hyperdx
Resolve production issues, fast. An open source observability platform unifying session replays, logs, metrics, traces and errors powered by ClickHouse and OpenTelemetry.
Runssource: GitHubhomepageTypeScriptMITcommit 0ebb689a9d71
TypeScript, MIT licensed. The project labels itself: alerting, analytics, apm, application monitoring, clickhouse, dashboard, frontend monitoring and kubernetes.
hyperdx runs. An Argusic agent installed it in 20.4 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
All unit test suites pass across 4 packages with Node 22.23.1 (7120 tests, 0 failures). Lint passes with warnings only.
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 96.7 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.
- Node 18.19.1 lacks Array.prototype.toSorted() — 55 app tests fail1 minute
- Install time
- 20 minutes
- Cold machine to finish
- 21 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- yarn ci:unit for packages/common-utils (2485 passed), packages/api (954 passed), packages/app (3681 passed), plus tsc --noEmit for packages/cli (0 errors)
- Model tokens used
- 86,263
- Exact commit tested
- 0ebb689a9d71
- 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.
tokens used 86,263 All verification is done. Here's the summary: **Install** — `yarn install` completed in 53s with peer dependency warnings but no errors. 2677 pac… **Node version** — The container has Node 18.19.1 but the project requires Node 22 (per `.nvmrc`). … **Tests passed (Node 22.23.1):** - `packages/common-utils` — 2485 passed, 34 suites - `packages/api` — 954 passed, 48 suites - `packages/app` — 3681 passed, 226 suites - `packages/cli` — `tsc --noEmit` exited 0 (no errors) - `yarn lint:fix` — completed with warnings only (no errors) **What wasn't verified** — Docker is not available in this container, so the full dev stack (ClickH…
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 20.4 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 MIT, as reported by its host.
- Measured 3 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.
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
Topics (from GitHub)
alertinganalyticsapmapplication-monitoringclickhousedashboardfrontend-monitoringkuberneteslog-managementlogsmetricsmonitoringobservabilityopentelemetryreactself-hostedsession-replaytracestypescript
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/hyperdx)Questions
- Does hyperdx run?
- Yes. hyperdx runs. Argusic installed and launched it on a clean machine in 20 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test hyperdx?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 0ebb689a9d71. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test hyperdx?
- The run that produced this verdict cost $0.07: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does hyperdx take to install?
- 20.4 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 hyperdx need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing hyperdx?
- 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 hyperdx?
- All 3 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.