The personal AI operating system

The operating system for an AI workforce.

amoOS plans the work, dispatches it to Claude and Codex agents across a fleet of machines, verifies what comes back, and compiles everything it learns into a Brain. It gets better with every run.

920 agent sessions · 342 PRs merged · 27 Jul – 29 Sep 2026

951

Tasks completed

Across every active project

342

Pull requests merged

Verified before merge

9.2B

Tokens in 30 days

Across 12 models

98%

Prompt-cache hit rate

Context reused, not re-paid

Cross-machine dispatchCompiled BrainMorning & evening briefsTelegram remote controlPush-to-talk voiceVerification gateLesson captureUsage-aware routingProject reviewsNetwork radarYouTube & X absorbSession recyclingDevice plane100+ MCP toolsCross-machine dispatchCompiled BrainMorning & evening briefsTelegram remote controlPush-to-talk voiceVerification gateLesson captureUsage-aware routingProject reviewsNetwork radarYouTube & X absorbSession recyclingDevice plane100+ MCP tools

More than a dispatcher

amoOS isn’t a wrapper around a coding agent. It is the layer above them: it decides what to work on, where to run it, how to check it, and what to remember.

Execution

A fleet, not a laptop

Claude Code and Codex agents run on always-on machines, each in its own isolated worktree. Close the laptop and the work keeps going.

amonode

Linux · always on

60%

amomini

Mac mini

18%

amom4pro

Cockpit

22%

Memory

A Brain that compounds

A compiled wiki of projects, decisions and lessons with provenance. Every agent reads it before it works.
Trust

Evidence before done

Definition-of-done checks and independent verify passes gate every merge.
Channels

Reachable from anywhere

Telegram is the remote control, a native app takes voice, the web is the command center, and 100+ MCP tools live inside every AI client.
TelegrammacOS voiceWeb command centerClaude CodeCodexClaude Desktop

The operating loop

01

Plan

Goals become sprints with executable, dependency-aware tasks.

02

Dispatch

Each task goes to the right model on the right machine.

03

Observe

Live sessions, capacity and questions stream into one place.

04

Verify

Work is accepted on evidence, not on an agent saying “done”.

05

Learn

Reports and lessons compile into the Brain for the next run.

The command center

Sprint progress, live sessions on every machine, capacity, interventions and verification signals in one view. Longer autonomous runs are only useful if they stay legible.

Command center

3 nodes online

Sprint

18 / 24

tasks done

Fleet

7 live

sessions

Capacity

62%

weekly used

brain-recall-eval

amonode · Opus 5.5

running

gateway-rate-limits

amomini · Codex

verifying

voice-latency-pass

amonode · Sonnet 5.5

running

digest-composition

amomini · Opus 5.5

needs you

usage-pacing-v2

amonode · Codex

merged

Merged this sprint

57

Illustrative data

Human in the loop

Step away without disappearing.

Safe, reversible work keeps moving. When an agent hits a real judgment call it asks instead of guessing, and the question lands on your phone with options. One tap and the worker resumes.

  • 134 decisions answered from the phone in the last 30 days.
  • Destructive actions always ask first; nothing irreversible is guessed.
  • Unanswered questions fail safe: gated actions are denied on timeout, never assumed.

amoOS

online · your fleet

Message

FAQ

What is amoOS?

amoOS is a personal AI operating system built and run by Aung Myint Oo. It plans work as sprints, dispatches tasks to Claude Code and Codex agents running on a fleet of machines, verifies the results, and compiles everything it learns into a persistent Brain that makes the next run faster.

Is amoOS just a coding-agent dispatcher?

No. Dispatch is one part. amoOS also includes sprint planning, a verification gate, a compiled knowledge Brain with provenance, learning loops that attach lessons to future tasks, morning and evening briefs, content and relationship tools, usage telemetry, and four ways to reach it: Telegram, a macOS voice app, a web command center and MCP.

Which AI models does amoOS use?

amoOS is model-agnostic. It routes work by difficulty: Claude Opus 5.5 for coordination, hard tasks and review, Claude Sonnet 5.5 for standard and mechanical work, Codex (GPT models) for bulk execution on a separate subscription pool, and small or local models for utility jobs. Twelve models ran in September 2026.

Does amoOS keep working when the laptop is closed?

Yes. Agents run on always-on machines, a Linux box and a Mac mini, each in an isolated git worktree. The laptop is only the cockpit for planning and review, and decisions can be answered from a phone through Telegram.

How does amoOS get better over time?

Every session ends with a bounded handoff that records what happened, which lessons helped, and new lesson candidates. Reviewed lessons are attached automatically to future tasks, and project knowledge is compiled into canonical Brain pages that every agent reads before it works.

How does amoOS make sure agent work is correct?

Work is accepted on evidence, not on an agent saying it is done. Definition-of-done checks and independent verify passes run before merge, genuine judgment calls are escalated to a human, and destructive actions always ask first.

How much work does amoOS actually do?

Between 27 July and 29 September 2026 amoOS ran 920 agent sessions, completed 951 tasks and merged 342 pull requests across three machines. In the last 30 days of that window it processed 9.2 billion tokens at a 98% prompt-cache hit rate.

Can I use amoOS?

amoOS is a personal system that Aung Myint Oo builds and runs every day, not a commercial product. Follow @amodev on X for updates on how it works and what it ships.