Aetox-Harness

Assistant

Fits your work — you don't fit yourself to it

"2 hours of meeting audio — summarise it, with who took which action"

You get back: a summary with the topics, the decisions, and the minute each was said at, plus the action items and who owns them — a file in your folder, not text in a chat box.

It knows you in parts — your profile, this machine, each desk, each worker, and the routes you repeat — so it reaches deeper context than a general assistant, without carrying all of it in every request.

recording meetings · receipts · tidying files · disk space · slides · live research · browser · automation · clips and content · teams · open tasks and reminders

Developer

Code that runs — and it remembers what this project decided

It opens the real project: real files, an editor, a terminal, a language server and git. It reads the AETOX.md, AGENTS.md or CLAUDE.md the project already has, runs the project's own tests before it says done, and writes down plainly what it could not verify.

Memory is per project — open a folder and you get that folder's memory: your coding patterns, the decisions already agreed, the team's conventions. Nothing needs explaining twice, and what a chat is about is pulled in when it is needed.

The system makes the model write better code — that is the only thing we sell for code, not the model's own brilliance. The model is yours; whichever one you have.

Don't take our word for it — run it yourself. Every task, starting file and hidden test is published. Run any of them · All the numbers, speed and tokens included →

real files · terminal · git · diagnostics · symbol · MCP tools · the project's own tests · sub-agents

Scripted walkthrough of the real app, not a benchmark run.

Quick start

1. Install from the Microsoft Store or with the installer above. Pick one channel and stay with it: the Store build and the installer build keep separate settings, chats and keys.
2. Connect a model. Open Settings › Models and sign in with ChatGPT, GitHub Copilot or OpenRouter, or paste a key, or point it at LM Studio or Ollama on your machine.
3. Give it a job. Pick Assistant for files, documents and the web, or Code for a project folder.

One instruction. It works out the steps

The point is not "it can do many things". It is that you hand over one job and it works out what has to happen first, with a real tool at every step, checking each step's result before moving on — and you see every step and can stop at any of them.

Jobs that really take several steps

• "Build a booking system for my shop" — reads the ask as a spec → designs the database → writes the migration → builds the screens → runs the tests → opens a browser and books a slot itself → reports what is still missing
• "Research five competitors and make me a comparison table" — drives the browser across tabs, reads page by page → collates → writes an .xlsx
• "2 hours of meeting audio, summarise it" — transcribes → splits topics and decisions with the minute each was said → pulls the actions and their owners → writes the file
• "Fix this bug" — reads the stack trace → finds the files involved → fixes → runs the project's tests → loops back if it does not pass

A job too big for one worker — handed to a team, back as the finished set, with who did what.

Why the same model does better

The model thinks. The environment does the rest — with real parts, not simulations.

• Hands. A real browser with its own tabs and profile, a real terminal, real writers for .docx, .xlsx and .pptx, and a real cut room. It does not describe the spreadsheet to you — it writes the file and opens it to look.
• Eyes. OCR and document readers that read a scanned page instead of asking a vision model to guess, and a page captured as it was instead of remembered.
• What the model cannot know. Skills keep it: the 1280×720 frame of a slide room, how an attacker breaks a path check, how a Thai tax invoice rounds — and how you work.
• A check before it hands back. It runs the tests, opens the file it made, and says plainly what it could not prove.

It learns how you work

An environment that starts from zero every morning is a tool you rent again each day. Aetox keeps what it finds out about you.

• A way of working counts only when it repeats. One day is an episode; three different days is how you work. It does not decide from a single conversation that this is who you are.
• You say yes. Anything proposed from outside a turn waits for you, and every line it keeps can be undone from under the answer that added it.
• Split by who has to read it — but the work can reach all of it. The architecture decides who reads what: your profile rides with every job, what it knows about this machine with the desks that need it, each room's ways with that room, each worker's with that worker. At work it can search the whole history, open any day's note, and pull in the documents on your machine — so nothing has to ride in every request, and nothing is forgotten.
• It is a folder of plain .md files, not a black box. Your profile, what it knows about this machine, each room's notes, one note per day of work, and the routes it has seen you repeat — written where you can open, edit, copy into another agent, or delete them. Nothing it learns is trapped in a format only Aetox reads.

Where it runs is your choice, and so is the model: 31 providers, cloud and local, asked for their model lists at run time so a new model appears without a new Aetox. Today the whole of it runs on your machine. Every provider

Measured results

Code — five public test suites × 2 public OpenAI models (gpt-6-luna, gpt-5.6-terra, medium reasoning) × 3 rounds, against four other harnesses (OpenCode · Codex CLI · omp · pi). Every task is judged by hidden tests, on the same account, with no user settings on any of them.