If you've been developing your coding skills to be able to work on AI projects, you've probably come across some open source AI resources.

I know I have. In fact, I think I use open source more than APIs and proprietary software these days.

When it comes to AI development, closed source software is often very limited due to a company's legal or corporate reasons.

If you've read my previous articles on using LangChain, you already have a fair grasp of the power of these open source frameworks.

So, in this article, I decided to gather other popular choices that you might find helpful for your AI development needs. Let's take a deeper look.

First, what is open source?
Open source in software development refers to the practice of making a project's source code available to the public for use, modification, and redistribution.

With its decentralized nature, it encourages open collaboration and feedback loops, which are important in helping projects succeed.

Some key principles of ai database open source development include:

Free redistribution: Open source software should not restrict anyone from selling or giving away the software as part of a software distribution. If yes, no royalties or fees are required for such distribution.
Source Code Availability: Open source software must contain source code, which should be easily accessible and distributable. If the software product is not distributed with source code, there should be a well-known method of obtaining it.
Non-discriminatory: Open source licenses must not discriminate against any individual or group, which means they must actually be accessible to anyone.
This model has grown in popularity, especially recently on sharing platforms like Twitter and Product Hunt. In fact, 80 percent of respondents to a survey said they had increased their use of open source in the past year, with 41 percent claiming it was a "significant increase."