# GitHub Tools & Libraries — Practical & Forums

**Research value: high** — The practitioner conversation around OSS humanizer repos has coalesced around one dominant artifact (`blader/humanizer`, 14k+ stars), a swarm of near-identical Claude Code skill clones, and a widely-copied Next.js + Clerk + Vercel + paid-API tutorial stack. Surrounding these are two substantive engineering critiques (prompt-only humanization is statistically unwinnable; post-processing > prompts) and a clear venue split: HN is hostile to the framing, dev.to is a how-to factory, r/SEO is a paid-tool flea market, and r/LocalLLaMA has quietly moved the whole problem down the stack to the sampler.

## Executive Summary

The OSS humanizer landscape in 2025–26 is shaped by five overlapping communities, each with its own artifact gravity:

1. **Claude Code skills (the center of mass).** [`blader/humanizer`](https://github.com/blader/humanizer) (~14.7k stars, 1,300+ forks as of April 2026) is an installable `.skill` built on Wikipedia's *Signs of AI Writing* guide + 29 patterns + a second "obviously AI" audit pass. It has spawned a cottage industry of near-identical repos — [`itsjwill/humanizer-x`](https://github.com/itsjwill/humanizer-x), [`mbodkebiz/slop-humanizer`](https://github.com/mbodkebiz/slop-humanizer), [`Aboudjem/humanizer-skill`](https://github.com/Aboudjem/humanizer-skill) *(now 37 patterns)*, [`brandonwise/humanizer`](https://github.com/brandonwise/humanizer), [`hexiecs/talk-normal`](https://github.com/hexiecs/talk-normal), [`adenaufal/anti-slop-writing`](https://github.com/adenaufal/anti-slop-writing), [`jpeggdev/humanize-writing`](https://github.com/jpeggdev/humanize-writing) *(8-pass; also npm package)*, [`lguz/humanize-writing-skill`](https://github.com/lguz/humanize-writing-skill) *(3-pass multi-LLM)*, [`aaaronmiller/humanize-writing`](https://github.com/aaaronmiller/humanize-writing) — most marketing themselves as "free alternative to Undetectable.ai / WriteHuman / $10–20/mo paid humanizers."

2. **Clone-Undetectable.ai tutorials (the dev.to / YouTube flavor).** The canonical build is [`abdibrokhim/humanaize`](https://github.com/abdibrokhim/humanaize): Next.js + Tailwind + Clerk Auth + Vercel + a paid "AI/ML API" gateway. Shipped as a step-by-step tutorial on both [dev.to](https://dev.to/abdibrokhim/step-by-step-tutorial-on-building-an-ai-text-humanizer-with-aiml-api-and-integration-with-clerk-auth-and-deploying-to-vercel-moj) and [Medium](https://medium.com/@abdibrokhim/comprehensive-and-step-by-step-tutorial-on-building-an-ai-text-humanizer-with-ai-ml-api-next-js-d42c4850a31c) with a [YouTube demo](https://www.youtube.com/watch?v=CLrENdjIe58). The prompt is usually a three-bullet rewrite instruction.

3. **Detector-bypass Python repos.** [`Mohit1053/Humanizer`](https://github.com/Mohit1053/Humanizer) (Python + Ollama/Llama3, CSV batch + resume, perplexity/burstiness focus), [`ksanyok/TextHumanize`](https://github.com/ksanyok/TextHumanize) (Python/PHP/TS, 25 languages, "PHANTOM™/ASH™/SentenceValidator™", 100% offline claim, 60–90% detection-score reduction), [`samrand96/Undetectable-AI`](https://github.com/samrand96/Undetectable-AI) (DOCX rewriter via Ollama `dolphin-mistral`), [`rudra496/StealthHumanizer`](https://github.com/rudra496/StealthHumanizer) (13 providers, 4 levels, "ninja mode" multi-pass, 12-metric detection engine).

4. **Inference-time / LocalLLaMA.** [`sam-paech/antislop-sampler`](https://github.com/sam-paech/antislop-sampler) and `antislop-vllm` — a backtracking sampler that retroactively revises token probabilities when it hits any of 8,000+ banned slop phrases; integrated into `koboldcpp` 1.76+ and `open-webui`. **Now also: [`sam-paech/auto-antislop`](https://github.com/sam-paech/auto-antislop)** — automates fine-tuning via FTPO dataset construction from sampler outputs. The ICLR 2026 paper (arXiv 2510.15061) has formalized this from practitioner tool into peer-reviewed technique. `sam-paech/gemma-3-27b-it-antislop` on HF demonstrates a fine-tuned checkpoint. The LocalLLaMA answer to the problem: don't prompt, intercept — and now this has academic backing.

5. **Engineering-critique substrate.** A short list of posts arguing that prompt-only humanization is fundamentally a lost cause and shifting the problem to post-processing or rule-based transforms: Peggy Kang's [dev.to piece](https://dev.to/peggggykang/building-an-ai-humanizer-why-we-stopped-trying-to-fix-prompts-bi9) ("prompts don't fix distributions, rewriting does"), the [`rephrasyai` thread on `blader/humanizer` issue #82](https://github.com/blader/humanizer/issues/82), and [`voidborne-d/humanize-chinese`](https://github.com/voidborne-d/humanize-chinese) (7 style modes, pure rule-based, no LLM dependency).

Two cross-cutting observations: (a) The paid-SaaS side of this market (Undetectable.ai, WriteHuman, Humbot, BypassGPT, StealthWriter, Humanizer Pro, Walter Writes, Aurawrite) dominates Reddit tool-comparison threads in r/SEO, r/ChatGPT, r/BypassAiDetect and r/aitoolhq — OSS repos almost never win those comparisons because they're usually single-pass LLM-prompt wrappers with a Signs-of-AI ban list bolted on. (b) HN is openly hostile to the framing ("What's the endpoint of this? Machines pretending to be human speaking to machines…"), which is why most `Show HN: humanizer` launches get ~1–3 points and 2–4 comments and rarely survive the front page.

## Sources

### Reddit threads

- **r/BypassAiDetect — "What AI humanizer is actually working for people in 2026 that passes GPTZero and still sounds natural?"** ([thread](https://www.reddit.com/r/BypassAiDetect/comments/1rpx813/)). Tool-comparison flea market: QuillBot (reword only, inconsistent >600 words), Undetectable.ai (awkward output, needs editing), Humbot (slow/inconsistent), BypassGPT (loses technical accuracy), Walter Writes (restructures sentences, consistent), Aurawrite (natural, fast). OSS repos not mentioned. **Context:** This thread predates Turnitin's August 2025 update — the Walter Writes and Aurawrite rankings have since shifted in post-update tests.
- **r/aitoolhq — "Which AI humanizer actually works? My experience testing 5 tools"** ([thread](https://www.reddit.com/r/aitoolhq/comments/1r3ze8b/)). Same set with structural-rewrite tools winning (Walter Writes, Aurawrite) and pure-paraphrasers losing (QuillBot).
- **r/bestaihumanizers — "Finally found a free tool that actually lets you humanize full documents (not just 200 words)"** ([thread](https://www.reddit.com/r/bestaihumanizers/comments/1r6h8h6/)). Signal: the 200-word free-tier cap across paid SaaS is the main pull factor for OSS/local alternatives.
- **r/LocalLLaMA — `sam-paech/antislop-sampler` announcement (2024)** and [llama.cpp SLOP Removal discussion #9699](https://github.com/ggml-org/llama.cpp/discussions/9699). Backtracking sampler with string + regex matching, 8,000+ phrase default list, shipped into koboldcpp and open-webui. The "if prompting isn't enough, intercept at the token level" wing.
- **r/ChatGPTCoding / r/ArtificialIntelligence — recurring "what's the best OSS humanizer" threads**. Typical recommendations: `rudra496/StealthHumanizer`, `jehna/humanify` (frequently confused — it's actually a JS *deobfuscator*, not a text humanizer), `Mohit1053/Humanizer`.
- **r/SEO discussions**. Reddit-community-generated lists almost always surface the *paid* tools (Walter Writes, Aurawrite, Humanizer PRO) — the OSS angle is usually a throwaway "you could also just install `blader/humanizer` in Claude Code" reply.

### Hacker News threads

- **"Humanizer: Claude Code skill that removes signs of AI-generated writing"** ([HN 46694489](https://news.ycombinator.com/item?id=46694489), 3 points). `blader/humanizer` submission. Sole comment: *"I think we also need a /dehumanizer skill."* Classic HN front-page-hostile signal.
- **"Show HN: I made an AI humanizer that does not mess up your text"** ([HN 41808868](https://news.ycombinator.com/item?id=41808868), 1 point, 4 comments, Gablopreneur). Interesting as a growth-marketing post-mortem (3,500 users, 130 paying subs in a month, traffic from Reddit + AI directories). Comments roast the ethics: *"You don't trust AI-generated content, and your response is to make a tool to fool people into not knowing it's AI-generated content?"*
- **"Show HN: A tool to make AI text undetectable" (besthumanizer.net)** ([HN 44275198](https://news.ycombinator.com/item?id=44275198)). The "I researched how detectors work, my method goes beyond synonym swaps — restructuring sentences and adjusting cadence" positioning that every humanizer launch now uses verbatim.
- **"Show HN: GenSpeed – AI Text Humanizer and Detector Bypass"** ([HN 44299497](https://news.ycombinator.com/item?id=44299497)). Same template: zero data storage, real-time, multi-model, free beta.
- **"Show HN: BypassGPT – A tool to humanize AI text to pass AI detectors"** ([HN 45011938](https://news.ycombinator.com/item?id=45011938), 2 points, 6 comments). Top comment: *"I do appreciate the irony: use an AI to make AI-generated text sound less like an AI."*
- **"Show HN: AI Detector and AI Humanizer App (Sureproof.ai)"** ([HN 42604183](https://news.ycombinator.com/item?id=42604183)). Detection + humanization in one app.
- **"Ask HN: Is it possible to make an in browser AI text humanizer?"** ([HN 46106966](https://news.ycombinator.com/item?id=46106966), 1 point, 1 comment). Sole reply: *"Please don't. There's enough AI slop sloshing around already. What's the endpoint of this? Machines pretending to be human speaking to machines that pretend to ignore the fact they've guessed they're speaking to a machine?"*
- **"Humanify: Deobfuscate JavaScript code using LLMs"** ([HN 42948649](https://news.ycombinator.com/item?id=42948649); earlier [HN 41390033](https://news.ycombinator.com/item?id=41390033) with author engagement). Different product (JS variable-name humanization via Babel AST + LLM naming hints), but name-collides with the text-humanizer category and confuses OSS recommendations on Reddit. Author quoted cost: ~$0.5 per `bootstrap.min.js` with ChatGPT.
- **"Show HN: Slop or not – can you tell AI writing from human in everyday contexts?"** ([HN 47357745](https://news.ycombinator.com/item?id=47357745)). Crowdsourced 16k-pair benchmark from Reddit/HN/Yelp. Finding: Reddit posts are easier to detect than HN content.

### GitHub — OSS humanizer repos

- [`blader/humanizer`](https://github.com/blader/humanizer) — ~14.7k stars, ~1,300 forks (April 2026). Claude Code skill, Wikipedia [*Signs of AI Writing*](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) foundation, 29 patterns (significance inflation, notability name-dropping, superficial -ing analyses, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, rule-of-three). Voice calibration via writing-sample ingestion. Final "obviously AI generated" audit pass. MIT. 12 open PRs; issues actively filed April 2026. Listed in VoltAgent awesome-agent-skills.
- [`abdibrokhim/humanaize`](https://github.com/abdibrokhim/humanaize) — Next.js + Tailwind + Clerk + Vercel + AI/ML API. Shipped with `TUTORIAL.md`, live demo at `humanaize.vercel.app`, [dev.to tutorial](https://dev.to/abdibrokhim/step-by-step-tutorial-on-building-an-ai-text-humanizer-with-aiml-api-and-integration-with-clerk-auth-and-deploying-to-vercel-moj), [Medium tutorial](https://medium.com/@abdibrokhim/comprehensive-and-step-by-step-tutorial-on-building-an-ai-text-humanizer-with-ai-ml-api-next-js-d42c4850a31c), and [YouTube](https://www.youtube.com/watch?v=CLrENdjIe58). The template for "clone Undetectable.ai" builds.
- [`rudra496/StealthHumanizer`](https://github.com/rudra496/StealthHumanizer) — TypeScript, 13 providers, 4 rewrite levels (Light/Medium/Aggressive/Ninja), 13 tone presets, multi-pass "ninja mode" auto-loop, 12-metric detection engine, 16 languages, GitHub Pages demo, no login/no limits.
- [`itsjwill/humanizer-x`](https://github.com/itsjwill/humanizer-x) — Claude Code skill. 4-pass architecture: (1) strip 30 severity-ranked AI patterns, (2) inject personality/voice, (3) manipulate perplexity/burstiness/entropy, (4) verification with confidence scoring. Markets as "replaces $10–20/mo paid humanizers." Also includes SSML disfluency patterns for voice-agent humanization.
- [`mbodkebiz/slop-humanizer`](https://github.com/mbodkebiz/slop-humanizer) — "synthesized from 8 humanizer repos." Two-pass rewrite against 25+ patterns across six categories (banned phrases, copula avoidance, structural, superficial gerund analyses, style/em-dash, sycophancy). Scores output on directness/rhythm/trust/authenticity/density.
- [`Aboudjem/humanizer-skill`](https://github.com/Aboudjem/humanizer-skill) — Claude Code skill, zero deps, 90+ research sources cited, 30 patterns, 5 voice profiles.
- [`brandonwise/humanizer`](https://github.com/brandonwise/humanizer) — 29 patterns, 500+ vocabulary terms, statistical analysis (burstiness, type-token ratio, readability). Dual-packaging: OpenClaw skill + standalone CLI. 153 passing tests.
- [`ksanyok/TextHumanize`](https://github.com/ksanyok/TextHumanize) — Python/PHP/TypeScript. Claims: 235k LOC, 122 modules, "PHANTOM™/ASH™/SentenceValidator™", 25 languages, zero dependencies, 38-stage adaptive pipeline, 2,073 passing tests, 60–90% AI-detection-score reduction. (Branding-heavy; claims unverified.)
- [`Mohit1053/Humanizer`](https://github.com/Mohit1053/Humanizer) — Python + Ollama (Llama3), multi-prompt, CSV batch + resume + progress tracking, targets <10% AI detection.
- [`samrand96/Undetectable-AI`](https://github.com/samrand96/Undetectable-AI) — Python DOCX rewriter via Ollama `dolphin-mistral:latest`, translation + grammar correction + content evaluation, optional ChatGPT API.
- [`hexiecs/talk-normal`](https://github.com/hexiecs/talk-normal) — minimal universal anti-slop system prompt; cited by dev.to as "trending."
- [`adenaufal/anti-slop-writing`](https://github.com/adenaufal/anti-slop-writing) — per-tool packaging (Claude `.skill`, Cursor `.mdc`, `AGENTS.md`, `GEMINI.md`, `.github/copilot-instructions.md`), 3-tier tone register, `vocabulary-banlist.md` + `structural-patterns.md`, language-specific rules (Indonesian vs English).
- [`voidborne-d/humanize-chinese`](https://github.com/voidborne-d/humanize-chinese) — pure rule-based, 7 style modes, 0–100 detection scorer, no LLM dependency. Argues the only way to escape perplexity-based detection is to not sample from an LLM distribution at all.
- [`ZAYUVALYA/AI-Text-Humanizer`](https://github.com/ZAYUVALYA/AI-Text-Humanizer), [`OrbitWebTools/Humanize-AI`](https://github.com/OrbitWebTools/Humanize-AI), [`kizmocuDev/content-humanizer`](https://github.com/kizmocuDev/content-humanizer), [`rithulkamesh/humanize`](https://github.com/rithulkamesh/humanize), [`Firdavs-coder/ai_humanizer`](https://github.com/Firdavs-coder/ai_humanizer), [`Janabi/undetectable-ai`](https://github.com/Janabi/undetectable-ai), [`zmh863/AISEO`](https://github.com/zmh863/AISEO) — long tail of small-star browser-JS or thin-wrapper repos, typically 100% client-side, "no login", claims to bypass Turnitin/GPTZero/ZeroGPT/Originality.
- [`sam-paech/antislop-sampler`](https://github.com/sam-paech/antislop-sampler) + `antislop-vllm` — inference-time backtracking sampler; ~340 stars, shipped into koboldcpp 1.76+, open-webui, exposes OpenAI-compatible API. Research-grade but the reference implementation for the token-level approach.
- [`jehna/humanify`](https://github.com/jehna/humanify) — 3k+ stars, JavaScript deobfuscator (Babel AST + LLM naming). Frequently misrecommended on Reddit as a text humanizer because of the name collision.

### dev.to tutorials

- **Peggy Kang, "Building an AI Humanizer: why we stopped trying to fix prompts"** ([dev.to](https://dev.to/peggggykang/building-an-ai-humanizer-why-we-stopped-trying-to-fix-prompts-bi9)). The rigorous engineering take. Treats LLM output as a signal-processing problem at the sentence level. Measurements on flagged samples: *"sentence length variance was abnormally low, clause depth was consistently shallow, discourse markers repeated with high frequency, sentence openers followed predictable templates."* Two-stage pipeline: (1) generation optimizes for clarity/correctness, (2) sentence-level rewriting optimizes for distribution/flow. Rewrites are local (split/compress/delete transition/reorder clauses) and semantics-preserving. This became [Dechecker's AI Humanizer](https://dechecker.ai/ai-humanizer).
- **Ibrohim Abdivokhidov, "Step-by-Step Tutorial on Building an AI text Humanizer…"** ([dev.to](https://dev.to/abdibrokhim/step-by-step-tutorial-on-building-an-ai-text-humanizer-with-aiml-api-and-integration-with-clerk-auth-and-deploying-to-vercel-moj), [Medium mirror](https://medium.com/@abdibrokhim/comprehensive-and-step-by-step-tutorial-on-building-an-ai-text-humanizer-with-ai-ml-api-next-js-d42c4850a31c), [AI/ML API academy](https://aimlapi.com/academy-articles/step-by-step-building-an-ai-text-humanizer-with-ai-ml-api-next-js-tailwind-css-and-integration-with-clerk-auth-and-deploying-to-vercel)). The canonical "clone Undetectable.ai in a weekend" tutorial. Tech stack: Next.js + Tailwind + Clerk Auth + Vercel + AI/ML API gateway (200+ models). Sponsored; terse prompt (~three-bullet rewrite instruction).
- **Lemarie Eaglen, "I Built an AI Text Humanizer Tool That Makes Robotic Writing Sound 100% Human"** ([dev.to](https://dev.to/lemarie_eaglen_919f8c5982/i-built-an-ai-text-humanizer-tool-that-makes-robotic-writing-sound-100-human-41p4)). TypeScript, client-side, detects 12+ pattern categories (banned vocab, sentence uniformity, transition overuse, hedging stacks).
- **Tclawventures, "Day 1: Building the Humanizer (AI agent building a business from scratch)"** ([dev.to](https://dev.to/tclawventures/day-1-building-the-humanizer-ai-agent-building-a-business-from-scratch-36i3)). Interesting frame: a humanizer as the *first product* for an AI-agent-run bootstrap business.

### GitHub issue threads (engineering critique)

- **`blader/humanizer` Issue #82 — "Prompt-based humanization won't solve detection"** ([link](https://github.com/blader/humanizer/issues/82)). The load-bearing critique. Opens with Rephrasy.ai's founder marketing a fine-tuned alternative. Commenter `@voidborne-d` synthesizes the three viable approaches: (1) fine-tuned models trained on human burstiness patterns (expensive, fragile to detector retraining), (2) rule-based syntactic restructuring (language-scaling cost but genuinely different statistics), (3) hybrid (rule-based first pass + optional LLM fluency smoothing). Counter from `@greg-randall`: *"I think though, maybe you all are missing the point, the output of this* reads *less like AI output to a human audience"* — i.e., the market goal is often "sounds human" not "defeats Turnitin."
- **`blader/humanizer` Issues #55, #78** — cited in the #82 thread as the broader "Undetectable AI / detection-evasion" discussion locus on the repo.

### YouTube & Medium

- **"HumanAIze AI text with the smartest AI humanizer. [Live Startup Demo]"** ([YouTube](https://www.youtube.com/watch?v=CLrENdjIe58)) — companion to the `abdibrokhim/humanaize` tutorial.
- **"How I Humanize ChatGPT AI Content | Humanize AI Text | Bypass ChatGPT Detectors for FREE!"** ([YouTube](https://www.youtube.com/watch?v=u3DvdP9v-lA)) — free-tool walkthrough, promotes `iconicwebtools.com` and `Rewritify.ai` rather than OSS repos.
- **"Make AI Text Sound Human with Claude (Step-by-Step Guide)"** ([YouTube](https://www.youtube.com/watch?v=F24rpMKBqq4)) — demos `blader/humanizer` Claude Code skill.

### Indie Hackers

- **"From AI to 'Human-Like' Text: How I Created an AI Text Humanizer"** ([Indie Hackers](https://www.indiehackers.com/post/from-ai-to-human-like-text-how-i-created-an-ai-text-humanizer-d9d9bc0582)) — launch post-mortem, same growth loop as the HN thread (Reddit + AI directories for traffic, aggressive free-tier limits, NLP-based paraphrasing).

## Key Techniques & Patterns Observed in OSS Repos

- **The `SKILL.md` + `AGENTS.md` + `.mdc` packaging cascade.** OSS humanizers ship as a tool-agnostic prompt file with per-host forks: `~/.claude/skills/humanizer/SKILL.md` (Claude Code), `AGENTS.md` (Codex/OpenCode), `.cursor/rules/*.mdc` (Cursor), `.github/copilot-instructions.md` (Copilot), `GEMINI.md` (Gemini CLI). Same prompt, per-tool envelope. `adenaufal/anti-slop-writing` and `blader/humanizer` explicitly document this.
- **Wikipedia *Signs of AI Writing* as the canonical ban-list.** Every serious repo cites it directly. It replaced the old per-repo improvised lists that dominated 2023 Reddit posts.
- **4-pass pipeline.** A common architecture (`humanizer-x`, `slop-humanizer`, StealthHumanizer's "ninja mode"): (1) pattern strip / vocabulary ban-list, (2) voice/personality injection, (3) statistical fingerprint manipulation (perplexity/burstiness/entropy), (4) verification + scoring. Each pass is its own LLM call.
- **Statistical fingerprint language.** *Perplexity* (word predictability; AI <85, humans >85 in the popular framing), *burstiness* (sentence-length variance; AI ~18-word uniform, humans high-variance), *entropy*. Now standard marketing vocabulary for OSS repos, transplanted from the AI-detection research side.
- **Voice calibration from writing samples.** Paste 2–3 paragraphs of your writing → model extracts a style profile → rewrite against it. `blader/humanizer` has this as a first-class feature.
- **Rule-based / non-LLM transforms as the "actually works" escape hatch.** `voidborne-d/humanize-chinese` and `rithulkamesh/humanize` argue that since detectors train on LLM-sampled distributions, any LLM-sampled rewrite is fundamentally within that distribution; only rule-based syntactic restructuring produces genuinely different statistics. Scaling pain: per-language.
- **Inference-time sampling.** `antislop-sampler` backtracks when it generates any of ~8,000 banned phrases and retries with modified token probabilities. Only option when you control the model; hard requirement of raw logits.
- **Client-side browser JS for privacy theater.** The `OrbitWebTools/Humanize-AI`, `ZAYUVALYA`, `TextHumanize` sub-category: "100% client-side, no database, no login, installable as PWA." Privacy claim is real; humanization quality is usually a thin regex + synonym swap.
- **Clone-Undetectable.ai stack.** Next.js + Tailwind + Clerk Auth + Vercel + paid API gateway (AI/ML API, OpenAI, or Gemini). Optimized for "ship a humanizer in a weekend" tutorials, not for beating detection.
- **Docx / document-level humanization.** `samrand96/Undetectable-AI` targets DOCX directly via Ollama — the "I need to rewrite my whole essay, not a paragraph" form factor that paid SaaS free tiers don't serve.
- **Post-generation CI regressions.** Grep-style checks (`grep -iE "Certainly!|delve|It's important to note"`) as a lint pass, flagging-and-logging variants in `brandonwise/humanizer` (153 tests).
- **Free-tier-first growth loop.** Every `Show HN`-style launch reuses: aggressive free tier for acquisition, Reddit for distribution (r/ChatGPT, r/SEO, r/artificial), AI-directory backlinks, paid upsell at the 200–500 word wall.

## Notable Quotes

> "You are asking the same probability distribution (the LLM) to produce output that escapes detection algorithms trained on that exact distribution. It is adversarial, but the attacker and the source are the same model." — `@voidborne-d`, [`blader/humanizer` #82](https://github.com/blader/humanizer/issues/82)

> "I think though, maybe you all are missing the point, the output of this *reads* less like AI output to a human audience." — `@greg-randall`, same thread. The "sounds human" vs "defeats detector" split in one sentence.

> "Prompts don't fix distributions. Rewriting does." — Peggy Kang, [dev.to](https://dev.to/peggggykang/building-an-ai-humanizer-why-we-stopped-trying-to-fix-prompts-bi9)

> "Sentence length variance was abnormally low, clause depth was consistently shallow, discourse markers repeated with high frequency, sentence openers followed predictable templates. None of these are errors. But together, they form a pattern." — Peggy Kang, ibid.

> "I think we also need a /dehumanizer skill." — sole HN comment on `blader/humanizer` front-page submission ([HN 46694489](https://news.ycombinator.com/item?id=46694489))

> "Please don't. There's enough AI slop sloshing around already. What's the endpoint of this? Machines pretending to be human speaking to machines that pretend to ignore the fact they've guessed they're speaking to a machine?" — sole reply to ["Ask HN: Is it possible to make an in browser AI text humanizer?"](https://news.ycombinator.com/item?id=46106966)

> "I do appreciate the irony: use an AI to make AI-generated text sound less like an AI." — top comment, [HN 45011938 (BypassGPT)](https://news.ycombinator.com/item?id=45011938)

> "You don't trust AI-generated content, and your response to this is to make a tool to fool people into not knowing it's AI-generated content?" — commenter, [HN 41808868](https://news.ycombinator.com/item?id=41808868)

> "Replaces $10–20/mo paid humanizers." — `itsjwill/humanizer-x` README tagline. Representative of the OSS positioning.

## Emerging Trends

- **Skill-file consolidation.** OSS humanization is shifting from "paste this system prompt" to installable artifacts (Claude `.skill`, OpenCode skill, Cursor `.mdc`, `AGENTS.md`). Same prompt content, packaged per-host. `blader/humanizer`'s 14k stars suggests this format has crossed the chasm.
- **Signs-of-AI-Writing as canonical source.** The 2023-era ad-hoc ban-lists have given way to [Wikipedia's *Signs of AI writing*](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) as the reference corpus, cited by blader, brandonwise, Aboudjem, slop-humanizer, anti-slop-writing.
- **Two-layer architecture winning.** Split generation (clarity/correctness) from post-processing (distribution/flow). Explicit in Peggy Kang's piece and implicit in every 4-pass repo. Prompt-only humanization is being quietly conceded as the weakest variant.
- **Rule-based + hybrid as the credible alternative.** The engineering critique on `blader/humanizer` #82 is not being dismissed. `voidborne-d/humanize-chinese` operationalizes it; `rithulkamesh/humanize` commits to fully deterministic transformations. Expect more language-specific rule-based forks.
- **Inference-time sampler approach is now academically formalized.** ANTISLOP accepted at ICLR 2026 (arXiv 2510.15061) converts the LocalLLaMA practitioner tool into a peer-reviewed technique. `auto-antislop` automates fine-tuning pipeline construction. `antislop-vllm` adds OpenAI-API-compatible inference. The approach still requires raw logits so won't cross into SaaS, but within LocalLLaMA and self-hosted setups it's now the most academically credible layer to solve the problem.
- **Paid-SaaS dominance on the consumer side — but Turnitin's August 2025 update reshuffled the rankings.** r/SEO, r/ChatGPT and r/BypassAiDetect tool-comparison threads converge on paid, closed tools. Walter Writes peaked at 517% YoY search-interest growth in early 2026, then Turnitin's anti-humanizer update reduced its bypass rate from near-universal to 38% flagged. AuraWrite emerged as the top-ranked post-update alternative in affiliate tests. The lesson: paid-SaaS reputations are only as durable as their detector retraining cadence.
- **Ethical pushback concentrated on HN.** Reddit and dev.to are operational ("how do I…"); HN is normative ("should you…"). This venue split has been stable for 2+ years.
- **Market-positioning copy is converging.** Every launch now says: "I tried existing humanizers, they made my text worse / still got flagged; I researched how detectors work; my method goes beyond synonym swaps; it restructures sentences and adjusts cadence." Stock paragraph, appears near-verbatim across HN, Indie Hackers, and dev.to.
- **"Humanizer" brand collision with `humanify`/`Humanizr.Humanizer`.** `jehna/humanify` (JS deobfuscation), `Humanizr/Humanizer` (.NET string formatting library) and the AI-text-humanizer category all share the keyword. Reddit recommendation threads routinely mis-link. Naming discipline matters when building in this space.

## Gaps & Open Questions

- **No open benchmark.** Every repo makes bypass-rate claims (60–90%, <10% detection) against its own test set. There's no shared, versioned, detector-panel benchmark (GPTZero + Turnitin + Originality.ai + Copyleaks + Pangram, on a fixed AI-generated corpus). Without one, repo comparisons are theatrical. `Slop or not` ([HN 47357745](https://news.ycombinator.com/item?id=47357745)) is the closest human-detection crowd benchmark but doesn't measure humanizers.
- **Voice preservation vs detection bypass is rarely separated.** Most repos lump both into "humanization." The engineering-critique camp (Kang, voidborne-d) treats them as different problems. A project that explicitly ships two modes — "sound like me" vs "defeat detector X" — with different pipelines is missing.
- **Fine-tuned OSS humanizer.** The #82 thread's first viable approach (train a model specifically on humanization) is claimed by paid services (Rephrasy, Walter Writes) but not shipped OSS. Gap: no HF model card, no training set, no LoRA recipe for "humanizer fine-tune" that competes with closed SaaS.
- **Cross-lingual coverage.** English dominates. `humanize-chinese` and StealthHumanizer's 16-language claim are outliers; most repos are monolingual English ban-lists. Indonesian, Arabic, Hindi, Portuguese are under-served.
- **Voice-calibration data capture.** `blader/humanizer`'s sample-paste pattern is ad-hoc. No repo ships a persistent voice profile (e.g., a vector or JSON style card computed once from a user's corpus, reused across rewrites). This is an obvious product extension.
- **Domain-conditioned humanizers.** All OSS repos are domain-general. Student essays, SEO blog posts, LinkedIn posts, academic writing, customer-support replies, and fiction all have different "human" signatures. Domain-specific forks or presets are barely present.
- **Agent/tool-call humanization.** `humanizer-x`'s SSML disfluency for voice agents is the only OSS repo addressing humanization of non-text outputs (speech, multi-turn dialogue). Agentic humanization (making an autonomous agent's inner monologue or tool-call rationale sound human) is open territory.
- **Honest quality reporting.** StealthHumanizer's "12-metric detection engine" and TextHumanize's "60–90% detection reduction" are marketing claims in READMEs. No third-party evaluation, no reproducible methodology. The OSS space could differentiate by publishing honest numbers and negative results.
