hajimari
Hajimari is a beautiful & customizable browser startpage/dashboard with Kubernetes application discovery.
Runssource: GitHubhomepageGoApache-2.0commit b07024d99794
Go, Apache-2.0 licensed. The project labels itself: dashboard, kubernetes and startpage.
hajimari runs. An Argusic agent installed it in 8 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Hajimari server binary builds and runs on port 3000, serving the Svelte frontend (HTTP 200) and all JSON API endpoints (startpage, apps, bookmarks) with the in-memory store.
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.
- internal/hajimari/crdapps/apps.go:72: logger.Error called with printf-style formatting directive instead of logger.Errorf1 minute
- Install time
- 8 minutes
- Cold machine to finish
- 8 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- curl -s -o /dev/null -w "%{ http_code}" http://localhost:3000/ returned 200; curl -s http://localhost:3000/startpage returned 200 with valid JSON; curl -s -o /dev/null -w "%{http_code}" http://localhost:3000/apps returned 200; curl -s -o /dev/null -w "%{http_code}" http://localhost:3000/bookmarks returned 200
- Model tokens used
- 92,837
- Exact commit tested
- b07024d99794
- 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.
- 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.
- App launched on port 3000 with `memory: true` config (avoiding the `/data` filesystem and kubecon…
- `GET /` → HTTP 200 (1574 bytes) - serves the Svelte frontend
- `GET /startpage` → HTTP 200 (1315 bytes) - returns JSON with config defaults
- `GET /apps` → HTTP 200 (3 bytes - empty array, no Kubernetes cluster available)
- `GET /bookmarks` → HTTP 200 (3 bytes - empty array)
The app works as a standalone web server without Kubernetes by using the in-memory store. A real Ku…
exec
/bin/sh -lc "echo '{\"install_succeeded\": true, \"launch_succeeded\": true, \"install_minutes\": 8…
succeeded in 0ms:
{"install_succeeded": true, "launch_succeeded": true, "install_minutes": 8, "errors": [{"msg": "int…
tokens used
92,837Replay 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 1 error 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 Apache-2.0, 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 | 1/3 | Runs | 100.00 | 0.05 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.12 |
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/hajimari)Questions
- Does hajimari run?
- Yes. hajimari runs. Argusic installed and launched it on a clean machine in 8 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test hajimari?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit b07024d99794. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test hajimari?
- The run that produced this verdict cost $0.05: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does hajimari 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 hajimari need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing hajimari?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for hajimari?
- 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.