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DevinCodex CloudCursor

HyperTeams vs cloud coding agents

Devin, Codex, and Cursor run on their cloud and open a PR. HyperTeams makes what runs on your machine something others can trust.

The short version

These three take your repository to their cloud, change it there, and hand it back as a PR. The goal — work that needs nobody watching — is the same as ours, but the price is different: your code goes onto their infrastructure, and the scope narrows to "code." HyperTeams does not move execution. Code is still changed on your machine, and work that is not code — research, a first draft, tidying data — lines up in the same list. Connect is what makes that list safe for other people to see.

Side by side

Devin
Autonomous agent in a cloud VM
Codex Cloud
OpenAI's async PR agent
Cursor
Editor-first, with background agents
HyperTeams control room
Runs the work on your machine
HyperTeams Connect
Adds team, approval, and records
Where it runs
HyperTeams control room
In their cloud VM.
In their cloud; also reachable from CLI and IDE.
In the editor; background agents run in their cloud.
Execution stays on that machine — the control room sits on top rather than taking it over.
It does not touch execution; it receives what the control room reports.
Several jobs at once
HyperTeams control room
A separate VM per task, running in parallel.
Queue several cloud tasks and collect them as PRs.
You can dispatch several background agents.
One card per job — hand it five and five run side by side, in five folders.
The control room does this
How far along it is
HyperTeams control room
Session logs on their web app.
A task list and the PR diff.
In the editor sidebar.
Status, a summary of the answer, and an unread marker, all in one list.
The control room does this
If you close the window
HyperTeams control room
It runs on their side, so your machine does not matter.
It runs on their side, so your machine does not matter.
Background agents keep running on their side.
Resident, and it records progress: it continues after you close the window, and a tunnel shows it on your phone.
The control room does this
At a set time
HyperTeams control room
Recurring runs come from the API or an outside scheduler.
Scheduled from outside, e.g. GitHub Actions.
A person starts it.
You pick a time on screen, and a failure notifies you.
The control room does this
Several machines in one place
HyperTeams Connect
One cloud on their side; "my machine" is not a concept.
Likewise, one cloud on their side.
Scoped to the machine with the editor open.
It stops at that one machine. It does not know what runs on the next one.
A control room per machine, all of them in one list — including which machine is awake.
When someone else assigns work
HyperTeams Connect
The team assigns work from Slack; it runs with their cloud's permissions.
It inherits your GitHub permissions.
The person at the editor is assumed.
It assumes the owner of the machine. The screen is behind one password.
Accounts and roles, policy per folder and command, and pre-approval per item.
Retracing it later
HyperTeams Connect
Session logs stay on their side.
PRs and task logs.
In the editor's history.
It keeps that machine's run history. There is no notion of an approval.
Who approved what, and when, kept as a policy audit trail.
What it cost
HyperTeams Connect
Per task, on their dashboard.
As plan usage.
As request usage.
It shows cost per job on that machine, with no owner attached.
Attributed to a person and a team, with ceilings.
Price and license
A subscription plus usage-based charges.
Included in a ChatGPT plan, or metered through the API.
Subscription.
One command to install, using the Claude account you already have.
Only the features that cross into a team are metered in credits.
Where it runsHyperTeams control room
  • Devin
    In their cloud VM.
  • Codex Cloud
    In their cloud; also reachable from CLI and IDE.
  • Cursor
    In the editor; background agents run in their cloud.
  • HyperTeams control room
    Execution stays on that machine — the control room sits on top rather than taking it over.
  • HyperTeams Connect
    It does not touch execution; it receives what the control room reports.
Several jobs at onceHyperTeams control room
  • Devin
    A separate VM per task, running in parallel.
  • Codex Cloud
    Queue several cloud tasks and collect them as PRs.
  • Cursor
    You can dispatch several background agents.
  • HyperTeams control room
    One card per job — hand it five and five run side by side, in five folders.
  • HyperTeams Connect
    The control room does this
How far along it isHyperTeams control room
  • Devin
    Session logs on their web app.
  • Codex Cloud
    A task list and the PR diff.
  • Cursor
    In the editor sidebar.
  • HyperTeams control room
    Status, a summary of the answer, and an unread marker, all in one list.
  • HyperTeams Connect
    The control room does this
If you close the windowHyperTeams control room
  • Devin
    It runs on their side, so your machine does not matter.
  • Codex Cloud
    It runs on their side, so your machine does not matter.
  • Cursor
    Background agents keep running on their side.
  • HyperTeams control room
    Resident, and it records progress: it continues after you close the window, and a tunnel shows it on your phone.
  • HyperTeams Connect
    The control room does this
At a set timeHyperTeams control room
  • Devin
    Recurring runs come from the API or an outside scheduler.
  • Codex Cloud
    Scheduled from outside, e.g. GitHub Actions.
  • Cursor
    A person starts it.
  • HyperTeams control room
    You pick a time on screen, and a failure notifies you.
  • HyperTeams Connect
    The control room does this
Several machines in one placeHyperTeams Connect
  • Devin
    One cloud on their side; "my machine" is not a concept.
  • Codex Cloud
    Likewise, one cloud on their side.
  • Cursor
    Scoped to the machine with the editor open.
  • HyperTeams control room
    It stops at that one machine. It does not know what runs on the next one.
  • HyperTeams Connect
    A control room per machine, all of them in one list — including which machine is awake.
When someone else assigns workHyperTeams Connect
  • Devin
    The team assigns work from Slack; it runs with their cloud's permissions.
  • Codex Cloud
    It inherits your GitHub permissions.
  • Cursor
    The person at the editor is assumed.
  • HyperTeams control room
    It assumes the owner of the machine. The screen is behind one password.
  • HyperTeams Connect
    Accounts and roles, policy per folder and command, and pre-approval per item.
Retracing it laterHyperTeams Connect
  • Devin
    Session logs stay on their side.
  • Codex Cloud
    PRs and task logs.
  • Cursor
    In the editor's history.
  • HyperTeams control room
    It keeps that machine's run history. There is no notion of an approval.
  • HyperTeams Connect
    Who approved what, and when, kept as a policy audit trail.
What it costHyperTeams Connect
  • Devin
    Per task, on their dashboard.
  • Codex Cloud
    As plan usage.
  • Cursor
    As request usage.
  • HyperTeams control room
    It shows cost per job on that machine, with no owner attached.
  • HyperTeams Connect
    Attributed to a person and a team, with ceilings.
Price and license
  • Devin
    A subscription plus usage-based charges.
  • Codex Cloud
    Included in a ChatGPT plan, or metered through the API.
  • Cursor
    Subscription.
  • HyperTeams control room
    One command to install, using the Claude account you already have.
  • HyperTeams Connect
    Only the features that cross into a team are metered in credits.

Checked against each product's official documentation as of 2026-09-06. Their products change, and when they do this table becomes wrong — tell us and we will fix it.

  • Devin docs
  • Codex Cloud docs
  • Cursor docs

What the control room does, and what Connect does

HyperTeams is two products. Which of them answers each row above is exactly where the boundary sits.

HyperTeams control room

Runs the work on your machine

A program you install with one command. It assigns work per folder, runs jobs in parallel, and keeps going after you close the window. While you work alone, this is all you need.

The rows it answers
  • Where it runs
  • Several jobs at once
  • How far along it is
  • If you close the window
  • At a set time

HyperTeams Connect

Makes it something others rely on, unattended

It starts paying the moment there is a second machine and a second person: pooling machines into one list, filtering work others assign through approval, keeping an audit trail of what happened, and splitting cost per person.

The rows it answers
  • Several machines in one place
  • When someone else assigns work
  • Retracing it later
  • What it cost

When do you move up a rung?

  1. L1 · Observe

    You watch several machines. The moment there is a second one.

  2. L2 · Share

    The team sees what was asked for. The moment you start sharing one password.

  3. L3 · Delegate

    Others assign work and see the results. This is where approval, policy, and audit earn their place.

Where they diverge

Where the code goes

HyperTeams control room
these cloud agents

The repository is uploaded and changed on their cloud. An air-gapped network or an export restriction stops you right here.

HyperTeams control room

The code never leaves that machine. What goes out is the question sent to the model, and what gets uploaded is a setting with steps.

When it is not only code

HyperTeams control room
these cloud agents

Opening a PR is the finish line. Work that does not end in a commit — research, a first draft — has no seat here.

HyperTeams control room

The core noun is "work." Code or not, it becomes one card, and a PR is not the only way it can finish.

What stops a run somebody else started

HyperTeams Connect
these cloud agents

Repository access is execution access. Once through, the instruction runs as given.

HyperTeams Connect

Individual runs can be held. You set "ask me first" per folder and per command, and the approval is recorded.

Which one should you pick?

When to just use a cloud agent

  • When the finish line is a PR

    Turning a defined backlog into PRs? That is their answer outright — they live next to the repo and the review flow is already in GitHub.

  • When there is no machine to leave on

    HyperTeams needs at least one machine to run on. If you close the laptop and have no server, their side is the right one.

  • When you are choosing on benchmarks

    That contest is about raw coding performance, and HyperTeams is not in it — it runs whichever engine you already use.

When HyperTeams is the answer

  • When the code must not leave

    HyperTeams control room

    An air-gapped network or an export restriction stops you the moment the repository goes to their cloud. HyperTeams does not move execution, so the code never leaves that machine.

  • When the work is not only code

    HyperTeams control room

    Work that does not finish at a PR — research, a first draft, tidying survey answers — lines up in the same list. The core noun is "work," not "commit."

  • When work assigned by others needs filtering

    HyperTeams Connect

    If "repository access is execution access" makes you uneasy, per-item pre-approval and a record of who approved what fill that gap.

Running both

  1. 1

    Leave your cloud agent alone

    Keep the work that ends in a PR where it is.

  2. 2

    Split the front doors

    Code goes there; work that is not code, and work other people assign, comes here. They barely overlap.

  3. 3

    Pool the records in one place

    Wherever it ran, "who asked for what, and what did it cost" has to be answerable on one screen. That is Connect's job.

Common questions

A layer, not a replacement

Put it on top of what you already run

One command to install. Nothing you use today gets removed or moved — you only add the command screen above it.

curl -fsSL https://hyperteams.net/install.sh | bash
View the install script source
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