AIHOT
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Runssource: GitHubhomepageTypeScriptMITcommit 589f79eff094
TypeScript, MIT licensed. The project labels itself: ai, content curation, llm, mcp, news aggregator, rss, self hosted and typescript.
AIHOT runs. An Argusic agent installed it in 4 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
The project installs cleanly with Node.js 24.11 and PostgreSQL 16, typechecks across 6 packages, passes all 128 backend tests and 11 frontend tests, the API server returns 200 on /openapi-v1.json, and the web server serves /, /all, /about, and /feed.xml at HTTP 200.
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 100 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.
- Install time
- 4 minutes
- Cold machine to finish
- 13 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- npm test: 128 passed, 0 failed in 29s; npm run build -w @aihot/web: built in 561ms; node --test apps/web/tests/*.test.ts: 11 passed; API /openapi-v1.json: HTTP 200; Web /, /all, /about, /feed.xml: HTTP 200; typecheck: all 6 packages passed
- Model tokens used
- 168,862
- Exact commit tested
- 589f79eff094
- 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.
**Frontend tests**: All 11 frontend tests passed. tokens used 168,862 I've successfully set up and verified the entire project. Let me compile the final report. Here's the comprehensive result of what I verified: **Install**: Ran `npm ci` → all 395 packages installed without errors. Node.js 24.11 was downloaded… **Type checking**: `npm run typecheck` passed across all packages (contracts, backend, api, worker,… **Database**: All 35 migrations applied cleanly to `aihot_test`. Seed data imported: 38 topics, 18 … **Backend tests**: All 128 tests passed in 29s. **Frontend build**: Built in 561ms (web server). **Launch**: API server starts on port 3001, serves `openapi-v1.json` at HTTP 200. Web server starts… **Frontend tests**: All 11 frontend tests passed.
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 4 minutes, faster than the median of the 66 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 67 ai projects Argusic has installed and timed, AIHOT was the 29th fastest to reach a running state, and 51 of 67 reached one at all.
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 4 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
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)
aicontent-curationllmmcpnews-aggregatorrssself-hostedtypescript
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/aihot)Questions
- Does AIHOT run?
- Yes. AIHOT runs. Argusic installed and launched it on a clean machine in 4 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test AIHOT?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 589f79eff094. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test AIHOT?
- 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 AIHOT take to install?
- 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 AIHOT need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing AIHOT?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does AIHOT compare with the alternatives?
- Of the 67 ai projects Argusic has installed and timed, AIHOT was the 29th fastest to reach a running state, and 51 of 67 reached one at all.
- Where is the evidence for AIHOT?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.