Defai：Defi的未来

在仅仅一周的时间内，DeFai迅速成为一个备受推崇的项目领域，预计在未来几个月内表现出色。

Original source: DWF Ventures X account

原始资料：DWF Ventures X帐户

Author: DWF Ventures

作者：DWF Ventures

Compiled by: ShenChao TechFlow

编译者：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.

在短短的一个星期内，DeFai迅速成为一个热门项目区域，我们可以期望在未来几个月内看到Defai的出色表现。

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

那么，是什么使Defai如此重要？它解决了什么核心问题？让我们一起探索。

Introduction

介绍

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.

近年来，DEFI已取得了重大进展 - 从第一波协议（例如，现在称为@skyecosystem，@uniswap和@compoundfinance）到现在超过3000种不同的DEFI协议。

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

尽管DEFI的进步对整个行业都有意义，但它们也面临着一些关键挑战。

Challenges

挑战

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.

第一个主要问题是Defi产品的运营复杂性日益增加。无论是由于基础体系结构的复杂性还是参与所需的众多步骤，这都导致某些Defi产品的用户采用较低。

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.

第二个问题是找到最有效和有吸引力的产量策略的过程依赖于手动操作，并且相对效率低下。例如，诸如集中流动性提供和贷款之类的产品要求存款人进行持续的积极管理。

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

尽管诸如自动流动性管理方案和账户抽象之类的解决方案有助于减少运营摩擦，但预计DeFai将从根本上解决这些问题。

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

为了应对以上两个挑战，出现了一个全新的范式。

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）和分散财务（DEFI）的结合，旨在简化和自动化复杂的Fefi操作，弥合现有解决方案与用户友好的体验之间的差距。

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可以根据预设参数自动执行用户的任务。这些代理可以与智能合约和帐户互动，而无需人工干预，可以学习用户的偏好和行为，从而进一步优化用户体验。

@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是第一支认识到Defai趋势并迅速采取行动的球队。今天，加密货币空间欢迎一个全新的类别 - 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，将我对Defi的爱与我们在@HeyanOnai开发的新兴技术相结合，这只是一种有趣的尝试，但现在已经成为现实。 Defai已经到达，并留在这里。 Defai的浪潮才刚刚开始！”

Classification of DeFAI Projects

DEFAI项目的分类

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

DEFAI项目可以归类为以下类型，每个类型都解决了Defi面临的不同问题：

Abstraction

抽象

Analysis

分析

Optimization

优化

Infrastructure

基础设施

Abstraction

抽象

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

抽象类别中的项目旨在简化DEFI，即使产品复杂性提高，用户也更容易参与。

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

这些项目通过各种方法来实现其目标，例如支持文本到作用功能以及自动化多步骤和多链流程。

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.

这些方法有效地简化了参与DEFI的过程，分为两个简单的步骤：首先，根据用户需求和兴趣确定最佳机会；其次，允许代理使用单个命令完成所有必要的操作。

Some projects go further to expand these capabilities.

一些项目进一步扩大了这些功能。

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不仅提供了研究工具和自动执行功能，而且还为开发人员提供了一个将自己的Defi协议直接集成到代理生态系统中的框架，从而扩展了代理的服务功能。

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

同时，@griffaindotcom推出了各种专业代理，用户可以利用这些代理来进一步简化特定的流程，例如快速完成令牌狙击。

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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.

此类别中的项目与抽象类别有一些相似之处，但是他们的重点是汇总和分析来自各种来源的链上数据和数据，以识别Defi和代币中的趋势和机会。

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.

通过用户界面，用户可以向代理查询与项目技术指标（技术分析），基本属性（基本分析）和市场情绪有关的信息。此外，这些代理中的大多数在X平台上运行自己的帐户，积极共享分析结果并与社区互动。

@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是该类别中的领导者之一，其特征是其自定义大语模型（LLM）框架，数据索引器和专有算法以供趋势识别。由于其相对准确的预测，它迅速融入了CT社区文化中，逐渐建立了与意见领袖（KOL）相似的声誉。

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.

另一位新兴代理@AcolyTai通过其独特的甲骨文提供动态交互功能，使与代理组的协作能够根据汇总数据为用户提供响应。将来，它甚至将支持私人数据集的使用。

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Optimization

优化

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

优化类别中的项目包括使用AI来优化收益率和投资组合配置的代理和协议。

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.

协议通常合并了基于以前的回测策略直接部署用户存款的AI模型。另一方面，代理商更多地专注于提供灵活性，使用户可以自定义其投资策略和方法。

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

例如， @sturdyfinance的SN10（基于Bittensor子网）是AI驱动的收益率优化