This is a submission for the Hermes Agent Challenge

What I Built

I built Hermes Meter, a Hermes Agent application-layer concept for turning AI agent activity into a local-first physical desk display.

The project is based on my AI Desk Meter repo.

Live project page:

• AI Desk Meter GitHub Pages

The idea is simple:

Hermes Agent should not only work in the background. It should have a visible local status surface that tells the developer what the agent is doing, how active it is, whether it is stale, and whether the local runtime is healthy.

AI agents are becoming more capable, but they often feel invisible.

A task starts. The model thinks. Tools run. Tokens generate. Usage changes. The agent may be active, idle, stalled, offline, or rate-limited.

But the developer often has to guess what is happening.

Hermes Meter turns that invisible agent state into a tiny local dashboard.

The first hardware direction is an ESP32-S3 AMOLED desk display that can show agent/runtime state such as:

• active / idle / stale / error
• current usage percentage
• weekly usage percentage
• reset countdowns
• burn-rate state
• connection status
• data confidence
• local model / provider state
• optional pixel mascot or “Musing…” style status animation

The goal is not to bypass usage limits or scrape private dashboards.

The goal is visibility, diagnostics, and local-first agent awareness.

Demo

The core Hermes Meter flow looks like this:

Hermes Agent / local AI tool / runtime logs
  ↓
Python host daemon
  ↓
Normalized JSON payload
  ↓
Wi-Fi POST / BLE / stdout
  ↓
ESP32-S3 AMOLED display
  ↓
Physical desk status meter

Instead of checking a browser tab, cloud dashboard, or terminal log, the developer gets an always-visible local device.

Example display states:

Hermes: Musing...
Mode: active
Current: 50%
Weekly: 11%
Burn: normal
Reset: 01:22:00
Source: local/mock
Confidence: estimated

Example JSON payload:

{
  "schema": "ai-desk-meter.v1",
  "service": "hermes-agent",
  "current_percent": 50,
  "weekly_percent": 11,
  "current_reset_seconds": 4920,
  "weekly_reset_seconds": 547200,
  "burn_rate": "normal",
  "status": "Musing...",
  "mode": "active",
  "updated_at": 1760000000,
  "source": "local-runtime",
  "confidence": "estimated"
}

Example Hermes Agent state mapping:

agent.planning        → Musing...
agent.tool_running    → Working
agent.streaming       → Generating
agent.waiting         → Idle
agent.no_output       → Stalled
agent.timeout         → Timeout
agent.offline         → Offline
agent.error           → Error

That gives Hermes Agent a physical, glanceable status surface.

Code

Core repository:

• AI Desk Meter — local-first physical desktop usage dashboard for AI coding tools, with an ESP32-S3 AMOLED target, Python host daemon, JSON protocol, docs, examples, firmware scaffold, and enclosure notes.

Live project page:

• AI Desk Meter GitHub Pages

Supporting local-agent infrastructure:

• ARC-Neuron LLMBuilder — local AI build-and-memory system focused on model promotion, benchmark receipts, and governed model improvement.
• arc-lucifer-cleanroom-runtime — local-first runtime direction for receipts, replay, rollback, ranked memory, and sandboxed AI execution.
• LuciferAI_Local — local/offline assistant direction using local model execution and GGUF / llamafile workflows.
• ARC-StreamMemory — local visual/session memory direction for agent-readable frame and screen evidence.
• TizWildin Entertainment HUB — public hub for the broader software, AI, automation, and audio ecosystem.

This Hermes Agent challenge build focuses on the physical status-meter pattern:

Hermes Agent
  ↓
runtime/provider state
  ↓
host daemon
  ↓
meter payload
  ↓
ESP32-S3 display
  ↓
glanceable local agent awareness

My Tech Stack

• Hermes Agent
• AI Desk Meter
• ESP32-S3 AMOLED display target
• Python host daemon
• JSON state protocol
• Wi-Fi POST / BLE direction
• local runtime/provider adapters
• mock/manual providers
• firmware scaffold
• C++ / PlatformIO direction
• local-first diagnostics
• physical desk display UX
• optional mascot/status animation

The core pattern is:

Agent state
  ↓
normalized meter payload
  ↓
local transport
  ↓
tiny hardware display
  ↓
developer awareness

How I Used Hermes Agent

Hermes Agent is the agentic workflow that benefits from a visible local meter.

A Hermes-style agent can plan, call tools, stream model output, inspect files, run tasks, and produce results.

Hermes Meter focuses on the operator side:

What is the agent doing right now?

In this pattern, Hermes Agent emits or exposes simple state changes:

• planning
• using tools
• generating
• waiting
• retrying
• stalled
• timed out
• completed
• offline
• error

The AI Desk Meter host daemon converts those states into a normalized payload.

The physical display renders the result.

That gives the developer a small hardware “agent heartbeat.”

Instead of wondering whether the agent is frozen, still working, or disconnected, the developer can glance at the desk meter.

Why This Matters

Agent UX is not only about chat windows.

If agents become part of real developer workflows, they need better local presence.

A physical meter can show:

• whether the agent is alive
• whether generation is active
• whether a run is stale
• whether usage is high
• when reset windows are approaching
• whether a provider is offline
• whether the current state is exact, estimated, mock, or unknown

This is especially useful for local-first and multi-provider setups.

A developer might use:

• Hermes Agent
• local GGUF models
• cloud coding tools
• CLI assistants
• local ARC runtimes
• desktop dashboards
• physical meters

Hermes Meter gives those systems a shared output surface.

It makes the agent visible outside the terminal.

What Makes It Different

Most agent projects focus only on the model or the tool call.

Hermes Meter focuses on the human operator loop.

The model can be powerful, but the developer still needs to know:

Is it working?
Is it idle?
Is it stuck?
Is it offline?
Is usage getting high?
Can I trust this reading?
When does it reset?

The meter turns that state into a simple, local, glanceable display.

That matters because invisible automation is harder to trust.

Visible automation is easier to supervise.

Safety / Ethics Boundary

This project is for visibility and diagnostics only.

It is not designed to:

• bypass usage limits
• evade quotas
• rotate accounts
• scrape private dashboards
• automate abuse
• hide usage

The goal is to show local state and make agent/runtime behavior easier to understand.

The meter is an awareness tool, not a bypass tool.

Current Status

AI Desk Meter is currently a DIY starter package with:

• README and GitHub Pages project page
• Python host daemon direction
• mock/manual provider flow
• normalized JSON payload
• ESP32-S3 AMOLED firmware scaffold
• examples
• enclosure notes
• polished HTML spec guide
• roadmap toward multi-AI meter support

The first target is a small ESP32-S3 AMOLED board.

The host computer does the usage collection and estimation.

The display receives a simple JSON state and renders it.

Future Roadmap

Next steps for Hermes Meter:

• Add a Hermes Agent provider adapter
• Add local runtime state mapping
• Add “Musing / Working / Generating / Stalled / Timeout” display modes
• Add local GGUF / llamafile status integration
• Add ARC runtime receipt/status integration
• Add BLE pairing
• Add OTA update path
• Add more display skins
• Add multi-agent / multi-provider mode
• Add physical alert states for stalled generation
• Add a tiny “agent heartbeat” animation

Closing Thought

Open agents should not be invisible.

If Hermes Agent is planning, thinking, generating, stalled, or complete, the developer should be able to see that state locally.

That is the core idea of Hermes Meter:

Hermes Agent for work.

AI Desk Meter for visibility.

Local JSON for state.

ESP32-S3 for physical presence.

A tiny display for agent awareness.