tanuki.py VS E2B

I came across this open source library a few times across Reddit + HN and something that piqued my interest was their concept around "test-driven alignment".

Hey guys, I've spent the last few weeks building Tanuki: https://github.com/Tanuki/tanuki.py

Hi r/python, Jack here! I'm one of the creators of MonkeyPatch, an easy way to build LLM-powered functions and apps that get cheaper and faster the more you use them.

Check out the ‘function_modeler’. Currently it’s OpenAI only, but local models are on the immediate roadmap.

https://github.com/monkeypatch/monkeypatch.py/blob/master/sr...

Hi HN, Jack here! I’m one of the creators of MonkeyPatch, an easy way to build LLM-powered functions and apps that get cheaper and faster the more you use them.

For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in < 5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior.

After about 200-300 calls, these functions will begin to get cheaper and faster. We’ve seen 8-10x reduction in cost and latency in some use-cases! This happens via progressive knowledge distillation - MonkeyPatch incrementally fine-tunes smaller, cheaper models in the background, tests them against the constraints defined by the developer, and retains the smallest model that meets accuracy requirements, which typically has significantly lower costs and latency.

As an LLM researcher, I kept getting asked by startups and friends to build specific LLM features that they could embed into their applications. I realized that most developers have to either 1) use existing low-level LLM clients (GPT4/Claude), which can be unreliable, untyped, and pricey, or 2) pore through LangChain documentation for days to build something.

We built MonkeyPatch to make it easy for developers to inject LLM-powered functions into their code and create tests to ensure they behave as intended. Our goal is to help developers easily build apps and functions without worrying about reliability, cost, and latency, while following best software engineering practices.

We’re only available in Python currently but actively working on a Typescript version. The repo has all the instructions you need to get up and running in a few minutes.

The world of LLMs is changing by the day and so we’re not 100% sure how MonkeyPatch will evolve. For now, I’m just excited to share what we’ve been working on with the HN community. Would love to know what you guys think!

Open-source repo: https://github.com/monkeypatch/monkeypatch.py

Sample use-cases: https://github.com/monkeypatch/monkeypatch.py/tree/master/ex...

Benchmarks: https://github.com/monkeypatch/monkeypatch.py#scaling-and-fi...

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.