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Claimer

claimer

I asked someone recently, “What’s the most remarkable thing about the Solana ecosystem?” Here’s what he said: Oh meme coins and PumpFun, right? No, don’t smirk… if I asked you what you knew about Solana, your answer would most definitely be “meme coins,” “PumpFun,” or maybe based on more recent events, “Pengu airdrop.” No, it’s not your fault, it’s more like that’s what the prevailing narrative has been about the ecosystem. This sentiment is more than backed up by Messari’s State of Solana report for Q3, 2024 which shows that Solana’s average daily DEX volume has continued to rise impressively since Q4, 2023. The most impressive increase by far was the $0.4B to $1.2B jump between Q4 2023 and Q1 2024. Why is this particular timing significant? Because it ties into PumpFun’s launch on January 19, 2024 —a direct indication of the impact PumpFun has had on the volume of transactions in the Solana ecosystem. That’s not the only indication. According to Dexu.AI, the top 10 tokens in the Solana ecosystem by MarketCap are almost all meme coins (excluding stables, of course). And all of these tokens have enjoyed a massive price surge over the meme coin super cycle of last here. See here for context👇🏼 Source: https://www.coingecko.com/en/chains/solana And it is also important to point out that Solana has been a hub for meme coin trading since way before PumpFun came into existence. OG tokens like $POPCAT and $BONK come to mind. Based on all this it’s easy to see why Solana has gained a reputation for being “The blockchain for meme coins.” But is that all there is to the blockchain? That would be a huge NO! Peeking Under The Hood Solana is one of the most stacked blockchains currently existing performance-wise. To start with Solana combines a unique consensus mechanism known as “Proof of History” with Proof of Stake (PoS) to deliver incredible performance largely unmatched in the current Web3 space. Speed To start with, Solana can theoretically handle up to 65,000 transactions per second (TPS), benchmarking it as one of the fastest L1s around. Just so you understand, this is way faster than Avalanche, Ethereum, BSC, Algorand, and Cardano combined! In addition to this, Solana offers near-instant finality, making it an excellent choice for projects that require quick confirmation of transactions. A huge reason why the blockchain is so fast is its unique consensus framework that allows for the hyper-efficient rapid processing of large transaction volumes. Resilience But it's not just that Solana is fast, the blockchain’s network is also very resilient, making it capable of handling significant load. $DOGS and $NOTairdrops caused outages in the TON blockchain because of the sheer number of demand placed on it by airdrop claimers. The number of people who received the airdrop was about 45 million and 35 million respectively. $BONK airdropped to over 37 million users, making it one of the largest-ever airdrop events on Solana. Yet there was NO network outage recorded during the event. This clearly demonstrates that Solana is capable of handling the huge demand required by sectors like GameFi, AI and even RWA. We’ll get to that in a moment. Reach It’s also important to point out that despite being powered by SVM, Solana’s reach is near-boundless. This is thanks to its integration of interoperability protocols like The Rome Protocol, Wormhole, and LayerZero– a particularly significant development that connects Solana to over 70 other chains. Correcting The Misconception Now ask yourself: Is it possible that the only thing all this artillery is good for is launching and trading meme coins? Sounds absurd, doesn’t it? Now you’re beginning to understand just how incredible Solana as a product really is. Memes may have hugely outshadowed utility plays on Solana but don’t make the popular mistake of assuming they are absent. Solana is still home to tons of innovative projects. What I find particularly interesting is there are tons of innovative solutions already built on Solana and they cut across all of the major DeFi sectors. Here’s a walkthrough of each sector 👇🏽 #1. DeFAI DeFAI is a newly-emerged sector in the DeFi ecosystem that focuses on integrating AI with core DeFi components in order to supercharge on-chain activities and operations. By this, I mean integrating AI with traditional DeFi tools and processes in order to arrive at a much more efficient product. For example, trading bots have long been a staple in the degen trading landscape with tools like Bonkbot, Bananagunbot and so on. Now although these tools offer much better position entry and exit accuracy + speed, they still have a lot of limitations. They can’t research tokens and spot gems on their own
They can’t set entry and exit targets themselves
They rely on human input to carry out their functions. A DeFAI improvement on this like Griffain’s Agent Dora is able to 100% autonomously scrape for data, develop key actionable insights, and take appropriate trading action. And guess what? Griffain is built on Solana! I should also mention that Griffain actually has a vast collection of specialist AI agents capable of performing different tasks from Whale watching to yield farming and even training other AI agents. Other examples of projects in this category native to Solana include: MOBY


NEUR


BUZZ #2. DePIN DePIN is short for Decentralized Physical Infrastructure Network. This is a DeFi sector that involves using decentralized/blockchain systems to deploy, manage, and operate physical infrastructure. The way this works is pretty simple: Protocols here usually offer incentives to users in exchange for them performing a specific action like running a node. Rewards are allocated based on participation in the network in order to drive the message that active participation will be rewarded.\ DePIN has actually proved to be a revolutionary tool in improving existing Web2 systems and even breaking ground where Tradfi has failed. Some sectors where DePIN projects have made a name for themselves include the following:\ Data storage (Filecoin)
Telecommunication (Helium)
Energy (Power Ledger)
IoT (IOTEX)
Computing, and many more. It will interest you to know that Solana is actually a treasure trove of DePIN projects. Almost all the biggest names in this sector are on the Solana blockchain. Examples include: IONET
Grass
Render
GenesysGo An excellent use case example of Solana DePIN projects doing crazily well is Helium Beacons using Starlink to successfully cell service for over 20 thousand Hurricane Helene victims in NC. Check it out here. #3. DeSCI DeSCI stands for Decentralized Science, and it’s one of the fastest-growing sectors in Web3. DeSCI projects work to apply blockchain technology to scientific research, with a focus on how scientific research is funded, conducted, published, shared, and accessed. DeSCI is so important because it introduces the much-needed transparency layer into the field of scientific research, ensuring that all data from funding to peer review processes are transparent, immutable, and verifiable by anyone. Examples of DeSCI projects on Solana include: Pump.science


Molecule #4. RWA According to Artemis.xyz, RWA was the best-performing sector in 2024. This becomes even more astonishing when you consider that the meme coin supercycle that led to the birth of icons like $GOAT, $SUNDOG, $HIPPO, and $PNUT happened last year too! Institutional players like Blackrock and Franklin Templeton have all identified real-world assets as the next logical evolution of TradFi and DeFi and they’ve been pouring billions of dollars into the sector. So this is definitely a sector worth paying attention to. And it’s interesting to point out that a number of solid RWA projects are also present in the Solana ecosystem. Some examples include: Parcl
Homebase
Collector Crypt Other Notable Mentions There are literally lots of incredible products in the Solana ecosystem so I definitely won’t be able to give you a walkthrough of every one of them. The best I can do is provide you with 10 more project recommendations I think you should take a look at… you’ll be surprised: Vodra - vodra.io
Moonwalk Fitness - moonwalk.fit
SendAI - solanaaihackathon.com
Top Hat AI - tophat.one
Solana Mobile - solanamobile.com
EnsoFi - __a__pp.ensofi.xyz
Sponge - app.spongehq.xyz
Bullet - projectbullet.xyz
Hivemapper - docs.hivemapper.com
Save - save.finance Taking all that I’ve said thus far into account, this is my thesis: Solana has too much quality to be relegated to a “meme coin chain” narrative. We’ve already seen several incredible products, some of which I’ve highlighted already. There will be more. You have no idea what’s coming. I asked someone recently, “What’s the most remarkable thing about the Solana ecosystem?” Here’s what he said: I asked someone recently, “What’s the most remarkable thing about the Solana ecosystem?” Here’s what he said: Oh meme coins and PumpFun, right? Oh meme coins and PumpFun, right? Oh meme coins and PumpFun, right? No, don’t smirk… if I asked you what you knew about Solana, your answer would most definitely be “meme coins,” “PumpFun,” or maybe based on more recent events, “Pengu airdrop.” No, it’s not your fault, it’s more like that’s what the prevailing narrative has been about the ecosystem. This sentiment is more than backed up by Messari’s State of Solana report for Q3, 2024 which shows that Solana’s average daily DEX volume has continued to rise impressively since Q4, 2023. The most impressive increase by far was the $0.4B to $1.2B jump between Q4 2023 and Q1 2024. Why is this particular timing significant? Because it ties into PumpFun’s launch on January 19, 2024 —a direct indication of the impact PumpFun has had on the volume of transactions in the Solana ecosystem. That’s not the only indication. According to Dexu.AI, the top 10 tokens in the Solana ecosystem by MarketCap are almost all meme coins (excluding stables, of course). And all of these tokens have enjoyed a massive price surge over the meme coin super cycle of last here. See here for context👇🏼 Source: https://www.coingecko.com/en/chains/solana https://www.coingecko.com/en/chains/solana https://www.coingecko.com/en/chains/solana And it is also important to point out that Solana has been a hub for meme coin trading since way before PumpFun came into existence. OG tokens like $POPCAT and $BONK come to mind. Based on all this it’s easy to see why Solana has gained a reputation for being “The blockchain for meme coins.” But is that all there is to the blockchain? That would be a huge NO! Peeking Under The Hood Solana is one of the most stacked blockchains currently existing performance-wise. To start with Solana combines a unique consensus mechanism known as “Proof of History” with Proof of Stake (PoS) to deliver incredible performance largely unmatched in the current Web3 space. Speed To start with, Solana can theoretically handle up to 65,000 transactions per second (TPS), benchmarking it as one of the fastest L1s around. Just so you understand, this is way faster than Avalanche, Ethereum, BSC, Algorand, and Cardano combined! In addition to this, Solana offers near-instant finality, making it an excellent choice for projects that require quick confirmation of transactions. A huge reason why the blockchain is so fast is its unique consensus framework that allows for the hyper-efficient rapid processing of large transaction volumes. Resilience But it's not just that Solana is fast, the blockchain’s network is also very resilient, making it capable of handling significant load. $DOGS and $NOTairdrops caused outages in the TON blockchain because of the sheer number of demand placed on it by airdrop claimers. The number of people who received the airdrop was about 45 million and 35 million respectively. $BONK airdropped to over 37 million users, making it one of the largest-ever airdrop events on Solana. Yet there was NO network outage recorded during the event. This clearly demonstrates that Solana is capable of handling the huge demand required by sectors like GameFi, AI and even RWA. We’ll get to that in a moment. Reach It’s also important to point out that despite being powered by SVM, Solana’s reach is near-boundless. This is thanks to its integration of interoperability protocols like The Rome Protocol, Wormhole, and LayerZero– a particularly significant development that connects Solana to over 70 other chains. Correcting The Misconception Now ask yourself: Is it possible that the only thing all this artillery is good for is launching and trading meme coins? Sounds absurd, doesn’t it? Now you’re beginning to understand just how incredible Solana as a product really is. Memes may have hugely outshadowed utility plays on Solana but don’t make the popular mistake of assuming they are absent. Solana is still home to tons of innovative projects. What I find particularly interesting is there are tons of innovative solutions already built on Solana and they cut across all of the major DeFi sectors. Here’s a walkthrough of each sector 👇🏽 #1. DeFAI DeFAI is a newly-emerged sector in the DeFi ecosystem that focuses on integrating AI with core DeFi components in order to supercharge on-chain activities and operations. By this, I mean integrating AI with traditional DeFi tools and processes in order to arrive at a much more efficient product. For example, trading bots have long been a staple in the degen trading landscape with tools like Bonkbot, Bananagunbot and so on. Now although these tools offer much better position entry and exit accuracy + speed, they still have a lot of limitations. They can’t research tokens and spot gems on their own They can’t set entry and exit targets themselves They rely on human input to carry out their functions. They can’t research tokens and spot gems on their own They can’t set entry and exit targets themselves They rely on human input to carry out their functions. A DeFAI improvement on this like Griffain’s Agent Dora is able to 100% autonomously scrape for data, develop key actionable insights, and take appropriate trading action. And guess what? Griffain is built on Solana! I should also mention that Griffain actually has a vast collection of specialist AI agents capable of performing different tasks from Whale watching to yield farming and even training other AI agents. Griffain Griffain Other examples of projects in this category native to Solana include: MOBY NEUR BUZZ MOBY MOBY MOBY MOBY NEUR NEUR NEUR NEUR BUZZ BUZZ BUZZ BUZZ #2. DePIN DePIN is short for Decentralized Physical Infrastructure Network. This is a DeFi sector that involves using decentralized/blockchain systems to deploy, manage, and operate physical infrastructure. The way this works is pretty simple: Protocols here usually offer incentives to users in exchange for them performing a specific action like running a node. Rewards are allocated based on participation in the network in order to drive the message that active participation will be rewarded.\ DePIN has actually proved to be a revolutionary tool in improving existing Web2 systems and even breaking ground where Tradfi has failed. Some sectors where DePIN projects have made a name for themselves include the following:\ Data storage (Filecoin) Telecommunication (Helium) Energy (Power Ledger) IoT (IOTEX) Computing, and many more. Data storage (Filecoin) Telecommunication (Helium) Energy (Power Ledger) IoT (IOTEX) Computing, and many more. It will interest you to know that Solana is actually a treasure trove of DePIN projects. Almost all the biggest names in this sector are on the Solana blockchain. Examples include: IONET Grass Render GenesysGo IONET IONET IONET Grass Grass Grass Render Render Render GenesysGo GenesysGo GenesysGo An excellent use case example of Solana DePIN projects doing crazily well is Helium Beacons using Starlink to successfully cell service for over 20 thousand Hurricane Helene victims in NC. Check it out here . here here here #3. DeSCI DeSCI stands for Decentralized Science, and it’s one of the fastest-growing sectors in Web3. DeSCI projects work to apply blockchain technology to scientific research, with a focus on how scientific research is funded, conducted, published, shared, and accessed. DeSCI is so important because it introduces the much-needed transparency layer into the field of scientific research, ensuring that all data from funding to peer review processes are transparent, immutable, and verifiable by anyone. Examples of DeSCI projects on Solana include: Pump.science Molecule Pump.science Pump.science Pump.science Pump.science Molecule Molecule Molecule Molecule #4. RWA According to Artemis.xyz, RWA was the best-performing sector in 2024. This becomes even more astonishing when you consider that the meme coin supercycle that led to the birth of icons like $GOAT, $SUNDOG, $HIPPO, and $PNUT happened last year too! Institutional players like Blackrock and Franklin Templeton have all identified real-world assets as the next logical evolution of TradFi and DeFi and they’ve been pouring billions of dollars into the sector. So this is definitely a sector worth paying attention to. And it’s interesting to point out that a number of solid RWA projects are also present in the Solana ecosystem. Some examples include: Parcl Homebase Collector Crypt Parcl Parcl Parcl Homebase Homebase Homebase Collector Crypt Collector Crypt Collector Crypt Other Notable Mentions There are literally lots of incredible products in the Solana ecosystem so I definitely won’t be able to give you a walkthrough of every one of them. The best I can do is provide you with 10 more project recommendations I think you should take a look at… you’ll be surprised: Vodra - vodra.io Moonwalk Fitness - moonwalk.fit SendAI - solanaaihackathon.com Top Hat AI - tophat.one Solana Mobile - solanamobile.com EnsoFi - __a__pp.ensofi.xyz Sponge - app.spongehq.xyz Bullet - projectbullet.xyz Hivemapper - docs.hivemapper.com Save - save.finance Vodra - vodra.io Moonwalk Fitness - moonwalk.fit SendAI - solanaaihackathon.com Top Hat AI - tophat.one Solana Mobile - solanamobile.com EnsoFi - __a__pp.ensofi.xyz Sponge - app.spongehq.xyz Bullet - projectbullet.xyz Hivemapper - docs.hivemapper.com Save - save.finance Taking all that I’ve said thus far into account, this is my thesis: Solana has too much quality to be relegated to a “meme coin chain” narrative. We’ve already seen several incredible products, some of which I’ve highlighted already. There will be more. You have no idea what’s coming.

Beating the "Meme Coins" Allegations: Explore the Solana Ecosystem

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 

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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%”

There are more AI models than what you see in the news and on social media. There are hundreds, including open-source models, private ones, and the tech giant’s own: Gemini, Claude, OpenAI, Grok, Deepseek. What is a model really? Is it just a block box of data? Almost! You can think of it as a zip file of the internet with a bit of C++ code that communicates with the zip file. I credit this analogy to Andrej Karpathy, although I’m not sure if it was his idea, but he is the real industry expert. An AI model is a neural network utilizing a set of massive data to recognize specific patterns. Now is the time to take advantage of them and choose wisely whether for business, personal assistance, or creativity enhancement. The objective of this guide is not about “model training”; it is geared for individuals new to the field of AI who want a better understanding and to leverage the technology. You can build with AI, not over it, so after reading this guide, the knowledge gained shall be understanding general concepts, usage, and measuring accuracy. In this AI guide, you will learn the following, so you can skip to any section, but if you’re a beginner, read this entire article: Category of Models
Corresponding Tasks of Models
Naming Convention of Models
Accuracy Performance of Models
Benchmark References As a beginner or just hearing about the popular tools, make note that there isn’t one type, multi-use case model that does everything you ask it to do. From the interface, it may appear that you are just typing to a chatbot, but there is a lot more being executed in the background. Business analysts, product managers, and engineers adopting AI can identify the objective they have and select from a category of AI models. Here are 4 categories of models among many: Natural Language Processing (general)
Generative (Image, Video, Audio, Text, Code)
Discriminative (Computer Vision, Text Analysis)
Reinforcement Learning While most models specialize in one category, others are multi-modal with different levels of accuracy. Every model has been trained on specific data and, therefore, can do specific tasks related to the data it was trained on. Here’s a list of common tasks each category of models can do: Natural Language Processing Enables computers to interpret, understand, and generate natural human language using tokenization and statistical models. Examples are chatbots, and the most common one is ChatGPT, which stands for “generative pre-trained transformer”. Most models are actually pre-trained transformers. Generative (Image, Video, Audio, Text, Code) They are Generative Adversarial Networks (GANs), which use two sub-models known as a generator and a discriminator. Realistic imagery, audio, text, and code can be produced based on tons of data it was trained on. Stable Diffusion is the most popular method of generating images and videos. Discriminative (Computer Vision, Text Analysis) These use algorithms designed to learn different classes of datasets for decision-making. They include sentiment analysis, optical recognition, and sentiment analysis. Reinforcement Learning Using trial-and-error methods and human enforcement to produce goal-oriented outcomes, such as robotics, game playing, and autonomous driving. Naming Convention of Models Now that you understand the types of models and their tasks, the next step is to identify the model quality and performance. This begins with the name of the models. Let’s break down a model naming. There’s the official convention for naming AI models, but the most popular ones will just have a name followed by the version number, such as chatGPT#, Claude #, Grok #, Gemini #. However, the smaller open-source and task-specific models will have longer names. This can be seen on huggingface.co, which will contain the organization name, model name, parameter size, and lastly the context size. Let’s elaborate with examples: mistralai/Mistral-Small-3.1-24B-Instruct-2053 Mistralai is the organization
Mistral-Small is the model name
3.1 is the version number
24B-Instruct is the parameter count in billions of training data
2053 is the context size or token count google/gemma-3-27b Google is the organization
Gemma is the model name
3 is the version number
27B is the parameter size in billions Additional details, which you will see and need to know, are the quantization format in bits. The higher the quantization format, the more computer RAM and storage are required to operate the model. A quantization format is represented in floating point, such as 4, 6, 8, and 16. Other formats can include GPTQ, NF4, and GGML, which indicate usage for specific hardware configurations. Accuracy Performance of Models If you’ve seen news headlines about a new model release, do not immediately trust the results that are claimed. AI performance competition is so competitive right now that companies cook up the performance numbers for marketing hype. How many people will test them on their own instead of trusting the marketing hype? Not many at all, so don’t fall for the “hallucinated figures”. References https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/ and https://lmarena.ai/?leaderboard The real way to determine model quality is to check benchmark scores and leaderboards. There have been several tests which you could say are semi-standardized or maybe fully standardized, but in reality, we are testing “black boxes” with tons of variables. The best measure is to check answers and responses from AI with facts and other scientific sources. Leaderboard websites will show sortable rankings with votes, confidence interval scores, usually in a percentage value. The common benchmarks are tests that prompt the AI model with questions and get answers that are measured. They can include: AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, WinoGrande, GSM8K, HumanEval. Here are brief descriptions of the benchmarking methods: AI2 Reasoning Challenge (ARC) – 7787 multiple-choice science questions from grade school HellaSwag – commonsense reasoning exercises through sentence completion MMLU – Massive multitask language understanding for problem solving TruthfulQA – assess truthfulness by encouraging falsehoods and avoiding responses like “I’m not sure”. WinoGrande – Winograd schema challenge with two near-identical sentences based on a trigger word GSM8K – 8,000 grade school level math questions HumanEval – measures the ability to generate correct Python code across 164 challenges Leaderboard websites for reference: https://www.vellum.ai/blog/llm-benchmarks-overview-limits-and-model-comparison https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/ https://scale.com/leaderboardhttps://artificialanalysis.ai/leaderboards/models https://epoch.ai/data/notable-ai-models https://openlm.ai/chatbot-arena/ https://lmarena.ai/?leaderboard Originally published here There are more AI models than what you see in the news and on social media. There are hundreds, including open-source models, private ones, and the tech giant’s own: Gemini, Claude, OpenAI, Grok, Deepseek. What is a model really? Is it just a block box of data? Almost! You can think of it as a zip file of the internet with a bit of C++ code that communicates with the zip file. I credit this analogy to Andrej Karpathy, although I’m not sure if it was his idea, but he is the real industry expert. An AI model is a neural network utilizing a set of massive data to recognize specific patterns. Now is the time to take advantage of them and choose wisely whether for business, personal assistance, or creativity enhancement. The objective of this guide is not about “model training”; it is geared for individuals new to the field of AI who want a better understanding and to leverage the technology. You can build with AI, not over it, so after reading this guide, the knowledge gained shall be understanding general concepts, usage, and measuring accuracy. In this AI guide, you will learn the following, so you can skip to any section, but if you’re a beginner, read this entire article: Category of Models Corresponding Tasks of Models Naming Convention of Models Accuracy Performance of Models Benchmark References Category of Models Corresponding Tasks of Models Naming Convention of Models Accuracy Performance of Models Benchmark References As a beginner or just hearing about the popular tools, make note that there isn’t one type, multi-use case model that does everything you ask it to do. From the interface, it may appear that you are just typing to a chatbot, but there is a lot more being executed in the background. Business analysts, product managers, and engineers adopting AI can identify the objective they have and select from a category of AI models. Here are 4 categories of models among many: Natural Language Processing (general) Generative (Image, Video, Audio, Text, Code) Discriminative (Computer Vision, Text Analysis) Reinforcement Learning Natural Language Processing (general) Generative (Image, Video, Audio, Text, Code) Discriminative (Computer Vision, Text Analysis) Reinforcement Learning While most models specialize in one category, others are multi-modal with different levels of accuracy. Every model has been trained on specific data and, therefore, can do specific tasks related to the data it was trained on. Here’s a list of common tasks each category of models can do: Natural Language Processing Natural Language Processing Enables computers to interpret, understand, and generate natural human language using tokenization and statistical models. Examples are chatbots, and the most common one is ChatGPT, which stands for “generative pre-trained transformer”. Most models are actually pre-trained transformers. Generative (Image, Video, Audio, Text, Code) Generative (Image, Video, Audio, Text, Code) They are Generative Adversarial Networks (GANs), which use two sub-models known as a generator and a discriminator. Realistic imagery, audio, text, and code can be produced based on tons of data it was trained on. Stable Diffusion is the most popular method of generating images and videos. Discriminative (Computer Vision, Text Analysis) Discriminative (Computer Vision, Text Analysis) These use algorithms designed to learn different classes of datasets for decision-making. They include sentiment analysis, optical recognition, and sentiment analysis. Reinforcement Learning Reinforcement Learning Using trial-and-error methods and human enforcement to produce goal-oriented outcomes, such as robotics, game playing, and autonomous driving. Naming Convention of Models Naming Convention of Models Now that you understand the types of models and their tasks, the next step is to identify the model quality and performance. This begins with the name of the models. Let’s break down a model naming. There’s the official convention for naming AI models, but the most popular ones will just have a name followed by the version number, such as chatGPT#, Claude #, Grok #, Gemini #. However, the smaller open-source and task-specific models will have longer names. This can be seen on huggingface.co , which will contain the organization name, model name, parameter size, and lastly the context size. huggingface.co Let’s elaborate with examples: mistralai/Mistral-Small-3.1-24B-Instruct-2053 mistralai/Mistral-Small-3.1-24B-Instruct-2053 Mistralai is the organization Mistral-Small is the model name 3.1 is the version number 24B-Instruct is the parameter count in billions of training data 2053 is the context size or token count Mistralai is the organization Mistral-Small is the model name 3.1 is the version number 24B-Instruct is the parameter count in billions of training data 2053 is the context size or token count google/gemma-3-27b google/gemma-3-27b Google is the organization Gemma is the model name 3 is the version number 27B is the parameter size in billions Google is the organization Gemma is the model name 3 is the version number 27B is the parameter size in billions Additional details, which you will see and need to know, are the quantization format in bits. The higher the quantization format, the more computer RAM and storage are required to operate the model. A quantization format is represented in floating point, such as 4, 6, 8, and 16. Other formats can include GPTQ, NF4, and GGML, which indicate usage for specific hardware configurations. Accuracy Performance of Models Accuracy Performance of Models If you’ve seen news headlines about a new model release, do not immediately trust the results that are claimed. AI performance competition is so competitive right now that companies cook up the performance numbers for marketing hype. How many people will test them on their own instead of trusting the marketing hype? Not many at all, so don’t fall for the “hallucinated figures”. References https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/ and https://lmarena.ai/?leaderboard https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/ https://lmarena.ai/?leaderboard The real way to determine model quality is to check benchmark scores and leaderboards. There have been several tests which you could say are semi-standardized or maybe fully standardized, but in reality, we are testing “black boxes” with tons of variables. The best measure is to check answers and responses from AI with facts and other scientific sources. Leaderboard websites will show sortable rankings with votes, confidence interval scores, usually in a percentage value. The common benchmarks are tests that prompt the AI model with questions and get answers that are measured. They can include: AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, WinoGrande, GSM8K, HumanEval. Here are brief descriptions of the benchmarking methods: AI2 Reasoning Challenge (ARC) – 7787 multiple-choice science questions from grade school AI2 Reasoning Challenge (ARC) HellaSwag – commonsense reasoning exercises through sentence completion HellaSwag MMLU – Massive multitask language understanding for problem solving MMLU TruthfulQA – assess truthfulness by encouraging falsehoods and avoiding responses like “I’m not sure”. TruthfulQA WinoGrande – Winograd schema challenge with two near-identical sentences based on a trigger word WinoGrande GSM8K – 8,000 grade school level math questions GSM8K HumanEval – measures the ability to generate correct Python code across 164 challenges HumanEval Leaderboard websites for reference: Leaderboard websites for reference: https://www.vellum.ai/blog/llm-benchmarks-overview-limits-and-model-comparison https://www.vellum.ai/blog/llm-benchmarks-overview-limits-and-model-comparison https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/ https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/ https://scale.com/leaderboard https://artificialanalysis.ai/leaderboards/models https://scale.com/leaderboard https://artificialanalysis.ai/leaderboards/models https://epoch.ai/data/notable-ai-models https://epoch.ai/data/notable-ai-models https://openlm.ai/chatbot-arena/ https://openlm.ai/chatbot-arena/ https://lmarena.ai/?leaderboard https://lmarena.ai/?leaderboard Originally published here here

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