ed25519 VS OpenAI-DotNet

The first step was to generate an ed25519 - ssh key for my Github account.

From the GitLab documentation, ED25519 keys are recommended, as they are more secure and performant than RSA keys, according to the book Practical Cryptography With Go.

Correction regarding Ed25519: The Edwards Digital Signature Algorithm (EdDSA) https://ed25519.cr.yp.to/ was developed by a team including Daniel J. Bernstein, Niels Duif, Tanja Lange, Peter Schwabe, and Bo-Yin Yang. The first paper came out in 2011.

Les concepteurs de l'algorithme estimation :

Ed25519

Ed25519 для транзакций с одной подписью, и

> For the ED25519, the key size should be 256 or larger.

This tripped my bullshit filter. ED25519 is 256 bits always: https://ed25519.cr.yp.to/

You can obtain an OpenAI API key by visiting the OpenAI website.

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At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides open-source AI and machine learning models that can easily be deployed on HuggingFace itself or third-party systems such as Amazon SageMaker or Azure ML.  You can interface with these deployments through an API and control the scaling of these models, which makes them perfectly suited for production environments. These models range in size but are generally small AI models that are good at doing one specific task. With capabilities to fine-tune these models, or use the pre-trained model for specific tasks, embedding them into various applications becomes more efficient, enhancing automation and performance. Combining these models can create new and intricate AI applications. In this case, by utilizing HuggingFace models, you wouldn’t have to depend on a production application on a third-party provider such as OpenAI or Google, ensuring a more targeted and customizable approach to deploying deep learning solutions in your operations.

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