E2B
promptr
E2B | promptr | |
---|---|---|
35 | 16 | |
6,108 | 881 | |
3.0% | - | |
9.9 | 8.4 | |
5 days ago | about 2 months ago | |
TypeScript | JavaScript | |
Apache License 2.0 | MIT License |
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.
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!
promptr
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Ask HN: What are some actual use cases of AI Agents?
I taught https://github.com/KillianLucas/open-interpreter how to use https://github.com/ferrislucas/promptr
Then I asked it to add a test suite to a rails side project. It created missing factories, corrected a broken test database configuration, and wrote tests for the classes and controllers that I asked it to.
I didn't have to get involved with mundane details. I did have to intervene here and there, but not much. The tests aren't the best in the world, but IMO they're adding value by at least covering the happy path. They're not as good as an experienced person would write.
I did spend a non-trivial amount of time fiddling with the prompts I used to teach OI about Promptr as well as the prompts I used to get it to successfully create the test suite.
The total cost was around $11 using GPT4 turbo.
I think in this case it was a fun experiment. I think in the future, this type of tooling will be ubiquitous.
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Ask HN: What apps have you created for your own use?
I made a CLI tool called Promptr that allows you to make changes to a codebase via plain English instructions:
https://github.com/ferrislucas/promptr
There’s a templating system (liquidjs) included which is useful if you have a library of prompts that you want to reference often.
You can think of it as a junior engineer that needs explicit instructions.
Here are a few example PR’s implemented by Promptr - see the commits for the prompt that was used to produce the code:
https://github.com/ferrislucas/promptr/pull/38
https://github.com/ferrislucas/promptr/pull/41
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Another Major Outage Across ChatGPT and API
https://github.com/ferrislucas/promptr
You just prompt it directly or with a file, and it applies the changes to your file system. There's also a templating system that allows you to reference other files from your prompt file if you want to have a shared prompt file that contains project conventions etc.
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ReactAgent: LLM Agent for React Coding
This is exactly the use that Promptr is intended for https://github.com/ferrislucas/promptr
* full disclosure: I’m the author of Promptr
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Ask HN: How do you use AI to get things done faster?
I’ve been experimenting with pairing a tool I wrote called Promptr [1] with another tool called Open Interpreter [2].
I start with a prompt that teaches Open Interpreter how to use Promptr, and then I discuss what I’m trying to accomplish. It’s certainly not perfect, but there’s definitely something good that happens when you can iterate using dialog with a robot that can modify your file system and execute commands locally.
[1] Promptr: https://github.com/ferrislucas/promptr
[2] Open Interpreter: https://github.com/KillianLucas/open-interpreter
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Coders Can Survive–and Thrive–In a ChatGPT World
I wrote a great tool for this: https://github.com/ferrislucas/promptr
It’s great for making changes to existing code because it automatically includes the relevant files for context.
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ChatGPT Changed How I Write Software
For those looking for specific examples of useful code being authored by AI, you can check out this tool:
https://github.com/ferrislucas/promptr
The README links to example PR’s comprised of commits written by GPT4. The prompts used to produce the code are noted in the commit messages.
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Ask HN: What's your favorite GPT powered tool?
Promptr is a coding assistant tool that allows you to ask GPT to produce or modify code, and the results will be automatically applied to your file system.
https://github.com/ferrislucas/promptr
From the README:
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Any recommended tools for accessing codebases?
I'm also interested in this problem. It can be theoretically solved by giving GPT long-term memory about a specific codebase through vector embedding generation (using OpenAI's embeddings API). The semantic embeddings can then be stored in a vector (or vector-supported) database such as Pinecone alongside metadata for querying. Some of the key considerations are how to compare vectors for similarity (there are many algorithms) and how to use metadata to better support your use case. The following resources can be helpful to further understand this technique: - https://platform.openai.com/docs/guides/embeddings - https://www.mlq.ai/fine-tuning-gpt-3-question-answer-bot/ - https://www.pinecone.io/learn/javascript-chatbot/ A couple of semi-related projects I've been looking into: - https://github.com/ferrislucas/promptr - https://github.com/pashpashpash/vault-ai
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5-Apr-2023
Promptr is a CLI tool for operating on your codebase using GPT. (https://github.com/ferrislucas/promptr)
What are some alternatives?
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
chatgpt-shell - ChatGPT and DALL-E Emacs shells + Org babel 🦄 + a shell maker for other providers
lmql - A language for constraint-guided and efficient LLM programming.
IncognitoPilot - An AI code interpreter for sensitive data, powered by GPT-4 or Code Llama / Llama 2.
gish - GPT command line
Selefra - The open-source policy-as-code software that provides analysis for Multi-Cloud and SaaS environments, you can get insight with natural language (powered by OpenAI).
plz-cli - Copilot for your terminal
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
telegram-chatgpt-concierge-bot - Interact with OpenAI's ChatGPT via Telegram and Voice.
ChatIDE - AI Coding Assistant in your IDE - ChatGPT (OpenAI) and Claude (Anthropic) in a VSCode extension.