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Description

DefiLlama is dedicated to providing precise data while avoiding advertisements or sponsored materials, ensuring transparency in its operations. It features a comprehensive listing of DeFi projects spanning various blockchains. Most of the adapters utilized on DefiLlama are developed and managed by their respective community members, with all modifications organized through the DefiLlama/DefiLlama-Adapters GitHub repository. The platform gathers information on a protocol by invoking specific endpoints or executing blockchain queries. It calculates the Total Value Locked (TVL) of a protocol and presents the results. Currently, our Software Development Kit (SDK) is limited to supporting EVM-compatible chains; therefore, if your project operates on any of these chains, creating an SDK-based adapter is advisable. Conversely, if your project is situated on a different chain, the fetch adapter would be the appropriate solution. An adapter functions as a method that accepts both a timestamp and the block height (specifically on Ethereum), returning the token balances secured within your protocol's smart contracts at that specific moment. This structured approach ensures that users can reliably track and access the necessary data for their DeFi projects.

Description

Llama Guard is a collaborative open-source safety model created by Meta AI aimed at improving the security of large language models during interactions with humans. It operates as a filtering mechanism for inputs and outputs, categorizing both prompts and replies based on potential safety risks such as toxicity, hate speech, and false information. With training on a meticulously selected dataset, Llama Guard's performance rivals or surpasses that of existing moderation frameworks, including OpenAI's Moderation API and ToxicChat. This model features an instruction-tuned framework that permits developers to tailor its classification system and output styles to cater to specific applications. As a component of Meta's extensive "Purple Llama" project, it integrates both proactive and reactive security measures to ensure the responsible use of generative AI technologies. The availability of the model weights in the public domain invites additional exploration and modifications to address the continually changing landscape of AI safety concerns, fostering innovation and collaboration in the field. This open-access approach not only enhances the community's ability to experiment but also promotes a shared commitment to ethical AI development.