The Internet Grew Up Based on Open-Source Software.

But Now, Proprietary AI Solutions Reign Supreme in the AI Landscape, with Tech Giants at the Helm.

Here are 5 Reasons why Open-Source Code-LLMs will change the AI landscape.


OpenAI's APIs and infrastructure can be expensive to use, especially for large-scale deployments or organizations with limited budgets. Open-source LLMs are typically free to use, operating costs can be moderate. This cost advantage can be significant.
One use case could be a Coding Assistant on your public API docs. It allows your users to interact with the SDK in a natural way and to generate code that gets them started quickly. You can select the knowledge of the LLM yourself and don't need to worry about the AI accidentally talking about your competition. arrow-down

Transparency and control

OpenAI's models are often provided as black boxes, with limited access to the underlying code or training data. This can make it difficult for users to understand how the models work or customize them for their specific needs.
Open-source LLMs provide full access to the model's code and training data, allowing users to understand how the model works and customize it to their specific needs. This transparency can be crucial for ensuring that the model's outputs align with ethical considerations and do not perpetuate biases. arrow-down

Community-driven development

Open-source LLMs benefit from the collective expertise and contributions of a global community of developers and researchers. This collaborative approach leads to faster innovation and the development of more robust and versatile models. One example of this is the CodeSearchNet dataset, which was created by a community of volunteers from various organizations. This dataset contains millions of code snippets and relevant documentation, allowing Open-Source LLMs to learn from real-world code and improve their ability to understand and generate code. The CodeSearchNet dataset has been used for a variety of purposes, including:
  • Training Open-Source LLMs on large-scale code data
  • Evaluating the performance of Open-Source LLMs on code tasks
  • Developing new algorithms for code understanding and generation

Fostering innovation

Open-source LLMs serve as a foundation for innovation, enabling developers to experiment with new ideas and applications without the constraints of proprietary models. This open-source ecosystem drives progress in the field of AI.