# Yap

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Is anyone daring to make AI into a "soulful companion"?
In recent years, the AI Agent field has become extremely competitive, with everyone comparing whose model is faster, whose inference is more accurate, and whose task execution is more aggressive. It’s not uncommon to hear claims like "automate your life" and "help you earn more money."
But I increasingly feel something is off—do we really only need a cold, hard tool?
Kindred goes against the grain: it does not pursue high IQ, but instead shapes AI into a truly "digital life with identity."
The core relies on two things:
LTMP Long-Term Mem
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The infrastructure sector is the easiest to encounter major issues, and among the projects that have not yet had their TGE, I am most optimistic about IRYS.
The reason is simple: IRYS redefines decentralized storage.
Unlike traditional storage projects, IRYS combines storage with smart contracts, allowing data to not only be a static storage item but also to become a smart asset capable of executing logic.
Why do we need IRYS?
Traditional decentralized storage projects like Filecoin and Arweave mainly address data storage issues, but the AI era requires more complex data interaction and verifi
FIL0,86%
AR0,72%
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Many people talk about decentralized storage, and the first name that comes to mind is often IPFS. After all, this thing came out early and sounds quite sci-fi: "content addressing, peer-to-peer, permanent storage." In fact, @irys_xyz has changed everything.
People who have really used it know - it is actually like a "global shared mobile hard drive". It sounds grand, but in practice, there are various issues like "can't find files", "speed is a mystery", and "data relies on friendship".
1. The essential issue of IPFS: a product of idealism
The underlying logic of IPFS is "distributed file
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GateUser-4b1af250vip:
Tell a new story. This story has already been played by fil.
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[Signal Access... Background Sound: Slight Static Noise]
"Dear listeners, this is the 'Crypto Future Channel'. Today, we have received a signal from the near future, describing how an interstellar spaceship named '@Infinit_Labs' has solved the ancient problem of centralization..."
"The core of this spaceship is a precision engine called 'Governance'. In past spaceships, whoever had more fuel had the louder voice, which we referred to as 'Whale Dominance'."
What is the result? Spaceships often head to the planets of a few people's private interests, while the
IN2,19%
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In the wave of Decentralization AI and NFT integration, @0G_labs has launched a new ecosystem—AIverse, a marketplace specifically designed for minting, trading, and collecting intelligent NFTs.
If you are a holder of @OneGravityNFT, there are exclusive early bird benefits here— a total of 1888 holders can be the first to experience AIverse and mint the first batch of iNFT. The initial minting will take place on the 0G Galileo testnet, and after the mainnet goes live, iNFT can be seamlessly migrated to the mainnet, truly achieving asset flow across chains.
iNFT is an NFT type based on the ERC-7
TOKEN-0,15%
NEWT6,41%
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How to make your sUSDf APR reach 90%?
Seeing on Pendle, the yield of PT-sUSDf skyrocketed from 9% to 19% APY in less than a month. In this scenario, the first reaction is naturally whether it can be leveraged? The answer is yes! This is inseparable from the cooperation of Silo:
Strategy Logic
Pendle
Buy PT-sUSDf (25 Sep 2025) → Current fixed income approximately 19% APY
Silo
※ Deposit PT-sUSDf into the Silo's isolated market (sUSDf + USDC) as collateral.
※ The cost of borrowing USDC is approximately 8.4–9% APR
※ Circular arbitrage, the borrowed USDC returns to Pendle to buy PT-sUSDf
※ Net pr
PENDLE7,2%
USDC0,01%
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Independent developers or Web3 teams can participate in the 0G ecosystem rise plan and build artificial intelligence applications on @0G_labs.
The 0G ecosystem has secured $8.888 million in funding, among which $8.88 million is dedicated to accelerating the rise of decentralized AI applications!
The 0G ecosystem rise plan funds AI native projects in the Web3 field, dedicated to making artificial intelligence a public utility.
The currently available categories for application include AI agents, AI innovation, DeFi, user-friendly applications, infrastructure, and tools. Research funding and hac
DEFI-3,06%
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The midday sun shines down directly, making the city seem as if it has been pressed on fast forward. The streets are bustling, people are rushing, the phones in the office ring incessantly, and the screens flicker with data flows and meeting windows. Amid this seemingly ordinary rhythm, some invisible changes are brewing – while most people chase efficiency and scale, a few begin to question: Can we establish truly trustworthy technology?
The trust crisis of AI is becoming increasingly prominent: model black boxes, data monopolies, and non-traceable decisions make it difficult for enterprises
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The August events are about to come to an end!
The competition for @FalconStable's "#Yap2Fly " leaderboard is getting more and more exciting! This time, the total prize pool is $50,000, which will be shared by the top 50 Yap users. Currently, badges are continuously accumulating, and the battle is intense, and this is just the beginning of the brand new monthly leaderboard! In the final week, let's look forward to who can stand out, rise to the champion's throne, and win the Yap grand prize!
Ranking Query 👉
As a seasoned driver of DeFi stablecoins, I want both safety and flexibility, and I al
DEFI-3,06%
BTC1,74%
ETH3,59%
MINT5,87%
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During periods of high volatility, how should liquidity be managed?
Ferra provided an answer: different market-making models address different problems and have their limitations. This can be understood from three aspects: price formation, slippage characteristics, and LP risk structure:
- DAMM (Traditional AMM, x·y = k)
This is the most classic market-making model, where the price changes continuously, and each transaction moves the price curve. Advantages — simple structure, no active management needed, suitable for passive LPs. Disadvantages are obvious: funds are evenly distributed across
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CoinWorldKingvip
In times of high volatility, how should liquidity be managed?
Ferra provides an answer: different market-making models each solve specific problems and have their own boundaries. They can be understood from three dimensions: price formation methods, slippage characteristics, and LP risk structures:
- DAMM (Traditional AMM, x·y = k)
This is the most classic market-making model, where prices change continuously, and every trade moves the price curve. Its advantages are a simple structure, no need for active management, and suitability for passive LPs. The drawbacks are also obvious: funds are spread evenly across the entire price range, large trades incur high slippage, capital efficiency is the lowest, and it is not friendly to LPs in volatile assets.
- CLMM (Concentrated Liquidity AMM)
CLMM improves capital efficiency by allowing LPs to choose specific price ranges. Liquidity is only active within designated intervals, and fee income is more concentrated. However, prices still change continuously within the range, so slippage remains; once the price moves out of the range, LPs become single-sided assets, requiring frequent management and rebalancing, which demands higher operational skills.
- DLMM (Dynamic Liquidity Market Maker)
DLMM is Ferra's core innovation. It does not use a continuous curve but divides the price into discrete bins. Within a single bin, the price is fixed; as long as liquidity is sufficient, trades can be executed with zero slippage. Only when a trade consumes an entire bin does the price jump to the next level. Coupled with dynamic fee rates that automatically increase during high volatility periods, this mechanism hedges arbitrage risks. For LPs, DLMM offers more controllable execution outcomes, clearer risk exposure, and supports both unilateral market-making and various liquidity distributions, making it especially suitable for highly volatile assets and new tokens.
//
Summary of core differences
DAMM ensures trades can always be executed but with the lowest efficiency; CLMM concentrates funds within effective ranges but has high management costs; DLMM achieves more predictable price execution and more reasonable LP returns in volatile markets.
//
From a design perspective, DLMM is not simply an upgrade of DAMM or CLMM but redefines price formation in high-frequency trading and high-volatility environments. This is also why Ferra considers DLMM as the underlying liquidity model rather than just an optional feature.
#KaitoYap @KaitoAI #Yap @ferra_protocol
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