tugtainer
An application for automated Docker container updates with a web UI
Runssource: GitHubPythonMITcommit 514f436c1ab3
Python, MIT licensed. The project labels itself: angular, auto update, container management, container monitoring, container update, containers, dashboard and devops.
tugtainer runs. An Argusic agent installed it in 8 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Backend app starts, serves API on port 8000, runs all 16 Alembic migrations, and all 263 unit tests pass.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 96 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- 'alembic' bare command not found in subprocess1 minute
- Default DB_URL points to /tugtainer/tugtainer.db (unwritable)1 minute
- Python 3.12 available but project requires >=3.130.5 minutes
- AGENT_SECRET not set, auth would fail at runtime0.5 minutes
- Install time
- 8 minutes
- Cold machine to finish
- 8 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- Model tokens used
- 56,959
- Exact commit tested
- 514f436c1ab3
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- How the result was proved
- runs before 2026-09-07 did not record the command used to check the result
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
Here's the full report: ## Errors encountered & fixes | Error | Fix | |---|---| | Python 3.12 in container, project requires ≥3.13 | `uv sync` auto-downloaded CPython 3.13.15 | | `alembic` not found by subprocess (start.py ran bare command) | Changed `alembic` → `sys.executab… | Default DB URL points to `/tugtainer/tugtainer.db` (not writable) | Created `.env` with `DB_URL=s… | `AGENT_SECRET` missing (startup would fail auth checks) | Set `AGENT_SECRET=CHANGE_ME!` and `DISA… No third-party keys were required — the app runs with `AGENT_ENABLED=false` and `DISABLE_AUTH=true`… ## Verification - `backend.start` launched and served `GET /api/public/health` → `"OK"` - **All 263 tests pass** (241 backend + 3 agent + 19 shared) in ~4.4s
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 8 minutes.
- Recovered from all 4 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
- Measured 2 times, so the result is not a one-off.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 8 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.03 | |
| Argusic Runner | 1/3 | Runs | 100.00 | 0.10 |
Topics (from GitHub)
angularauto-updatecontainer-managementcontainer-monitoringcontainer-updatecontainersdashboarddevopsdockerdocker-automationhomelabimage-watcherorchestrationpythonrest-apiself-hostedupdate-checkerupdate-toolweb-uiwebserver
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/tugtainer)Questions
- Does tugtainer run?
- Yes. tugtainer runs. Argusic installed and launched it on a clean machine in 8 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test tugtainer?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 514f436c1ab3. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test tugtainer?
- The run that produced this verdict cost $0.10: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does tugtainer take to install?
- 8 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does tugtainer need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing tugtainer?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for tugtainer?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.