Le Hanh STONE🐦‍⬛🪨

🎉Omnichain Carnival Wave 1 is now live with early access! TL;DR -Early access for Wave 1 starts on 3/26 at 14:00 UTC and lasts for 4 days -Register during early access to receive a 15% bonus in wave 1 -3% of the total supply will be allocated in Wave 1. Lead Wave 1 with extra rewards! Register at: carnival.stakestone.io Anticipate Wave 2, plus even more to come. Learn more about omnichain carnival: @official_42951/stakestone-omnichain-carnival-6a4fc10ebe4f" target="_blank" rel="nofollow noopener">medium.com/@official_4295…

I'm an Early Pumper! Use my referral code 36785278 for early access to @pumpmarkets and bonus PUMP points ⛽ pump.markets

🚨 Testnet Early Access 🚨 Sign up now for your chance to be an Early Pumper ⛽ The earlier you join, the more points you earn... 🏆 See the link in bio @pumpmarkets to get started 🔗

🚀🚀 Introducing the first 404 protocol Launchpad: ZKFair CyberArmy on the Element ZKFair Marketplace launched by @ZKFCommunity! 1. Name: ZKFair CyberArmy 2. Amount: 9,999 3. Price - Whitelist: 1 USDC - Public Sale: 3 USDC 4. Introduction: CyberArmy is a NFT collection consisting of 9,999 warriors, adhering to the 404 protocol. Each with its own unique elements symbolizing fairness, revolution, and the spirit of never giving up. 5. Utility: permanent access to ZKFair future launch pool airdrops and other ecosystem potential airdrops. 6. Mint Rules - Whitelist: each wallet can mint a maximum of 1 (FCFS) - Public Sale: each wallet can mint a maximum of 1 (FCFS) PS: if the collection is sold out during the whitelist period, there will be no public sale. 7. Mint Time - Whitelist: Mar.2nd, 12:00, UTC - Public Sale: Mar.3rd, 12:00, UTC 8. Whitelist Check: you can check it on the mint page after Mar.2nd, 11:00, UTC. 9. Mint Page element.market/zkfair/launchp… 10. Get a Whitelist🔥🔥 - For EPG/EPS holder: element.market/launchpad/even… - For public user: element.market/launchpad/even… 🤩🤩LFG!! #ElementNFT #ZKFair #NFT #NFTCommunity #Whitelist #Erc404NFT

Hello, I am hanhle0.edu and I’m an Open Campus ID holder. I just minted for free on @opencampus_xyz and earned my first #OCPoints. Join me in the future of decentralized education ✊ Comment below for a referral code!

Introducing #OC100! Join us, along with @ForbesWeb3 and @animocabrands, as we search for the top 100 creators and thought leaders in Web3. 25 exceptional judges. 8 diverse categories. 3 community votes. 100 extraordinary creators. Learn more at 100.opencampus.xyz

I just joined @wen_exchange Early Access to claim $WEN Pre-Token Airdrop 👀 WEN is No.1 NFT marketplace on @Blast_L2 with native yield and zero trading fee. Use this ref link for extra tokens👇: wen.exchange/earlyaccess/R1… twitter.com/wen_exchange/s…

@KIPprotocol ..;

KIP Explainer Series: #2 What is RAG, and Why is KIP decentralizing it? TLDR: RAG is an innovative technique used in Generative AI, involving 3 key Value Creators in AI (app owners,📱, model owners 🤖, data owners 📚 ). KIP's successful decentralisation of RAG essentially gives a framework for the decentralisation of all of AI, and is a necessary first step to fight encroaching AI Monopolies. 1⃣ RAG IN A NUTSHELL ​​AI models are trained by feeding in data. They learn from the data, adjusting their internal weights to recognize patterns, enabling them to make predictions or decisions based on new data. The model can then answer user queries from its newly-gained "native" knowledge. But this training process requires the entire dataset to be exposed to the model, and essentially results in the data being 'absorbed' into the model. If the data includes confidential or copyrighted information, there's a risk that the model will spit that information out verbatim at some point in the future. So what if you don't wish to put your data at risk? That's where Retrieval-Augmented Generation, or RAG comes in. RAG is a sophisticated technique that enables AI models to generate answers it doesn't natively know, by retrieving data & information from external knowledge bases & databases it is given access to. It's like an intelligent assistant who does not know the answer to your question, but is able to expertly research to find the answer from external data sources. 1. User Query Input: The process begins with a user posing a question or query to a chatbot running a RAG system. For example, "What are the symptoms of COVID-19?" 2. Retrieval from External Databases: The model initiates the retrieval phase by searching through linked external knowledge bases & databases, such as medical journals, health websites, and clinical databases, to retrieve only relevant chunks of data and info related to the user query. 3. Data Processing, Filtering and Generation: Retrieved data undergoes processing and filtering to extract key information and eliminate irrelevant data points. The AI model synthesizes the retrieved data with contextual cues from the user query to generate a response. In the case of the COVID-19 symptoms query, RAG might generate a response listing common symptoms such as fever, cough, and shortness of breath, but also potentially including information the latest medical research papers that was not available when the model was trained - a higher quality response. 4.Response Delivery: The generated response is presented to the user via the chatbot interface. Thus, RAG allows external data to be used to answer AI queries without needing that data to be "absorbed" first by a model through the training process. RAG techniques are getting more sophisticated all the time, and in our research paper here, we show that quality of answers under RAG can outperform trained models. arxiv.org/pdf/2311.05903… ​​2⃣ IMPORTANCE OF RAG RAG is going to become increasingly important because: ​​​ 1. Model training is a highly technical and specialised activity, and often very expensive to do - not everyone will have the necessary skillsets or resources to able to train models. 2. There is a lot of data (confidential, proprietary etc.) whose owners may never feel comfortable to expose fully to models they don't fully own or control. One important point you may also have noticed is: Under a RAG framework, app owners,📱, model owners 🤖 and data owners 📚 work together and each contribute to the answering of user queries. Thus, in a equitable state of affairs, each party should be fairly compensated for their contributions. But there is currently no easy way to do this without compromising each party's independence or ownership rights. (Incidentally, this problem is exactly what prompted us to start building KIP, more than a year ago.) This is the "money problem". ​​​3⃣ "THE MONEY PROBLEM" WITH RAG & CENTRALISED AI Let's imagine a situation where one entity owns all three levers of AI value creation: there's no need to split payments collected from the users between the parties, as it's just internal accounting. But the flipside of that is: if we are not ok with ONE ENTITY OWNING ALL 3 LEVERS OF AI VALUE CREATION ( 📱, 🤖 , 📚 ), we must solve the issue of how to split money between the different industries of AI Value Creators. Without solving "The Money Problem", ( 📱, 🤖 , 📚 ) cannot each maintain their independence and freedom to trade. And a monopoly is already forming right now. Here's our opinion on how the monopolistic battleplan of OpenAI will work: - OpenAI obviously has some of the most powerful models - closed-source models like GPT-4, which were trained using our collective knowledge as published and scraped from the open internet over many years. That powers their apps like ChatGPT, and the user-made GPTs. - Via their Copyright Shield - that is, their commitment to pay the legal fees of anyone found to be uploading copyrighted data to their platform - they embolden and encourage their users to upload data to their closed platform without fearing legal consequences. - Given that OpenAI is a centralised, closed-source web2 platform, we should ask ourselves: does the data uploaded by users - whether to ChatGPT or the GPTs apps - still belong to the uploaders? - So with their existing models, unapologetic scraping of any and all data, Copyright Shield, and their huge war-chest, you have probably the most voracious data vacuum cleaner ever created, sucking in data and resources to feed their models. Put all the above together (and their 7 tttttrillion dollar raise for hardware) and it's not difficult to see that total monopolisation of AI development by one or a few companies will be inevitable, unless something is done. For reasons we've already shared, we passionately believe that AI monopolisation is bad for humanity, and are actively fighting against it. 4⃣THE SIGNIFICANCE OF DECENTRALISING RAG RAG involves all 3 core levers of AI value creation ( 📱, 🤖 , 📚 ). Thus, by building a framework for decentralising RAG, KIP essentially builds a framework for decentralising control over value creation in AI, thus giving a level playing field for all value creators to fight AI monopolies. We allow AI to function efficiently as a collaborative effort involving millions of small- and large-scale creators, without the need for one huge company to coordinate each of the core functions. We will do that by first solving 3 base level problems that have been a barrier to the decentralisation of RAG: 1. Ownership: Ensuring that ( 📱, 🤖 , 📚 ) can publish easily and securely to web3 easily, creating their web3 "trading entity" in the form of ERC 3525 Semi-Fungible Tokens, thus enabling them to prove their digital property rights on chain. 2. Connectivity: Ensuring smooth off-chain and on-chain interactions, providing an open environment for 📱, 🤖 , 📚 to connect to each other easily and freely 3. Monetisation: Providing a common framework for recording & accounting for the contributions of each AI Value Creator, as well as an automated revenue share and withdrawals. By bringing about decentralised RAG (d/RAG), KIP is crafting the first crucial blueprint for fighting AI Monopolies. Unlocking digital property rights for each AI value creator, and empowering each to transact while remaining independent, is the exact opposite of what Big Tech is trying to achieve. KIP Protocol arms AI Value Creators with the weapons ⚔️ necessary to fight the monopolists in AI.

Cycle 10 is live! Learn more about what @KIPprotocol is building ( 📱, 🤖 , 📚 ) Get your 🔑

Unleash the thrill with Ten! Seize your opportunity to win 1000 USDC, Hardware Wallets, Crypto Domains, and exclusive roles in the Ten community. Stage 1 ✨ is available on @Galxe ! More epic adventures & bigger prizes coming 🔜! Join Now: galxe.com/ten/campaign/G…

Earn on Ten Testnet by helping us battle-harden the only encrypted Layer 2 for Ethereum! Shape the future of Web3 in collaboration with @CoinList. This is a chance for Users, Developers, and Node Runners to earn from a pool of 22,500,000 $TEN Join now - coinlist.co/ten-testnet?ut…

#TestOnTen x.com/tenprotocol/st… @Lanhle0 '';

twitter.com/RariChain/stat… //...

⚡️Dropping today on RARI ⚡️ Ten emerging artists selected in the recent contest from @forbesweb3 and @rarible Feb 12-Feb 23 | All OE 24h | All .002 ETH From over 2600+ entries, a panel of superstar judges selected these pieces to be showcased to spotlight the potential from web3 art community. Learn more about these impressive finalists 👇 mirror.xyz/0xAf2C35857711… 1/12

The rise of Token Centric Architecture has me hyped ☺ Curious to see all the new modular experiences projects like #Smartlayer will help build🧠 #smartlayer

I just got whitelisted for the @volmexfinance Perpetual Futures Exchange 🎉 Perpetual futures contracts on the $BVIV Index and $EVIV Index will be a game-changer! 0x1F2Ade21b98Db2c787416cCe720f8e2eFd0B07De Faucet: faucet.volmex.finance Trade: perpetuals.volmex.finance

📢An exciting benefit on @Uniswap 💰VIP3 pass holders can now get 20% Cashback instantly after trading in Uniswap Detailed info: dapp.vip3.io/benefit/uniswap Freemint VIP3 pass here: vip3.io/mint 🎁Bonus for the first 150 users who claimed Uniswap Cashback: extra 30 USDT future Coupon offered by @OfficialApeXdex #giveaway #uniswap #memecoin #Airdrop