humanize-text
Open-source text humanization pipeline with every intermediate step published. Two LLM rewrites at temp 1.3, then two hops across different NMT engines. Four documented methodologies you can read, modify, and run locally.
Runs with mockssource: GitHubhomepagePythonMITcommit 79bb396acfc3
Python, MIT licensed. The project labels itself: ai humanizer, ai tools, ai writing, humanize ai text, humanize text, n8n, paraphrasing and python.
humanize-text runs, with stand-ins for the services it depends on. An Argusic agent installed it in 1.5 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Project installs in a venv, all 40 unit tests pass, CLI entry point works, and real OpenRouter LLM rewriting (Steps 1-2 of the pipeline) is verified end-to-end.
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 92 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.
- pip install fails due to externally-managed-environment (PEP 668) in container1 minute
- test_openrouter_provider failed because pre-existing OPENROUTER_API_KEY env var overrode test config value per code design1 minute
- Install time
- 2 minutes
- Cold machine to finish
- 10 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- 40/40 pytest tests passed; real OpenRouter chat_completions returned 'pineapple'; full 4-step pipeline executed with real LLM (Chinese then Japanese rewrite) + mocked Google/Niutrans; CLI --help and --json output verified
- Model tokens used
- 60,580
- Exact commit tested
- 79bb396acfc3
- 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
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
All verified. Here's the final report: **Install**: Created a venv (container blocks system pip). `pip install -e ".[legacy]"` succeeded. … **Bug fix**: `test_Argusic_provider` failed because the container's `OPENROUTER_API_KEY` env var ov… **Real LLM verification**: The Argusic API key in the environment is valid — `chat_completions` ret… **Full pipeline**: Steps 1–2 (LLM rewrites) ran against real Argusic API. Google Translate (`deep_t… tokens used 60,580 All verified. Here's the final report: **Install**: Created a venv (container blocks system pip). `pip install -e ".[legacy]"` succeeded. … **Bug fix**: `test_Argusic_provider` failed because the container's `OPENROUTER_API_KEY` env var ov… **Real LLM verification**: The Argusic API key in the environment is valid — `chat_completions` ret… **Full pipeline**: Steps 1–2 (LLM rewrites) ran against real Argusic API. Google Translate (`deep_t…
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
- Installed in 1.5 minutes.
- Recovered from all 2 errors without a human stepping in, which says the failures are documented well enough to solve.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
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 with mocks | 92.00 | 0.05 |
Topics (from GitHub)
ai-humanizerai-toolsai-writinghumanize-ai-texthumanize-textn8nparaphrasingpythonresponsible-aitext-humanizertext-processingtext-rewritingwriting-assistant
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/humanize-text)Questions
- Does humanize-text run?
- humanize-text runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 2 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test humanize-text?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 79bb396acfc3. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test humanize-text?
- 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 humanize-text take to install?
- 1.5 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 humanize-text need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing humanize-text?
- 2 things broke in the recorded run, and 2 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for humanize-text?
- 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.