gpt-code-search
E2B
gpt-code-search | E2B | |
---|---|---|
6 | 35 | |
136 | 6,256 | |
- | 2.4% | |
10.0 | 9.9 | |
11 months ago | 4 days ago | |
Python | TypeScript | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gpt-code-search
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Show HN: Open-source code search with OpenAI's function calling
Thanks for the clarification. I was confused since the About link at the top of the linked-to repo has the URL https://wolfia.com prominently displayed. Both projects are very interesting and cool. Thanks!
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Open-source code search tool with OpenAI's GPT-4 and function calling
Key Features: - Efficient: Code search, retrieval, and answering all performed with OpenAI's GPT-4 function calling. - Privacy-centric: Code snippets only leave your device when you ask a question and the LLM requires the relevant code. - Ready-to-use: No need for pre-processing, chunking, or indexing. Get started right away! - Universal: It works with any code on your device. Why is it important? This tool aids in leveraging the power of GPT-4 to scan your codebase, eliminating the need to manually copy and paste code snippets or share your code with another third-party service. The tool addresses these issues by letting GPT-4 identify the most relevant code snippets within your codebase, saving you the need to copy and paste or send your code elsewhere. Notably, it fits right into your terminal, sparing you the need for a new UI or window. Here are the types of questions you can ask: - Help with debugging errors and locating the relevant code and files - Document extensive files or functionalities formatted as markdown - Generate new code based on existing files and conventions - Ask general questions about any part of the codebase Despite a few limitations like the inability to load context across multiple files at once and limited search depth, this tool is a considerable step towards a more efficient coding experience. For those seeking an even more powerful tool that uses vector embeddings and a more robust search and retrieval system, check out Wolfia Codex, the cloud-based big brother to gpt-code-search. That's it!
If you want to get smarter in AI, look here first. All the information has been extracted on Reddit for your convenience but you can find the GitHub repo here.
E2B
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Ask HN: Who is hiring? (May 2024)
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
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Show HN: Add AI code interpreter to any LLM via SDK
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...
- Open Source Python Code Interpreter for Any LLM
- Show HN: Open-Source Infrastructure for AI Code Interpreters
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We're building cloud runtime for AI agents and gradually open-sourcing everything
Hey folks, we're building an open source runtime for AI agents at E2B.
- Show HN: Run LLM-generated code in sandboxed envs
- Sandboxed cloud environments for AI agents & apps with a single line of code
- We're building a cloud for AI agents & AI apps, It's free and we're gradually open-sourcing the infra. Would love to hear your feedback!
- [P] We're building a cloud for AI agents & AI apps, It's free and we're gradually open-sourcing the infra. Would love to hear your feedback!