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AI · alternatives · 2026

24 alternatives to Stun LLMs with thousands of invisible Unicode characters

I made a free tool that stuns LLMs with invisible Unicode characters. *Use cases:* Anti-plagiarism, text obfuscation against LLM scrapers, or just for fun! Even just one word's worth of “gibberified” text is enough to block most LLMs from responding coherently.

Gibberifier is a free tool that embeds invisible Unicode characters into text to disrupt large language model processing. It's designed for writers, content creators, and website owners who want to protect their work… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…

    2023 · github.com · its alternatives →

  2. 2

    After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…

    2024 · github.com · its alternatives →

  3. 3

    We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…

    Mar 2026 · github.com · its alternatives →

  4. 4

    New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.

    2023 · github.com · its alternatives →

  5. 5

    New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.

    2023 · github.com · its alternatives →

  6. 6

    Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…

    2023 · github.com · its alternatives →

  7. 7

    Detect and identify hidden unicode in text strings

    2024 · invisiblecharacterviewer.com · its alternatives →

  8. 8

    G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…

    2023 · github.com · its alternatives →

  9. 9

    We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.

    2023 · github.com · its alternatives →

  10. 10

    Protect your projects from hallucinated LLM imports

    2025 · antislopsquat.vercel.app · its alternatives →

  11. 11

    Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…

    2024 · github.com · its alternatives →

  12. 12

    Good morning HN! For a while now I have been toying with this idea and now finally have a working prototype. This project allows you to encode secret messages into ordinary looking text by using arithmetic coding with a probability model derived from an LLM. By encrypting the message and then decompressing the encrypted message using the arithmetic coder, you get output which looks just like randomly sampled output from the LLM. Except, it actually encodes your secret messages in the specific choices of tokens. By using authenticated encryption, only a user who knows the key can know that a…

    2025 · github.com · its alternatives →

  13. 13

    Hide secret messages in plain text using invisible Unicode.

    2025 · chinmay29hub-stegmoji.vercel.app · its alternatives →

  14. 14IB

    Hey HN -- I'm a solo dev. Built this because I got tired of AI crawlers reading my HTML in plain text while robots.txt did nothing. The core trick: shuffle characters and words in your HTML using a seed, then use CSS (flexbox order, direction: rtl, unicode-bidi) to put them back visually. Browser renders perfectly. textContent returns garbage. On top of that: email/phone RTL obfuscation with decoy characters, AI honeypots that inject prompt instructions into LLM scrapers, clipboard interception, canvas-based image rendering (no img src in DOM), robots.txt blocking 30+ AI crawlers, and…

    Mar 2026 · obscrd.dev · its alternatives →

  15. 15

    A CLI tool for managing and semantically diffing LLM prompts. Goes beyond text diff by detecting meaning-level changes using embeddings (OpenAI or local). Useful for versioning, testing, and CI/CD workflows.

    2025 · github.com · its alternatives →

  16. 16

    Invisible character generator

    2025 · invisiblecharacter.dev · its alternatives →

  17. 17

    Ultra-fast, low latency LLM prompt injection/jailbreak detection ⛓️ - arekusandr/last_layer

    2024 · github.com · its alternatives →

  18. 18AS

    We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!

    2023 · heatmap.demos.watchful.io · its alternatives →

  19. 19

    Hide secret messages inside LLM-generated text.

    Jun 2026 · stegveil.dev · its alternatives →

  20. 20

    Generate invisible text & blank Unicode characters

    Feb 2026 · textoinvisibles.com · its alternatives →

  21. 21AS

    2015 · unicode-highlight.herokuapp.com · its alternatives →

  22. 22

    Open-source agent skill + stdlib HTTP service that strips invisible Unicode, C2PA/EXIF/XMP, and (best-effort) statistical text marks from files you own. Claude / Gemini-SynthID / OpenAI / Kirchenbauer / keyed-Gumbel class.

    Aug 2026 · github.com · its alternatives →

  23. 23GP

    When working on data science projects I often want to just save my work, without having to think deeply about what’s changed. I found the intentionality of Git to feel like friction in these times, and I built GitEase to simplify my workflow. Instead of having to think about add/commit/push/pull with GitEase I just think about `ge load, ge save, ge share, ge undo`. And for even less thinking, I use an LLM (text-da-vinci-003) to generate commit messages by summarizing the diff. Take a look, try it out, and I’d welcome any contributions & feedback. pip install gitease $ ge —help…

    2023 · medium.com · its alternatives →

  24. 24UO

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