Hey HN,

I am building Agentis, an AI-native programming language written in vanilla Rust. Instead of treating prompts as external library calls, "everything is a prompt" is a core language primitive.

There is no standard library. No string.split(). If an agent needs to process data, it asks the LLM.

I also completely fused the language with a VCS. Code isn't stored in text files; it's a binary, SHA-256-hashed DAG.

A few core concepts I implemented:

1. Cognitive Budget: Every operation costs fuel. This prevents runaway agent loops and forces efficient prompt design.

2. Evolutionary Branching: I use explore blocks to fork execution. If a solution passes the validate block, the branch survives. If it fails, it's silently discarded.

3. Content-Addressed Code: Since the AST is hashed, there are no merge conflicts. You import by hash.

I just completed Phase 4 of my implementation and am currently gearing up for Phase 5. The CLI supports local models (Ollama), Claude, Anthropic API, etc.

It’s highly experimental, and I'm at the stage where I'd love to discuss these core concepts with the community and find people interested in collaborating.

I'd love to hear your thoughts on this architecture, the evolutionary branching, or the VCS model.