Paste text below — or load it from a URL — and it highlights sentences that match known LLM clichés, including the tells catalogued in Wikipedia’s Signs of AI writing guide. Chain patterns like “no X, no Y” get a badge counting their items; tap or hover a highlight to see which cliché it hit.
Wikipedia:Signs of AI writing
This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles, drafts, comments, and other content. It is a field guide to help detect undisclosed AI-generated content on Wikipedia: while some of the signs may be broadly applicable, some may not apply in a non-Wikipedia context.[a] Not all text featuring these indicators is AI-generated, as the large language models that power AI chatbots are trained on human writing, including Wikipedia. Many elements of AI writing can be found in editorials, blogs, or fan fiction.
Yuchen Jin on Twitter / X
Karpathy’s “LLM Wiki” pattern: stop using LLMs as search engines over your docs. Use them as tireless knowledge engineers who compile, cross-reference, and maintain a living wiki. Humans curate and think.Diagram generated by my Claude agent knowledge worker. https://t.co/5u5i1GeFK8 pic.twitter.com/NIaq3KlAok— Yuchen Jin (@Yuchenj_UW) April 4, 2026
How To Write With An LLM
Two rules keep an LLM from pasteurizing your writing: never take a word it suggests, and never let it encourage you. Then hand it all the tedious work.
Pushback on use of hidden prompts to snare AI peer reviews
The invisible messages, which instruct LLMs to use telltale phrases in a peer-review report, catch AI misuse but also erode trust, some say.
Sakana AI on Twitter / X
We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025!Paper: https://t.co/2FRiVF1UXJCode: https://t.co/rx4G7dq1SWBiological systems are capable of… pic.twitter.com/UdUYfqRXBS— Sakana AI (@SakanaAILabs) June 12, 2025
Lorem Ipsum Generator
Easily generate Lorem Ipsum placeholder text in any number of characters, words sentences or paragraphs. Learn about the origins of the passage and its history, from the Roman era to today.
NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs'...
Large language models (LLMs) are increasingly used in peer review at major AI conferences, yet novelty remains a persistent weak point. Existing benchmarks assess novelty as a single holistic...
How do LLMs affect writing? Talks by visitors to the AI STORIES project | UiB
The AI STORIES project is hosting a day of research talks focused on analysing AI-generated writing, with approaches ranging from the qualitative analysis of a professor of writing to large-scale computational analysis finding that prompting an LLM to edit a text to improve its grammar leads to significiant shifts in meaning.
sosumi.ai - Apple Docs for LLMs
sosumi.ai provides Apple Developer documentation in an AI-readable format by converting JavaScript-rendered pages into Markdown.
Yixin Dong on Twitter / X
We’re excited to announce that XGrammar has partnered with Outlines! 🎉XGrammar is now the grammar backend powering Outlines, enabling structured LLM generation with higher speed.Check out Outlines — an amazing library for LLM structured text generation! 🚀 pic.twitter.com/6VJgCeAauN— Yixin Dong (@yi_xin_dong) August 21, 2025
Tropes - AI Writing Pattern Directory
The definitive reference for identifying AI writing patterns. Learn to spot the tropes that give away AI-generated text.
Is there a broadly used lexicon for *book* highlights yet? (eg. for your Xteink/KOReader, or highlighted.app, highlights) (I figure this could be handy for sense-making on @semble.so, and for everyone with their new fancy xteink devices…)
LLM is a learned distribution p on sequences of tokens. If you just sample a bunch of text from p willy nilly and put it in the training data and train you just get back p. But if you sample a bunch of text from p and throw out whatever's bad and train on what's left then you learn p(x|x is not bad)
