A deliberately simple watermark model shows how much room a text watermark has: the entropy available at each token, how a small bias becomes detectable, and why length matters.
Source: blog.gaborkoos.com
![AI Agents List [2026] | Frameworks, Agentic Harnesses & Useful Repos](https://images.unsplash.com/photo-1697577418970-95d99b5a55cf?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w5ODE4OTh8MHwxfHNlYXJjaHw4fHxhaSUyMGFnZW50fGVufDB8MHx8fDE3OTA0NDU0OTV8MA&ixlib=rb-4.1.0&q=80&w=400)
The original was basically my own notepad that I decided to post before losing another version somewhere on my desktop lol. This one keeps that idea, with a much bigger list and a bit more organisation. There are frameworks for building your own agents, coding tools you can actually open and use, research agents, browser automation, voice agents, and a dedicated section for agentic harnesses. Also included the useful boring stuff: testing, tracing, permissions and checking whether a project is still maintained.
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The laboratory, founded by Elon Musk and recently supported by a $1 billion grant from Microsoft, has designed text generators that create readable passages virtually indistinguishable from those written by humans. OpenAI’s machine learning approach scrapes massive amounts of data from the web and analyzes it for statistical patterns that allow it to realistically predict what letters or words will likely be written next. When users feed a word or phrase or longer text snippets into the generator, it expands on the words with convincingly humanlike text.
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This post is a walkthrough of how LLMs work. Modern LLMs are mostly built by stacking transformer blocks over and over, so understanding the transformer machinery gets you most of the way there.I’ll cover the core mechanisms inside modern transformer-based LLMs, without all that sticky math stuff. Don’t get me wrong, you should learn the math, but this can serve as an introduction.Most modern LLMs share the same transformer-family skeleton. The differences come from what each one was trained on, the scale and configuration choices, and the post-training done on top. By the end, you should be able to read many modern LLM papers or model cards and know which piece of the architecture each section is talking about.
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