Selefra VS E2B

[Feature]Modules support filtering, while labels support customization of any format. by @FelixsJiang in #30

Selefra Project Repository: github.com/selefra/selefra

Analyze cloud resources using GPT (https://github.com/selefra/selefra)

Selefra: Selefra is an open-source Policy as Code tool that can use natural language to write rules for security compliance checks, cost configuration checks, and architecture rationality checks on current cloud services.

GitHub: https://github.com/selefra/selefra

Website: https://www.selefra.io/

Here, we strongly encourage you to try Selefra and enjoy a faster and more efficient cloud security analysis and resolution experience. You can find more information about Selefra on our official website (https://www.selefra.io/) or GitHub (https://github.com/selefra/selefra), or follow our Twitter account (https://twitter.com/SelefraCorp) for more real-time updates.

Check out and star GitHub: https://github.com/selefra/selefra

E2B | https://e2b.dev | San Francisco, CA | Full-time | In-person

[E2B](https://e2b.dev) is building a secure open-source runtime that will power next billion of AI apps & agents.

We found an early traction with making it easy for developers to add [code interpreting](https://github.com/e2b-dev/code-interpreter) to their AI apps with our SDK built on top of our [agentic runtime](https://github.com/e2b-dev/e2b). We have paying customers from seed to enterprise companies.

We're hiring:

- Frontend/Product Engineer

- Infrastructure Engineer

Check the roles here https://e2b.dev/careers

Hi, I'm the CEO of the company that built this SDK.

We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

You can think of us as giving small cloud computers to LLMs.

We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

Our long-term plan is to build an automated AWS for AI apps and agents.

Happy to answer any questions and hear feedback!

[0] https://e2b.dev/

[1] https://github.com/e2b-dev

[2] https://github.com/firecracker-microvm/firecracker

[3] https://e2b.dev/docs

[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

Hey folks, we're building an open source runtime for AI agents at E2B.