Defai：Defi的未來

在僅僅一周的時間內，DeFai迅速成為一個備受推崇的項目領域，預計在未來幾個月內表現出色。

Original source: DWF Ventures X account

原始資料：DWF Ventures X帳戶

Author: DWF Ventures

作者：DWF Ventures

Compiled by: ShenChao TechFlow

編譯者：Shenchao TechFlow

In just over a week, DeFAI has rapidly emerged as a hot project area, and we can expect to see strong performance in DeFAI over the coming months.

在短短的一個星期內，DeFai迅速成為一個熱門項目區域，我們可以期望在未來幾個月內看到Defai的出色表現。

So, what makes DeFAI so important? What core issues does it address? Let’s explore together.

那麼，是什麼使Defai如此重要？它解決了什麼核心問題？讓我們一起探索。

Introduction

介紹

In recent years, DeFi has made significant progress—from the first wave of protocols (such as Maker, now known as @SkyEcosystem, @Uniswap, and @compoundfinance) to now over 3000 different DeFi protocols.

近年來，DEFI已取得了重大進展 - 從第一波協議（例如，現在稱為@skyecosystem，@uniswap和@compoundfinance）到現在超過3000種不同的DEFI協議。

While the advancements in DeFi are significant for the entire industry, they have also exposed some key challenges.

儘管DEFI的進步對整個行業都有意義，但它們也面臨著一些關鍵挑戰。

Challenges

挑戰

The first major issue is the increasing operational complexity of DeFi products. Whether due to the complexity of the underlying architecture or the numerous steps required to participate, this has led to lower user adoption for some DeFi products.

第一個主要問題是Defi產品的運營複雜性日益增加。無論是由於基礎體系結構的複雜性還是參與所需的眾多步驟，這都導致某些Defi產品的用戶採用較低。

The second issue is that the process of finding the most capital-efficient and attractive yield strategies relies on manual operations and is relatively inefficient. For example, products like concentrated liquidity provision and lending require depositors to engage in continuous active management.

第二個問題是找到最有效和有吸引力的產量策略的過程依賴於手動操作，並且相對效率低下。例如，諸如集中流動性提供和貸款之類的產品要求存款人進行持續的積極管理。

While solutions such as automated liquidity management protocols and account abstraction have helped reduce operational friction, DeFAI is expected to fundamentally resolve these issues.

儘管諸如自動流動性管理方案和賬戶抽象之類的解決方案有助於減少運營摩擦，但預計DeFai將從根本上解決這些問題。

To address the above two challenges, a brand new paradigm has emerged.

為了應對以上兩個挑戰，出現了一個全新的範式。

DeFAI is the combination of artificial intelligence (AI) and decentralized finance (DeFi), aimed at simplifying and automating complex DeFi operations, bridging the gap between existing solutions and user-friendly experiences.

Defai是人工智能（AI）和分散財務（DEFI）的結合，旨在簡化和自動化複雜的Fefi操作，彌合現有解決方案與用戶友好的體驗之間的差距。

In the form of AI agents, DeFAI can automatically execute tasks for users based on preset parameters. These agents can interact with smart contracts and accounts without human intervention and can learn user preferences and behaviors, further optimizing the user experience over time.

以AI代理的形式，DeFai可以根據預設參數自動執行用戶的任務。這些代理可以與智能合約和帳戶互動，而無需人工干預，可以學習用戶的偏好和行為，從而進一步優化用戶體驗。

@danielesesta: " @DWFLabs was the first team to recognize the DeFAI trend and quickly take action. Today, the crypto space welcomes a brand new category—DeFAI.

@danielesesta：“ @DWFlabs是第一支認識到Defai趨勢並迅速採取行動的球隊。今天，加密貨幣空間歡迎一個全新的類別 - defai。

Initially, it was just a fun attempt to combine my love for DeFi with the emerging technologies we are developing at @heyanonai, but now it has become a reality. DeFAI has arrived and is here to stay. The wave of DeFAI has just begun!"

最初，將我對Defi的愛與我們在@HeyanOnai開發的新興技術相結合，這只是一種有趣的嘗試，但現在已經成為現實。 Defai已經到達，並留在這裡。 Defai的浪潮才剛剛開始！”

Classification of DeFAI Projects

DEFAI項目的分類

DeFAI projects can be categorized into the following types, each addressing different issues faced by DeFi:

DEFAI項目可以歸類為以下類型，每個類型都解決了Defi面臨的不同問題：

Abstraction

抽象

Analysis

分析

Optimization

最佳化

Infrastructure

基礎設施

Abstraction

抽象

Projects in the abstraction category aim to simplify DeFi, making it easier for users to engage even as product complexity increases.

抽像類別中的項目旨在簡化DEFI，即使產品複雜性提高，用戶也更容易參與。

These projects achieve their goals through various means, such as supporting text-to-action functionality and automating multi-step and multi-chain processes.

這些項目通過各種方法來實現其目標，例如支持文本到作用功能以及自動化多步驟和多鏈流程。

These methods effectively simplify the process of participating in DeFi into two simple steps: first, identifying the best opportunities based on user needs and interests; second, allowing the agent to complete all necessary operations with a single command.

這些方法有效地簡化了參與DEFI的過程，分為兩個簡單的步驟：首先，根據用戶需求和興趣確定最佳機會；其次，允許代理使用單個命令完成所有必要的操作。

Some projects go further to expand these capabilities.

一些項目進一步擴大了這些功能。

For example, @HeyAnonai not only provides research tools and automated execution capabilities but also offers developers a framework to integrate their own DeFi protocols directly into the agent ecosystem, thereby expanding the service capabilities of the agents.

例如，@heyanonai不僅提供了研究工具和自動執行功能，而且還為開發人員提供了一個將自己的Defi協議直接集成到代理生態系統中的框架，從而擴展了代理的服務功能。

Meanwhile, @griffaindotcom has introduced various specialized agents that users can utilize to further simplify specific processes, such as quickly completing token sniping.

同時，@griffaindotcom推出了各種專業代理，用戶可以利用這些代理來進一步簡化特定的流程，例如快速完成令牌狙擊。

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Analysis

分析

Projects in this category share some similarities with the abstraction category, but their focus is on aggregating and analyzing on-chain data and data from various sources to identify trends and opportunities in DeFi and tokens.

此類別中的項目與抽像類別有一些相似之處，但是他們的重點是匯總和分析來自各種來源的鏈上數據和數據，以識別Defi和代幣中的趨勢和機會。

Through a user interface, users can query agents for information related to project technical indicators (technical analysis), fundamental attributes (fundamental analysis), and market sentiment. Additionally, most of these agents operate their own accounts on the X platform, actively sharing analysis results and interacting with the community.

通過用戶界面，用戶可以向代理查詢與項目技術指標（技術分析），基本屬性（基本分析）和市場情緒有關的信息。此外，這些代理中的大多數在X平台上運行自己的帳戶，積極共享分析結果並與社區互動。

@aixbt_agent is one of the leaders in this category, characterized by its custom large language model (LLM) framework, data indexer, and proprietary algorithms for trend identification. It has quickly integrated into the CT community culture, gradually establishing a reputation similar to that of opinion leaders (KOLs) due to its relatively accurate predictions.

@AIXBT_AGENT是該類別中的領導者之一，其特徵是其自定義大語模型（LLM）框架，數據索引器和專有算法以供趨勢識別。由於其相對準確的預測，它迅速融入了CT社區文化中，逐漸建立了與意見領袖（KOL）相似的聲譽。

Another emerging agent, @AcolytAI, provides dynamic interaction capabilities through its unique oracle, enabling collaboration with agent groups to provide users with responses based on aggregated data. In the future, it will even support the use of private datasets.

另一位新興代理@AcolyTai通過其獨特的甲骨文提供動態交互功能，使與代理組的協作能夠根據匯總數據為用戶提供響應。將來，它甚至將支持私人數據集的使用。

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Optimization

最佳化

Projects in the optimization category include agents and protocols that utilize AI to optimize yields and portfolio configurations.

優化類別中的項目包括使用AI來優化收益率和投資組合配置的代理和協議。

Protocols typically incorporate AI models that directly deploy user deposits based on previous backtesting strategies. Agents, on the other hand, focus more on providing flexibility, allowing users to customize their investment strategies and methods.

協議通常合併了基於以前的回測策略直接部署用戶存款的AI模型。另一方面，代理商更多地專注於提供靈活性，使用戶可以自定義其投資策略和方法。

For example, @SturdyFinance's SN10 (based on the Bittensor subnet) is an AI-driven yield optimization

例如， @sturdyfinance的SN10（基於Bittensor子網）是AI驅動的收益率優化