compreendendo Vulnerabilidades e Salvaguardas em Oracles Blockchain

Расшифровка хаков Oracle: понимание уязвимостей и мер безопасности в блокчейн-оракулах

Oracle Hack'lerinin Kodunu Çözmek: Blockchain Oracle'larındaki Güvenlik Açıklarını ve Korumaları Anlamak

Giải mã các cuộc tấn công của Oracle: Tìm hiểu các lỗ hổng và biện pháp bảo vệ trong Blockchain Oracles

解码预言机黑客：了解区块链预言机中的漏洞和保护措施

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

The wait is over! - it’s time to celebrate the champion startups of the world. Shout out to all of the participants in our ! You did a wonderful job showcasing your incredible startups and powering through another year. 2023 Startups of the Year is a community-driven voting platform brought to you by HackerNoon that recognizes and celebrates tech startups changing technology and the world for the better. The award started on May 8th, 2023, where the internet nominated and voted for 30k startups around the world. The voting period remained open until December 31, 2023, and now we are here with the winner announcement after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 To see the top 3 startups in each respective city, visit and pick a city. hackernoon.com/startups You’ll get a little surprise 😉 Here are the Startups of the Year Regional Announcements: North America Europe Asia Africa South America Oceania Misc/Remote From all of us at HackerNoon, thank YOU for your excellent campaign perseverance 🥺 This year has been awesome! Startups of the Year 2023 is concluded with 623k+ total votes, up from 215k+ votes in the inaugural startup competition. We look forward to seeing you again in the next campaign (stay tuned 😉)! Without further ado, let us kick off the announcement by celebrating the cities with top votes per region for 2023 🎉 North America (11,697 votes): Winner: Bloomfilter Cleveland, Ohio (8,835 votes) - Winner: SerpApi Austin, Texas , California (5,564 votes) - Winner: Finch San Francisco (4,258 votes) - Winner: RELAYTO SoMa, California - (3339 votes) Winner: Windscribe Toronto, Canada, North America (3304 votes) - Winner: Vouched Seattle (2716 votes) - Winner: Seccuri Palo Alto, CA, North America (2671 votes) - Winner: Salsa Valley Miami, FL, North America (2,613 votes) - Winner: Majhi Group Manhattan, New York (2423 votes) - Winner: Flieber, Inc. New York City, NY, North America See the complete announcement for the . Startups of the Year, North America, here Europe (45,612 votes) - Winner: Algomo London, UK (2,344votes) - Winner: Data Lake Warsaw, Poland (1,955 votes) - Winner: Hoken Tech Bari, Italy (1716 votes) - Winner: Not8 Amsterdam, Netherlands (1657 votes) - Winner: KYVE Frankfurt Am Main, Germany (1566 votes) - Winner: BazaIT Kyiv, Ukraine (1496 votes) - Winner:  Unity Network Tallinn, Estonia (1,478 votes) - Winner: Volcaino Ghent, Belgium (1,288 votes) - Winner: Owl Me Dublin, Ireland (1198 votes) - Winner: Groover Paris, France See the complete announcement for the . Startups of the Year, Europe, here Oceania (3,350 votes) - Winner: Sharepass Melbourne (2,107 votes) - Winner: The Catfisher Project Perth, Australia (220 votes) - Winner: Gemini AI Solutions Auckland, New Zealand, Oceania (213 votes) - Winner: Nexustorage Wellington, New Zealand, Oceania (138 votes) - Winner: Coviu Sydney (124 votes) - Winner: Bopple Brisbane, Australia, Oceania (124 votes) - Winner: Regional CFO New South Wales, Australia, Oceania (106 votes) - Winner: Edit on the Spot Gold Coast, Australia, Oceania (93 votes) - Winner: Knowledge Satnav Canbera, Australia, Oceania , Australia, Oceania (48 votes) - Winner: Peptalk360 North Melbourne, Melbourne See the complete announcement for the . Startups of the Year, Oceania, here Asia (16,345 votes) - Winner: Serenity Shield Singapore (13,588 votes) - Winners: QUIXY Telangana, India (10,168 votes) - Winner: HealthySure Mumbai, India (3,865 votes) - Winner: InterviewBuddy Visakhapatnam, India (3,733 votes) - Winner: Omniflo Bengaluru, India (4589 votes) - Winner: MeNow Tel Aviv (3877 votes) - Winner: ForestVPN Dubai (2478 votes) - Winner: TechGraph Powai, Mumbai, India (1004 votes) - Winner: Reelo Ahmedabad, GJ, India (875 votes) - Winner: Debt Hunter Hong Kong, China See the complete announcement for the . Startups of the Year, Asia, here Africa (8,179 votes) - Winner: GO PLATFORM Algiers, Algeria (767 votes) - Winner: Tudu Accra, Ghana (415 votes) - Winner: Treford Lagos, Nigeria (265 votes) - Winner: Phalconwise Ogbomosho, Nigeria (157 votes) - Winner: AbuErdan Cairo (151 votes) - Winner: Eazipay Lekki (144 votes) - Winner: KaziConnect Dar es Salaam, Tanzania (101 votes) - Winner: faceATT Gauteng, South Africa (79 votes) - Winner: Dimagi Cape Town (71 votes) - Winner: Sabi Addis Ababa See the complete announcement for the . Startups of the Year, Africa, here South America (376 votes ) - Winner: UpGirl Santiago, Chile (217 votes) - Winner: Klever Sao Paulo, Brazil (191 votes) - Winner: Tributo Simple Buenos Aires, Argentina (127 votes) - Winner: Light-it Montevideo, Uruguay (87 votes) -Winner: Thousand Antioquia, Colombia (108 votes) - Winner: GetLavado Lima (101 votes) - Winner: Cogniflow Medellin (73 votes) - Winner: TPAGA Bogota (73 votes) - Winner: Reliv Quito (47 votes) - WInner: Quantex Cordoba See the complete announcement for the . Startups of the Year, South America, here Misc/Remote: Featured Startups (1500 votes) - Winner: CIC Chain Remote, Earth (1193 votes) - Winner: Reship Colony US Misc (146 Votes) - Winner: Partful UK, Misc (116 votes) - Winner: GoMining British Virgin Islands Misc (283 Votes) - Winner: tomorrowSpace Germany Misc (225 votes) - Winner: Dtravel Cayman Islands See the complete announcement for Misc/Remote startups . here Once again, HUGE CONGRATULATIONS and BIG PROPS to all of the top Startups of each region this year! The whole team at HackerNoon would like to thank you for your perseverance in the campaign, and we look forward to seeing you in the next campaign. About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Stand Up for the Champion Startups of the Year! 

বছরের চ্যাম্পিয়ন স্টার্টআপের জন্য দাঁড়ান!

Setzen Sie sich für die Champion-Startups des Jahres ein!

¡Defiende a las startups campeonas del año!

Défendez les startups championnes de l’année !

वर्ष के चैंपियन स्टार्टअप के लिए खड़े रहें!

今年のチャンピオンスタートアップのために立ち上がろう!

올해의 챔피언 스타트업을 위해 일어서세요!

Defenda as startups campeãs do ano!

Встаньте на защиту стартапов-чемпионов года!

Yılın Şampiyon Startup'ları için Ayağa Kalkın!

Hãy ủng hộ các công ty khởi nghiệp vô địch của năm!

为年度冠军初创企业挺身而出！

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Machine Coding and Low-Level Design (LLD) rounds are integral components of technical interviews for software engineering positions. These rounds test your ability to transform your coding skills into real-world solutions and require a deep understanding of data structures, algorithms, and software architecture. To help you prepare effectively, I've compiled a comprehensive guide with resources, practice questions, and informative videos to ace these rounds. Most of the resources are based on , but you can get an idea and code with your preferred language. Java programming language Below are some sample questions for the machine coding round: Sample Questions Design a Parking Lot: Another classic LLD question. You are asked to design a parking lot management system. Design an Online Shopping System: A more complex LLD problem that tests your ability to design a scalable e-commerce system. Design an Online Auction System Recently asked me from one of the top companies - more about this later. Recently, I was researching LLD, and I was confused about how and where to start, so I came up with a strategy of my own. I started with the theory first with the help of by Yogita Sharma. She breaks down complex concepts into simple analogies and examples that you will remember easily. I would also suggest you start here and learn what are low-level design basics first and a few design patterns. sudoCode Then, move on to questions and practice them through the different resources mentioned below. Resources: Theory Short Video Lessons - Low-Level Design Course (sudoCode) Everything you need for LLD Interviews in one place - Grokking the Low-Level Design Interview The OG Book - (go through this only if you have enough/extra time to explore, probably months cause it takes you deep into theory) Design Patterns Questions and More Industry - (do at least 2-3 from this resource to get an idea of how the questions are framed) Standard Questions workat.tech - This YouTube playlist consists of most of the questions/solutions, Use case diagram creation, different design patterns, and more. (Mode of Language - Hindi) Concept && Coding - OG Again, most of the standard questions explanation with solutions. Soumyajit Bhattacharyay Mock Interviews Keerti Purswani (OG, GOAT for HLD and System Design) Guarav Sen Tips and Tricks Practice 😀 Only writing code and practicing can make you comfortable with these kind of interviews, and time management is must. Mostly time limit to complete the problem statement is 90-120mins for most companies. I have also practiced some of the machine coding questions myself. You can check out my GitHub repo for the same; I will keep updating it from time to time. Github - System Design and follow me for the latest updates. Star the repository, This is all you need. In conclusion, the Machine Coding and LLD rounds can be challenging, but with the right resources, practice, and understanding, you can excel in these interviews. Remember, it's not just about finding the right answer but also about demonstrating your problem-solving and design skills. So, immerse yourself in practice and theory, and you'll be well-prepared to tackle these technical interviews successfully. Good luck on your coding journey! All the best, and keep grinding.

How to Master Machine Coding/LLD Round: Resources and Tools

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Stories, News & Mentions about KLEER

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