Corach Rambler improved from his reappearance to finish a staying-on fourth to Le Milos in the Coral Gold Cup last season and, for all connections were reluctant to be drawn into discussing targets, Newbury's headline jumps race could be on the cards again.

'I have to apologise to everyone' - Lucinda Russell takes the blame as Corach Rambler finishes last on reappearance

Corach Rambler finished last on his seasonal reappearance in the Edinburgh Gin Chase, with course specialist Elvis Mail striking for Nick Alexander and 3lb claimer Bruce Lynn.

Corach Rambler finishes last on reappearance as Elvis Mail takes Edinburgh Gin spoils

Tipstar champion Katie Midwinter shares her best bets for Saturday's action. Course-and-distance winner BEFORE MIDNIGHT, now 5lb lower than when a respectable sixth in the Red Rum Handicap Chase at Aintree in April, runs off a mark of 135 with 7lb claimer Ben Sutton aboard.

Katie Midwinter's top tips: Wisdom makes sense in feature

On paper, there appears to be no clear-cut favorite among a dozen sophomore fillies assembled for the $300,000 Valley View Stakes (G3T) at Keeneland.

It's Anybody's Race in Wide Open Valley View

Conditional Daire Davis was given a 21-day ban for improper riding following a chaotic incident that caused two of his rivals to unseat in the 2m5f mares' novice hurdle at Ludlow on Thursday.

Conditional handed 21-day ban following chaotic incident during Ludlow race

When Penn National shut down for a one-month planned break Sept. 23, Tyler Conner, the track's third-leading rider this year in both wins and earnings, wasn't sure what he'd do to keep his income flowing.

Riding Six Tracks In Six Days, Conner's Mileage Surpassed Only By His Work Ethic

SPORT have you covered for the horse racing with tips and free bets for the day’s fixtures at Hereford, Huntingdon and Leicester. Tom Lunn has gone through the racecards, form, going and

Tuesday horse racing tips from Tom Lunn at Hereford, Huntingdon and Leicester

Back Down Under, a six-race maiden rated 76, is drawn in stall one and while her most recent effort at Down Royal was a step in the right direction, she looks up against it.

'He's a strong traveller so the race should suit' - key quotes and analysis for the competitive Birdcatcher

Friday's card at Belmont at the Big A will be bolstered by a Pick 6 carryover of $22,140 after the multi-race wager went unsolved on Thursday's nine-race card. The $1 Pick 6 returned $2,460 to bettors who selected 5-of-6 winners correctly.

Pick 6 Carryover Of $22,140 For Friday’s Card At Aqueduct

Our latest research for 2023 reveals that the global Stacker Cum Reclaimer market is anticipated to reach USD 215008 million by 2031, propelled by a steady CAGR of 9.9% over the analysis period ...

Stacker Cum Reclaimer Market 2031: In-Depth Research Report

Claimer

claimer

Before starting, let’s keep in mind the definition of a traditional “bridge:” a structure designed to attach and cross over two (or more) previously unconnected places. It’s not that different in the crypto world, because, in reality, all distributed ledger platforms are isolated from the others. They have their own assets and features and, if we want to connect them, we’ll need a digital bridge. This means that you simply can’t send, for instance, Bitcoin to Ethereum, Ether to Bitcoin, or GBYTEs to Ethereum. Not directly, at least. I Luckily, bridges are here to make the connection possible. f you try to send an asset from A chain to B chain without a bridge in the middle, your wallet simply can’t do that. They’re protocols or platforms that enable seamless and secure interoperability between different networks, facilitating the transfer of digital assets across numerous ecosystems. With its help, you could use a native asset from the A chain in the B chain without issues —to take advantage of liquidity mining or P2P contracts, for example. Let’s dive a bit more into these platforms. Types of bridges in crypto If we consider their inner workings, we can separate cross-chain bridges into three : In the "Lock and Mint" model, users secure tokens on the source chain, and equivalent tokens are minted on the destination chain. Conversely, in the reverse direction, burning these unlocks the ‘originals’ on the source chain. main types Lock and Mint, Burn and Mint, and Lock and Unlock. ‘wrapped’ tokens The next approach, "Burn and Mint," involves burning tokens on the source chain and re-issuing (mining) them on the destination chain. Meanwhile, "Lock and Unlock" allows users to lock tokens on the source chain and unlock them from a liquidity pool on the destination chain, with liquidity on both sides being supported by monetary incentives to providers. In other words, for them to create (mint) or unlock B-compatible tokens in the same amount locked or burned by the A-chain user. So, we can say they’re not the same coins from the beginning, but an equivalent in the destination network. Of course, users can recover the “original coins” in the A chain as well by reversing the process. an action in the A chain (lock or burn/erase tokens) must be performed and proved to some actors on chain B, Cross-chain bridges and decentralization Now, if we consider the amount of external control or involved middlemen in the process (centralization), we can classify cross-chain bridges into trust-based and trustless. rely on user trust in a federation or custodial entity, operating in a centralized manner and keeping control of user funds. While quick and cost-effective for large transactions, Trust-based bridges their focus on transaction speed may compromise safety, fraud prevention, and regulatory exposure. In contrast, trustless bridges operate on programmable agents (smart contracts, autonomous agents, chain code, etc.), offering a higher level of decentralization and user control over their own funds. These bridges, while providing more regulatory freedom, available chains, and flexibility, may face challenges due to the potential vulnerabilities of their code. On the other hand, centralization levels vary among trust-based bridges, where the size of the group determines centralization. Examples like Binance Bridge exhibit high centralization with a single custodian, posing a single point of failure. Multi-signature schemes mitigate risks, but collusion or compromise remains a concern. However, challenges arise in implementing these bridges, making them difficult to scale. Economic incentive-based bridges, exemplified by protocols like XClaim and TBTC, prioritize security through collateralization. Yet, high collateral requirements may lead to increased fees, potentially impacting competitiveness. It’s important, then, to find a balance without losing control. Light client-based bridges verify transfers using light clients of source chains, avoiding central parties. Counterstake Bridge is a user-friendly and decentralized cross-chain bridge available to import and export digital assets from and to the Obyte ecosystem and EVM-based networks. It’s a trustless and economic-incentive-based bridge that applies its novel method to be secure and cost-effective without sacrificing user control. The key to its operation is in the name itself: counter-stake. Counterstake ( in this example). Then, an equivalent amount of the same token is claimed on the foreign chain, like Ethereum, through an equivalent stake in the foreign chain's native asset. Users initiate the process by locking their native assets, such as GBYTE, on the asset’s home chain Obyte Next, there’s a challenging period of three days where participants can challenge the legitimacy of the claimed funds by counter-staking 1.5 times the original stake. This back-and-forth process can continue for many days (in very rare cases), until one side has a stake 1.5 times greater than the other side and it stays unchallenged, determining the validity of the claim. The protocol incentivizes honest participation by offering rewards to those supporting legitimate claims. Indeed, even if a whale (rich holder) stakes against a legitimate claim, the increasing length of challenging periods, the community input, and the public nature of the involved crypto networks would lead to the whale losing all the staked funds. , and the coins are claimed without further issues.  Users can be self-claimers and pass through the challenging period, or speed this up by using the services of an assistant. That’s why, in practice, most challenging periods end without a challenge, after only three days Exchange quickly with assistants Crossing this bridge doesn’t have to take days for average users. It could be quicker with the help of assistants. Acting as a facilitator, the assistant helps users by claiming transfers on their behalf, and immediately delivering the claimed coins to the user with a deduction for a predetermined reward. This reward should cover network fees on the destination chain and compensate the assistant adequately for the associated risks and capital locked in claims. Assistants, whether operating individually or in pooled setups, also function as watchdogs, challenging fraudulent claims and aiding users in expediting their transfers securely. To utilize an assistant's services, users indicate the reward they are willing to pay when sending a transfer. All of this happens in the background, though. When using the Counterstake Bridge, average users will see a simple interface including “You send [X coins on A chain],” “You Get [X coins on B chain],” the assistant’s predetermined reward for that amount, and a space to fill in the Obyte or MetaMask address to receive the funds. To use the bridge, you’ll need both wallets installed. Once the funds have been sent through the above interface, you can check their whole path on the same website: Sent – Mined – Claimed – Claim Confirmed. It’s important to note that, for now, Counterstake only works with the (for Obyte-based tokens) and MetaMask wallet (for Ethereum-based and BSC-based tokens). Obyte wallet “Claim Confirmed” means that an assistant has received your money and sent the coins to your wallet. You don’t need to do anything after sending the transfer. Just wait between 20 – 30 minutes to receive your assets on the destination chain. It may also happen that the available assistants don’t have the amount you need at the moment, so you can choose between self-claiming or waiting for them to refill their pools. Participate in Counterstake Governance —if they want to participate. and adjust critical parameters, including the stake growth factor for consecutive challenging periods, the initial stake value, challenging periods for regular and large transfers, the threshold for deeming a transfer large, minimum stake, and the price oracle used. Since no central party is behind Counterstake, the same users are in charge of decisions They can shape Two programmable agents, the export agent on the home chain and the import agent on the foreign chain, each have their governance modules. Token holders on the home chain vote on export agent parameters, while holders of the minted token on the foreign chain vote on the import agent parameters. Participants actively engage in shaping the protocol's dynamics, ensuring adaptability and responsiveness to the evolving needs of the Counterstake Bridge ecosystem. This decentralized governance employs challenger voting, allowing active participants to make prompt decisions without requiring quorums. In the realm of crypto bridges, the stands out for its trustless and decentralized approach. Offering secure and transparent cross-chain transfers, the protocol's unique features ensure versatility, user-friendliness, and reliability. Counterstake Bridge Featured Vector Image by storyset / Freepik

Counterstake and More: Learn to Transfer Crypto with Bridges 

কাউন্টারস্টেক এবং আরও অনেক কিছু: ব্রিজ দিয়ে ক্রিপ্টো স্থানান্তর করতে শিখুন

Counterstake und mehr: Erfahren Sie, wie Sie Krypto mit Bridges übertragen

Counterstake y más: aprenda a transferir criptomonedas con puentes

Counterstake et plus : apprenez à transférer des crypto-monnaies avec des ponts

काउंटरस्टेक और अधिक: ब्रिजेज के साथ क्रिप्टो ट्रांसफर करना सीखें

カウンターステークなど: ブリッジを使用して暗号通貨を転送する方法を学ぶ

Counterstake 및 기타: 브리지를 사용하여 암호화폐를 전송하는 방법 알아보기

Counterstake e mais: aprenda a transferir criptografia com pontes

Counterstake и многое другое: научитесь передавать криптовалюту с помощью Bridges

Counterstake ve Daha Fazlası: Köprülerle Kripto Aktarmayı Öğrenin

Counterstake và hơn thế nữa: Tìm hiểu cách chuyển tiền điện tử bằng Bridges

Counterstake 及更多：学习使用网桥传输加密货币

Demystifying the Technical Properties of Sharding: Why it is Great

Desmitificando las propiedades técnicas de la fragmentación: por qué es genial

Démystifier les propriétés techniques du sharding : pourquoi c'est génial

साझाकरण के तकनीकी गुणों का रहस्योद्घाटन: यह महान क्यों है

シャーディングの技術的特性の謎解き: シャーディングが優れている理由

Desmistificando as propriedades técnicas do sharding: por que é ótimo

Làm sáng tỏ các thuộc tính kỹ thuật của Sharding: Tại sao nó lại tuyệt vời

揭开分片技术特性的神秘面纱：为什么它很棒

If “The future is decentralization” becomes a reality, there is no doubt blockchain will be at the heart of that future. After all, the concept and the ideas of decentralization were born based on the introduction of blockchain. Satoshi Nakamoto, the pseudonymous developer behind bitcoin, published a back in the fourth quarter of 2008. That was when a few rare people saw the glimpses of what the future was going to be. white paper Fast forward a decade, and today, blockchain is a widely spread idea, at least in the tech industry. Tech heads are trying to employ different ideas to remove the central authority and disrupt various industries, of course for their own benefit. There is no sure-shot of which industries will be disrupted and where organizations will fail, but here are a few likely to get disrupted: Banking: Let’s start with the obvious one: Banking. Banking (and Financial services) are probably a disruption-priority when one thinks of implementing blockchain in conventional industries. Traditionally, ‘fees’, ‘time consuming’, ‘paperwork’, ‘fraud’, and many such words/terms have been associated with the banking industry. And blockchain offers elimination of such beliefs. Transfer fees, cross-border transaction charges, currency rates, and the time for the money to reflect in the other person’s bank are a few barriers the end-consumers don’t like. Cryptocurrency like bitcoin can eliminate the central authority in such cases. With bitcoin or any other “decentralized ledger for payments” currency, one can transfer millions of dollars anywhere with minimal charges and super-fast speed. For e.g., Ashton Kutcher did an almost $4 million transaction  instantly using ripple on . the ellen show significant parts of the banking industry such as clearance and settlements, Loans and credit, trade finance, etc. can also benefit using blockchain. Other Real estate Real estate is perhaps the eminent asset class industry blockchain can disrupt. Traditional real estate obstacles such as availability for only high-income people, lack of transparency, Lack of Liquidity, realtor and banking fees, transaction speed, frauds, etc. can be overcome using blockchain. Blockchain is time-stamped, decentralized, and secure series of immutable records. Meaning, if an individual list his/her verified property, everyone in the loop can know the ownership details, i.e., no fraud. Middlemen like banks, lawyers, and realtors can be cut off using . In short, 3-5% cost cut. Moreover, banking benefits like speed, no-limit transactions also come with blockchain implementation. smart contracts Another benefit of blockchain for both parties is . The low-income buyer can own a fraction of the property, which means the seller will get cash immediately without waiting for the right party to buy the whole property. tokenization In a nutshell, blockchain has a lot to offer in the real state industry. Supply Chain The supply chain is a complex industry. There can be a hundred stages -- cargos, trains, inventory, etc.-- in a global supply chain. And a distributed, digital ledger can be of tremendous value here. Applications of blockchain in transparency, agreements, tracking, and payments during the whole supply chain are practical. For e.g., Every individual throughout the supply chain can have access to the previous block’s details and other relevant information. Big players such as Walmart are already embracing blockchain in their supply chain and reaping benefits. ’s implementation of blockchain technology is an excellent example of Blockchain in the supply chain. Tokens.net Intellectual Property Stealing the original creation and/or idea is common cheating in the world. People use intellectual property(IP) for personal and commercial use all the time, and the creators don’t bother taking legal actions against them. The reason? They will need to provide proof such as patent, trademark, or copyright for that publicly available piece, which will take time and money. The digital ledger technology can help save IP. Given the immutable, time-stamped, and secure system of a blockchain, one can save their findings or creation on the digital ledger. This can seamlessly work as proof when legalities are the question. In short, blockchain will keep “digital thieves” from fraud and stealing intellectual property. Healthcare Blockchain can save lives. This might be an overstatement, but there’s a potential. First off, the usage of blockchain in the medicine supply chain can eliminate fraudulent drug dealers. Every legit pharmaceutical company and medical shop can register for a common blockchain and connect with their databases. If the numbers do not stack up, the frauds can be caught given the transparency and immutability of the blockchain. Another place where it can save lives is in hospitals. If a global blockchain is built with data of every medical phase of every individual patient and made accessible to legit doctors, they can save lives in emergencies. Moreover, an ideal drug for the patient can be provided based on the patient’s history. Additionally, medical insurance companies can also eliminate fraud claimers. Education Online education and distance-learning are already a reality, and blockchain can take it further. Using blockchain, every record from KG-Ph.D along with the individual’s achievements and skillsets can be made accessible to institutions and the concerned students. These records are immutable, transparent, and verified, which makes institutions and organizations find relevant students and eliminate fakes. Moreover, it helps institutions and students in collaborations. In a nutshell, whenever and wherever a student-background verification is due, blockchain can help. Also, mutual learning from any part of the world is a plus. Cyber Security: The final industry (in this list) where blockchain can benefit is cybersecurity. Blockchain provides decentralized storage solutions, which means it’s not stored on a specific server. And although the blocks of the ledger are available publically, advanced cryptography keeps the sensitive data secured from hackers. This concept sums up for IoT security, Private messaging, etc. like cyber threats. Final Words: Blockchain can disrupt any industry where transactions, data transfer, and security are involved. With its characteristics like immutability, cryptography, and decentralization, Blockchain will likely disrupt the above-stated industries. What are your thoughts? Which industry do you believe will use blockchain at scale? : Youth's Era is a consultant for Tokens.net Disclosure

Top 7 industries Blockchain will Most likely disrupt

What’s a Bitcoin fork? A fork is basically an alteration of the current Bitcoin protocol changing the rules. As an example, if you were playing an online game and wanted to change the rules, all other players will need to agree on the rule change. If successful, the changes are implemented and the game continues as normal. However, if the consensus was not large enough then two versions of the game are created (version #1 = old rules, version #2 = new rules). This is now a in the game. The same analogy applies to Bitcoin’s code. fork Post forking there is an “original Bitcoin” and a “new Bitcoin.” For example, Bitcoin Cash changed the block size from 1 MB to 8. The supporters switched to a new coin “Bitcoin Cash”, and those who preferred the original rules kept using the original Bitcoin. There are also soft forks, that allow the new rules to be compatible with the old rules. Reasons to care about a fork: Switch over to the new rules and the new coin because it’s better than using the original Bitcoin. The fork could impact the Bitcoin community, Bitcoin’s adoption, and Bitcoin’s price. Profit from the fork by selling the new coins that are delivered to every Bitcoin holder as every Bitcoin holder gets free coins at a hard fork. This doesn’t happen automatically and the new coins must be claimed. Each new coin also has a different claiming mechanism. The dangers of Bitcoin forks: The more recent forks seem similar to each other, whilst the main reason behind the creation being “marketing buzz” than an ideology. Bitcoin forks have become the new ICOs with many seeking out free coin. Quick money for developers: Some forks aren’t true copies of Bitcoin’s history. The developers receive a large amount of the new coin, which they sell at market once the coin starts trading. Scams: Some forks are simply just scams. An example of a reported scam is Bitcoin Platinum. Scams can come in the form of forks that are created to short Bitcoin’s price (e.g., Bitcoin Platinum) or something more elaborate such as forks that are created to steal users’ real Bitcoins(e.g., Bitcoin Gold fake wallet). Hence, claiming coins from a fork has a considerable amount of risk. How to safely claim coins from a fork Read about the project. Find out who the developers are, and their track record. Examine their roadmap and how far along they have come. Check reviews about the project, and the team. The claiming process is quite complicated, and there is a risk of losing the coins. For example, a forked coin must implement replay protection. It means the network can separate the new coin from the original. If the coins are left on an exchange that supports the fork then the exchange will often extract the new coins for the user. However, the one rule to always follow before claiming any coin is to move the Bitcoin to a new wallet with a new seed phrase to reduce the chances of losing existing Bitcoin. Warning: claiming and selling fork-coins involves considerable risks and requires some advanced knowledge. By attempting these procedures, you accept all of these risks: Working with and exposing your Bitcoin private keys. Installing and running certain software. Trading and possibly identifying yourself on various exchanges. The rule when Claiming Bitcoin Forks: Claiming all these forks require using the private keys of the Bitcoin wallet, which contain the BTC at the time when the coin is forked. So transfer all bitcoins to a new hardware wallet to maintain security standards while claiming! Three Important factors needed for profiting from Bitcoin Forks This is the time and date (measured in Bitcoin block height) when the fork took place. A Bitcoin address that received bitcoin value after the fork won’t receive any fork-coins. Fork Height: Fork-coins are awarded in direct proportion to the amount of bitcoin in each address (e.g. 1.582 fork-coins for 1.582 BTC), however, this ratio can vary. Reward Ratio: Very few exchanges will allow deposits of minor forks. Hence preparatory research must be undertaken on how to exit from the new coin holdings. Exchange Deposit Availability: Claiming the Bitcoin Forks Export your Private Keys: To claim most fork-coins, it’s necessary to export the private keys from the old wallet. In most cases, a file will be generated that contains all addresses and their respective private keys. Certain wallets, especially hardware wallets, won’t allow you to export the private keys. In such cases, it’s necessary to enter your hardware wallet’s seed phrase into a tool (such as Ian Coleman’s BIP39 Tool ). Another option is to import the seed into a compatible HD wallet, such as Electrum. The tool or wallet will regenerate the private keys and their addresses, which allows this info to be imported to the fork-coin in the correct format. Check the Balance of your Addresses and Make a List: To save time, only import private keys that contain a value. Use findmycoins.ninja and btcdiv.com sites to check the claimable fork-coin balance of your old Bitcoin addresses. Record all valid addresses and private key combinations. Two Reliable “Do It Yourself” Claiming Methods: BitPie & Bither are two Bitcoin wallets that allow for claiming several fork coins. There’s seems to be a partnership between the two, while it seems Bither rejects non-BitPie addresses when claiming. Bither is featured on the Bitcoin.org site, indicating some reliability. 1. BitPie & Bither: Ymgve has an excellent script, supports SegWit addresses, has lower mining fees than BitPie/Bither, and accepts any address for sending. The coins may be sent directly to the exchange’s deposit account. However, the script requires the use of the command line and hence, more complicated. 2. Ymgve’s Fork Claimer: Bitcoin Forks Claiming Services: As an alternative to the DIY approach, there are services that charge a percentage (usually around 5%) of the fork-coin profits to handle the process. The following list all seem to have received a lot of positive feedback. Besides CoinPanic, they’ll require your private keys. Reddit user Camku. Nbysy is another Redditor who offers a fork-coin service, with good feedback. CoinPanic is a service, that doesn’t demand your private keys for claiming. Dig.Walleting. Loyce has a claiming service on the BitcoinTalk forum, with positive feedback. 37 Bitcoin Forks in 2017–2018 Follow Me and Decentralised Capital and learn new developments in financial markets and all things Blockchain. Follow me to stay updated Follow us at Bullion Asset Management (Xgold) to stay updated on Fractionalised, Digitized, Physical Gold on Blockchain here. Follow me on Twitter!

What the Fork? Bitcoin Forks and how to claim them

Introduction With the rise of based crypto currencies, smart contracts have become increasingly more popular. blockchain Crypto startups are utilizing smart contracts to create intelligent agreements that can be executed in a “ ” environment. Smart contracts take out the middleman and execute on contract terms automatically, based on rules put in place. Although smart contracts harbor a lot of promise, we have seen a worrying history of hacked smart contracts, where hundreds of millions of dollars worth of crypto currencies have been lost or stolen. trustless My growing interest in crypto currencies and blockchain based has led me to research and investigate these hacks, to gain a better understanding of technology how they were hacked. , where we will dive deep into the most infamous hacks. This article is part 1 of a 3 part series We will study how strokes of genius, or subtle mistakes caused earthquakes in the crypto world. Aside: This article is based on the amazing lecture of at I highly recommend watching this talk, and all other materials on the channel. Leonid Beder Blockchain Academy — How to Not Destroy Millions. Kin Ecosystem Foundation Youtube Smart Contracts A smart contract is a set of promises, specified in digital form, including protocols within which the parties perform on these promises — Nick Szabo, 1996 Let’s break down this definition term by term. Contract An example of a common contract is a . sales contract The seller to deliver the goods in exchange for the buyer. promises The buyer to pay the desired price. promises Digital form means that the contract has to be programmed in machine readable code. We want the contract to be Digital form deterministic in nature, thus providing the same outcome based on the same input, every single time. Protocols For example, say the parties agree that the purchased good is to be paid in Bitcoin. The obvious protocol of choice would then be the Bitcoin protocol. Smart this just sounds cool. Popular implementation of smart contract programming languages Ethereum Implements a nearly Turing-complete language on its blockchain, a prominent smart contract framework. We say nearly Turing-complete b/c in order to be completely Turing complete we need unlimited computational power. Bitcoin Implements a Turing-incomplete scripting language that allows the creation of limited smart contracts, such as: multisignature accounts payment channels escrows time locks atomic cross-chain trading oracles Solidity by Example Solidity is an OOP language for writing smart contracts (mostly?) on Ethereum. We’ll start with a simple example of a smart contract named Greeter, which will: Initialize with a greeting message (eg., “Hello World!”) Provide a method to read/query the greeting message. Provide a method to write/modify the greeting message. For these examples we will use the official online Remix IDE found @ https://remix.ethereum.org It is best practice at the moment to specify explicitly which version of solidity we are using, because we don’t know which bugs can be introduced in future releases. Solidity has a similar syntax to JS. We are defining a contract named Additionally, we have a public state variable of type string called greeting and two methods (constructor and setGreeting). Greeter. Fundamentals: Execution Model Every smart contracts needs to be compiled to byte code before it can be deployed to the network. Deployment means being stored on the Ethereum network. This can be a production, test or private network. Every transaction or message call in Ethereum is executed by the Etherum Virtual Machine (EVM) on every Ethereum node. (miner or just a full node). Since is running on the EVM, and is executed simultaneously by in the network — there should a mechanism to used by each contract. The mechanism that limits resources in Ethereum is gas. every smart contract every single operation every node limit the resources Every operation executed has a deterministic (yet sometimes hard to predict) cost measured in units of gas. It’s hard to determine gas costs because conditional control flows can change which operations get executed in run time. Image the following program if (condition is true) { // do something super simple which is very cheap in gas terms } else { // do something really complicated which will be very expensive! } Every gas unit consumed by a transaction must be paid for in Ether, based on a gas/Ether price which is set by the sender of the transaction. Each sender determines what he’s willing to pay. The idea is the more important or urgent it is to you, the more you’d be willing to spend. Transactions have also a on how much gas the transaction can consumer gas limit parameter that is an upper bound Why is the gas limit parameter needed? We have an upper bound b/c it isn’t always clear how much the execution of a transaction will cost, and we want to cap how much Ether we spend on it! Let’s upload the contract to the Remix IDE, create and deploy it. Remix can work against main net and test nets. It’s really convenient to work against an Ethereum simulation. When using the simulation we can pretend to have millions of Ether, and not wait on Ethereum transactions to be processed which make development a lot faster. Running smart contracts via Remix is completely free, so I recommend you give it a try at home :) Once the contract is deployed we see it in the Deployed Contracts section in the box (see the arrow!). Notice that we can easily change the constructor argument and redeploy to the network. Fundamentals: Special Variables and Functions There are special variables and functions which always exist in the global namespace. Here are the ones we’ll discuss today (Ethereum address): sender of the message (current call). Thsi belongs to the original caller of the transaction. This can either be a users account or the address of another smart contract. The Ethereum security model guarantees that msg.sender cannot be faked. msg.sender (unit): number of wei sent with the message. wei is the smallest unit of Ether (think cents to dollars.) So this specifies how much ether we are sending with the function for buying, transacting, etc... msg.value Fundamentals: Function and State Variables Visibility In Solidity, there are four types of visibilities: For functions: : can be called externally from other accounts / contracts. It is important to note that we cannot call this code from our script. We would need to create an additional transaction which would invoke this externally. This is very good for methods that need to be publicly accessible but you want to avoid using them in your own code. external (default) can be called by everyone. public : : can only be called internally from our smart contract or a smart contract that inherits from our smart contract. internal can only be called internally and only from the contract itself. private: For state variables: : can be accessed by everyone. Solidity automatically generates a getter for a public variable. public (default): can only be accessed internally and only from the contract itself. internal : can only be accessed internally and only from the contract itself. private Fundamentals: Function Modifiers Modifiers can be used to modify the behavior of functions. This lets us add functionality before, after or even around the invocation of the function. (Logging for example) Modifiers are very similar to before/after/around/hooks in other programming languages. For example, let’s augment our Greeter smart contract to: Have an owner (in our case, the deployer of the smart contract) Make sure that only the owner can further modify the greeting We’re going to see this pattern a lot, in the coming examples as well as in the wild. Additionally, let’s add a condition that says only the owner can change the greeting. It is important to note that require is a predicate in Solidity. If its evaluation returns false, then an exception is thrown, and all changes done in contract are completely rolled back. The underscore is equivalent to a yield. This basically means execute in this place the remaining code of the function. Fundamentals: Fallback Function A smart contract can have exactly one unnamed function. This function is invoked when a call to the contract is made and none of the other functions match the given function identifier. A common use for fallback functions is when Ether is being transferred to the contract. This triggers the invocation of the fallback function. So, in order to set up an account to be able to receive Ether the bare minimum (in most cases) is setting this function up. Fundamentals: Payable Modifier In order to receive Ether, . This is to protect us from accidentally sending ether to a contract that doesn’t expect it. every function must be marked as payable When sending Ether as part of a function call, the function must be marked as payable. When sending Ether directly to a contract, its fallback function must be marked as payable. If no such function exists, the contract receive Ether through regular transactions. cannot Events Example This is a fancy way to do some logging in Ethereum. Aside: We have a function called that logs returns and event. This could be useful for the case of having an off-chain client that reads the loggings and manages all the donations. Donate We have a function called which is (we added the payable modifier). We also have a function called which is , so sending Ether should fail in this case (payable modifier is missing). donate payable troll not payable Let’s deploy this contract to Remix so that we can verify the results of sending money via each of these methods. In the gif above we can see that invoking the method results in a transaction where a value of 5 wei is transferred, and when we invoke the method no money is transferred. donate troll Let’s Destroy Some Ether We can see in Reward that we have a mapping between We use an owner concept here so our owner can call the method, deciding that a specific address is supposed to get some amount of a magic token. Additionally, we have the method which is publicly accessible by everyone. This method gives you the reward that belongs to your address, and then emit an event for the reward. address and a uint. setReward claimReward Can anyone spot a problem with this? Notice that when we declare the mapping, it is a map from rewards address to unsigned integer. We are subtracting here without checking that the necessary funds are available! So any scenario where we reach a negative value in rewards will result in an underflow. Aside: A , will change what should be a negative value to an extremely large positive one. Check out the link, for a further explanation on how underflows work. n underflow Consider the following example. A reward claim of 500 units is made. Now, our hacker will try and claim 1000. In theory, this shouldn’t work because we only have 500 left. After running this transaction will have an infinite amount of coins! the claimer Bugs: Overflow / Underflow Solidity can handle up to 256 bit numbers is when a number gets incremented above its maximum value. So adding 1 to 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF results in 0. Overflow is the inverse case, when the number is unsigned, decrementing will underflow the number. This will work the other way around, where subtracting 1 from 0x00000000000000000000 results in 0xFFFFFFFFFFFFFFFFFFFFF. Underflow Mitigation #1: Test for Correctness Test for correctness before performing any operation. A quick fix for this is to implement a check to verify that the claim is smaller than the current account balance. Mitigation #2: SafeMath Always use the (de-facto) standard library. SafeMath Find nice, relatively stable, smart contracts in . OpenZeppelin’s Github repo What the library does is implement mathematical operations in a safe way, such that if an overflow occurs it rolls back the transaction. Assert is very similar to revert it rolls back bad transaction. In the example below we have imported SafeMath, injected it for use on , and therefore the library will revert any transactions that cause an underflow on line 30. unit256 Setting Function Visibility Can you spot an error in the Charity contract? Let’s break it down. We have a payable fallback function and a withdrawal function where the owner can withdraw his funds. You can imagine that at some point the owner may want to withdraw his funds so he can do something with them. Now, let’s take a look at the method. It’s using the owner paradigm. It has no privacy. Anyone can change the owner, thus transferring the funds to himself / herself. setOwner In Solidity functions are public by default! Hack Scenario: Alice deposits 1000 wei for this example. Bob tries to withdraw with but fails because he is not the owner of the charity. Now, Bob calls into setOwner, changing the owner to himself. Bob takes all the funds. All this because someone forgot to define visibility! Mitigation #3: Always Define Visibility! Always define visibility Limit function visibility when possible Conclusion This has been a primer on the Solidity language, and we have examined some of the simple, dangerous mistakes that can be made during smart contract development. The next post in this series will dive into infamous, genius and nuanced hacks, that have so far in the past several years. drained hundreds of millions of dollars worth of crypto If you’re looking for resources to ramp up on the blockchain world, I highly recommend (again) checking out the , as it has a wonderful inventory of high quality, technical discussions and lectures. Huge thanks to for building this lecture and teaching it at Blockchain Academy! Kin Ecosystem Youtube channel Leonid Beder If this post was helpful, please subscribe and click the clap 👏 button below to show your support! ⬇⬇ You can follow me on , and . Instagram Linkedin Medium

How to Not Destroy Millions in Smart Contracts (Pt. 1)

First claimed his attention was theology

Twitter v. Elon Musk Court Filing by Potter Anderson & Corroon LLP, July 12, 2022 is part of . You can jump to any part in this filing . This is part 25 of 31. HackerNoon’s Legal PDF Series here FACTUAL ALLEGATIONS VIII. Defendants purport to terminate the merger agreement B. Twitter’s representations in its SEC filings supply no basis for termination 131. Nor can defendants show that Twitter has made any representation or collection of representations the inaccuracy of which is “reasonably likely to result in” a Company Material Adverse Effect. They do not even try. Notwithstanding that defendants have received mountains of information regarding Twitter’s processes, far beyond what they are entitled to under the merger agreement, their termination notice asserts only that “[p]reliminary analysis by Mr. Musk’s advisors” of the vast data set Twitter provided to Musk after signing “causes Mr. Musk to strongly believe” Twitter’s reported estimates have been inaccurate. Ex. 3 at 6. Musk’s claimed “belie[f]” is of course no proof of misrepresentation, much less of a Company Material Adverse Effect — which can be established only by clearing an extraordinarily high bar that is nowhere in sight here. Continue reading here

Musk publicly claimed Twitter's SEC filings inaccurate, while unable to provide evidence in court

Musk afirmó públicamente que las presentaciones de la SEC de Twitter son inexactas, aunque no pudo proporcionar evidencia en la corte

Musk a publiquement affirmé que les documents de la SEC de Twitter étaient inexacts, alors qu'il était incapable de fournir des preuves devant le tribunal

कस्तूरी ने सार्वजनिक रूप से दावा किया कि ट्विटर की एसईसी फाइलिंग गलत है, जबकि अदालत में सबूत देने में असमर्थ है

Musk は公に Twitter の SEC 提出書類が不正確であると主張したが、法廷で証拠を提出することはできなかった

Musk alegou publicamente que os registros da SEC do Twitter eram imprecisos, embora não pudesse fornecer evidências no tribunal

Musk công khai tuyên bố hồ sơ SEC của Twitter không chính xác, trong khi không thể cung cấp bằng chứng trước tòa

马斯克公开声称 Twitter 向美国证券交易委员会提交的文件不准确，但无法在法庭上提供证据

FTX + SBF Chapter 11 (Bankruptcy) Court Filing by John J. Ray III, Nov 17, 2022 is part of . You can jump to any chapter in this filing . This is part 6 of 20. HackerNoon’s Legal PDF Series here I- THE PREPETITION DEBTORS A. The Dotcom Silo 33. The Dotcom Silo includes FTX.com, the trade name for the business conducted by the parent company in the Dotcom Silo, FTX Trading Ltd., which is organized in Antigua. FTX.com is a digital asset trading platform and exchange. It was founded by Messrs. Bankman-Fried, Wang and Singh and commenced operations in May 2019. The Dotcom Silo also holds certain marketplace licenses and registrations in certain non-U.S. jurisdictions, including the European Union and Japan. The FTX.com platform is not available to U.S. users. 34. In addition to its core digital asset exchange, the Dotcom Silo offered an off-exchange portal that enabled users to connect and request quotes for spot digital assets and trade directly. The portal enabled users to lend their digital assets to other users for spot trading and matched users wanting to borrow with those willing to lend. 35. The FTX.com platform grew quickly since its launch to become one of the largest cryptocurrency exchanges in the world. Mr. Bankman-Fried claimed that, by the end of 2021, around $15 billion of assets were on the platform, which according to him handled approximately 10% of global volume for crypto trading at the time. Mr. Bankman-Fried also claimed that FTX.com, as of July 2022, had “millions” of registered users. These figures have not been verified by my team. 36. The Dotcom Silo’s unaudited consolidated balance sheet as of September 30, 2022 is the latest balance sheet that was provided to me with respect to the Dotcom Silo. It shows total assets of $2.25 billion as of September 30, 2022. Because such balance sheet was produced while the Debtors were controlled by Mr. Bankman-Fried, I do not have confidence in it, and the information therein may not be correct as of the date stated. 37. The chart below summarizes certain information regarding the Dotcom Silo’s consolidated assets as reflected in the September 30, 2022 balance sheet: (1) Amounts shown in thousands of U.S. Dollars.
(2) The balance sheet for non-Debtor FTX Digital Markets Ltd. is consolidated to Debtor FTX Trading Ltd.’s
balance sheet. The September 30, 2022 Balance Sheet of non-Debtor FTX Digital Markets Ltd. reflects an
asset position of $149,336, as follows: Cash and Cash Equivalents ($82,564), Restricted Cash ($10,000),
U.S. Dollar Denominated Stablecoins ($63), Related Party Receivables ($45,944), Prepaid Expenses and
Other Current Assets ($4,922), Property and Equipment, Net ($5,565) and Other Non-Current Assets
($278) (amounts in thousands of U.S. Dollars).
(3) Non-debtor FTX Digital Markets Ltd. has a net intercompany accounts payable of $30 million due to
entities controlled by Debtor FTX Trading Ltd.
(4) In the above table, assets shown reflect the elimination of intercompany entries within and between the
WRS Silo and Dotcom Silo.
(5) Customer custodial fund assets are comprised of fiat customer deposit balances. Balances of customer
crypto assets deposited are not presented.
(6) Loans Receivable of $250 million at FTX US consists of a loan to BlockFi Inc. of $250 million in FTT
tokens.
(7) Intangible assets (in the amount of $343 million) are not reflected above. These consist of values
attributable to customer relationships and trade names.
(8) Goodwill balance (in the amount of $359 million) is not reflected above. 38. To my knowledge, the Dotcom Silo Debtors do not have any long-term or funded debt. The Dotcom Silo Debtors may have significant liabilities to customers through the FTX.com platform. However, such liabilities are not reflected in the financial statements prepared by these companies while they were under the control of Mr. Bankman-Fried. The chart below summarizes certain information regarding the Dotcom Silo’s consolidated liabilities as reflected in the September 30, 2022 balance sheet: (1) Amounts shown in thousands of U.S. Dollars.
(2) In the above table, liabilities shown reflect the elimination of intercompany entries within and between the
WRS Silo and Dotcom Silo.
(3) The balance sheet for non-Debtor FTX Digital Markets Ltd. is consolidated to Debtor FTX Trading Ltd.’s
balance sheet. The September 30, 2022 Balance Sheet of non-Debtor FTX Digital Markets Ltd. reflects
total liabilities in the amount of $1,278, as follows: Accounts Payable and Accrued Expenses ($1,259),
Accounts Payable, Related Party ($19) (amounts in thousands of U.S. Dollars).
(4) Customer custodial fund liabilities are comprised of fiat customer deposit balances. Balances of customer
crypto assets deposited were not recorded as assets on the balance sheet and are not presented. 39. The Debtors in the Dotcom Silo are organized in jurisdictions around the world, with the parent company FTX Trading Ltd. organized in Antigua. The Debtors in the Dotcom Silo also own 100% of the equity interests in over a dozen non-Debtor companies. Continue Reading Here

FTX handled $15 billion USD in assets, or '~10% of global volume for crypto trading', claimed SBF 

FTX manejó $ 15 mil millones de dólares en activos, o '~ 10% del volumen global para el comercio de criptomonedas', afirmó SBF

FTX a géré 15 milliards de dollars d'actifs, soit "~ 10% du volume mondial pour le trading de crypto", a déclaré SBF

एसबीएफ ने दावा किया कि एफटीएक्स ने संपत्ति में $15 बिलियन अमरीकी डालर या 'क्रिप्टो ट्रेडिंग के लिए वैश्विक मात्रा का ~10%' संभाला

FTX は 150 億ドルの資産を処理しました。これは「世界の仮想通貨取引量の約 10%」にあたると SBF は主張しています。

A FTX movimentou US$ 15 bilhões em ativos, ou '~ 10% do volume global para negociação de criptomoedas', afirmou a SBF

FTX đã xử lý 15 tỷ USD tài sản hoặc '~10% khối lượng giao dịch tiền điện tử toàn cầu', SBF tuyên bố

SBF 称，FTX 处理了 150 亿美元的资产，或“约占全球加密货币交易量的 10%”

Authors:
(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);
(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);
(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.2 Anchor Regression 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation B Experiments 2.1 Data Augmentation Many different data augmentation methods have been proposed in recent years with several applications in mind. Still most augmentations we mention here use human-designed transformations based on domain knowledge which leave the target variable invariant. For instance, Cutout [10] is an image-specific augmentation technique that is successfully used to train models on CIFAR10 and CIFAR100 [25], but was determined to be unsuitable for larger image datasets like ImageNet with higher resolution [9]. Other augmentation methods for images such as random crop, horizontal or vertical mirroring, random rotation, or translation [29, 43] may similarly apply to a certain group of image datasets while being inapplicable to others, e.g. datasets of digits and letters. In an attempt to automate the augmentation process and reduce human involvement, policy or searchbased automated augmentation methods were developed. In AutoAugment [7] a neural network is trained with Reinforcement Learning (RL) to combine an assortment of transformations in varying strengths to apply on samples of a given dataset and improve the model accuracy. Methods such as RandAugment [8], Fast AutoAugment [30], UniformAugment [32] and TrivialAugment [36] aim at reducing the cost of the pretraining search phase in automated augmentation with randomized transformations and reduced search space. Alternatively, in order to adapt the augmentation policy to the model during training, PopulationBased Augmentation [16] and Online Hyperparameter Learning [31] use multiple data augmentation workers that are updated using evolutionary strategies and RL, respectively. Adversarial AutoAugment [53] and AugMax [47] optimize for the augmentation policy that deteriorates the training accuracy and improves its robustness. DivAug [34] finds the policy which maximizes the diversity of the augmented data. Having a separate search phase for optimal augmentation policy is computationally expensive and may exceed the required computation to train the downstream model [8, 48]. In addition, these methods and their online counterparts need to be trained separately on every single dataset. While OnlineAugment [44] and DDAS exploit meta-learning to avoid this problem, they still rely on a set of predefined class invariant transformations that require domain-specific information. Generic transformations such as Gaussian or adversarial noise [10, 28, 45] and dropout [3] are also effective in expanding the training dataset. Generative models such as Generative Adversarial Networks (GAN) [13] and Variational Auto-Encoders (VAE) [22] are trained in [1, 6, 44] to synthesize samples close to the low dimensional manifold of the data for classification. Mixup [51] is a popular data augmentation using a convex combination of pairs of samples from different classes and their softened labels for augmentation. Mixup is only evaluated on classification problems, even though it is claimed that the application to regression is straightforward. Various extensions of Mixup have been proposed to prevent data manifold intrusion [46], use more complex mixing strategies [33, 50] or account for saliency in augmented samples [20, 21]. These methods were predominantly designed to excel in classification tasks. In particular, Mixup for regression was studied in [5, 18, 49, 52] but it was reported to adversely impact the predictions in regression problems when misleading augmented samples are generated from a pair of faraway samples. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Authors: Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.1 Data Augmentation 2.2 Anchor Regression 2.2 Anchor Regression 3 Anchor Data Augmentation 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation A Additional information for Anchor Data Augmentation B Experiments B Experiments 2.1 Data Augmentation Many different data augmentation methods have been proposed in recent years with several applications in mind. Still most augmentations we mention here use human-designed transformations based on domain knowledge which leave the target variable invariant. For instance, Cutout [10] is an image-specific augmentation technique that is successfully used to train models on CIFAR10 and CIFAR100 [25], but was determined to be unsuitable for larger image datasets like ImageNet with higher resolution [9]. Other augmentation methods for images such as random crop, horizontal or vertical mirroring, random rotation, or translation [29, 43] may similarly apply to a certain group of image datasets while being inapplicable to others, e.g. datasets of digits and letters. In an attempt to automate the augmentation process and reduce human involvement, policy or searchbased automated augmentation methods were developed. In AutoAugment [7] a neural network is trained with Reinforcement Learning (RL) to combine an assortment of transformations in varying strengths to apply on samples of a given dataset and improve the model accuracy. Methods such as RandAugment [8], Fast AutoAugment [30], UniformAugment [32] and TrivialAugment [36] aim at reducing the cost of the pretraining search phase in automated augmentation with randomized transformations and reduced search space. Alternatively, in order to adapt the augmentation policy to the model during training, PopulationBased Augmentation [16] and Online Hyperparameter Learning [31] use multiple data augmentation workers that are updated using evolutionary strategies and RL, respectively. Adversarial AutoAugment [53] and AugMax [47] optimize for the augmentation policy that deteriorates the training accuracy and improves its robustness. DivAug [34] finds the policy which maximizes the diversity of the augmented data. Having a separate search phase for optimal augmentation policy is computationally expensive and may exceed the required computation to train the downstream model [8, 48]. In addition, these methods and their online counterparts need to be trained separately on every single dataset. While OnlineAugment [44] and DDAS exploit meta-learning to avoid this problem, they still rely on a set of predefined class invariant transformations that require domain-specific information. Generic transformations such as Gaussian or adversarial noise [10, 28, 45] and dropout [3] are also effective in expanding the training dataset. Generative models such as Generative Adversarial Networks (GAN) [13] and Variational Auto-Encoders (VAE) [22] are trained in [1, 6, 44] to synthesize samples close to the low dimensional manifold of the data for classification. Mixup [51] is a popular data augmentation using a convex combination of pairs of samples from different classes and their softened labels for augmentation. Mixup is only evaluated on classification problems, even though it is claimed that the application to regression is straightforward. Various extensions of Mixup have been proposed to prevent data manifold intrusion [46], use more complex mixing strategies [33, 50] or account for saliency in augmented samples [20, 21]. These methods were predominantly designed to excel in classification tasks. In particular, Mixup for regression was studied in [5, 18, 49, 52] but it was reported to adversely impact the predictions in regression problems when misleading augmented samples are generated from a pair of faraway samples. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

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