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Distributed 9P graphics engine for real-time AI generation

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Peribus – "Track hand on A, display on B, blink LED on C" – one prompt

by kanzeee·Mar 9, 2026·2 points·0 comments

AI Analysis

●●●BangerWizardryBig BrainBold Bet

One prompt orchestrates cameras, GPIOs, and screens across five machines via 9P filesystem.

Strengths
  • 9P filesystem abstraction makes distributed hardware feel local
  • LLM agents write code that composes resources across networked machines
  • Plan 9 philosophy applied to modern AI orchestration workflows
Weaknesses
  • No security model documented as experimental danger warning
  • Very early with only 12 commits and limited testing
Target Audience

Developers building distributed AI workflows and robotics researchers

Post Description

Peribus is a Plan 9-inspired workspace where a single prompt — typed or spoken — generates live UI and orchestrates hardware across every machine on your network. Cameras, screens, GPIOs, sensors, speakers — you name it. The LLM sees your entire network as directories and writes code that composes them. The flashy version: "Track my hand on camera. Map fingers to a piano on machine 2. Play notes on machine 3. Classify the melody on machine 4. Compose a harmony and display sheet music on all five." One prompt. Five machines. It works.

But the real power is incremental, voice-driven workflows. Picture a logistics dispatcher: "Open a map." Done. "Load orders.csv from the warehouse server." Done. "Plot the delivery addresses." Done. "Shortest route." Done. "Pull GPS from the delivery truck." Done. "Recalculate with live traffic and truck position. Keep updating." Done. Six machines, one voice conversation. Each step builds on the last — the canvas accumulates state, every element is versioned with full undo/redo, nothing breaks. That's not a demo, that's a Tuesday morning. Simpler things work too. "Create a button" — a button appears on the canvas. "Make it transparent with shadows" — it updates live. "Create a 3D car game" — a driving simulation with traffic appears alongside your other widgets. "Add multiplayer with machine B" — done.

The mechanism:

echo "plot delivery addresses on map" > /n/llm/coder/input cat /n/llm/coder/OUTPUT > /n/machine_name/scene/parse

A single response can target multiple machines simultaneously through intrinsic routing — the agent's output is split by machine and streamed to each one:

cat /n/llm/coder/A > /n/A/scene/parse cat /n/llm/coder/B > /n/B/scene/parse cat /n/llm/coder/C > /n/C/scene/parse

cat blocks until the agent starts generating, then streams code into each machine's scene parser. Widgets appear in real time. The multiplexer (riomux) stitches machines at the 9P wire level — mount a Raspberry Pi, a workstation, a delivery truck's onboard computer, and they're just directories. The agent's context includes what's already on every screen, so each new request builds on existing state. No unnecessary APIs. No message brokers. No orchestration framework. Just files, reads, and writes. Plan 9's idea, pushed as far as it goes.

Experimental, no security model. Isolated networks only.

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