⛩️ LongLongDΞ 🍀

@ValorantUpdated @grok when will it be in Germany?

REMINDER: Night Market will return TOMORROW, 11th of December // #VALORANT

Chog, the Mascot of Monad. Launching soon on mainnet. premint.xyz/chognft/

We may have brushed off on @ChogNFT, they are now known as Chogluminati 😈 Welcome to the cult - join our discord if you dare 🔮

Phase 4 is live Stage 1 #FIRE is gone but we have another stages Fl @the10kSquad right now for the next mint 🔜

@kiaitwo @zama @OpenZeppelin Gzama mate

@0xlonglongde_ @zama @OpenZeppelin Great partnership

Gzama Welcome the next genesis operator to @zama 🤝🤝🤝 @OpenZeppelin 🤝🤝🤝 Zama's ecosystem is expanding and getting bigger. It won't be long before Zama becomes an integral part of blockchain. #ZamaCreatorProgram #FHE

@FaezehEnsafjo @zama @OpenZeppelin Zama is cooking 🧑‍🍳

@0xlonglongde_ @zama @OpenZeppelin When privacy and interoperability meet — mass adoption becomes possible. Zama is quietly solving challenges most people don’t even realize exist yet. 👀🔥

@forhad_nokib @zama @OpenZeppelin Gzama mate

@0xlonglongde_ @zama @OpenZeppelin Gzama fam

🤝

@zama @LayerZero_Core Congrats 🤝

JUST IN: We’re proud to announce our next genesis operator: @LayerZero_Core. LayerZero is the leading interoperability protocol enabling hundreds of billions of dollars to move annually across blockchains. Trusted by major asset issuers and fintech partners like PayPal USD, Google Cloud, and the State of Wyoming, LayerZero offers a protocol for universal blockchain development. Today, it connects $80B in tokenized assets and 600+ organizations across 150+ blockchains. As a Zama MPC operator, LayerZero will help secure the private FHE key that powers the Zama Protocol, ensuring confidentiality without compromising verifiability. Learn more about the Zama Protocol and its operators in our litepaper: #operations-and-governance" target="_blank" rel="nofollow noopener">docs.zama.ai/protocol/zama-… 🔔See you tomorrow for the next genesis operator reveal.

Gzama Welcome the next genesis operator to @zama 🤝🤝🤝 @LayerZero_Core 🤝🤝🤝 Zama's ecosystem is expanding and getting bigger. It won't be long before Zama becomes an integral part of blockchain. #ZamaCreatorProgram #FHE

Welcome @KakarotZkEvm to @zama 🤝 FHE is the future #ZamaCreatorProgram #Zama #FHE

@monad 0x2a15522102f7884dea6be9b44cd2fc21b8c461db

Reply to this post with your EVM wallet address for some MON on Monad mainnet. The MON will cover your first few gas fees so you can start using the chain right away. You must be following @monad to receive it.

@zama on @X ✅ #ZamaCreatorProgram #fhe

FHE of @zama “I keep your data secure.” Also FHE: “But in return, it takes you half a day just to add two numbers.” #ZamaCreatorProgram #FHE

@zama_fhe và FHE “pháp sư” chuyên cho máy tính xử lý dữ liệu mà không cần mở khóa Nói đơn giản thì: •FHE (Fully Homomorphic Encryption) = kiểu nấu ăn mà vẫn để nguyên hộp, kết quả vẫn ngon, mà không ai biết bên trong có gì 🍱 •Còn Zama = người đang biến trò “ảo thuật” đó thành công nghệ thật. Dữ liệu nói: “Tôi muốn được tính toán, nhưng đừng ai nhìn tôi trần trụi nha 😳” Zama: “Yên tâm em, anh xử được 😎” #ZamaCreatorProgram #FHE

Zero-Knowledge AI Training Attribution — a rarely discussed but high-impact domain for @zama_fhe FHE integration 🧠 Context — The Core Problem In the current AI landscape, data is the new scarce resource, yet there’s no transparent mechanism to verify or reward contribution. Centralized training systems (e.g., OpenAI, Anthropic) aggregate massive datasets, but: - Individual or institutional data contributors receive no recognition or reward. - There is no verifiable proof that someone actually contributed real training data. - The training process itself is a black box, turning AI models into private monopolies that absorb data without traceability. ⚙️ Challenges in Federated Learning Federated learning allows multiple parties to collaboratively train models without sharing raw data. However, it suffers from three major issues: - Lack of verifiable contribution: there’s no way to confirm whether a node actually trained on real, valid data instead of fake gradients. - No quantitative attribution: there’s no reliable way to measure how much each dataset improved the model’s performance. - Existing privacy techniques (like differential privacy) only obscure data — they don’t prove authenticity or quantify value. 🔐 Solution: ZK + FHE = Trustless AI Training Attribution Layer 1. FHE enables fully encrypted training aggregation - Each data provider keeps their raw data private, encrypting it using FHE encryption keys. - The aggregator or smart contract performs training directly on ciphertext, so no one ever sees the underlying data. - The resulting gradients or model weights are also encrypted — privacy is preserved end-to-end. 2. ZK (Zero-Knowledge Proofs) ensures authenticity and transparency - Each participant can generate a ZK proof to attest that: “I contributed valid data and executed proper training steps,” — without revealing the data or the model. - These proofs are recorded on-chain, creating an immutable contribution record. - The system can then distribute token rewards or reputation points proportional to verifiable participation. ⚙️ The Architecture: Incentivized Privacy-Preserving AI Training Network 1. Data Providers (Nodes) Supply original data (e.g., user behavior, medical, IoT) → encrypt using FHE. 2. FHE Aggregator Layer Performs training computation on ciphertext without data exposure. 3. ZK Attestation Layer Generates proofs confirming valid participation and correct computation. 4. Reward Mechanism (On-chain) Allocates tokens or credits based on contribution proofs — transparent yet private. 5. Model Deployment / Inference The trained model can be accessed, even for inference, through FHE-secured APIs. 🌐 Ecosystem Impact 🔸 1. DePIN for AI Data - Participants can tokenize and stake their datasets, earning rewards when used in training — without ever disclosing raw data. - This becomes a DePIN (Decentralized Physical Infrastructure Network) for AI data — similar to how Render or Akash tokenize GPU compute, but focused on data and model training. 🔸 2. Unlocking Dormant Enterprise Data - Industries such as healthcare, finance, and government hold massive sensitive datasets that cannot legally be shared. - With FHE + ZK, these institutions can participate in AI economies while staying compliant with GDPR, HIPAA, or data localization laws. - This unlocks billions in currently siloed data value. 🔸 3. Foundation for “AI Proof-of-Contribution Economy” - Every data batch or training iteration can be cryptographically attributed to its contributor. - Over time, AI models will carry verifiable on-chain lineage — showing who contributed what data, enabling transparent profit-sharing. 🎯 Strategic Conclusion FHE + ZK don’t just protect data — they redefine how AI is trained, validated, and incentivized. If Zama builds this layer, it could evolve into the “Compute & Attribution Layer” for the global data economy: - FHE → enables secure encrypted computation. - ZK → guarantees correctness and fairness. - Blockchain → provides settlement and incentive alignment. Together, they create a foundation for a decentralized AI ecosystem where data becomes a yield-generating, privacy-preserved asset. This would give rise to an entirely new AI Data DePIN Market, with Zama positioned at its computational core. #ZamaCreatorProgram #FHE #AI #DePIN

Overnads Whitelist Pass gives you the right to mint 2 GTD (guaranteed) NFTs on mainnet. Overlist also gives you the right to mint 2 GTD (guaranteed) NFTs on mainnet. A snapshot of Whitelist Pass holders will be taken within 48 hours at a random time. Please make sure your NFT is in the correct wallet. More details in discord

@zama_fhe FHE - powered Privacy-Preserving Data Oracle Layer 🧾 Current Limitations: Oracle Systems Are Public-by-Design — and That Restricts Enterprise Data Monetization Existing oracle providers like Chainlink, Pyth, Redstone, API3 offer public data feeds, such as: - Asset prices, funding rates, volatility indices. - Lending parameters for DeFi protocols. - NFT floor price feeds and generalized market data. 👉 The Problem: This public feed model does not attract enterprise-grade data providers, such as: - Quant firms with proprietary pricing logic. - Fintechs running private risk scoring AI. - Enterprise SaaS platforms with fraud detection or behavioral scoring models. - Prediction providers with hidden alpha signals. These actors will not publish raw data or model logic because: "Data + algorithm = core IP moat" 🔐 FHE Enables “Confidential Compute Oracle” Instead of Just “Public Data Feeds” Unlike traditional oracle flows: "The enterprise NEVER exposes raw data. Instead, it submits ENCRYPTED signal outputs to an FHE oracle node" Flow: 1. Enterprise keeps its dataset and model fully private. 2. It encrypts output signals using the oracle’s public FHE key. 3. The oracle coprocessor executes contract logic over encrypted signals — without ever seeing the raw input. 4. Smart contracts receive encrypted verdicts or yes/no conditions, which can still trigger on-chain logic. 💡 Outcome: - Data remains undisclosed. - DeFi protocols still react to encrypted intelligence. → Oracle layer shifts from feeding raw data → to selling compute access over encrypted intelligence. 🎩 This Creates a New Market Category: The Confidential Oracle Economy Role - Function in the Encrypted Oracle Market 1. Data / AI Model Provider - Supplies private signals (alpha strategies, credit scoring, anti-fraud models) — but never reveals the model or data 2. FHE Oracle Nodes - Execute decision logic/inference over encrypted data 3. DeFi / DAO Protocols - Consume these signals as black-box logic triggers 4. Economic Layer - Payment happens per encrypted compute request, not per data access 🚀 High-Conviction Verticals That Could Be Unlocked ✅ Private Credit Scoring for On-chain Lending - Traditional KYC/credit score leaks identity. - FHE → only returns encrypted approval verdicts, never exposing user data. ✅ Quant Strategy Oracles - Funds can sell encrypted long/short directional bias signals. - Vaults and structured products could auto-adjust strategies without ever reverse-engineering the source model. ✅ Hybrid FHE + ZK Compliance Layer - Compliance oracle verifies “This address meets regulatory criteria” without deanonymizing the user. - FHE processes identity checks → ZK proof ensures correctness → DeFi contract trusts the result fully privately. 🎯 Strategic Positioning: Zama Can Become the Chainlink of Confidential Compute Chainlink = Public Data Oracle Layer Zama FHE = Confidential Compute Oracle Layer If Chainlink monetizes public financial data, then Zama can monetize encrypted enterprise intelligence — a market 10X higher value because IP-based signals have elite pricing potential. 💬 Strategic narrative (can be used in direct protocol alignment): “Zama isn’t just a privacy layer — it is the execution layer for a Confidential Oracle Market, unlocking enterprise AI signals that no public oracle architecture can capture.” #ZamaCreatorProgram #Zama #FHE #OracleLayer

@zama_fhe FHE - powered Encrypted AI Logic & Hidden State for On-chain Gaming 🎮 On-chain Games Today = Public State Simulations, Not Real Strategy Environments All current on-chain games are built under full transparency, meaning: - AI/NPC logic is public. - Combat resolution formulas are readable on-chain. - Hidden mechanics like luck rolls, hidden cards, enemy intelligence models are fully detectable through chain analysis. 👉 Result: - Players or bots simply scan contract state → precompute the optimal strategy → farm rewards like an optimization machine. - AI/NPC logic is not actually AI — it's just a predictable set of public rules, making “AI gaming on-chain” mostly a marketing label. 🔐 FHE Enables an Actual “Encrypted Game Logic Execution Layer” Instead of revealing all NPC logic or PvP mechanics: - FHE allows all AI decision-making and combat calculations to be executed on ENCRYPTED game data. - Validators/coprocessors can still verify correctness, but cannot read the game’s strategic state: → Hidden player hands → NPC adaptive behavior model → Randomness seed logic → Long-term AI memory evolving over gameplay 🔥 This introduces something never before possible on-chain: "Real fog-of-war mechanics and hidden decision-making — executed on-chain without any trusted server" ZK-proofs alone cannot handle real-time adaptive AI logic, only static verification. FHE is the missing compute layer. ♟ Zama Could Become the “Encrypted AI Engine” for the Next Era of GameFi A potential architecture: 1. Game developers upload AI logic or NPC decision models as encrypted compute modules. 2. Player submits actions → encrypted on-device → uploaded as ciphertext. 3. FHE Coprocessors execute NPC/AI response logic without revealing internal AI state. 4. Combat results or AI outcomes are also encrypted and only partially revealed per game phase → introducing true strategic uncertainty. 🃏 Breakthrough Use Cases: Encrypted On-chain Poker, PvP AI Duels & Adaptive Dungeon Agents - Trustless On-chain Poker: Cards remain encrypted, gameplay still verified — no need for a centralized dealer. - AI Duels Submissions (like on-chain StarCraft bots): Each player’s AI strategy runs inside encryption, FHE computes the duel outcome → strategies aren’t leaked even after match ends. - Encrypted Dungeon NPCs: NPCs use AI models that adapt to player style via FHE-kept memory, turning blockchain into a living AI game environment, not just a static ledger. 💰 Market Positioning & Strategic Moat Stack Layer & Current State & FHE Opportunity 1. On-chain AI Simulation & Off-chain servers, vulnerable & transparent scripts & Zama → Encrypted AI Simulation Coprocessor for Games 2. Chainlink VRF & Only randomness, not logic execution & FHE → Encrypted Tactical Compute Oracle 3. GameFi Meta & Yield farming disguised as games & FHE Gaming → Real player agency / mind games on-chain "If Zama ships a lightweight “Confidential Game Compute SDK” — it could become the Unreal Engine for encrypted AI-native on-chain games" #Zama #ZamaCreatorProgram #FHE #OnchainGaming