Raypher–Running local AI agents (OpenClaw) on your own local computer

Hi HN, Kidiga here.

Most of us want to run autonomous agents (like OpenClaw or LangChain) locally on our daily-driver machines so they can actually interact with our IDEs and real workflows.

But giving a non-deterministic, hallucination-prone script raw access to your host OS is basically local remote code execution. It’s a matter of time before an agent wipes a directory or leaks an SSH key.

The current workarounds are terrible:

Containers: Shoving the agent in Docker completely lobotomizes it. It can no longer see or interact with your actual host OS where your real work lives.

The Hardware Air-Gap: Buying a dedicated Mac Mini or a cheap secondary laptop just to run agents safely is an expensive band-aid.

The Cloud: Spinning up a VPS adds annoying latency, costs monthly fees, and kills the "local-first" speed advantage.

I got fed up and built Raypher.

Raypher is a bare-metal security architecture specifically designed for AI agents. It lets you run agents directly on your host machine while keeping them on a cryptographically tight leash.

How the Alpha works under the hood:

Hardware Identity: We bind the agent's session cryptographically to your physical TPM 2.0 chip. If the agent is hijacked or moved to another machine, the keys fail.

Zero-Touch Interception: You don't have to rewrite your Python/Node agent scripts. Raypher currently uses transparent system proxy injection (netsh portproxy on Windows) to catch all agent traffic automatically.

The Policy Engine & DLP: Every request is evaluated locally. If it tries to phone home to an unapproved domain, it gets blocked. It also runs local DLP to automatically redact API keys before they hit the network stream.

(Transparency note: To maintain system stability for early testers, this MVP uses system proxy injection. The heavy kernel-level eBPF/WFP hooks are currently in development, but this Alpha proves the hardware-bound Policy Engine actually works).