🥷 StealthHumanizer — A Free Open-Source AI Text Humanizer with 13 Providers and Multi-Pass Ninja Mode
<h2> Why StealthHumanizer? </h2> <p>With the rise of AI-generated content, tools that can humanize text are in high demand. But most solutions are paid, require sign-ups, or limit your usage. I wanted to build something different — a completely free, open-source text humanizer that anyone can use without restrictions.</p> <p><strong>StealthHumanizer</strong> supports 13 text generation providers, 4 rewrite levels, 13 distinct tones, and a multi-pass "ninja mode" for maximum naturalness.</p> <h2> Features </h2> <h3> 🔄 13 AI Providers </h3> <p>StealthHumanizer works with OpenAI, Anthropic, Google, Mistral, Cohere, and many more providers. Switch between them freely — whatever works best for your content.</p> <h3> 📊 4 Rewrite Levels </h3> <p>From light touch-ups to complete rewrites, choose
Why StealthHumanizer?
With the rise of AI-generated content, tools that can humanize text are in high demand. But most solutions are paid, require sign-ups, or limit your usage. I wanted to build something different — a completely free, open-source text humanizer that anyone can use without restrictions.
StealthHumanizer supports 13 text generation providers, 4 rewrite levels, 13 distinct tones, and a multi-pass "ninja mode" for maximum naturalness.
Features
🔄 13 AI Providers
StealthHumanizer works with OpenAI, Anthropic, Google, Mistral, Cohere, and many more providers. Switch between them freely — whatever works best for your content.
📊 4 Rewrite Levels
From light touch-ups to complete rewrites, choose the level that fits your needs.
🎨 13 Tones
Professional, casual, academic, creative, persuasive, and more. Pick the tone that matches your content's purpose.
🥷 Multi-Pass Ninja Mode
Run multiple humanization passes for the most natural-sounding output. The ninja mode applies layered transformations to make text virtually indistinguishable from human writing.
🔓 No Login, No Limits
Zero friction. No account creation. No usage caps. Just open it and use it.
Tech Stack
-
Language: TypeScript
-
Architecture: Provider-agnostic plugin system
-
UI: Clean, minimal interface focused on usability
Live Demo
🔗 GitHub Repository
Why Open Source?
Text humanization should be accessible to everyone — students, content creators, developers, and researchers. Locking this behind paywalls creates an uneven playing field. StealthHumanizer levels it.
Built by Rudra Sarker — Open Source Developer
Connect: X/Twitter | LinkedIn | GitHub
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
mistralopen sourceopen-source
Chinese AI rivals clash over Anthropic’s OpenClaw exit amid global token crunch
Chinese tech companies are engaged in a public war of words as they compete to capitalise on US start-up Anthropic’s decision to pull its industry-leading Claude models from open-source AI agent tool OpenClaw. The development comes as AI agents have triggered a huge increase in demand for AI tokens – the core metric of AI usage – raising questions about the long-term ability of industry players to meet this demand amid a growing global crunch in computational power. On Sunday, Anthropic...
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
arXiv:2603.24591v2 Announce Type: replace Abstract: While large language models (LLMs) have accelerated 2D software development through intent-driven "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains a major challenge. The fundamental barrier is not just the steep learning curve for human creators, but that low-level sensor APIs and complex game engine hierarchies are ill-suited for LLM reasoning, routinely exceeding context windows and inducing syntax hallucinations. To bridge this gap, we contribute XR Blocks, an open-source, LLM-native WebXR framework. Unlike traditional engines, XR Blocks introduces a semantic "Reality Model" that aligns spatial computing primitives (users, physical environments, and agents) with natural language, providing a robust, conc
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Open Source AI
OpenClaw Changed How We Use AI. KiloClaw Made It Effortless to Get Started
OpenClaw is a powerful open-source AI agent, but self-hosting it is a pain. KiloClaw is OpenClaw fully hosted and managed by Kilo — sign up, connect your chat apps, and your agent is running in about a minute. No Docker, no YAML, no server babysitting. People are using it for personalized morning briefs, inbox digests, auto-building CRMs, browser automation, GitHub triage, and more. Hosting is $8/month with a 7-day free trial, inference runs through Kilo Gateway at zero markup across 500+ models, and it's free for open-source maintainers. Read All
WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
arXiv:2604.02570v1 Announce Type: new Abstract: Singular Value Decomposition (SVD) has become an important technique for reducing the computational burden of Vision Language Models (VLMs), which play a central role in tasks such as image captioning and visual question answering. Although multiple prior works have proposed efficient SVD variants to enable low-rank operations, we find that in practice it remains difficult to achieve substantial latency reduction during model execution. To address this limitation, we introduce a new computational pattern and apply SVD at a finer granularity, enabling real and measurable improvements in execution latency. Furthermore, recognizing that weight elements differ in their relative importance, we adaptively allocate relative importance to each elemen
![trunk/bac8607b42eebcd1173c3c8b6a6afa62ccb4c3b8: [vllm hash update] update the pinned vllm hash (#179439)](https://avatars.githubusercontent.com/u/133916390?s=60&v=4)
I tested speculative decoding on my home GPU cluster. Here's why it didn't help.
I spent Saturday night testing n-gram speculative decoding on consumer GPUs. The claim: speculative decoding can speed up LLM inference by 2-3x by predicting future tokens and verifying them in parallel. I wanted to see if that holds up on real hardware running diverse workloads. For the most part, it doesn't. But the journey was worth it, and I caught a benchmarking pitfall that I think a lot of people are falling into. The setup My home lab runs Kubernetes on a machine called Shadowstack. Two NVIDIA RTX 5060 Ti GPUs (16GB VRAM each, 32GB total). I use LLMKube, an open source K8s operator I built, to manage LLM inference workloads with llama.cpp. For this test I deployed two models: Gemma 4 26B-A4B : Google's Mixture of Experts model. 26B total params but only ~4B active per token. Runs a
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!