This repository was generated by Claude (Anthropic) as an experiment—to explore how Claude works and to reverse-engineer its approach. I spent a few hours discussing the idea with both Claude and ChatGPT, focusing on how to build a content-addressable, multilingual infrastructure for Python.
Two small PRs, each based on a single prompt, resulted in a CLI that captures the core idea. The goal was to make it relatable to others—let me know if it resonates with you.
Observations & Process: Claude asked a lot of clarifying questions, while ChatGPT "hallucinated" a broader "cognitive ecology" (likely because I’d mentioned the project under the name "mobius"). The final README reflects a blend of ChatGPT’s grandiose vision and Claude’s more grounded approach. It frames ouverture as a symbiotic post-LLM relationship—a bridge between existing knowledge (including code) and post-LLM AI systems.
The Bigger Picture: If ouverture succeeds, it could become an infrastructure like npmjs—but with less friction, less drama, and fewer barriers. The irony? The README’s vision remains relevant even without LLMs. The core idea—content-addressable, multilingual code—stands on its own.
Origins & Goals: This idea has been brewing for over a decade. My original goals were:
Code as a reusable resource: Write a function, store it, forget it, and retrieve it later—dependencies and all—without the hassle of reinventing wheels (e.g., leftpad or buried helper functions). Lowering cognitive barriers: Enable people to contribute to code without requiring English proficiency, aligning with the "think globally, act locally" ethos.
Inspirations: Key projects that shaped this thinking:
- Unison (content-addressable code) - Abstract Wikipedia (multilingual knowledge) - Situated Software
ouverture.py is my answer to [this Lobste.rs discussion on easing the production of micro-libraries](https://lobste.rs/s/ebbaed/how_ease_production_micro_librari...).