Why This Matters:
GitHub Copilot uses several LLM models, but other AI agents might perform better. This application also enables the use of GitHub Copilot for powering other AI applications, such as Proof-of-Concepts (POCs).
How It Works:
The application acts as a middleware layer, translating OpenAI API requests into interactions with GitHub Copilot models. It leverages Go's robust concurrency model and efficient HTTP handling to ensure low-latency responses, making it suitable for real-time applications. This project is a reverse-engineered proxy of the GitHub Copilot API, exposing it as an OpenAI compatible service. This allows integration with tools that support the OpenAI Chat Completions API.
Use Cases:
While the application uses models provided by GitHub Copilot, you can integrate other AI agents like QwenCode, Cline, Roo code, Crush, OpenCode, and etc. Or even power your own application.
I'd love to hear your thoughts on how this approach can be improved or extended. Whether you're building a new application or integrating it into an existing workflow, feel free to share your ideas or feedback here!
Original Author. I wrote this app as a side project to fight for privacy, freedom of speech and human rights.
E2E encryption is not a new idea, but it is hard to find a easy to use app. So I took privatebin as backend, because it is widely available and feature rich, and build a UI on top of it.
I knew that this app may be used for other purposes, but we never ban the usage of fire because it can burn houses, right?
Author of privapps Notebook, I just had first release couple of days ago. It was my side project and I didn't expect to monetize it, the notebook project is more like a missionary project.
I think e2e encryption and share is not new, especially compare to the privatebin, which has been out there for quite a long time, and it is mature with a lot of features, such as docker / K8s deployment.
If Droppler want to success, it might need to do slack integration, SSO and customize work. Since the space is very crowd there.
In addition, it can be very easy to be used for other purposes.
Edit: I read your site again, it is like try to make e2e encryption as a SaaS platform, for fast app development. This direction is kind of overlapping with vault:
https://spring.io/projects/spring-vault
Many cloud platform like AMZ, GCP have secrets management services, it can be a hard battle.
Why This Matters: GitHub Copilot uses several LLM models, but other AI agents might perform better. This application also enables the use of GitHub Copilot for powering other AI applications, such as Proof-of-Concepts (POCs).
How It Works: The application acts as a middleware layer, translating OpenAI API requests into interactions with GitHub Copilot models. It leverages Go's robust concurrency model and efficient HTTP handling to ensure low-latency responses, making it suitable for real-time applications. This project is a reverse-engineered proxy of the GitHub Copilot API, exposing it as an OpenAI compatible service. This allows integration with tools that support the OpenAI Chat Completions API.
Use Cases: While the application uses models provided by GitHub Copilot, you can integrate other AI agents like QwenCode, Cline, Roo code, Crush, OpenCode, and etc. Or even power your own application.
I'd love to hear your thoughts on how this approach can be improved or extended. Whether you're building a new application or integrating it into an existing workflow, feel free to share your ideas or feedback here!