OpenAI-DotNet VS age

Okay, now we've confirmed the API endpoint is working, let's connect it to OpenAI first, install the OpenAI package, navigate to the route directory, and run the command below in our terminal

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

OpenAI Account: Register for an OpenAI account to utilize AI-powered features in your ToolJet applications. Sign up here.

KodiBot is a desktop app that enables users to run their own AI chat assistants locally and offline on Windows, Mac, and Linux operating systems. KodiBot is a standalone app and does not require an internet connection or additional dependencies to run local chat assistants. It supports both Llama.cpp compatible models and OpenAI API.

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.

age

and echoing the result after converting to an age private key

I like that https://github.com/FiloSottile/age has small public keys.

> something fresh

It exists, it's called age..

Some random links

https://github.com/FiloSottile/age

https://www.reddit.com/r/crypto/comments/hr64hr/state_of_age...

https://github.com/FiloSottile/age/discussions/432

> (Acquiring keys, rotating keys, identifying compromised keys, and most importantly either reaches a large enough percentage of emails..

Oh nevermind, age doesn't do any of that. Indeed, it doesn't even do email https://github.com/FiloSottile/age/issues/93

Encrypted secrets thanks to SOPS and Age

I never heard of "Age" before this post. Thank you to share. If others are interested to learn more, here are two other interesting posts about Age:

https://github.com/FiloSottile/age/discussions/432

https://words.filippo.io/dispatches/age-authentication/

of all things I was able to resolve the issue via this github issue: https://github.com/FiloSottile/age/issues/370#issuecomment-1...

Why keep something secret on a public repo? Is that not an oxymoron?

Also, I’m terms of encryption something like age[0] makes it much easier to not shoot yourself in the foot.

[0] https://github.com/FiloSottile/age

Why RSA specifically? For backups, I recommend Tarsnap. But if you really don't want to pay for encrypted cloud hosting, then check out age encryption.