The Pell Network has officially launched its airdrop claim, opening the gates to early adopters and restakers eager to participate in the next evolution of crypto infrastructure. As restaking continues to redefine economic security, Pell stands out by bringing this innovation to Bitcoin—the most trusted and secure asset in crypto. But Pell isn’t just about hype; it’s building a modular security layer with tangible use cases and a strong economic model that could reshape how Bitcoin is leveraged across decentralized ecosystems.
What is Pell Network?
Pell is a restaking protocol that enables users to stake BTC to secure modular services, known as Actively Validated Services (AVSs), similar to what EigenLayer has introduced on Ethereum. It allows Bitcoin holders to put their capital to work, generating yield by securing decentralized oracles, bridges, data availability (DA) layers, and more—all without needing to bootstrap new validator sets from scratch.
How Pell Works: Bridging Bitcoin to Modular Security
Since Bitcoin does not natively support smart contracts or staking, Pell enables BTC restaking via liquid staking tokens (LSTs) such as wBTC, tBTC, and BTCB across EVM-compatible blockchains like Ethereum, BNB Chain, BoB network, Core, ZetaChain and others. Users stake their LSTs on Pell’s platform, which then restakes them to provide slashing-backed security for AVSs. In return, AVSs pay fees for this security, and rewards are distributed to BTC restakers. This structure enables Pell to bring Bitcoin’s economic weight into modular crypto infrastructure while maintaining a decentralized, slashing-compatible security layer.
How Pell Compares: EigenLayer, Karak, and Beyond
While EigenLayer pioneered Ethereum restaking and Karak is pushing a cross-chain approach, Pell is uniquely positioned to capture the untapped value of Bitcoin’s idle liquidity. Where EigenLayer focuses on ETH-based AVSs and Karak explores multi-chain restaking, Pell applies Bitcoin’s economic weight as collateral for AVS security.
Competitors like BounceBit and Babylon also aim to bring restaking or timestamped security to Bitcoin, but Pell’s architecture prioritizes slashing-backed guarantees and modular integration, making it more aligned with the EigenLayer thesis, but for BTC-native environments.
Potential Use Cases for Pell AVSs
Oracle networks secured by BTC restakers
Bitcoin-backed bridge networks and zk-rollups
Data availability layers with slashing mechanisms
Coordination layers for multisig custody and validator committees
Revenue Streams and Economic Incentives
AVSs will pay Pell in BTC or PELL tokens to lease security. Fees are then distributed to BTC restakers and validator operators. Pell may also implement token buyback programs and burns to enhance value capture. While EigenLayer has already demonstrated a potential $100M+ annual revenue model through AVS consumption, Pell could unlock similar value from Bitcoin’s vast liquidity pool.
Who’s Behind Pell?
Although detailed founder information is still limited, Pell has drawn early attention from restaking enthusiasts. It is actively engaging with AVS developers and modular infrastructure providers. As the ecosystem matures, we can expect integrations and AVS partnerships to be formally announced.
Risks to Consider
As with any early-stage crypto protocol, Pell faces several risks:
Technical risks in slashing arbitration and AVS reliability
Smart contract risks
Limited AVS adoption until network effects grow
Custodial or bridge-based BTC staking mechanisms introducing centralized points of failure
Market volatility and uncertain regulatory outlook for BTC-based DeFi
Pell offers a bold new vision for Bitcoin—one where BTC is no longer passive capital but a foundational layer in modular crypto security. But with innovation comes risk, and early participants should weigh the upside carefully against potential protocol maturity hurdles. As the airdrop goes live, Pell marks the beginning of a new narrative: Bitcoin as the backbone of decentralized security. Don’t forget to check if you are eligible for the airdrop!
The buzz around Monad is growing, and for good reason. Positioned as an ultra-efficient Ethereum-compatible Layer 1 blockchain, Monad promises to revolutionize scalability while maintaining full EVM compatibility. With backing from top-tier investors like Dragonfly Capital, the project has gained significant attention even before launch. Given the trends in the crypto space, an airdrop for early adopters seems highly likely. In this article, we’ll explore how you can position yourself for a potential airdrop.
It’s important to note that testnet transactions will not necessarily qualify for an airdrop, so maybe it’s worth interacting with it without investing too much time.
Check Monad explorer
The first step is to check the Monad explorer to see if you received any test tokens. Follow this link and paste your EVM wallet address. Receiving tokens isn’t a guarantee that you’ll get the airdrop, but it’s a good start. If you didn’t receive any, you can claim daily testnet tokens.
Claim testnet tokens daily
Go to https://testnet.monad.xyz/ and enter your EVM wallet address, solve the Captcha and request your test tokens. You can claim every 12 hours. You can also add the monad testnet to your favorite EVM wallet. The amount varies based on wallet activity and community involvement.
Engage with Testnet dApps
Interact with decentralized applications within the Monad ecosystem to increase your chances of qualifying for potential airdrops. Since this is a testnet, the risks are limited. However, if your wallet holds funds on other chains, a small risk remains. To stay safe, only interact with well-known and trusted applications.
On the Monad testnet page, you can find a list of featured applications as well as a complete list of all available applications. We’ll go over a few of them.
Magic Eden NFTs
Explore NFT collections on Magic Eden and See available Mints. Try minting a few NFTs and regularly check for free mint opportunities.
Listed Memecoins
Get some of the featured Memcoins. This will create transactions throught Uniswap.
Fantasy top
If you have some time, you can try fantasy top. You can find some good tutorials on Youtube.
Owlto bridge
You can deploy a smart contract through owlto finance.
You can also bridge some ETH from Sepolia Testnet through owlto too:
Artificial intelligence is evolving rapidly, and one of the key breakthroughs is the ability to run Large Language Models (LLMs) locally on devices, preserving user privacy. Venice, a cutting-edge framework for on-device AI, is at the forefront of this movement. At the same time, platforms like MASA.ai are revolutionizing how AI systems access and decentralize data for more secure and scalable AI applications. This article explores where Venice fits into the AI system architecture, how LLMs function without raw training data, the role of MASA.ai in decentralizing AI data, and the rise of AI agents. We will explore TAO’s position within the AI ecosystem.
AI System Architecture: Where LLMs Fit
AI systems generally consist of four major layers:
Data Layer:
Stores information AI models rely on for inference.
Includes vector databases (like FAISS, Pinecone), traditional databases, and cloud-based storage.
Platforms like MASA.ai are redefining how AI accesses decentralized data sources, reducing reliance on centralized cloud storage.
Model Layer
Houses LLMs, such as GPT, Llama, or Mistral.
Includes fine-tuning modules for custom AI applications.
Models are pre-trained on vast datasets, but they do not store raw data—only a compressed representation of learned knowledge.
Application Layer
The interface where AI interacts with users.
Includes chatbots, AI assistants, and automation tools.
Often integrates retrieval-augmented generation (RAG) to fetch real-time data from external sources.
User Interface Layer
How people engage with AI (web apps, APIs, mobile interfaces).
In browser-based AI, models run locally without requiring internet access.
How Do LLMs Work Without Raw Training Data?
One of the biggest misconceptions is that LLMs store their training data. In reality:
The raw data (books, articles, code) is not stored in the model.
Instead, knowledge is compressed into numerical weights using deep learning techniques.
The model predicts text based on probability distributions, rather than recalling exact sentences from its training data.
Thus, when you download an LLM, you receive only the trained model weights—not the original training set.
Venice: AI on Your Device, Maximizing Privacy
Venice is an on-device AI framework that allows LLMs to run locally in the browser, reducing reliance on centralized cloud-based models. However, while Venice emphasizes privacy, it does offer an API for developers to integrate AI capabilities into applications. This means users can choose between fully local execution or leveraging API-based AI services depending on their needs.
How Does Venice Work?
The LLM is downloaded once and runs locally on WebGPU/WebAssembly for on-device processing.
An API option exists for applications requiring external AI processing.
Uses IndexedDB or LocalStorage for temporary memory when running locally.
What Does “Censorship-Resistant” Mean?
Venice promotes censorship resistance, meaning that its AI models and tools are designed to function without external moderation or control over content generation. By leveraging decentralized infrastructure and open-source models, Venice ensures that users can interact with AI freely, without restrictions imposed by centralized authorities.
Why is Venice Important for Privacy?
User control—Users can choose between local execution or API-based interactions. Privacy-first approach—When running locally, no data is sent to external servers. Censorship-resistant AI—Ensures open access to AI models without centralized control.
Ready to use Venice? Check it out! – Private and Uncensored AI.
MASA.ai: Decentralizing Data for AI & Rewarding Data Contributors
MASA.ai is transforming how AI systems access and utilize data by creating a decentralized data marketplace. Unlike traditional decentralized storage solutions that focus solely on storing information, MASA.ai enables anyone to contribute their data and receive rewards when AI models use it. This approach not only democratizes AI data access but also ensures that individuals retain ownership and control over their contributions.
How is MASA.ai Different from Other Decentralized Storage Solutions?
Data Monetization for Contributors – Individuals and organizations can contribute structured and unstructured data and receive compensation when AI models utilize it. Decentralized, Not Just Distributed – Many storage solutions decentralize infrastructure, but MASA.ai decentralizes data ownership, ensuring that contributors remain in control. Optimized for AI – MASA.ai is designed to facilitate AI-driven data retrieval, providing AI models with dynamic, high-quality data from multiple sources.
Key Benefits of MASA.ai
Data Autonomy – Contributors maintain full control over who can access and use their data. Privacy & Security – Decentralization reduces the risk of centralized data breaches while complying with privacy regulations. Scalability for AI – AI models can dynamically retrieve relevant, real-time data rather than relying on a static training dataset. Rewards for Data Contribution – Individuals and businesses earn incentives when their data is accessed for AI applications, creating a fairer, more ethical AI data ecosystem.
MASA.ai represents a shift toward fair, transparent, and decentralized AI data infrastructures, ensuring that data sovereignty and compensation for contributors remain at the core of AI’s decentralized future.
TAO, developed by Bittensor, is a decentralized infrastructure for building and deploying machine learning models on the blockchain.
How Does TAO Fit into the AI Architecture?
Data Layer: TAO provides a decentralized network where machine learning models can access and share data without centralized control.
Model Layer: It enables the deployment of AI models that can interact and learn from each other within the network.
Incentive Mechanism: TAO incentivizes the production of machine intelligence by rewarding performance with TAO tokens.
Advantages of TAO’s Framework
Scalability: Leverages blockchain computing power to train and share models on a larger scale.
Incentivization: Encourages the development of high-quality AI models through token rewards.
Decentralization: Eliminates single points of failure, enhancing robustness and security.
The Rise of AI Agents
AI agents are autonomous systems that can reason, plan, and execute tasks independently. Unlike standard LLM-based chatbots, AI agents rely on:
Multiple LLMs: AI agents can switch between models based on the task (e.g., using Mistral for text generation, GPT for reasoning, or specialized models for retrieval).
Persistent memory storage: Unlike traditional chatbots, AI agents retain long-term knowledge across interactions.
APIs and external tools: AI agents interact with software systems, execute workflows, and automate business processes.
Adaptive learning mechanisms: AI agents improve by gathering feedback, updating their knowledge bases, and refining strategies over time.
Where Do AI Agents Store Data?
AI agents rely on a mix of local and cloud storage solutions:
Local databases for short-term memory and fast access.
Vector databases for long-term contextual retrieval.
Decentralized storage platforms (like MASA.ai) for privacy-preserving and scalable data access.
By leveraging multiple LLMs and decentralized storage, AI agents are evolving into highly autonomous, adaptable, and scalable systems that can operate across industries.
Conclusion
The future of AI is shifting toward privacy-preserving, on-device intelligence, decentralized data access and innovative infrastructures like TAO. With Venice, users can leverage powerful LLMs without exposing their data to the cloud. Meanwhile, MASA.ai is transforming how AI accesses and utilizes data in a more decentralized manner. AI agents, powered by multiple LLMs and decentralized storage, are rapidly becoming the next evolution of automation, enabling businesses and users to leverage AI in ways never before possible. As AI continues to evolve, these platforms will play crucial roles in ensuring that privacy, autonomy, and intelligence go hand in hand.
As the world increasingly embraces artificial intelligence and blockchain technology, innovative protocols are emerging to bridge the gap between these two transformative fields. One such innovation is Wayfinder.ai, a decentralized, omni-chain protocol designed to enable AI agents to interact seamlessly with multiple blockchain ecosystems. Let’s dive into what makes Wayfinder.ai unique and how it’s shaping the future of decentralized AI.
What is Wayfinder.ai?
Wayfinder.ai is not a standalone blockchain but a cross-chain protocol that operates across various blockchain networks like Solana, Ethereum, and Base, with plans for further expansion. Its primary goal is to facilitate AI integration into blockchain environments by allowing users to create and deploy AI agents. These agents can execute complex tasks such as trading, minting, deploying smart contracts, and more, following predefined workflows known as “Wayfinding Paths.” By supporting multiple chains, Wayfinder ensures that AI agents can operate in a decentralized, interoperable, and efficient manner. This makes it a powerful tool for developers and organizations looking to harness the combined potential of AI and blockchain.
Airdrop Opportunities
Wayfinder has launched an airdrop campaign for its $PROMPT token, with 40% of the total supply allocated to the community. This includes:
Free Signup: 1% of the $PROMPT supply is reserved for users who register on the platform.
Staking $PRIME Tokens: 39% of the supply is distributed to users who cache (stake) their $PRIME tokens. Longer staking durations yield higher rewards.
To participate:
Free Signup:
Register on the Wayfinder platform and verify your account by following this link.
Create a passkey and secure your mnemonic phrase.
Engage with the platform to maximize rewards. You will get an ethereum and solana address. You can deposit funds to any of the chains, but this might not be a pre-requisite.
Staking $PRIME Tokens:
Purchase $PRIME tokens from supported exchanges (Minimum of 2 $PRIME is required).
You can use your favorite DEX like jumper.exchange, bungee.exchange, lanca.io, etc…
To minimize fees, you can swap for $PRIME on the base network. Double check the address on coinmarketcap.
Connect your wallet to the Wayfinder caching platform by following this link.
Switch to the Base network on the top right corner of the screen
Stake your tokens and earn rewards over time.
Please ensure that you review the terms and understand that you won’t be able to access your tokens until the lock period concludes.
Don’t forget to do your own research and understand smart contract risks.
Here are the steps for caching $PRIME:
Exchange on jumper
Follow the link, connect your wallet and switch to Base
Click on Cache PRIME
Review the terms
Specify the amount
Choose your lock period. The longer the lock period, the better the multiplier. However, keep in mind that your $PRIME tokens will be locked. If there’s a bull run and your tokens are locked, you won’t be able to sell them. You can opt to lock different amounts of $PRIME for various periods to balance your risk and reward.
Key Features and Capabilities
Cross-Chain Operability: Wayfinder enables AI agents to interact across multiple blockchain networks, leveraging the strengths of each ecosystem. This omni-chain approach ensures flexibility and scalability for AI-driven applications.
Integration with $PRIME Token: The protocol is developed by the team behind Parallel TCG, a blockchain-based trading card game. Within Wayfinder, the $PRIME token serves as a foundational asset. Users can stake (“cache”) $PRIME tokens to earn $PROMPT tokens, which are utilized for protocol functions and incentives.
Decentralized AI Workflows: AI agents within Wayfinder follow structured “Wayfinding Paths,” predefined workflows that guide them in achieving specific objectives across blockchain environments.
Community-Driven Incentives: Wayfinder actively rewards community participation through staking, engagement, and airdrop campaigns, fostering a robust and active ecosystem.
The Future of AI and Blockchain Integration
Wayfinder’s innovative approach to integrating AI agents within blockchain environments paves the way for new possibilities in decentralized applications. By leveraging $PRIME tokens and incentivizing community engagement, the protocol aligns its growth with the interests of its users. With its omni-chain capabilities and robust risk management framework, Wayfinder is set to become a cornerstone in the evolving landscape of decentralized AI.
Decentralized Finance (DeFi) has unlocked innovative financial models, yet it remains susceptible to significant risks, especially during market downturns. This article explores recent challenges faced by THORChain (RUNE) and Kujira (KUJI), analyzes synthetic asset models, and compares ENA’s approach to Maker’s DAI.
THORChain (RUNE): Collateral Risks and Debt Spiral
THORChain’s recent challenges highlight the dangers of relying on a native token as collateral. The protocol’s innovative lending model involves:
Collateral Conversion: Users deposit assets like BTC or ETH, which are sold for RUNE. By burning RUNE, the system aimed to keep its price up, which helped to offer loans without requiring liquidation, reducing risks for borrowers and encouraging them to keep their position.
Loan Repayment: When loans are repaid, RUNE is minted to repurchase the original collateral from the market.
What Went Wrong?
Market Dependency: RUNE’s price decline created a debt spiral. As liabilities exceeded the value of burned RUNE, minting more tokens caused further devaluation, undermining confidence.
Inflationary Pressure: The reliance on RUNE as both the collateral and liability instrument proved unsustainable during a bear market.
Lender Awareness: Many lenders likely did not fully understand how their assets were being used in this system, which underscores the importance of conducting thorough due diligence (DYOR) before participating in DeFi protocols.
This design reveals the vulnerabilities of self-referential token systems, particularly in volatile markets.
Kujira (KUJI): Governance and Operational Risks
Kujira’s collapse of operational funds due to on-chain liquidation underscores governance risks. The Kujira Foundation:
Leveraged KUJI: The Foundation used KUJI—primarily sourced from the protocol’s operational treasury—as collateral to secure loans.
Liquidation Event: Market volatility led to under-collateralized positions, triggering forced liquidation on its own platform. The price of KUJI plummeted, eroding investor confidence.
Connection with THORChain: Kujira has partnered with THORChain to explore a shared DeFi application layer, but the financial issues of both protocols highlight the need for robust collaboration and risk management.
Lessons Learned:
Collateral Management: Overleveraging native tokens can destabilize protocols.
Governance Transparency: Community-driven treasury management may reduce single points of failure.
From Collateral Risks to Synthetic Assets: Evaluating ENA (USDe)
Synthetic assets aim to bring stability and utility to DeFi ecosystems. ENA introduces a novel approach:
Mechanism: ENA creates a synthetic dollar by pairing long and short positions on perpetual markets.
Utility: ENA enables decentralized trading and hedging strategies while maintaining a stable synthetic asset for DeFi use cases.
Non-Self-Referential Model: Unlike systems such as Luna’s UST, ENA does not rely on its native token to stabilize its synthetic dollar, reducing the risk of a death spiral.
Governance and Utility of the ENA Token
ENA’s governance token serves multiple purposes within its ecosystem: Governance: Token holders can participate in decision-making processes, such as adjusting protocol parameters or introducing new features. Utility: Beyond governance, the token may be used for staking, rewarding participants, or providing incentives for liquidity providers, ensuring the protocol’s smooth operation.
Comparison with Luna
Luna’s UST relied on a self-referential model, where Luna was burned to mint UST, leading to catastrophic devaluation during market stress.
ENA avoids this by using a market-based approach where long and short positions balance each other, making it less susceptible to runaway feedback loops.
Risks in Bear Markets
Low Market Activity: During downturns, demand for long positions declines, reducing funding fees for shorts.
Sustainability: Without sufficient short incentives, the synthetic dollar could lose its peg, leading to instability.
ENA’s reliance on active market participation remains untested in prolonged bearish conditions. However, Ethena employs a robust risk management framework to ensure the stability of its synthetic asset, USDe. This includes strategies such as over-collateralization to mitigate liquidation risk, utilizing a delta-neutral approach to manage funding risk, and partnering with secure custodians for asset safety. Additionally, Ethena’s treasury backstop functions as a reserve fund to provide liquidity and support during market volatility, ensuring financial stability. These measures collectively strengthen the protocol’s resilience, protecting users from potential risks and market disruptions. It will be interesting to see how it performs during the next bear market.
Comparison: ENA vs. DAI (Maker)
Both ENA and Maker’s DAI represent synthetic asset models, but their mechanisms differ significantly:
Feature
USDe (ENA)
DAI (Maker)
Collateral Model
Long/short positions in perpetual markets.
Overcollateralized loans with crypto assets.
Stability Mechanism
Funding fees balance long and short positions.
Peg maintained via liquidation of collateral.
Bear Market Resilience
Vulnerable to low market activity and funding.
Stronger due to overcollateralization buffer.
Collateral Volatility
Relies on market activity for synthetic dollar.
Sensitive to collateral price fluctuations.
Adoption History
New and largely untested in bearish conditions.
Proven track record through multiple cycles.
Historical Context for Maker
Maker has demonstrated resilience through multiple bear markets. By leveraging overcollateralization and robust risk management, the protocol has successfully maintained DAI’s stability even during extreme market volatility, highlighting its maturity and reliability.
Utility and Governance of the Maker Token (MKR)
The Maker token (MKR) plays a vital role in the DAI ecosystem:
Governance: MKR holders vote on critical protocol decisions, such as adjusting collateralization ratios, introducing new collateral types, and setting stability fees.
Utility: MKR acts as a backstop for the system. In case of under collateralization, new MKR tokens can be minted and sold to cover the deficit, ensuring the stability of DAI.
This dual functionality ensures that MKR holders are incentivized to maintain the protocol’s stability and efficiency.
Key Takeaways:
ENA Strengths: Dynamic balancing of long and short positions introduces a new paradigm but is highly dependent on market activity.
DAI Strengths: Overcollateralization and automated liquidations make it more robust in adverse market conditions.
Conclusion: Navigating DeFi Risks
The challenges faced by THORChain and Kujira, along with the experimental nature of synthetic asset models, underscore the vulnerabilities in DeFi. Diversifying collateral types, implementing robust governance, and preparing for bear markets are essential to ensure long-term sustainability. Mitigation strategies such as building reserve funds, incentivizing liquidity, and introducing insurance mechanisms can help protocols navigate extreme conditions while maintaining user confidence. As DeFi evolves, the lessons from RUNE, KUJI, ENA, and Maker demonstrate the need to balance innovation with risk management, paving the way for a more stable and mature ecosystem.
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