Crypto X AI的下一輪不僅會留在Memecoin曲目中。

熊市更適合深入研究，了解更多可行的敘述將使我們能夠在未來的潮流中脫穎而出。

Crypto X AI's next round will not only stay in the Memecoin track. The bear market is more suitable for in-depth research, and understanding more feasible narratives will enable us to ride the wave to the top in the future.

Crypto X AI的下一輪不僅會留在Memecoin曲目中。熊市更適合深入研究，了解更多可行的敘述將使我們能夠在未來的潮流中脫穎而出。

While organizing various reports on the AI track these days, I reflected on the stack released by Coinbase Ventures @cbventures regarding Crypto + AI.

如今，在組織AI軌道上的各種報告時，我在Coinbase Ventures @cbventures發布的有關Crypto + AI的Coinbase Ventures發布的堆棧中進行了反思。

JK @jonathankingvc envisions an ideal scenario for the combination of Crypto and AI: AI Agents interacting across various Crypto infrastructures. Software code (smart contracts) created by AI leads to a surge in Dapp numbers and an enhanced user experience, allowing users to own and govern their own AI large models and profit from them. This can be divided into:

JK @JonathankingVC設想了一個理想的情況，可以在各種加密基礎架構上進行互動的加密和AI：AI代理的組合。由AI創建的軟件代碼（智能合約）導致DAPP數字和增強的用戶體驗激增，使用戶可以擁有並控制自己的AI大型模型並從中獲利。這可以分為：

A computing layer primarily based on decentralized computing providers like Aethir.

一個計算層主要基於分散的計算提供商等分散的計算層。

A data layer centered around training datasets to expand AI large models.

一個數據層以培訓數據集為中心，以擴展AI大型模型。

A middleware layer composed of various new AI-based infrastructures (training/privacy inference/Agent platforms).

中間件層由各種新的基於AI的基礎架構（培訓/隱私推論/代理平台）組成。

An application layer.

應用層。

As it stands, there are very few Crypto + AI application layer products that allow retail investors to feel the impact directly, and the experience is poor. The most direct reason is that the foundational layers beneath the application layer have not yet been well established.

就目前而言，很少有加密貨幣 + AI應用層產品使散戶投資者能夠直接感受到影響，而且體驗很差。最直接的原因是，尚未確定應用層以下的基礎層。

Recently, starting from the model itself, FLock.io @flock_io has realized that training large models specifically for the Crypto track can validate its path through on-chain incentives to promote decentralized AI and model training. Although it is working on a larger narrative of decentralized AI model training, validating the feasibility of a standout product in a segmented field during the early stages can help FLock.io accumulate more early supporters, leading to the emergence of FLock.io's Web3 Agent Model.

最近，從模型本身開始，Flock.io @flock_io意識到，專門針對加密訓練的訓練大型模型可以通過鏈上激勵措施來驗證其路徑，以促進分散的AI和模型培訓。儘管它正在研究分散的AI模型培訓的更大敘事，但在早期階段驗證了在分段領域中脫穎而出的產品的可行性可以幫助Flock.io積累更多的早期支持者，從而導致Flock.io的Web3代理模型的出現。

If the application layer's Crypto AI Agent is your smart assistant, then its large model is equivalent to your assistant's brain. Only with deep experience and knowledge can it handle every interaction and execute each instruction correctly.

如果應用程序層的加密AI代理是您的智能助手，則其大型模型等同於您助手的大腦。只有具有深厚的經驗和知識，它才能處理每種互動並正確執行每個指令。

The most prominent metric of FLock.io's Web3 AI Agent large model is 75.93% FC precision matching accuracy, which simply means it understands Web3 better. Instructions that other models cannot recognize or that produce nonsensical outputs are addressed by the Web3 AI Agent large model through collaboration with industry partners like IO.net, based on the AI Arena Task 1 collaboration framework. By reducing single data source bias through decentralized training, AI Agents calling upon the Web3 AI Agent Model will be more practical and accurate.

Flock.io的Web3 AI代理大型模型最突出的指標是75.93％FC精確匹配精度，這僅意味著它可以更好地理解Web3。 Web3 AI代理大型模型通過與AI Arena Task 1 Collaboration Framework這樣的行業合作夥伴的協作來解決其他模型無法識別或產生荒謬輸出的說明。通過通過分散培訓來減少單個數據源偏差，呼籲Web3 AI代理模型的AI代理將更加實用和準確。

At this point, we must mention the ecological growth flywheel derived from FLock.io's natural incentive properties as a Crypto product.

在這一點上，我們必須提及從Flock.io的自然激勵性能作為加密產品中得出的生態增長飛輪。

By leveraging the basic $FLock incentives, more quality model trainers are encouraged to join, bringing higher quality data and training skills.

通過利用基本的$羊群激勵措施，鼓勵更多優質的模型培訓師加入，帶來更高質量的數據和培訓技能。

These contributions help build better AI large models;

這些貢獻有助於建立更好的AI大型模型；

More powerful AI large models can attract more feature-rich and practical Agents and Dapps to call upon;

更強大的AI大型模型可以吸引更多功能豐富，實用的代理商和Dapps。

Practical DApps and AI Agents, supported by the Web3 model, possess strong market competitiveness to generate more revenue;

由Web3模型支持的實用DAPP和AI代理具有強大的市場競爭力來產生更多的收入；

More calls to the large model by Agents and Dapps will generate more model incentives for trainers;

代理商和DAPP對大型模型的更多電話將為培訓師帶來更多的模型激勵措施；

This new data and usage feedback can further optimize and enhance the AI large model, while the prosperity of the ecosystem will also attract more quality model trainers to join through rewards and recognition.

這些新數據和使用反饋可以進一步優化和增強AI大型模型，而生態系統的繁榮也將吸引更多優質的模型培訓師通過獎勵和認可加入。

Through this continuously looping positive feedback, the FLock.io ecosystem can achieve sustained growth and self-enhancement. AI Infra is still in its early stages, let alone the Crypto AI field. We look forward to seeing more foundational BUIDLers like FLock.io in the industry. Only by laying a solid foundation for the model can we witness a more stable and flourishing application layer explosion.

通過這種不斷循環的積極反饋，Flock.io生態系統可以實現持續的增長和自我增強。 AI Infra仍處於早期階段，更不用說加密AI領域了。我們期待在行業中看到更多的基本造物主。只有為模型奠定堅實的基礎，我們才能目睹更穩定和繁榮的應用層爆炸。