Alternatives
Products that do what Stun LLMs with thousands of invisible Unicode characters does
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.
- 1LC
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
- 2WT
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
- 3RL
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
- 4BT
2024 · github.com
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RAG-ready web scraping that cuts your LLM token costs
Apr 2026 · geekflare.com
- 7UL
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
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2025 · fontgenerator.cool
- 10LA
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
- 11FG
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
- 12LC
2023 · github.com
- 13GL
Hey HN, I wanted a grammar checker that didn’t send my writing to someone's servers, so we built Grammit, a Chrome extension that runs grammar checks locally using an LLM. Your text never leaves your computer during checking. Here’s a 2-minute overview: https://www.loom.com/share/baf501ee6cf14a919a7384128246ed67 Because it uses an LLM, it catches more than spelling and grammar. For example, it can correct some wrong statements like “The first US president was Benjamin Franklin.” Grammit also includes an in-page writing assistant that can rephrase or draft new text. It…
2025 · chromewebstore.google.com
- 14IM
2024 · github.com
- 15IJ
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
- 16SE
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
- 17IB
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
- 18LP
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
- 19AS
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
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Generate invisible text & blank Unicode characters
Feb 2026 · textoinvisibles.com
- 22AS
2015 · unicode-highlight.herokuapp.com
- 23
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.
9d ago · github.com
- 241B
Jun 2026 · llm-wiki.net
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