[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-77286":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},77286,"humanize-text","lynote-ai\u002Fhumanize-text","lynote-ai","Free open-source AI text humanizer to convert AI-generated content into undetectable, human-like writing. Bypass Turnitin, GPTZero, and all major AI detectors. No sign-up required. Try our unlimited free online tool","https:\u002F\u002Flynote.ai\u002Fai-humanizer",null,"Python",1147,60,28,11,0,25,79,1068,112,18.36,"MIT License",false,"main",true,[27,28],"ai-humanize","ai-humanizer","2026-06-12 02:03:42","\u003Ch1 align=\"center\">AI-Humanizer\u003C\u002Fh1>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"presentation\u002Fai-humanizer-banner.png\" alt=\"AI-Humanizer turns AI drafts into human-like writing\" width=\"900\"\u002F>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize\u002Fstargazers\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fmolly554\u002Fai-humanize?style=social\" alt=\"Stars\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize\u002Fnetwork\u002Fmembers\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002Fmolly554\u002Fai-humanize?style=social\" alt=\"Forks\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize\u002Fblob\u002Fmain\u002FLICENSE\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmolly554\u002Fai-humanize\" alt=\"License\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwww.python.org\u002F\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.10+-blue.svg\" alt=\"Python\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Flynote.ai\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTry-Lynote.ai-brightgreen?style=for-the-badge\" alt=\"Lynote.ai\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flynote-ai\u002Fhumanize-text-zh\">中文版 →\u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n## What is AI-Humanizer?\n\nAn open-source toolkit that explores **4 proven approaches** to rewrite AI-generated text into natural, human-like content. Built for researchers, developers, and writers who want to understand and experiment with AI text humanization techniques.\n\n> **Want the best results without the hassle?**\n> [Lynote.ai](https:\u002F\u002Flynote.ai) combines ALL methods below into one intelligent pipeline — it automatically analyzes your text and selects the optimal approach for each passage.\n>\n> **[Try Lynote.ai Free →](https:\u002F\u002Flynote.ai)**\n\n---\n\n## Techniques\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"presentation\u002Ftechniques-overview.png\" alt=\"AI-Humanizer techniques overview\" width=\"820\"\u002F>\n\u003C\u002Fp>\n\nThis toolkit implements 4 independent humanization approaches. Each has strengths and trade-offs — understanding them helps you pick the right tool for your use case.\n\n### Method 1: Multi-Language Translation Chain\n\nTransforms text through a chain of distant language pairs (e.g., EN → ZH → JA → FI → EN), leveraging the structural differences between languages to naturally reconstruct sentence patterns.\n\n- Uses multiple NMT engines: Google Translate, Niutrans, MyMemory, Apertium\n- Distant language pairs (Finnish, Japanese) produce more thorough restructuring\n- Three processing tiers: Standard, Advanced, Focus\n\n> **Limitation:** Single translation chains may lose nuance in long-form academic content. Terminology accuracy decreases with more translation hops.\n\n### Method 2: Multi-Turn LLM Rewriting\n\nUses large language models with context-aware multi-round rewriting. Each round progressively adjusts sentence rhythm, vocabulary diversity, and structural variety.\n\n- DeepSeek API with high temperature settings (1.1–1.3) for natural variation\n- Burstiness-targeted prompts that deliberately vary sentence length and complexity\n- 2–3 rewriting rounds with cross-round context awareness\n\n> **Limitation:** Used alone, semantic drift increases with each round. Requires careful prompt engineering to maintain original meaning.\n\n### Method 3: Detection-Guided Feedback Loop\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"presentation\u002Fdetection-feedback-loop.png\" alt=\"Detection-guided feedback loop for text humanization\" width=\"760\"\u002F>\n\u003C\u002Fp>\n\nA closed-loop system that rewrites text, runs it through multiple detection signals, and iteratively refines passages that still trigger detection.\n\n- Four-signal fusion: Binoculars (GPT-2 dual-model perplexity), RoBERTa classifier, statistical features, diversity metrics\n- Document-level rewrite → sentence-level deep rewrite → rule-based post-processing\n- AI vocabulary replacement (30+ English signal words)\n- Sentence rhythm disruption: merging short sentences, breaking uniform-length patterns\n\n> **Limitation:** Requires local deployment of detection models. Resource-intensive (GPU recommended). Pipeline complexity makes debugging harder.\n\n### Method 4: Mixed-Engine Translation\n\nCombines outputs from different neural machine translation architectures in a single pass, exploiting the distribution shift between engines.\n\n- Each NMT engine introduces different structural biases\n- Mixing engines prevents single-model fingerprint patterns\n- Effective for short-to-medium content\n\n> **Limitation:** Higher API costs due to multi-engine calls. Configuration and engine selection require experimentation per language pair.\n\n---\n\n## Lynote.ai — The All-in-One Solution\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Flynote.ai\">\n    \u003Cimg src=\"presentation\u002Flynote_banner.png\" alt=\"Lynote.ai\" width=\"500\"\u002F>\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\nEach open-source method above addresses **part** of the problem. In practice, no single approach works best for every text type, length, or language.\n\n**[Lynote.ai](https:\u002F\u002Flynote.ai)** unifies all 4 approaches into one adaptive pipeline:\n\n- **Intelligent Method Selection** — Automatically analyzes each text passage and selects the approach (or combination of approaches) most likely to produce the best result\n- **Adaptive Multi-Stage Processing** — Dynamically chains methods based on real-time analysis, not a fixed pipeline\n- **Proprietary Post-Processing** — Additional optimization layers beyond what's available in this open-source toolkit\n- **10+ Languages Supported** — English, Chinese, Japanese, Korean, Spanish, French, German, and more\n- **Paste & Go** — No local GPU, no model downloads, no configuration. Just paste your text and get results\n- **Optimized for Real Content** — Academic papers, blog posts, marketing copy, technical documentation\n\n> **Why not just run all 4 methods yourself?**\n> You can! But Lynote.ai's advantage is knowing *which* method to apply *where* — and combining them in ways that preserve meaning while maximizing naturalness. It's not just \"run everything\"; it's intelligent orchestration.\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Flynote.ai\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTry_Lynote.ai_Free-brightgreen?style=for-the-badge\" alt=\"Try Lynote.ai Free\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n## Comparison\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"presentation\u002Fcomparison-pipeline.png\" alt=\"Open-source single methods compared with an adaptive all-in-one pipeline\" width=\"820\"\u002F>\n\u003C\u002Fp>\n\n| | Open-Source (Single Method) | Lynote.ai |\n|---|---|---|\n| Methods Available | 1 at a time, manual selection | All methods, auto-selected |\n| Processing | Fixed pipeline | Adaptive, per-passage optimization |\n| Setup | Local Python + GPU for detection models | Zero setup, browser or API |\n| Languages | Depends on engine configuration | 10+ languages out of the box |\n| Best For | Research, experimentation, learning | Production use, real-world content |\n\nSee [`examples\u002Fcomparison\u002F`](examples\u002Fcomparison\u002F) for side-by-side text samples.\n\n---\n\n## Quick Start\n\n| Method | Who It's For | How |\n|--------|-------------|-----|\n| [Lynote.ai](https:\u002F\u002Flynote.ai) | Everyone — best results, zero setup | Visit [lynote.ai](https:\u002F\u002Flynote.ai) |\n| Docker | Developers with Docker experience | `docker compose up` |\n| Source Install | Python developers | See below |\n| Google Colab | Quick experimentation | *Coming soon* |\n\n### Source Installation\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize.git\ncd AI-Humanizer\npip install -r requirements.txt\ncp config\u002Fconfig.example.toml config\u002Fconfig.toml\n# Edit config.toml with your API keys\npython -m src.humanizer --input \"Your AI-generated text here\"\n```\n\n### Docker\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize.git\ncd AI-Humanizer\ndocker compose up -d\n# API available at http:\u002F\u002Flocalhost:8000\n```\n\n---\n\n## Documentation\n\n- [Installation Guide](docs\u002Finstallation.md)\n- [API Reference](docs\u002Fapi-reference.md)\n- [Techniques Deep Dive](docs\u002Ftechniques.md)\n- [Open-Source vs Lynote.ai Comparison](docs\u002Flynote-comparison.md)\n- [FAQ](docs\u002Ffaq.md)\n\n---\n\n## Contributing\n\nWe welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See [LICENSE](LICENSE) for details.\n\n---\n\n## Links\n\n- [Lynote.ai — AI Humanization Platform](https:\u002F\u002Flynote.ai)\n- [Chinese Version (中文版)](https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize-zh)\n- [Report a Bug](https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize\u002Fissues)\n- [Request a Feature](https:\u002F\u002Fgithub.com\u002Fmolly554\u002Fai-humanize\u002Fissues)\n\n### Recommended Projects\n\n- [MoneyPrinterTurbo](https:\u002F\u002Fgithub.com\u002Fharry0703\u002FMoneyPrinterTurbo) — AI short video generator\n- [AiToEarn](https:\u002F\u002Fgithub.com\u002Fyikart\u002FAiToEarn) — AI content publishing tool\n\n---\n\n## Star History\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fstar-history.com\u002F#molly554\u002Fai-humanize&Date\">\n    \u003Cimg src=\"https:\u002F\u002Fapi.star-history.com\u002Fsvg?repos=molly554\u002Fai-humanize&type=Date\" alt=\"Star History Chart\" width=\"500\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n\u003Cp align=\"center\">\n  \u003Cb>If this project helps you, please give it a ⭐ — it helps others discover it too!\u003C\u002Fb>\n\u003C\u002Fp>\n","AI-Humanizer 是一个开源的AI文本人性化工具包，通过四种方法将AI生成的文本转化为自然的人类写作。核心功能包括多语言翻译链、多轮大语言模型重写、检测引导反馈循环和混合引擎处理，旨在提升文本的自然度和可读性。项目采用Python编写，提供了灵活的技术手段来优化不同场景下的文本输出质量。适合需要改进AI生成内容流畅性和真实性的研究人员、开发者及作家使用，尤其适用于提高自动化写作系统的输出效果。",2,"2026-06-11 03:55:18","CREATED_QUERY"]