Lynote Humanize Text

Open-source AI humanizer: rewrite AI-generated drafts into natural, human-sounding prose — and see every step it takes.

Quick start · Examples · Try it online · Humanizer API · Discord · 中文

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✨ Before → After

Quantum computing has been proposed as a paradigm shift for solving certain optimization and simulation tasks. Unlike classical bits, qubits can represent superpositions, enabling parallel computation in specific problem structures.

Quantum computing has attracted much attention as an innovative method for solving certain optimization and simulation problems. Unlike traditional bits, qubits can assume a superposition state, which enables parallel computing in certain problem structures.

Excerpt from Example 01 — the full trace shows every intermediate step (Chinese → Japanese → Finnish → English).

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🚀 Quick start

git clone https://github.com/lynote-ai/humanize-text.git
cd humanize-text
pip install -r requirements.txt
cp config/config.example.toml config/config.toml   # add your LLM + Niutrans keys

python -m src.standard.pipeline --input "Your AI-generated text here"

--input takes either a text string or a path to a .txt file. Add --verbose to watch each step, or --json to get the full step-by-step trace as JSON.

Don't want to set up keys? Try the hosted version at lynote.ai — paste and go.

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Why this project

• Open, not a black box. Most humanizers hide what they do behind marketing claims. Here every step is readable code, and every run can be traced.
• Runs end-to-end with your own keys. DeepSeek (default), OpenRouter, Atlas Cloud, OrcaRouter, LiteLLM, or any OpenAI-compatible endpoint.
• Three ways to run it. Python CLI, a no-code n8n workflow, or Docker.
• Research included. We also open-source the structure-first data pipeline we use to train our own rewrite model.

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How it works

The open pipeline combines two LLM rewrites with two machine-translation hops across distant languages and different engines:

Step	Engine	From → To	Purpose
1	LLM (temp 1.3)	Input → Chinese	Humanizing rewrite + language shift
2	LLM (temp 1.3)	Chinese → Japanese	Second rewrite, carries Step 1 as history
3	Google Translate	Japanese → Finnish	First hop — distant-language restructuring
4	Niutrans	Finnish → English	Second hop — cross-engine reconstruction

Why this chain works

1. Steps 1–2 (LLM rewrite): A high-temperature rewrite while translating breaks the statistical patterns typical of AI text. Step 2 carries Step 1 as conversation history to keep the text coherent.
2. Steps 3–4 (multi-engine translation): Two different translation engines introduce compounding structural changes, so no single engine's fingerprint survives.
3. Distant languages: Chinese → Japanese → Finnish maximizes linguistic distance at each hop before reconstructing English.

Tiers

Tier	What it does	Best for
standard	2 LLM rewrites + 2 MT hops	The default balance
advanced	+ multi-round LLM rewriting	Deeper restructuring
focus	+ detection-guided feedback loop	Maximum restructuring

Technical details: docs/pipeline.md · docs/techniques.md

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Ways to run it

Method	Who it's for	How
Python CLI	Developers	See Quick start
n8n workflow	No-code automation users	Import n8n/humanize_standard.json
Docker	Self-hosting a REST endpoint	docker compose up -d — see docs/installation.md
Hosted	Anyone who wants zero setup	lynote.ai or the Humanizer API

DeepSeek (default):

[api_keys]
deepseek_api_key = "sk-..."
niutrans_api_key = "your-key"

[llm]
provider = "deepseek"

OpenRouter:

[api_keys]
openrouter_api_key = "sk-or-..."
niutrans_api_key = "your-key"

[llm]
provider = "openrouter"
model = "deepseek/deepseek-chat"   # any OpenRouter model slug

Atlas Cloud:

[api_keys]
atlascloud_api_key = "ak-..."
niutrans_api_key = "your-key"

[llm]
provider = "atlascloud"
model = "qwen/qwen3.5-flash"

OrcaRouter:

[api_keys]
orcarouter_api_key = "sk-orca-..."
niutrans_api_key = "your-key"

[llm]
provider = "orcarouter"
model = "deepseek/deepseek-chat"   # any OrcaRouter model slug

Override the endpoint with base_url in [llm], or via the LLM_BASE_URL / LLM_API_KEY environment variables. Full reference: docs/configuration.md.

1. Import n8n/humanize_standard.json into your n8n instance.
2. Configure the LLM API key and URL in the HTTP Request nodes (defaults to DeepSeek; point at https://openrouter.ai/api/v1/chat/completions to use OpenRouter).
3. Run — input text goes in, rewritten text comes out.

More: docs/n8n-guide.md

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