Hazumi News

I made this for myself, and it seemed like it might be useful to others. I'd love some feedback, both on the threat model and the tool itself. I hope you find it useful!

Backstory: I've been using many agents in parallel as I work on a somewhat ambitious financial analysis tool. I was juggling agents working on epics for the linear solver, the persistence layer, the front-end, and planning for the second-generation solver. I was losing my mind playing whack-a-mole with the permission prompts. YOLO mode felt so tempting. And yet.

Then it occurred to me: what if YOLO mode isn't so bad? Decision fatigue is a thing. If I could cap the blast radius of a confused agent, maybe I could just review once. Wouldn't that be safer?

So that day, while my kids were taking a nap, I decided to see if I could put YOLO-mode Claude inside a sandbox that blocks exfiltration and regulates git access. The result is yolo-cage.

Also: the AI wrote its own containment system from inside the system's own prototype. Which is either very aligned or very meta, depending on how you look at it.

Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning.

After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane.

So I built UltraContext. The mental model is git for context:

- Updates and deletes automatically create versions (history is never lost)

- Replay state at any point

The API is 5 methods:

  uc.create()   // new context (can fork from existing)
  uc.append()   // add message
  uc.get()      // retrieve by version, timestamp, or index
  uc.update()   // edit message → creates version
  uc.delete()   // remove message → creates version

Messages are schema-free. Store conversation history, tool calls, system prompts—whatever shape you need. Pass it straight to your LLM using any framework you'd like.

What it's for:

- Persisting conversation state across sessions

- Debugging agent behavior (rewind to decision point)

- Forking contexts to test different flows

- Audit trails without building audit infrastructure

- Multi-agent and sub-agent patterns

What it's NOT:

- Not a memory/RAG system (no semantic search)

- Not a vector database

- Not an Orchestration/LLM framework

UltraContext handles versioning, branching, history. You get time-travel with one line.

Docs: https://ultracontext.ai/docs