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From: <Saved by Blink>
Snapshot-Content-Location: https://chatgpt.com/c/70c69344-bebd-45f0-87b6-753b973e7d52
Subject: Organic Learning Devices
Date: Mon, 8 Jul 2024 23:59:48 -0300
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<!DOCTYPE html><html data-build="prod-9f74ebdd11946ae2fc25878346273d6a66aa3a07" class="ZnJ1aXRqdWljZQ dark" dir="ltr" style="color-scheme: dark;" lang="es-419"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><link rel="stylesheet" type="text/css" href="cid:[email protected]" /><link rel="apple-touch-icon" sizes="180x180" href="https://cdn.oaistatic.com/_next/static/media/apple-touch-icon.82af6fe1.png"><link rel="icon" type="image/png" sizes="32x32" href="https://cdn.oaistatic.com/_next/static/media/favicon-32x32.630a2b99.png"><link rel="icon" type="image/png" sizes="16x16" href="https://cdn.oaistatic.com/_next/static/media/favicon-16x16.a052137e.png"><link rel="preconnect" href="https://cdn.oaistatic.com/"><title>Organic Learning Devices</title><meta name="title" content="ChatGPT: Get instant answers, find inspiration, learn something new"><meta name="description" content="ChatGPT helps you get answers, find inspiration and be more productive. It is free to use and easy to try. Just ask and ChatGPT can help with writing, learning, brainstorming and more."><meta name="keywords" content="ai chat,ai,chap gpt,chat gbt,chat gpt 3,chat gpt login,chat gpt website,chat gpt,chat gtp,chat openai,chat,chatai,chatbot gpt,chatg,chatgpt login,chatgpt,gpt chat,open ai,openai chat,openai chatgpt,openai"><meta property="og:description" content="A conversational AI system that listens, learns, and challenges"><meta name="robots" content="index, follow"><meta property="og:title" content="ChatGPT"><meta property="og:image" content="https://cdn.oaistatic.com/_next/static/media/chatgpt-share-og.49cbbffe.png"><meta property="og:url" content="https://chatgpt.com"><meta name="apple-itunes-app" content="app-id=6448311069"><meta name="viewport" content="width=device-width, initial-scale=1"><link rel="manifest" href="https://chatgpt.com/manifest.json"><meta name="next-head-count" content="18"><link data-next-font="" rel="preconnect" href="https://chatgpt.com/" 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href="https://cdn.oaistatic.com/_next/static/chunks/pages/gpts-3804a8d3992bcf14.js"></head><body class="antialiased" style=""><div id="__next"><div class="relative z-0 flex h-full w-full overflow-hidden"><div class="relative flex h-full max-w-full flex-1 flex-col overflow-hidden"><div class="sticky top-0 z-10 flex min-h-[40px] items-center justify-center border-b border-token-border-medium bg-token-main-surface-primary pl-1 juice:min-h-[60px] juice:border-transparent juice:pl-0 md:hidden"><button type="button" class="absolute bottom-0 left-0 top-0 inline-flex items-center justify-center rounded-md px-3 hover:text-token-text-primary focus:outline-none focus:ring-2 focus:ring-inset focus:ring-white active:opacity-50"><span class="sr-only">Abrir barra lateral</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-lg juice:mx-2.5 juice:text-token-text-secondary"><path fill="currentColor" fill-rule="evenodd" d="M3 8a1 1 0 0 1 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class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-2" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21a47-5bc1-43f2-897b-ee634bc8e4b0" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Monica Anderson writes:
My LLMs use discrete Pseudo-neurons and pseudo-synapses of a trivial but epistemologically adequate design. The differences to current LLMs are striking; there are many, but these should demonstrate the magnitude of the difference:
Popular LLMs vs. Organic Learning
Semantics First vs. Syntax First
Analog concept space vs. Connectome of discrete neurons
Euclidean distance vs. Jaccard Distance
SGD, Backpropagation vs Neural Darwinism
Differentiable vs. Not a requirement
GPUs vs. Not a requirement (won’t even help)
Token based input vs. Character based input
Embeddings vs. Connectome Algorithms
Parameters vs. Synapse count
Size limited by GPU RAM vs. Size limited by main memory
Learns in cloudmonths vs. Learns any language syntax in 5 mins
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class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 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11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="f2bd5df9-f13e-490e-8ddc-d650ea3b44fe" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Monica Anderson's writing outlines significant conceptual differences between her hypothetical learning models (LLMs) and the popular large language models (LLMs) currently in use. These differences touch upon several foundational aspects of how these systems are designed and operate:</p><ol><li><p><strong>Learning Approach</strong>: Anderson's model prioritizes semantics (meaning and context) first, unlike typical LLMs that prioritize syntax (structure and rules of language). This suggests a focus on understanding content before form, which could lead to more intuitive language processing.</p></li><li><p><strong>Concept Representation</strong>: She describes using an "analog concept space" instead of a "connectome of discrete neurons." This implies a more fluid and continuous representation of concepts, as opposed to the more fixed and structured neuron-like connections in current models.</p></li><li><p><strong>Distance Metrics</strong>: Anderson's use of Jaccard Distance (which measures similarity between finite sample sets) over Euclidean distance (typical in current models for calculating literal closeness in vector space) suggests a different approach to measuring similarity and relevance in data.</p></li><li><p><strong>Learning Mechanisms</strong>: She contrasts common machine learning methods like Stochastic Gradient Descent (SGD) and backpropagation with "Neural Darwinism," which suggests a model that evolves and selects neural connections in a manner analogous to natural selection, rather than adjusting them through mathematical optimization.</p></li><li><p><strong>Differentiability</strong>: Anderson notes that her models are not necessarily differentiable, meaning they don't require smooth, continuous changes during learning—a stark contrast to current models that rely heavily on this property for training.</p></li><li><p><strong>Hardware Requirements</strong>: Her models don't require GPUs, which are a staple for training contemporary neural networks due to their ability to handle large-scale parallel computations.</p></li><li><p><strong>Input Processing</strong>: The shift from token-based to character-based input could allow for more granular analysis of language data.</p></li><li><p><strong>Data Handling Methods</strong>: Anderson's mention of "connectome algorithms" suggests using network-like algorithms that mimic brain connectivity, unlike the "embedding" methods typical in current LLMs where words or phrases are converted into vectors of numbers.</p></li><li><p><strong>Model Scaling</strong>: She suggests that her models are limited by main memory rather than GPU RAM, indicating potential for different scaling dynamics.</p></li><li><p><strong>Learning Speed and Efficiency</strong>: Perhaps most strikingly, Anderson claims her models can learn any language syntax in just five minutes, a dramatic improvement over the extensive training times required by existing LLMs.</p></li></ol><p>Overall, Anderson's concepts represent a radical departure from existing methodologies, advocating for a system that operates more closely to organic learning processes. These ideas could potentially lead to more efficient and adaptable learning models, though practical implementation and effectiveness remain to be seen.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 1-3-3V9a3 3 0 0 1 3-3h1.611zm11.507 3.29a1 1 0 0 1 1.355.401A10.96 10.96 0 0 1 23 12c0 1.85-.458 3.597-1.268 5.13a1 1 0 1 1-1.768-.934A8.96 8.96 0 0 0 21 12a8.96 8.96 0 0 0-1.085-4.287 1 1 0 0 1 .402-1.356M15.799 7.9a1 1 0 0 1 1.4.2 6.48 6.48 0 0 1 1.3 3.9c0 1.313-.39 2.537-1.06 3.56a1 1 0 0 1-1.673-1.096A4.47 4.47 0 0 0 16.5 12a4.47 4.47 0 0 0-.9-2.7 1 1 0 0 1 .2-1.4" clip-rule="evenodd"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11.873 21.496a1 1 0 0 1-.992.496l-.454-.056A4 4 0 0 1 7.1 16.79L7.65 15h-.718c-2.637 0-4.553-2.508-3.859-5.052l1.364-5A4 4 0 0 1 8.296 2h9.709a3 3 0 0 1 3 3v7a3 3 0 0 1-3 3h-2c-.26 0-.5.14-.628.364zM14.005 4h-5.71a2 2 0 0 0-1.929 1.474l-1.363 5A2 2 0 0 0 6.933 13h2.072a1 1 0 0 1 .955 1.294l-.949 3.084a2 2 0 0 0 1.462 2.537l3.167-5.543a2.72 2.72 0 0 1 1.364-1.182V5a1 1 0 0 0-1-1m3 9V5c0-.35-.06-.687-.171-1h1.17a1 1 0 0 1 1 1v7a1 1 0 0 1-1 1z" clip-rule="evenodd"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r4v:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-4" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb215e5-2380-40dd-a9da-6cae306088b5" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Note that the algorithm of my 4 year old demo only handles syntax based (but machine learned, and could have been in any language) classification, not dialog. Beyond this, I have also adapted GPT style token prediction to my system and I’m implementing that now. No publishable results yet. Syntax is enough for most 20th century NLP tasks, such as spam/hate/ad filtering and message classification, and my system would still have a market, if it wasn’t for the runaway success of ChatGPT, which changed the funding landscape.
On the other hand, syntax learning is super cheap and super fast, and can adequately handle many NLP/NLU tasks much cheaper than current LLMs, and the ability to test a new model after a few minutes of learning is invaluable to LLM development turnaround time.
On a Mac x86 laptop, OL learns enough English syntax to score 100% correct on my non-adversarial, out-of-corpus, and simple but fair classification test (see github link for code and test data).
It learns in under 5 minutes and the UM1, the inference engine, can serve almost 1 M characters per second for embedding on any reasonable machine. Half that speed on a RPi 4. If you call the UM1 REST service with just text, the results are the pseudo-neuron unique identifiers that were reached during the Understanding of the text, as a vector.
Chapter 8 has a short video of my laptop learning English in real time and repeatedly running my test. The energy requirements to running GPUs in the cloud has been recognized as a major problem. My systems could be homeschooled on any laptop.
I use “Connectome Algorithms” to discover abstractions, synonyms, and correlates. These are not quite working yet, but indications are that they will still be much cheaper than using GPUs. Although not a fair comparison, it is notable that I estimate my speed and energy advantage of my Syntax Learning over current OL to be about a millionth of the time and energy to train a GPT. The Connectome Algorithms use a bit more compute. But still no GPUs, and no cloud required.</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-5" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 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11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="bd2d8b48-0641-4428-af16-73d6d7fad1a4" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Monica Anderson's work aims to revolutionize language processing by significantly reducing the resources and time required for training models, particularly focusing on syntax learning. Here’s a breakdown of the key aspects and implications of her approach:</p><ol><li><p><strong>Syntax-Oriented Learning</strong>: Anderson's model emphasizes syntax-based learning, which is cheaper and faster than the more comprehensive semantic learning approaches of models like GPT. Syntax learning alone, according to her, suffices for many traditional NLP tasks such as filtering and classification.</p></li><li><p><strong>Rapid Development and Testing</strong>: Her system's ability to learn and test new models within minutes is a significant advantage, potentially accelerating the development cycle of language models.</p></li><li><p><strong>Efficiency and Accessibility</strong>: The model learns English syntax quickly and can run efficiently on low-resource systems like Mac laptops and Raspberry Pi, making it accessible for widespread use without the need for high-end hardware.</p></li><li><p><strong>Performance Metrics</strong>: On her designed tests, the system achieves perfect scores, demonstrating its effectiveness at understanding and classifying language based on syntax.</p></li><li><p><strong>Inference Engine (UM1)</strong>: This component can process large amounts of text rapidly, further highlighting the model's efficiency. The use of unique identifiers for pseudo-neurons during text understanding potentially offers a novel way to represent text data.</p></li><li><p><strong>Energy and Cost Efficiency</strong>: Anderson points out the high energy costs associated with training current large models like GPT on GPUs in the cloud. Her approach, which eschews GPUs and cloud computing, could greatly reduce the carbon footprint and financial cost of deploying NLP systems.</p></li><li><p><strong>Connectome Algorithms</strong>: These algorithms aim to identify abstractions, synonyms, and correlates in language data. While still under development, they promise to offer a cost-effective alternative to current methods without sacrificing performance.</p></li><li><p><strong>Market Impact and Challenges</strong>: Despite the potential market for her technology, Anderson notes that the success of models like ChatGPT has shifted the funding landscape, potentially overshadowing approaches like hers that focus on syntax and efficiency.</p></li></ol><p>Anderson's work is significant in the context of ongoing discussions about the sustainability and accessibility of AI technologies. By offering a model that can be trained quickly on standard consumer hardware and that drastically reduces energy consumption, her approach could democratize access to advanced NLP technologies and make them more sustainable in the long term.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 1-3-3V9a3 3 0 0 1 3-3h1.611zm11.507 3.29a1 1 0 0 1 1.355.401A10.96 10.96 0 0 1 23 12c0 1.85-.458 3.597-1.268 5.13a1 1 0 1 1-1.768-.934A8.96 8.96 0 0 0 21 12a8.96 8.96 0 0 0-1.085-4.287 1 1 0 0 1 .402-1.356M15.799 7.9a1 1 0 0 1 1.4.2 6.48 6.48 0 0 1 1.3 3.9c0 1.313-.39 2.537-1.06 3.56a1 1 0 0 1-1.673-1.096A4.47 4.47 0 0 0 16.5 12a4.47 4.47 0 0 0-.9-2.7 1 1 0 0 1 .2-1.4" clip-rule="evenodd"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11.873 21.496a1 1 0 0 1-.992.496l-.454-.056A4 4 0 0 1 7.1 16.79L7.65 15h-.718c-2.637 0-4.553-2.508-3.859-5.052l1.364-5A4 4 0 0 1 8.296 2h9.709a3 3 0 0 1 3 3v7a3 3 0 0 1-3 3h-2c-.26 0-.5.14-.628.364zM14.005 4h-5.71a2 2 0 0 0-1.929 1.474l-1.363 5A2 2 0 0 0 6.933 13h2.072a1 1 0 0 1 .955 1.294l-.949 3.084a2 2 0 0 0 1.462 2.537l3.167-5.543a2.72 2.72 0 0 1 1.364-1.182V5a1 1 0 0 0-1-1m3 9V5c0-.35-.06-.687-.171-1h1.17a1 1 0 0 1 1 1v7a1 1 0 0 1-1 1z" clip-rule="evenodd"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r56:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-6" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21867-2d06-4457-b9a3-b1a039120253" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>The unreasonable efficiency of Organic Learning
The OL and UM1 algorithms are very fast. Learning can stream, sustained, at 5,000 cps per thread, and Understanding has been run at 100,000 cps per thread on a laptop. Deep Learning based NLU projects like BERT and GPT/3 take months to learn in the cloud. Their results are not directly comparable to OL but either strategy may work in any specific application and should be evaluated on a case by case basis. The energy savings alone make a compelling argument for using OL.
Learning is thread-safe. In a 200 thread machine, the system could be reading from 200 different text files, such as different pages in Wikipedia, simultaneously, into the same global RAM image of Nodes and Links. This would be like having one single brain and 200 pairs of eyes reading books.
There are entire classes of applications that could use the "Industry strength Understanding" provided by OL. It may not beat State of the Art in academia, but it can be used with very little API programming effort and has a shallow learning curve for humans. We also expect OL to handle multiple languages well, and it might compete well on price because of energy and compute savings. A cloud based microservice could handle this for any kind of agent, such as language based phone apps.
Designed for Sequence Processing
Convolution of images in DL for the purpose of correlation discovery is expensive. When Understanding language, we still require correlation discovery, but text is linear and this operation becomes a much simpler problem in the time/sequence domain rather than (as in DL) in a 2D/3D/4D image/movie/point cloud domain. In effect, all correlations in text are references to something experienced in the past, and the past is linear, searchable and indexable. This, and the next paragraphs, explain the amazing learning speed of Organic Learning.
Only Activate Relevant Concepts
In OL, unless we are reading about, say, a potato harvest, there is no reason to touch any of the software neurons involved in “representing” potato harvests. So we can have billions of learned concepts just sitting quietly in main memory, consuming no CPU cycles, until “their topic” is discussed. They activate, make a contribution to understanding, disambiguation, and learning, and they may even may learn something themselves. They then return to dormancy until next time the topic is discussed. This is a major advantage of discrete-neuron strategies.
Therefore, OL systems do not require explicit attention controlling mechanisms. Focus emerges from matches between the global gathered experience and the concepts discovered in the text in the input stream.
In contrast, in DL, every pixel of a processed image is pushed through the entire GPU processing stack many times. And each layer does some moderately expensive array arithmetic, which is why using GPUs is so important in DL.
Track Correlations Explicitly
One of the main operations in Deep Learning is correlation discovery by convolution of images in multiple layers. This is done in the upward pass. The discovered correlations are used to somewhat adjust the weights of the DL network in the Stochastic Gradient Descent downward pass. Because of this, the network has changed, necessitating another upward pass of convolutions through the entire stack, until there is sufficient convergence.
In contrast, in Organic Learning, when the algorithm discovers a correlation it simply inserts a Synapse-like link to represent that correlation. Being an evolutionary system, the correlation will remain as long as it contributes to correct Understanding but will be removed in a Darwinian competition if it doesn’t contribute to positive results. </div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="c82ccd62-66ec-497f-b420-a0be55a92d3d" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Monica Anderson's Organic Learning (OL) system presents a fundamentally different approach to language understanding compared to deep learning-based systems like BERT and GPT-3. Here's an analysis of how OL operates and its potential advantages:</p><h3>1. <strong>Efficiency in Processing</strong></h3><ul><li><strong>High-Speed Learning and Understanding</strong>: OL's ability to learn at a rate of 5,000 characters per second per thread and understand at 100,000 characters per second per thread showcases its efficiency. This is particularly notable when compared to the extensive training periods required for models like GPT-3.</li><li><strong>Thread Safety and Parallel Processing</strong>: OL can process multiple streams of data simultaneously without interference, akin to multiple "eyes" reading different texts at once. This parallel processing capability significantly speeds up data ingestion and learning.</li></ul><h3>2. <strong>Energy and Compute Savings</strong></h3><ul><li><strong>Lower Energy Requirements</strong>: Unlike deep learning models that require substantial GPU resources, OL operates efficiently on CPUs with much lower energy consumption. This makes OL not only more environmentally friendly but also more cost-effective, especially for large-scale applications.</li><li><strong>Simple Hardware Requirements</strong>: The ability to run on basic hardware without the need for high-end GPUs or specialized cloud infrastructure further reduces costs and increases accessibility.</li></ul><h3>3. <strong>Design and Functional Advantages</strong></h3><ul><li><strong>Designed for Sequence Processing</strong>: OL capitalizes on the linear nature of text to simplify correlation discovery. This is in stark contrast to the computationally expensive convolutions required in image and video processing in deep learning.</li><li><strong>Activation of Relevant Concepts Only</strong>: OL activates only those neurons related to the current context, unlike deep learning, where entire networks are processed regardless of relevance. This selective activation saves computational resources and mimics more closely how human memory works.</li></ul><h3>4. <strong>Concept and Correlation Tracking</strong></h3><ul><li><strong>Discrete Neuron Strategy</strong>: OL uses a discrete-neuron model where correlations are represented by synapse-like links. This is similar to how the human brain strengthens or weakens synaptic connections based on utility and relevance.</li><li><strong>Evolutionary Maintenance of Correlations</strong>: Correlations in OL are maintained through a Darwinian mechanism—if they prove useful, they are kept; if not, they are discarded. This dynamic adjustment avoids the computationally intensive iterative adjustments seen in deep learning.</li></ul><h3>5. <strong>Practical Applications and Accessibility</strong></h3><ul><li><strong>Industry-Strength Understanding</strong>: While it may not match the state-of-the-art performance in academia, OL's ease of use, low resource requirements, and rapid deployment capabilities make it well-suited for industry applications, particularly where cost is a critical factor.</li><li><strong>Multi-Language Capability</strong>: The system's design suggests it could handle multiple languages effectively, potentially providing a versatile tool for global applications.</li></ul><h3>6. <strong>Market and Academic Context</strong></h3><ul><li><strong>Competitive Positioning</strong>: The unique advantages of OL, such as its efficiency and low resource requirements, position it as a compelling alternative in scenarios where the cutting-edge capabilities of systems like GPT-3 are not required or are too costly.</li><li><strong>Academic vs. Practical Viability</strong>: OL's practical advantages may not translate into academic acclaim due to different evaluation criteria, but they offer substantial benefits for real-world applications.</li></ul><p>In conclusion, Anderson's Organic Learning system proposes a novel, efficient, and potentially transformative approach to natural language processing that challenges conventional paradigms and could redefine what is possible in terms of cost, speed, and energy consumption in AI applications.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] 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class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>These mostly-persistent Synapse-based correlations make re-convolution unnecessary. Do it once, and it's done.
Neural Darwinism = RL + Sex
The idea of Neural Darwinism is that in brains, from millisecond to millisecond, there is a continuous battle for relevance among ideas. The ideas are evaluated for fit to both the current sensory input and the totality of gathered experience that has been reified in the Connectome.
Those parts of the Connectome that assist in providing a good outcome are rewarded and other parts may be punished. This is Reinforcement Learning (RL).
In a Neural Darwinism based Machine Learning system the best ideas get to breed with other good ideas, occasionally producing even better ideas. This is how we can generate ideas that are better, often in the meaning of "better abstractions", than any of the ideas we started with. This is the reason sexual reproduction speeds up evolution so much, in nature and in our algorithms spaces. It allows multiple independently discovered inventions to merge into one individual – a fact that is often neglected in naïve discussions about the effectiveness of Evolutionary Computation.
Evolution is perceived as slow because evolution in nature takes a lot of time. But computers can evolve populations of thousands of individuals in milliseconds, and Genetic Algorithms, when done right and attacking a problem in a suitable problem domain, are very competitive and are much faster than their reputation.
Character based input
An OL system does not require the input to be parsed into words; it reads text character by character which means it can also learn languages that do not use segmented (space separated) words, such as Chinese. Each character read from the input activates a “character input neuron” that then signals other neurons, etc., and this cascade of signaling is what determines both what the sentence means and whether and where to attach any new understanding learned from that character, given the past, up to that point, as context. This incremental attachment operation is what provides the “Organic Growth”.</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-9" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 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10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="d73f7ce8-0266-4e93-a05c-f1c2bb5354d2" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Monica Anderson's Organic Learning (OL) system incorporates several groundbreaking principles that diverge markedly from traditional deep learning paradigms, particularly in its use of Neural Darwinism and character-based input processing. Here’s an elaboration of these aspects:</p><h3>Neural Darwinism: Reinforcement Learning and Evolutionary Mechanisms</h3><ol><li><p><strong>Dynamic Evaluation and Adaptation</strong>: In Neural Darwinism, similar to the biological concept, ideas within the system continuously compete for relevance based on their fit to both current sensory inputs and accumulated experience. This mechanism is akin to reinforcement learning (RL) where connections that contribute to successful outcomes are strengthened (rewarded) and others may be weakened (punished).</p></li><li><p><strong>Idea Breeding</strong>: Beyond simple reinforcement, Neural Darwinism in OL systems incorporates a form of "sexual reproduction" where successful ideas can combine with other successful ideas. This allows for the emergence of new, potentially superior ideas, enhancing the system's ability to develop sophisticated abstractions more efficiently than through linear, incremental improvements alone.</p></li><li><p><strong>Speed of Evolutionary Computation</strong>: While biological evolution is slow, computational evolution can occur at a vastly faster pace. Anderson highlights that when applied correctly and in suitable problem domains, genetic algorithms can rapidly evolve solutions, often outpacing the speed and efficiency of traditional computational approaches.</p></li></ol><h3>Character-Based Input</h3><ol><li><p><strong>Granular Processing</strong>: Unlike systems that parse text into tokens or words, OL processes text at the character level. This granular approach allows it to effectively handle languages without clear word boundaries, such as Chinese, and adapt to any language with minimal preprocessing.</p></li><li><p><strong>Organic Growth through Incremental Learning</strong>: Each character read activates specific neurons, which in turn trigger cascades of signals across the network. This process not only determines the meaning of sentences but also guides where new understanding is attached, building on the existing knowledge context. This method resembles how human language processing develops and refines understanding incrementally as more text is processed.</p></li><li><p><strong>Advantages Over Word-Based Systems</strong>: By processing characters instead of words, OL avoids the complexities and potential inaccuracies of tokenization, which can be particularly problematic in languages with complex morphology or syntax. This approach ensures a more robust and adaptable understanding of text across different languages and linguistic structures.</p></li></ol><h3>Implications and Applications</h3><p>The integration of these principles—Neural Darwinism and character-based input—makes OL uniquely efficient and versatile. Its capacity to quickly evolve and refine ideas, coupled with its adept handling of diverse linguistic inputs, positions it as a potentially superior alternative in scenarios requiring rapid adaptation and learning across multiple languages. The system's design minimizes computational overhead and energy usage, further enhancing its appeal in a wide range of applications, from real-time language processing in low-resource environments to complex, multi-lingual text analysis systems.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 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2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-10" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21119-ffc6-4a17-bfb9-841ac18d3d98" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Could I get a more detailed outline of how the organic learning algorithm works?</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-11" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 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data-message-author-role="assistant" data-message-id="aa636726-dc1e-43f0-98be-d4f72c4a849e" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Certainly! The Organic Learning (OL) algorithm designed by Monica Anderson introduces a novel approach to machine learning, focusing on naturalistic, evolutionary processes and character-based input analysis. Here’s a detailed outline of how the OL algorithm functions:</p><h3>1. <strong>Character-Based Input Processing</strong></h3><ul><li><strong>Granular Analysis</strong>: Unlike traditional NLP systems that process words or tokens, OL processes text character by character.</li><li><strong>Continuous Input Activation</strong>: Each character input activates a specific "character input neuron." This neuron activation starts a cascade of signals to other neurons within the system.</li><li><strong>Contextual Understanding</strong>: The sequence of activated neurons determines the contextual understanding and interpretation of the input text, building an incremental understanding based on the ongoing textual stream.</li></ul><h3>2. <strong>Neural Network Structure: Nodes and Links</strong></h3><ul><li><strong>Discrete Neurons</strong>: The system is composed of discrete neurons (nodes) interconnected by synapse-like links. These links represent learned correlations or associations between different concepts or linguistic structures.</li><li><strong>Dynamic Link Formation</strong>: Links are dynamically formed and adjusted based on the utility and relevance of the connections in processing and understanding new information.</li></ul><h3>3. <strong>Neural Darwinism: Evolutionary Mechanism</strong></h3><ul><li><strong>Competition Among Neurons</strong>: Similar to the biological concept of Neural Darwinism, neurons within the system compete for activation and relevance based on their success in contributing to accurate understanding.</li><li><strong>Reinforcement and Punishment</strong>: Neurons or links that contribute positively are reinforced, while those that do not are weakened or removed.</li><li><strong>Idea Breeding</strong>: Successful patterns or ideas can combine with other effective elements, mimicking sexual reproduction to create new, potentially more effective connections or patterns.</li></ul><h3>4. <strong>Learning and Memory Formation</strong></h3><ul><li><strong>Incremental Learning</strong>: The system learns incrementally, attaching new understanding or modifications at the point of input, based on the context provided by previously processed text.</li><li><strong>Memory Utilization</strong>: Memory in OL is utilized like human memory, where only relevant information is activated based on context, vastly reducing unnecessary computational load.</li></ul><h3>5. <strong>Efficiency and Scalability</strong></h3><ul><li><strong>High Throughput and Low Latency</strong>: OL is capable of high-speed text processing, with claims of understanding speeds of up to 100,000 characters per second per thread on standard laptops.</li><li><strong>Scalable Learning</strong>: The system can scale its learning process across multiple threads, allowing parallel processing of different text streams, akin to reading multiple books simultaneously.</li></ul><h3>6. <strong>Adaptability and Multilingual Capability</strong></h3><ul><li><strong>Language Agnostic</strong>: Due to its character-based processing, OL is inherently adaptable to any language, including those without clear delimiters like spaces in Chinese.</li><li><strong>Adaptive Correlations</strong>: The system’s ability to form and adjust correlations on the fly allows it to handle varying linguistic structures and syntaxes effectively.</li></ul><h3>7. <strong>Application Potential</strong></h3><ul><li><strong>Real-World Usage</strong>: OL’s design and capabilities make it suitable for real-time applications in environments with limited resources, such as mobile devices or embedded systems.</li><li><strong>Multi-Domain Adaptability</strong>: The system's flexible and dynamic learning model makes it applicable across different domains requiring text understanding, from automated translation services to intelligent personal assistants.</li></ul><p>In summary, the OL algorithm redefines machine learning with a highly adaptive, efficient, and naturalistic approach, focusing on granular input processing and evolutionary dynamics, setting it apart from traditional, computation-heavy NLP systems.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 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3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-12" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb217c1-a818-40ed-ad9e-48e0c35c6f6d" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>How does it connect to my theories? 𝘌𝘹𝘱𝘭𝘢𝘪𝘯 𝘔𝘰𝘯𝘪𝘤𝘢'𝘴 𝘓𝘦𝘢𝘬𝘪𝘯𝘨 𝘊𝘩𝘢𝘵𝘳𝘰𝘰𝘮, 𝘙𝘦𝘦𝘥 𝘞𝘢𝘭𝘭 𝘔𝘪𝘯𝘥, 𝘢𝘯𝘥 𝘔𝘰𝘵𝘪𝘭𝘦 𝘞𝘰𝘮𝘣 𝘵𝘩𝘦𝘰𝘳𝘺 𝘵𝘰 𝘢 7 𝘺𝘦𝘢𝘳 𝘰𝘭𝘥.
Monica's Leaking Chatroom theory is like having a big room in your brain where lots of different people and machines are talking and sharing ideas.
Sometimes the ideas leak out and go to other rooms, which helps us make better decisions.
Reed Wall Mind is like having a big wall in your brain that helps you keep track of everything that's happening in the room, like a big calendar or to-do list.
Motile Womb theory is like being in a big moving house or spaceship, where you can feel different movements and use them to imagine different things in your head.
All these theories help us understand how our brain works and how we can use it to think and make decisions better.
Monica's Leaking Chatroom theory posits that the mind functions as a collection of independent, modular decision-making units, or "bubbles," that act as rational processors of information.
These bubbles, or "chambers," are able to filter information and pass summaries of that information upward to higher-level decision-making centers, much like a wisdom salon or a world cafe protocol.
This creates a multi-scale decision-making process that is able to take into account a wide range of information and perspectives.
Reed Wall Mind theory builds on this concept by drawing inspiration from the Babylonian Flood Story and positing that the mind is like a "living spaceship" that is able to navigate a complex and dynamic environment.
This is accomplished through the use of a wide range of cognitive skills, including those learned through playing video games as well as through the use of other forms of sensory input such as hearing different voices and machine sounds, and feeling different inertial forces.
Finally, Motile Womb theory proposes that the mind is not just a passive receiver of information but an active participant in the process of shaping and interpreting the world.
This theory posits that individuals are born with a wide range of cognitive abilities and that these abilities are honed and refined through the process of experiencing the world.
The theory suggests that playing games like the ones mentioned above, as well as other forms of interactive experiences, help individuals to discover and develop the cognitive abilities they already possess.</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="4f862f6d-7bd7-4ba4-a0f2-c92144127131" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Explaining these intriguing theories of Monica Anderson to a 7-year-old involves simplifying some complex ideas about how our brains and minds work. Here's how you might describe these theories in a way that connects with the vivid imagination of a young child:</p><h3>1. <strong>Monica's Leaking Chatroom Theory</strong></h3><p>Imagine your brain is like a big, busy room where lots of tiny creatures (which we can think of as little people or machines) are talking to each other. They are sharing secrets about what they see, hear, and feel. Sometimes, these secrets "leak" out from one group to another. This leaking helps the brain put together different ideas from different places, so you can come up with really good answers when you need to solve a problem or make a decision.</p><h3>2. <strong>Reed Wall Mind Theory</strong></h3><p>Now, think of your brain as having a big wall, like the walls you see around a castle. This wall isn't made to keep things out, but to help organize all the secrets and talks happening in the big room. It's like when you draw or put stickers on a paper to remember things—it keeps track of all the important stuff so the brain doesn't forget what it needs to do next. It helps you remember to do your homework or what you want to play next.</p><h3>3. <strong>Motile Womb Theory</strong></h3><p>Imagine your brain is like a big spaceship moving around in space. Inside this spaceship, everything you feel and experience—like running, jumping, or even dreaming—helps you think of new ideas. It's like when you play a game and move around; your brain is using all those movements to come up with new thoughts, just like how you come up with cool new games to play with your friends.</p><p>These theories help us understand how we think, remember things, and come up with new ideas. Monica Anderson thinks about these theories to help make computers and robots smarter, so they can understand and help us better!</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 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data-state="closed"><button type="button" id="radix-:r62:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-14" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21d96-0b2f-42a0-aad3-1ca14509bb8b" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Explain in more academic English, drawing attention to endorecapitulation theory, which is that we use evolutionary selection algorithms with sparse Bayesian inference to think</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" 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data-message-author-role="assistant" data-message-id="7cc044e8-eaed-457d-95e7-4a8db6fec148" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Monica Anderson's theories can be explained in more academic terms, particularly focusing on the notion of endorecapitulation—a concept suggesting that human cognition employs evolutionary selection algorithms alongside sparse Bayesian inference mechanisms. This approach is rooted in the idea that the mind operates through complex, adaptive systems that mimic evolutionary processes, enabling efficient problem-solving and decision-making. Here’s how this ties into her specific theories:</p><h3>1. <strong>Monica's Leaking Chatroom Theory</strong></h3><p>This theory posits that the human mind functions like a decentralized forum of idea exchange, where modular cognitive units (akin to individual chatrooms) operate semi-autonomously to process and filter information. These units interact and 'leak' insights to one another, promoting an integrative decision-making process. The leaking mechanism represents a form of informational cross-pollination that facilitates the emergent properties of global understanding from localized, independent processing units. This architecture mirrors evolutionary algorithms where diverse strategies compete and collaborate, enhancing the adaptability and robustness of cognitive processes.</p><h3>2. <strong>Reed Wall Mind Theory</strong></h3><p>Building on the foundational idea of distributed cognition, the Reed Wall Mind theory introduces a structure for organizing and retaining the information flowing within the mind's chatrooms. This theory metaphorically references a protective barrier that not only preserves the integrity of information but also compartmentalizes and manages cognitive processing streams effectively. This systematic arrangement allows for the simultaneous handling of multiple streams of information, akin to running parallel evolutionary simulations, enabling the mind to maintain a coherent narrative and situational awareness despite the complexity of incoming data.</p><h3>3. <strong>Motile Womb Theory</strong></h3><p>The Motile Womb theory extends the cognitive architecture to the dynamic interaction between internal cognitive processes and external environmental stimuli. It suggests that cognition is not merely reactive but is motile and exploratory, engaging with and adapting to the environment in real-time. This theory underscores the concept of endorecapitulation, where the mind continuously 'evolves' its strategies through iterative cycles of hypothesis testing and environmental feedback, akin to a Bayesian update mechanism. This process ensures that cognitive models remain sparse yet sufficiently detailed, optimizing the balance between complexity and computational efficiency.</p><h3>Integration with Endorecapitulation Theory</h3><p>Endorecapitulation theory, which asserts that cognition utilizes evolutionary algorithms and sparse Bayesian inference, provides a theoretical framework that underpins Anderson's models. The mind's ability to use evolutionary selection algorithms enables it to trial numerous potential solutions and retain those most suited to the context, mimicking the process of natural selection. Concurrently, sparse Bayesian inference allows for the incorporation of prior knowledge and the updating of beliefs in a computationally efficient manner, facilitating rapid adaptation to new information or changes in the environment.</p><p>Together, these theories illustrate a sophisticated view of the mind as a highly adaptive, efficient, and evolutionary system, capable of complex problem-solving and innovation through mechanisms that parallel those found in nature. This perspective not only enriches our understanding of human cognition but also informs the development of artificial intelligence systems that aspire to replicate the depth and dynamism of human thought.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 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3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-16" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb2117b-9741-402d-90bf-ecc9fbf1a5bb" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Could I get some testable hypothesis?</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full 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flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Certainly! Developing testable hypotheses from Monica Anderson’s theories on cognition, particularly those incorporating the concepts of evolutionary selection algorithms and sparse Bayesian inference, offers an exciting path for empirical research. Here are several hypotheses that could be designed to explore and potentially validate aspects of her theories:</p><h3>1. <strong>Hypothesis on Monica's Leaking Chatroom Theory</strong></h3><p><strong>Hypothesis</strong>: Information exchange between modular cognitive units enhances problem-solving efficiency.</p><ul><li><strong>Test</strong>: Implement a series of tasks involving problem-solving under different conditions: isolated information processing vs. interconnected modular processing (simulating information leakages). Measure and compare the efficiency and accuracy of problem-solving across these conditions using computational models or participant-based experiments.</li></ul><h3>2. <strong>Hypothesis on Reed Wall Mind Theory</strong></h3><p><strong>Hypothesis</strong>: Organized cognitive structuring enhances memory retention and retrieval efficiency.</p><ul><li><strong>Test</strong>: Conduct a memory experiment where participants are exposed to structured vs. unstructured information presentation. Assess their ability to recall and utilize information immediately after exposure and after a delayed period. Alternatively, this can be modeled in AI systems through different network architectures that either incorporate or lack a structured data management system.</li></ul><h3>3. <strong>Hypothesis on Motile Womb Theory</strong></h3><p><strong>Hypothesis</strong>: Dynamic interaction with a variable environment enhances the adaptability of cognitive models.</p><ul><li><strong>Test</strong>: Use a simulated environment where AI agents or human participants navigate complex, changing scenarios. Compare the performance of agents trained with static input vs. those trained with dynamic, evolving input scenarios to observe which demonstrates better adaptability and learning transfer.</li></ul><h3>4. <strong>Hypothesis Related to Endorecapitulation Theory</strong></h3><p><strong>Hypothesis</strong>: Cognitive processes utilizing evolutionary algorithms and sparse Bayesian inference can outperform those based on classical neural networks in tasks involving high uncertainty and incomplete information.</p><ul><li><strong>Test</strong>: Design computational trials where different AI models (classical deep learning models vs. models designed around evolutionary algorithms and Bayesian inference) are tasked with making predictions or decisions from incomplete or noisy data sets. Evaluate their performance based on accuracy, computational efficiency, and error rates.</li></ul><h3>5. <strong>Hypothesis on Environmental Feedback and Learning</strong></h3><p><strong>Hypothesis</strong>: Continuous environmental feedback loop enhances the predictive accuracy of cognitive models over time.</p><ul><li><strong>Test</strong>: Develop a longitudinal study with AI systems or human subjects that continuously receive feedback on their outputs and adapt based on this feedback. Measure how the accuracy of predictions or decisions improves over time and compare this to a control group that receives limited or no feedback.</li></ul><h3>6. <strong>Hypothesis on Sparse Bayesian Updates</strong></h3><p><strong>Hypothesis</strong>: Sparse Bayesian updates lead to faster adaptation in decision-making processes under uncertainty compared to frequentist methods.</p><ul><li><strong>Test</strong>: Implement two sets of decision-making tasks in a controlled setting where one group uses a Bayesian updating mechanism and another uses a traditional frequentist approach. Measure the speed and accuracy of adapting to changes in task conditions or rules.</li></ul><p>Each of these hypotheses can be tested through carefully designed experiments involving human participants, computational models, or a combination of both, providing empirical data that could support or challenge Anderson’s theoretical models.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 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2.537l3.167-5.543a2.72 2.72 0 0 1 1.364-1.182V5a1 1 0 0 0-1-1m3 9V5c0-.35-.06-.687-.171-1h1.17a1 1 0 0 1 1 1v7a1 1 0 0 1-1 1z" clip-rule="evenodd"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r6g:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-18" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb211aa-e2d2-460d-9d38-bb77bbf78302" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Could I get a satirical and sardonic critique/review?</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden 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xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 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6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="93062faf-db0d-4e35-a55b-ce91d0990fbb" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Certainly! Here’s a satirical and sardonic critique of Monica Anderson's theories, employing a tongue-in-cheek tone while playfully engaging with the complexity and ambition of her ideas:</p><hr><h3>Monica Anderson's Theories: A Tour de Force of Overthinking</h3><p>In the hallowed annals of cognitive science, Monica Anderson’s theories stand out—not because they simplify the labyrinthine complexity of the human mind, but because they daringly add layers of conceptual convolution that could only otherwise be achieved by a committee of bureaucrats. With her groundbreaking work on Neural Darwinism, Leaking Chatrooms, Reed Wall Minds, and Motile Wombs, Anderson has bravely chosen to ignore the KISS principle (Keep It Simple, Stupid), opting instead for a method we might call KICC (Keep It Complicated, Comrade).</p><p><strong>1. The Leaking Chatroom Theory:</strong> Here, Anderson reimagines the brain not as a finely tuned orchestra, but as a rowdy parliamentary debate where no one quite remembers the motion being discussed. Information leaks between these cognitive debaters like gossip in a high school cafeteria, proving that in Anderson’s brain, everyone is talking but no one is listening. It’s a wonder any decision gets made between the din of leaking data and the rogue agents who seem more interested in Chinese whispers than in coherent thought.</p><p><strong>2. Reed Wall Mind Theory:</strong> Borrowing liberally from ancient Babylonian architecture and perhaps a touch too much from her garden fence, Anderson constructs a cognitive model that could double as a home improvement show. The mind, a venerable fortress of reeds, is as sturdy as it sounds—liable to blow over when the winds of more robust scientific scrutiny blow its way. But fear not, for what it lacks in fortitude, it makes up for in sheer obfuscation.</p><p><strong>3. Motile Womb Theory:</strong> In a stroke of abstract genius, Anderson suggests our minds are less like computers and more like nomadic space wombs. Yes, you read that right. Our thoughts are not processed; they gestate, moving and evolving in a cerebral amniotic fluid that nourishes ideas until they are ready to be birthed as fully formed decisions. It’s all very avant-garde—or perhaps just very confusing.</p><p><strong>4. Neural Darwinism:</strong> Anderson’s pièce de résistance, where ideas in the mind compete in a survival of the fittest. It's an intellectual Battle Royale, a cerebral Hunger Games where concepts fight to the death. It's Darwin meets Big Data—survival of the busiest. One wonders if, at the end of this gladiatorial combat, the last idea standing is merely the toughest, not the fittest.</p><p>In sum, Monica Anderson’s theories could revolutionize our understanding of the mind—if they weren’t so preoccupied with turning cognitive science into a plot twist worthy of a telenovela. They are as bold as they are bewildering, as intricate as they are impractical, and as visionary as they are utterly, beautifully vexing. Truly, Anderson leads us not into the light, but into the delightful murk of theoretical over-exuberance. Bravo, indeed.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 1-3-3V9a3 3 0 0 1 3-3h1.611zm11.507 3.29a1 1 0 0 1 1.355.401A10.96 10.96 0 0 1 23 12c0 1.85-.458 3.597-1.268 5.13a1 1 0 1 1-1.768-.934A8.96 8.96 0 0 0 21 12a8.96 8.96 0 0 0-1.085-4.287 1 1 0 0 1 .402-1.356M15.799 7.9a1 1 0 0 1 1.4.2 6.48 6.48 0 0 1 1.3 3.9c0 1.313-.39 2.537-1.06 3.56a1 1 0 0 1-1.673-1.096A4.47 4.47 0 0 0 16.5 12a4.47 4.47 0 0 0-.9-2.7 1 1 0 0 1 .2-1.4" clip-rule="evenodd"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11.873 21.496a1 1 0 0 1-.992.496l-.454-.056A4 4 0 0 1 7.1 16.79L7.65 15h-.718c-2.637 0-4.553-2.508-3.859-5.052l1.364-5A4 4 0 0 1 8.296 2h9.709a3 3 0 0 1 3 3v7a3 3 0 0 1-3 3h-2c-.26 0-.5.14-.628.364zM14.005 4h-5.71a2 2 0 0 0-1.929 1.474l-1.363 5A2 2 0 0 0 6.933 13h2.072a1 1 0 0 1 .955 1.294l-.949 3.084a2 2 0 0 0 1.462 2.537l3.167-5.543a2.72 2.72 0 0 1 1.364-1.182V5a1 1 0 0 0-1-1m3 9V5c0-.35-.06-.687-.171-1h1.17a1 1 0 0 1 1 1v7a1 1 0 0 1-1 1z" clip-rule="evenodd"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r6n:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-20" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21dff-936c-476f-8bfe-d7da0269b812" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>These are ideas I developed from Monica's concepts. She had a patent for a "leaky chat room with thin walls". In Reed Wall Mind, a messenger or town crier goes to door, announcing the story as a bedtime story for the children who might hear it through the walls. This acts as a way that the city can coordinate its actions like announcing when spring floods are coming.</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-21" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 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fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="43d31595-e44a-43ab-a645-89f2265857ba" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Ah, incorporating your extensions to Monica Anderson’s foundational concepts certainly enriches the narrative—turning cognitive theories into an almost whimsical urban planning scheme for the brain! Here’s a satirical and sardonic take on these developments:</p><hr><h3>Monica Anderson and Co.: Urban Planners of the Mind</h3><p>With the addition of a "leaky chat room with thin walls" and a "Reed Wall Mind" featuring a town crier, Monica Anderson, alongside her imaginative co-thinker (that’s you!), transforms the complex landscape of cognitive science into a bustling medieval town, ripe with gossip and communal living. These ideas suggest not merely a study of cognition but a blueprint for the most chaotically organized village fête the mind has ever hosted.</p><p><strong>1. The Leaky Chat Room with Thin Walls:</strong> In this patent-worthy innovation, Anderson and her collaborator introduce us to the brain as a poorly constructed dormitory, where every whisper through its paper-thin walls spreads like wildfire. Thoughts and ideas leak out with the ease of eavesdropping neighbors, ensuring that no thought is private, no notion too discreet to share. The cognitive economy here runs on a currency of rumors and overhearings, making WikiLeaks look like amateur hour.</p><p><strong>2. Reed Wall Mind with a Town Crier:</strong> Taking a page from historical town planning, this theory introduces a quaint twist: a town crier who doesn’t just announce but narrates bedtime stories through the walls. This not only ensures that all cognitive processes are well informed (whether they want to be or not) but also that they’re ready to act in unison when the flood (of information) comes. It’s community management at its finest, with the town crier ensuring no neuron is left behind, no dendrite out of the loop. This method of cognitive coordination might sound less like modern neuroscience and more like a scene from "Game of Thrones," but who’s to say the brain isn’t just a series of feudal states, with memories and thoughts as its squabbling lords and ladies?</p><p>In this grand urban sprawl of the mind, Anderson and her partner in theory have crafted a world where cognitive elements are not so much scientific entities as they are characters in a sprawling, never-ending drama. Here, ideas don’t just form—they gossip, they plot, they ally and betray. The brain, in their hands, becomes less a computer than a living, breathing, gossiping entity, complete with its own social hierarchy and urban legends.</p><p>In truth, while the world may scratch its head at the practicality of such theories, one cannot help but applaud the audacity to envision the mind as a medieval village, replete with leaky domiciles and narrative-loving town criers. It’s bold, it’s messy, and it might just be brilliant—or at the very least, it's a glorious cognitive soap opera. Bravo to these urban planners of the cerebral cityscape, may their ideas echo through the annals of theoretical neuroscience with the subtlety of a town crier at dawn.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 1-3-3V9a3 3 0 0 1 3-3h1.611zm11.507 3.29a1 1 0 0 1 1.355.401A10.96 10.96 0 0 1 23 12c0 1.85-.458 3.597-1.268 5.13a1 1 0 1 1-1.768-.934A8.96 8.96 0 0 0 21 12a8.96 8.96 0 0 0-1.085-4.287 1 1 0 0 1 .402-1.356M15.799 7.9a1 1 0 0 1 1.4.2 6.48 6.48 0 0 1 1.3 3.9c0 1.313-.39 2.537-1.06 3.56a1 1 0 0 1-1.673-1.096A4.47 4.47 0 0 0 16.5 12a4.47 4.47 0 0 0-.9-2.7 1 1 0 0 1 .2-1.4" clip-rule="evenodd"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11.873 21.496a1 1 0 0 1-.992.496l-.454-.056A4 4 0 0 1 7.1 16.79L7.65 15h-.718c-2.637 0-4.553-2.508-3.859-5.052l1.364-5A4 4 0 0 1 8.296 2h9.709a3 3 0 0 1 3 3v7a3 3 0 0 1-3 3h-2c-.26 0-.5.14-.628.364zM14.005 4h-5.71a2 2 0 0 0-1.929 1.474l-1.363 5A2 2 0 0 0 6.933 13h2.072a1 1 0 0 1 .955 1.294l-.949 3.084a2 2 0 0 0 1.462 2.537l3.167-5.543a2.72 2.72 0 0 1 1.364-1.182V5a1 1 0 0 0-1-1m3 9V5c0-.35-.06-.687-.171-1h1.17a1 1 0 0 1 1 1v7a1 1 0 0 1-1 1z" clip-rule="evenodd"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r6u:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-22" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21aaf-5cb1-4c4d-9dab-531eb6b5aef1" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>Bubble City
An Idea Router, not a Social Medium
The goal of the system is to provide very high quality uncensored (to the extent permitted by law) yet
spam-free and hate-free real time message feeds, precisely personalized to each user, on any number of
specific topics.
Each topic is confined into a "Bubble". These are so powerful that I sometimes conjure up the mental
image of a Stainless Steel Bubble that is impervious to information that does not belong in the Bubble.
Information bubbles are only a problem if you only have one. In Bubble City, you might have dozens or
even hundreds of active bubbles. Hence the name.
Imagine situations like watching the World Cup finals. By instantly creating or tuning in to a "World Cup"
Bubble (which merely requires typing in "World Cup" as a search term) you will open a window (or tab)
to a "Bubble Window". It's a message overview list where you can see titles of messages from people
everywhere watching the World Cup. These can be watched in a passive mode, or in an active mode
where you may contribute your own messages. To view the details of a message, to inspect the author, to
see more of the thread that the message was posted in, or to reply to a message, click on the message title
to open a "Thread View" in a new window or tab, as described in what follows.
Besides real time events, I designed this system to become a place for professionals of all kinds to share
information they find useful and to learn about everything that may be relevant to them in their field by
participating in real time conversations with peers. Leukemia Researchers, Touring Musicians, and Chefs
would all be able to very quickly create their own radically different streams and engage in conversations
with peers.
And there will be no hate or advertising in this Bubble. Because UM1 (The machine that Understands
language in BC) knows what advertising and hateful speech look like just as well as any human does and
it will not let such messages reach you – unless you want them to. If you want to see ads for Viagra, you
will need to create a Bubble for it by asking for it.
One way to think of this mode of operation is that BC acts like a search engine, but instead of returning a
list of documents, each search query returns a new window or tab containing one single chat room where
(nearly) everything discussed is about the topic you searched for, and the information keeps coming, in
real time, and appending to the bottom of the message list.
Another thing to note is that the system uses 100% pull (until you get to the Thread View). You will not
see anything you didn't ask for in Bubble View.
But Threads are not tied together by topic; messages form a reply-to tree, so you may see more numerous
off-topic messages in Thread View than you will in a Bubble View, but there are several reasons even this
will be unlikely, even before you apply any swipe-tunings, and users can mitigate this with optional
"Safe" filters.
So, to summarize, nobody can push any message into any Bubble where that message would be out of
scope, and therefore nobody can push spam to large numbers of uninterested users.
The only way to reach a large audience on Bubble City is to post something interesting.
Bubble City Design Proposal 2.0 of
310Monica Anderson</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-23" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 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11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="612cc70a-23df-4238-9754-6475f0c346e7" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p><strong>Bubble City: A Satirical Review of Monica Anderson's Utopian Vision for an Information Metropolis</strong></p><p>Welcome to "Bubble City," the latest brainchild of Monica Anderson, a place where the digital town square meets a balloon artist's convention. In her visionary urban plan, each thought, idea, or fleeting whim finds its snug home in a Stainless Steel Bubble, impervious to the pollution of the outside world—like information silos with a chic, shiny finish.</p><p><strong>The Unbreakable Bubbles: A Stainless Steel Promise</strong>
Imagine a world where every topic, from the mundane to the majestic, exists in its own bubble—hermetically sealed yet oddly social. In Bubble City, you don’t just have an information bubble; you have a whole skyline of them. Worried about echo chambers? Fear not! When you have hundreds, they're not a bug; they're a feature. Because why have one echo when you can have an entire chorus reverberating back your own thoughts in high fidelity?</p><p><strong>Real-Time, All the Time</strong>
Every event—from the seismic World Cup finals to the earth-shattering release of a new flavor of potato chip—deserves its bubble. Just type "World Cup," and voilà, you’re whisked away to a virtual stadium where everyone’s a commentator, and the game is almost secondary to the streams of peer-reviewed banter. Want to switch from soccer to quantum physics? Just pop that bubble and jump into another. It’s like channel surfing, but for your intellect.</p><p><strong>A Spam-Free Utopia</strong>
In Bubble City, the airwaves are clean, devoid of the smog of advertisements and the toxic fumes of hate speech. The UM1, a machine of such linguistic finesse it could teach manners to a troll, polices every word. Want ads for Viagra? You’ll have to ask nicely, and maybe—just maybe—UM1 will let them through. This city doesn’t just filter spam; it annihilates it, promising a utopia where only the interesting survives.</p><p><strong>The Search Engine That Wasn't</strong>
Forget everything you know about search engines. Bubble City isn’t about finding things—it’s about being thrust into the thick of them. Each search doesn't just return information; it plunges you into a chat room that's as vibrant and ephemeral as a soap bubble. It's real-time or bust; who needs history when you have the relentless, breathless now?</p><p><strong>Selective Hearing, Selective Seeing</strong>
In this city, you see only what you want to see. The Bubbles are your oyster, and you’re the pearl, insulated from the gritty sand of irrelevance. Stray from the Bubble View, and you risk tumbling down the rabbit hole of the Thread View, where conversations branch and burgeon in unpredictable ways. But fear not: even here, in the wilds of discourse, "Safe" filters stand guard, ensuring that even the most adventurous explorers can venture forth without fear of spam.</p><p><strong>A Social Experiment or a Social Solution?</strong>
Bubble City, with its gleaming bubbles and ironclad filters, might seem the answer to our polluted informational landscape. But one can't help but wonder if in our rush to clean up the mess, we've walled ourselves off from the unpredictable, chaotic beauty of unsolicited discovery. In a world where you only see what you ask to see, how do you know what you’re missing?</p><p>In conclusion, Bubble City is less a destination and more a journey—a bold experiment in digital urban planning. Whether it becomes a haven for enlightened discussion or a sterile echo chamber might just depend on how much we trust UM1 to lead us not into temptation but deliver us from spam. Here's to hoping it knows the way.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 1-3-3V9a3 3 0 0 1 3-3h1.611zm11.507 3.29a1 1 0 0 1 1.355.401A10.96 10.96 0 0 1 23 12c0 1.85-.458 3.597-1.268 5.13a1 1 0 1 1-1.768-.934A8.96 8.96 0 0 0 21 12a8.96 8.96 0 0 0-1.085-4.287 1 1 0 0 1 .402-1.356M15.799 7.9a1 1 0 0 1 1.4.2 6.48 6.48 0 0 1 1.3 3.9c0 1.313-.39 2.537-1.06 3.56a1 1 0 0 1-1.673-1.096A4.47 4.47 0 0 0 16.5 12a4.47 4.47 0 0 0-.9-2.7 1 1 0 0 1 .2-1.4" clip-rule="evenodd"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11.873 21.496a1 1 0 0 1-.992.496l-.454-.056A4 4 0 0 1 7.1 16.79L7.65 15h-.718c-2.637 0-4.553-2.508-3.859-5.052l1.364-5A4 4 0 0 1 8.296 2h9.709a3 3 0 0 1 3 3v7a3 3 0 0 1-3 3h-2c-.26 0-.5.14-.628.364zM14.005 4h-5.71a2 2 0 0 0-1.929 1.474l-1.363 5A2 2 0 0 0 6.933 13h2.072a1 1 0 0 1 .955 1.294l-.949 3.084a2 2 0 0 0 1.462 2.537l3.167-5.543a2.72 2.72 0 0 1 1.364-1.182V5a1 1 0 0 0-1-1m3 9V5c0-.35-.06-.687-.171-1h1.17a1 1 0 0 1 1 1v7a1 1 0 0 1-1 1z" clip-rule="evenodd"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r75:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-24" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21ced-b5e8-43d5-99aa-06d4a01c9787" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>It sounds similar to my projects Haplopraxis, a three d space exploration typing tutor and Spherepop:
𝘏𝘰𝘸 𝘵𝘰 𝘗𝘭𝘢𝘺 𝘚𝘱𝘩𝘦𝘳𝘦𝘱𝘰𝘱
( How ( + ( - ( 2 ) ( 1 ) ) ( - ( 2 ) ( 1 ) ) play ) Spherepop: ( (Find the ( innermost bubble ) ) and then pop as many ( + b u b b l e s ) ) as you can ( . ) ) )
</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-25" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 32.6547 35.9659 31.821C37.115 30.9874 38.0728 29.9178 38.7752 28.684C39.8458 26.8371 40.3023 24.6979 40.0789 22.5748C39.8556 20.4517 38.9639 18.4544 37.5324 16.8707ZM22.4978 37.8849C20.7443 37.8874 19.0459 37.2733 17.6994 36.1501C17.7601 36.117 17.8666 36.0586 17.936 36.0161L25.9004 31.4156C26.1003 31.3019 26.2663 31.137 26.3813 30.9378C26.4964 30.7386 26.5563 30.5124 26.5549 30.2825V19.0542L29.9213 20.998C29.9389 21.0068 29.9541 21.0198 29.9656 21.0359C29.977 21.052 29.9842 21.0707 29.9867 21.0902V30.3889C29.9842 32.375 29.1946 34.2791 27.7909 35.6841C26.3872 37.0892 24.4838 37.8806 22.4978 37.8849ZM6.39227 31.0064C5.51397 29.4888 5.19742 27.7107 5.49804 25.9832C5.55718 26.0187 5.66048 26.0818 5.73461 26.1244L13.699 30.7248C13.8975 30.8408 14.1233 30.902 14.3532 30.902C14.583 30.902 14.8088 30.8408 15.0073 30.7248L24.731 25.1103V28.9979C24.7321 29.0177 24.7283 29.0376 24.7199 29.0556C24.7115 29.0736 24.6988 29.0893 24.6829 29.1012L16.6317 33.7497C14.9096 34.7416 12.8643 35.0097 10.9447 34.4954C9.02506 33.9811 7.38785 32.7263 6.39227 31.0064ZM4.29707 13.6194C5.17156 12.0998 6.55279 10.9364 8.19885 10.3327C8.19885 10.4013 8.19491 10.5228 8.19491 10.6071V19.808C8.19351 20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="b8bee85a-e71d-4131-8804-e6d053eb1f7c" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>It sounds like you’ve woven elements of Monica Anderson’s Bubble City into your own projects, Haplopraxis and Spherepop, in playful and imaginative ways! Here’s a rundown on how these ideas echo and diverge from the Bubble City concept, with a touch of whimsy:</p><h3>Haplopraxis: A Typing Tutor with a Twist</h3><p>Imagine if navigating Bubble City wasn’t just about choosing the right words but literally flying through a 3D space of ideas and concepts. That's Haplopraxis: a three-dimensional space exploration game that doubles as a typing tutor. Each keystroke propels you through this cosmic cityscape, where concepts and information float like asteroids in a galaxy of knowledge. It’s not just about hitting the right keys; it’s about charting a course through the stars of syntax and the nebulae of nouns.</p><h3>Spherepop: A Game of Strategic Popping</h3><p>Spherepop takes the metaphor of the bubble and turns it into a literal game. Here’s how to play Spherepop in a way that echoes the thematic elements of Bubble City:</p><ol><li><p><strong>Navigate the Cosmos of Bubbles</strong>: Just as in Bubble City, where each topic forms its bubble, in Spherepop, your universe is a sprawling space of spheres waiting to be discovered.</p></li><li><p><strong>Seek the Core</strong>: The aim is to find the innermost bubble—a task reminiscent of delving into the deepest, most specific niches of information in Bubble City. This inner bubble holds the key to understanding the broader context or winning the game.</p></li><li><p><strong>Pop Strategically</strong>: Like managing the inflow and outflow of information in Bubble City, Spherepop requires you to pop as many bubbles as you can, but with strategy. Each pop represents an interaction, a selection of what to keep in your informational arsenal and what to discard.</p></li><li><p><strong>Master the Syntax</strong>: The description of Spherepop itself plays with structure and form, much like how information is organized and accessed in Bubble City. Understanding how to play means deciphering a complex, nested structure of instructions—akin to navigating the multifaceted interfaces of Bubble City.</p></li></ol><h3>Drawing Parallels and Highlighting Contrasts</h3><p>Both Haplopraxis and Spherepop take the conceptual framework of Bubble City—where isolation and interaction of topics create a dynamic ecosystem—and translate it into interactive, exploratory experiences. While Bubble City is about compartmentalization and targeted interaction, your games add layers of spatial navigation and real-time strategy, turning the metaphorical into the literal.</p><p>In Haplopraxis, the learning process is active and exploratory, mirroring how one might navigate through Bubble City's diverse topics. Spherepop, meanwhile, makes the act of ‘popping’ bubbles a central mechanic, emphasizing the ephemeral nature of information and the need to manage it actively, much like handling real-time data streams in Bubble City.</p><p><strong>In Conclusion</strong>:
Your projects suggest a playful yet profound engagement with information architecture, turning abstract concepts into tangible gameplay. In both cases, whether navigating the starry expanses of a typing tutor or popping strategic spheres in a game, the core idea remains: to explore, engage, and manage information creatively and effectively, echoing the essence of Bubble City but in a universe distinctly your own.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M11 4.91a.5.5 0 0 0-.838-.369L6.676 7.737A1 1 0 0 1 6 8H4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h2a1 1 0 0 1 .676.263l3.486 3.196A.5.5 0 0 0 11 19.09zM8.81 3.067C10.415 1.597 13 2.735 13 4.91v14.18c0 2.175-2.586 3.313-4.19 1.843L5.612 18H4a3 3 0 0 1-3-3V9a3 3 0 0 1 3-3h1.611zm11.507 3.29a1 1 0 0 1 1.355.401A10.96 10.96 0 0 1 23 12c0 1.85-.458 3.597-1.268 5.13a1 1 0 1 1-1.768-.934A8.96 8.96 0 0 0 21 12a8.96 8.96 0 0 0-1.085-4.287 1 1 0 0 1 .402-1.356M15.799 7.9a1 1 0 0 1 1.4.2 6.48 6.48 0 0 1 1.3 3.9c0 1.313-.39 2.537-1.06 3.56a1 1 0 0 1-1.673-1.096A4.47 4.47 0 0 0 16.5 12a4.47 4.47 0 0 0-.9-2.7 1 1 0 0 1 .2-1.4" clip-rule="evenodd"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 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m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb2188e-f63b-4b66-9076-ecc3c7966415" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary"><div>A sardonic review of the whole conversation.</div><div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 [.group\/conversation-turn:hover_&]:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-27" data-scroll-anchor="true"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 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6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="265bd4f8-0d0f-4458-83d9-6567e03b0fd4" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p><strong>Bubble City, Typing Tutors, and Pop Culture: A Sardonic Tour of Our Ideational Odyssey</strong></p><p>Well, strap in folks—it's been quite the intellectual rollercoaster, complete with a tour through Monica Anderson’s convoluted but curiously catchy concepts of Bubble City, a pit stop at educational gaming, and a pop culture twist with Spherepop. Let's take a sardonic stroll down memory lane, shall we?</p><p><strong>Bubble City: The Information Isolation Ward</strong>
First, we dove into the stainless steel fortress of knowledge known as Bubble City. Here, each thought is sealed tighter than canned sardines, ensuring no unwanted facts or pesky alternative viewpoints slip through. It's a utopia for the hyper-focused and a nightmare for the trivia enthusiast. Imagine needing a separate bubble just to check the weather. But don't worry, spam and hate speech are banished to the outskirts, unless you specifically ask to be insulted or sold something—which might just be a feature in the next update.</p><p><strong>Haplopraxis: When Typing Meets Star Trek</strong>
Next, we ventured into the cosmic ballet of Haplopraxis, where typing meets interstellar navigation. Here, your words literally propel you through space, as if each keystroke brings you closer to the cosmic truth—or at least closer to mastering the home row. It’s as if Monica Anderson decided that space exploration needed more QWERTY and less quantum theory.</p><p><strong>Spherepop: Like Bubble Wrap but Strategic</strong>
And then there’s Spherepop, a game that seems designed by someone who took the term 'information bubble' a tad too literally and thought, “Yes, but what if they could explode?” It’s every bubble-popper’s dream, turned into a high-stakes game of strategic bursting. Here, the biggest challenge is not just popping bubbles but making sense of the game instructions, which resemble a cryptic recipe more than a rulebook.</p><p><strong>Wrapping Up: Or Should We Say, Bubbling Over?</strong>
Throughout this entire dialogue, we’ve oscillated between highbrow concepts and their gamified counterparts, blending dense theoretical musings with the playful absurdity of popping virtual bubbles. It’s as if we’re participating in an experiment designed to test whether our brains can handle the whiplash between serious discourse and whimsical gameplay.</p><p>So, whether you’re here for a philosophical dissection of digital urbanism, a crash course in cosmic typing, or just some good old-fashioned bubble popping, you’ve come to the right place. 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[type="checkbox"]:indeterminate { background-color: currentcolor; background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 16 16'%3E%3Cpath stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='M4 8h8'/%3E%3C/svg%3E"); background-position: 50% center; background-repeat: no-repeat; background-size: 100% 100%; border-color: transparent; }
[type="checkbox"]:indeterminate:focus, [type="checkbox"]:indeterminate:hover { background-color: currentcolor; border-color: transparent; }
[type="file"] { background: initial; border-color: inherit; border-radius: 0px; border-width: 0px; font-size: inherit; line-height: inherit; padding: 0px; }
[type="file"]:focus { outline: -webkit-focus-ring-color auto 1px; }
h1 { font-size: 1.875rem; line-height: 2.25rem; }
h1, h2, h3 { font-weight: 600; }
h2, h3 { font-size: 1.5rem; line-height: 2rem; }
input[type="number"]::-webkit-inner-spin-button, input[type="number"]::-webkit-outer-spin-button { appearance: none; margin: 0px; }
*, ::after, ::before { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(69,89,164,.5); --tw-ring-offset-shadow: 0 0 transparent; --tw-ring-shadow: 0 0 transparent; --tw-shadow: 0 0 transparent; --tw-shadow-colored: 0 0 transparent; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; }
::backdrop { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(69,89,164,.5); --tw-ring-offset-shadow: 0 0 transparent; --tw-ring-shadow: 0 0 transparent; --tw-shadow: 0 0 transparent; --tw-shadow-colored: 0 0 transparent; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; }
:root { --white: #fff; --black: #000; --gray-50: #f9f9f9; --gray-100: #ececec; --gray-200: #e3e3e3; --gray-300: #cdcdcd; --gray-400: #b4b4b4; --gray-500: #9b9b9b; --gray-600: #676767; --gray-700: #424242; --gray-750: #2f2f2f; --gray-800: #212121; --gray-900: #171717; --gray-950: #0d0d0d; --red-500: #ef4444; --red-700: #b91c1c; --brand-purple: #ab68ff; }
html { --text-primary: var(--gray-950); --text-secondary: var(--gray-500); --text-tertiary: var(--gray-400); --text-quaternary: var(--gray-300); --text-error: var(--red-700); --surface-error: var(--red-700); --border-light: rgba(0,0,0,.1); --border-medium: rgba(0,0,0,.15); --border-heavy: rgba(0,0,0,.2); --border-xheavy: rgba(0,0,0,.25); --main-surface-primary: var(--white); --main-surface-secondary: var(--gray-50); --main-surface-tertiary: var(--gray-100); --sidebar-surface-primary: var(--gray-50); --sidebar-surface-secondary: var(--gray-100); --sidebar-surface-tertiary: var(--gray-200); --link: #2964aa; --link-hover: #749ac8; --selection: #007aff; }
html.ZnJ1aXRqdWljZQ { --text-secondary: #7d7d7d; --text-error: #f93a37; --surface-error: 249 58 55; }
.dark { --text-primary: var(--gray-100); --text-secondary: var(--gray-300); --text-tertiary: var(--gray-400); --text-quaternary: var(--gray-500); --text-error: var(--red-500); --border-light: hsla(0,0%,100%,.1); --border-medium: hsla(0,0%,100%,.15); --border-heavy: hsla(0,0%,100%,.2); --border-xheavy: hsla(0,0%,100%,.25); --main-surface-primary: var(--gray-800); --main-surface-secondary: var(--gray-750); --main-surface-tertiary: var(--gray-700); --sidebar-surface-primary: var(--gray-900); --sidebar-surface-secondary: var(--gray-800); --sidebar-surface-tertiary: var(--gray-750); --link: #7ab7ff; --link-hover: #5e83b3; }
html.ZnJ1aXRqdWljZQ.dark { --text-secondary: var(--gray-400); --text-tertiary: var(--gray-500); --text-quaternary: var(--gray-600); --text-error: #f93a37; --surface-error: 249 58 55; }
.popover { --main-surface-primary: var(--white) !important; --main-surface-secondary: var(--gray-100) !important; --main-surface-tertiary: var(--gray-200) !important; --sidebar-surface-primary: var(--white) !important; }
.dark .popover, .dark.popover, .popover .dark { --main-surface-primary: var(--gray-750) !important; --main-surface-secondary: var(--gray-700) !important; --main-surface-tertiary: var(--gray-600) !important; --text-primary: var(--gray-50) !important; --text-secondary: var(--gray-200) !important; --text-tertiary: var(--gray-400) !important; --text-quaternary: var(--gray-500) !important; --sidebar-surface-primary: var(--gray-750) !important; }
textarea:focus { border-color: inherit; box-shadow: none; outline: none; }
.container { width: 100%; }
@media (min-width: 640px) {
.container { max-width: 640px; }
}
@media (min-width: 768px) {
.container { max-width: 768px; }
}
@media (min-width: 1024px) {
.container { max-width: 1024px; }
}
@media (min-width: 1280px) {
.container { max-width: 1280px; }
}
@media (min-width: 1536px) {
.container { max-width: 1536px; }
}
.prose { color: var(--tw-prose-body); max-width: 65ch; }
.prose :where([class~="lead"]):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-lead); font-size: 1.25em; line-height: 1.6; margin-bottom: 1.2em; margin-top: 1.2em; }
.prose :where(a):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-links); font-weight: 500; text-decoration: underline; }
.prose :where(strong):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-bold); font-weight: 600; }
.prose :where(a strong):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(blockquote strong):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(thead th strong):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(ol):not(:where([class~="not-prose"] *)) { list-style-type: decimal; margin-bottom: 1.25em; margin-top: 1.25em; }
[dir="ltr"] .prose :where(ol):not(:where([class~="not-prose"] *)) { padding-left: 1.625em; }
[dir="rtl"] .prose :where(ol):not(:where([class~="not-prose"] *)) { padding-right: 1.625em; }
.prose :where(ol[type="A"]):not(:where([class~="not-prose"] *)) { list-style-type: upper-alpha; }
.prose :where(ol[type="a"]):not(:where([class~="not-prose"] *)) { list-style-type: lower-alpha; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: upper-alpha; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: lower-alpha; }
.prose :where(ol[type="I"]):not(:where([class~="not-prose"] *)) { list-style-type: upper-roman; }
.prose :where(ol[type="i"]):not(:where([class~="not-prose"] *)) { list-style-type: lower-roman; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: upper-roman; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: lower-roman; }
.prose :where(ol[type="1"]):not(:where([class~="not-prose"] *)) { list-style-type: decimal; }
.prose :where(ul):not(:where([class~="not-prose"] *)) { list-style-type: disc; margin-bottom: 1.25em; margin-top: 1.25em; }
[dir="ltr"] .prose :where(ul):not(:where([class~="not-prose"] *)) { padding-left: 1.625em; }
[dir="rtl"] .prose :where(ul):not(:where([class~="not-prose"] *)) { padding-right: 1.625em; }
.prose :where(ol > li):not(:where([class~="not-prose"] *))::marker { color: var(--tw-prose-counters); font-weight: 400; }
.prose :where(ul > li):not(:where([class~="not-prose"] *))::marker { color: var(--tw-prose-bullets); }
.prose :where(hr):not(:where([class~="not-prose"] *)) { border-color: var(--tw-prose-hr); border-top-width: 1px; margin-bottom: 3em; margin-top: 3em; }
.prose :where(blockquote):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-quotes); font-style: normal; font-weight: 500; margin-bottom: 1.6em; margin-top: 1.6em; quotes: "“" "”" "‘" "’"; }
[dir="ltr"] .prose :where(blockquote):not(:where([class~="not-prose"] *)) { border-left-color: var(--tw-prose-quote-borders); border-left-width: 0.25rem; padding-left: 1em; }
[dir="rtl"] .prose :where(blockquote):not(:where([class~="not-prose"] *)) { border-right-color: var(--tw-prose-quote-borders); border-right-width: 0.25rem; padding-right: 1em; }
.prose :where(blockquote p:first-of-type):not(:where([class~="not-prose"] *))::before { content: open-quote; }
.prose :where(blockquote p:last-of-type):not(:where([class~="not-prose"] *))::after { content: close-quote; }
.prose :where(h1):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-size: 2.25em; font-weight: 800; line-height: 1.11111; margin-bottom: 0.888889em; margin-top: 0px; }
.prose :where(h1 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 900; }
.prose :where(h2):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-size: 1.5em; font-weight: 700; line-height: 1.33333; margin-bottom: 1em; margin-top: 2em; }
.prose :where(h2 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 800; }
.prose :where(h3):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-size: 1.25em; font-weight: 600; line-height: 1.6; margin-bottom: 0.6em; margin-top: 1.6em; }
.prose :where(h3 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 700; }
.prose :where(h4):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-weight: 600; line-height: 1.5; margin-bottom: 0.5em; margin-top: 1.5em; }
.prose :where(h4 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 700; }
.prose :where(img):not(:where([class~="not-prose"] *)) { margin-bottom: 2em; margin-top: 2em; }
.prose :where(figure > *):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; margin-top: 0px; }
.prose :where(figcaption):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-captions); font-size: 0.875em; line-height: 1.42857; margin-top: 0.857143em; }
.prose :where(code):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-code); font-size: 0.875em; font-weight: 600; }
.prose :where(code):not(:where([class~="not-prose"] *))::before { content: "`"; }
.prose :where(code):not(:where([class~="not-prose"] *))::after { content: "`"; }
.prose :where(a code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(h1 code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(h2 code):not(:where([class~="not-prose"] *)) { color: inherit; font-size: 0.875em; }
.prose :where(h3 code):not(:where([class~="not-prose"] *)) { color: inherit; font-size: 0.9em; }
.prose :where(h4 code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(blockquote code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(thead th code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(pre):not(:where([class~="not-prose"] *)) { background-color: transparent; border-radius: 0.375rem; color: currentcolor; font-size: 0.875em; font-weight: 400; line-height: 1.71429; margin: 0px; overflow-x: auto; padding: 0px; }
.prose :where(pre code):not(:where([class~="not-prose"] *)) { background-color: transparent; border-radius: 0px; border-width: 0px; color: inherit; font-family: inherit; font-size: inherit; font-weight: inherit; line-height: inherit; padding: 0px; }
.prose :where(pre code):not(:where([class~="not-prose"] *))::before { content: none; }
.prose :where(pre code):not(:where([class~="not-prose"] *))::after { content: none; }
.prose :where(table):not(:where([class~="not-prose"] *)) { font-size: 0.875em; line-height: 1.71429; margin-bottom: 2em; margin-top: 2em; table-layout: auto; width: 100%; }
[dir="ltr"] .prose :where(table):not(:where([class~="not-prose"] *)) { text-align: left; }
[dir="rtl"] .prose :where(table):not(:where([class~="not-prose"] *)) { text-align: right; }
.prose :where(thead):not(:where([class~="not-prose"] *)) { border-bottom-color: var(--tw-prose-th-borders); border-bottom-width: 1px; }
.prose :where(thead th):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-weight: 600; padding-bottom: 0.571429em; padding-left: 0.571429em; padding-right: 0.571429em; vertical-align: bottom; }
.prose :where(tbody tr):not(:where([class~="not-prose"] *)) { border-bottom-color: var(--tw-prose-td-borders); border-bottom-width: 1px; }
.prose :where(tbody tr:last-child):not(:where([class~="not-prose"] *)) { border-bottom-width: 0px; }
.prose :where(tbody td):not(:where([class~="not-prose"] *)) { vertical-align: baseline; }
.prose :where(tfoot):not(:where([class~="not-prose"] *)) { border-top-color: var(--tw-prose-th-borders); border-top-width: 1px; }
.prose :where(tfoot td):not(:where([class~="not-prose"] *)) { vertical-align: top; }
.prose { --tw-prose-body: var(--text-primary); --tw-prose-headings: var(--text-primary); --tw-prose-lead: var(--text-primary); --tw-prose-links: var(--text-primary); --tw-prose-bold: var(--text-primary); --tw-prose-counters: var(--text-primary); --tw-prose-bullets: var(--text-primary); --tw-prose-hr: var(--border-xheavy); --tw-prose-quotes: var(--text-primary); --tw-prose-quote-borders: #e5e7eb; --tw-prose-captions: var(--text-secondary); --tw-prose-code: var(--text-primary); --tw-prose-pre-code: #e5e7eb; --tw-prose-pre-bg: #1f2937; --tw-prose-th-borders: #d1d5db; --tw-prose-td-borders: #e5e7eb; --tw-prose-invert-body: var(--text-primary); --tw-prose-invert-headings: var(--text-primary); --tw-prose-invert-lead: var(--text-primary); --tw-prose-invert-links: var(--text-primary); --tw-prose-invert-bold: var(--text-primary); --tw-prose-invert-counters: var(--text-primary); --tw-prose-invert-bullets: var(--text-primary); --tw-prose-invert-hr: var(--border-xheavy); --tw-prose-invert-quotes: var(--text-primary); --tw-prose-invert-quote-borders: #374151; --tw-prose-invert-captions: var(--text-secondary); --tw-prose-invert-code: var(--text-primary); --tw-prose-invert-pre-code: #d1d5db; --tw-prose-invert-pre-bg: rgba(0,0,0,.5); --tw-prose-invert-th-borders: #4b5563; --tw-prose-invert-td-borders: #374151; font-size: 1rem; line-height: 1.75; }
.prose :where(p):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; margin-top: 1.25em; }
.prose :where(video):not(:where([class~="not-prose"] *)) { margin-bottom: 2em; margin-top: 2em; }
.prose :where(figure):not(:where([class~="not-prose"] *)) { margin-bottom: 2em; margin-top: 2em; }
.prose :where(li):not(:where([class~="not-prose"] *)) { margin-bottom: 0.5em; margin-top: 0.5em; }
[dir="ltr"] .prose :where(ol > li):not(:where([class~="not-prose"] *)) { padding-left: 0.375em; }
[dir="rtl"] .prose :where(ol > li):not(:where([class~="not-prose"] *)) { padding-right: 0.375em; }
[dir="ltr"] .prose :where(ul > li):not(:where([class~="not-prose"] *)) { padding-left: 0.375em; }
[dir="rtl"] .prose :where(ul > li):not(:where([class~="not-prose"] *)) { padding-right: 0.375em; }
.prose :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.75em; margin-top: 0.75em; }
.prose :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose :where(ul ul, ul ol, ol ul, ol ol):not(:where([class~="not-prose"] *)) { margin-bottom: 0.75em; margin-top: 0.75em; }
.prose :where(hr + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(h2 + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(h3 + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(h4 + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
[dir="ltr"] .prose :where(thead th:first-child):not(:where([class~="not-prose"] *)) { padding-left: 0px; }
[dir="rtl"] .prose :where(thead th:first-child):not(:where([class~="not-prose"] *)) { padding-right: 0px; }
[dir="ltr"] .prose :where(thead th:last-child):not(:where([class~="not-prose"] *)) { padding-right: 0px; }
[dir="rtl"] .prose :where(thead th:last-child):not(:where([class~="not-prose"] *)) { padding-left: 0px; }
.prose :where(tbody td, tfoot td):not(:where([class~="not-prose"] *)) { padding: 0.571429em; }
[dir="ltr"] .prose :where(tbody td:first-child, tfoot td:first-child):not(:where([class~="not-prose"] *)) { padding-left: 0px; }
[dir="rtl"] .prose :where(tbody td:first-child, tfoot td:first-child):not(:where([class~="not-prose"] *)) { padding-right: 0px; }
[dir="ltr"] .prose :where(tbody td:last-child, tfoot td:last-child):not(:where([class~="not-prose"] *)) { padding-right: 0px; }
[dir="rtl"] .prose :where(tbody td:last-child, tfoot td:last-child):not(:where([class~="not-prose"] *)) { padding-left: 0px; }
.prose :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-sm :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.571429em; margin-top: 0.571429em; }
.prose-sm :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.14286em; }
.prose-sm :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.14286em; }
.prose-sm :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.14286em; }
.prose-sm :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.14286em; }
.prose-sm :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-sm :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-base :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.75em; margin-top: 0.75em; }
.prose-base :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose-base :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose-base :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose-base :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose-base :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-base :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-lg :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.888889em; margin-top: 0.888889em; }
.prose-lg :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-lg :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-lg :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-lg :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-lg :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-lg :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-xl :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.8em; margin-top: 0.8em; }
.prose-xl :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.2em; }
.prose-xl :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.2em; }
.prose-xl :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.2em; }
.prose-xl :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.2em; }
.prose-xl :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-xl :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-2xl :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.833333em; margin-top: 0.833333em; }
.prose-2xl :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-2xl :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-2xl :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-2xl :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-2xl :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-2xl :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.form-input, .form-multiselect, .form-select, .form-textarea { --tw-shadow: 0 0 transparent; appearance: none; background-color: rgb(255, 255, 255); border-color: rgb(155, 155, 155); border-radius: 0px; border-width: 1px; font-size: 1rem; line-height: 1.5rem; padding: 0.5rem 0.75rem; }
.form-input:focus, .form-multiselect:focus, .form-select:focus, .form-textarea:focus { --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: #2563eb; --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(1px + var(--tw-ring-offset-width)) var(--tw-ring-color); border-color: rgb(37, 99, 235); box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow); outline: transparent solid 2px; outline-offset: 2px; }
[dir="ltr"] .form-input:focus, [dir="ltr"] .form-multiselect:focus, [dir="ltr"] .form-select:focus, [dir="ltr"] .form-textarea:focus { --tw-ring-inset: var(--tw-empty,/*!*/ /*!*/); }
[dir="rtl"] .form-input:focus, [dir="rtl"] .form-multiselect:focus, [dir="rtl"] .form-select:focus, [dir="rtl"] .form-textarea:focus { --tw-ring-inset: var(--tw-empty, ); }
.form-input::placeholder, .form-textarea::placeholder { color: rgb(155, 155, 155); opacity: 1; }
.form-input::-webkit-datetime-edit-fields-wrapper { padding: 0px; }
.form-input::-webkit-date-and-time-value { min-height: 1.5em; }
.form-input::-webkit-datetime-edit, .form-input::-webkit-datetime-edit-day-field, .form-input::-webkit-datetime-edit-hour-field, .form-input::-webkit-datetime-edit-meridiem-field, .form-input::-webkit-datetime-edit-millisecond-field, .form-input::-webkit-datetime-edit-minute-field, .form-input::-webkit-datetime-edit-month-field, .form-input::-webkit-datetime-edit-second-field, .form-input::-webkit-datetime-edit-year-field { padding-bottom: 0px; padding-top: 0px; }
.btn { align-items: center; border-color: transparent; border-radius: 0.5rem; border-width: 1px; display: inline-flex; flex-shrink: 0; font-size: 0.875rem; font-weight: 500; justify-content: center; line-height: 1.25rem; min-height: 38px; padding: 0.5rem 0.75rem; pointer-events: auto; }
.btn.focus-visible, .btn:focus { outline: transparent solid 2px; outline-offset: 2px; }
.btn:focus-visible { outline: transparent solid 2px; outline-offset: 2px; }
.btn:disabled { cursor: not-allowed; opacity: 0.5; }
.ZnJ1aXRqdWljZQ .btn { border-radius: 9999px; padding-left: 0.875rem; padding-right: 0.875rem; }
.btn:active { opacity: 0.8; }
.btn-green, .btn-primary { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(16,163,127,var(--tw-bg-opacity)); color: rgba(255,255,255,var(--tw-text-opacity)); }
@media (hover: hover) and (pointer: fine) {
.btn-green:hover, .btn-primary:hover { --tw-bg-opacity: 1; background-color: rgba(26,127,100,var(--tw-bg-opacity)); }
}
.btn-green.focus-visible, .btn-primary.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(25,195,125,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-green:focus-visible, .btn-primary:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(25,195,125,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-blue { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(0,102,222,var(--tw-bg-opacity)); color: rgba(255,255,255,var(--tw-text-opacity)); }
@media (hover: hover) and (pointer: fine) {
.btn-blue:hover { --tw-bg-opacity: 1; background-color: rgba(29,78,216,var(--tw-bg-opacity)); }
}
.btn-blue.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(29,78,216,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-blue:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(29,78,216,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.ZnJ1aXRqdWljZQ .btn-primary { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(13,13,13,var(--tw-bg-opacity)); color: rgba(255,255,255,var(--tw-text-opacity)); }
@media (hover: hover) and (pointer: fine) {
.ZnJ1aXRqdWljZQ .btn-primary:hover { --tw-bg-opacity: 1; background-color: rgba(33,33,33,var(--tw-bg-opacity)); }
}
.ZnJ1aXRqdWljZQ .btn-primary.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(155,155,155,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.ZnJ1aXRqdWljZQ .btn-primary:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(155,155,155,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.ZnJ1aXRqdWljZQ .btn-primary:is(.dark *) { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(249,249,249,var(--tw-bg-opacity)); color: rgba(13,13,13,var(--tw-text-opacity)); }
@media (hover: hover) and (pointer: fine) {
.ZnJ1aXRqdWljZQ .btn-primary:hover:is(.dark *) { --tw-bg-opacity: 1; background-color: rgba(236,236,236,var(--tw-bg-opacity)); }
}
.btn-danger { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(239,68,68,var(--tw-bg-opacity)); color: rgba(255,255,255,var(--tw-text-opacity)); }
@media (hover: hover) and (pointer: fine) {
.btn-danger:hover { --tw-bg-opacity: 1; background-color: rgba(185,28,28,var(--tw-bg-opacity)); }
}
.btn-danger.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(248,113,113,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-danger:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(248,113,113,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
@media (hover: hover) and (pointer: fine) {
.btn-danger:disabled:hover { --tw-bg-opacity: 1; background-color: rgba(185,28,28,var(--tw-bg-opacity)); }
}
.btn-danger-outline { --tw-border-opacity: 1; --tw-text-opacity: 1; background-color: var(--main-surface-primary); border-color: rgba(220,38,38,var(--tw-border-opacity)); border-width: 1px; color: rgba(220,38,38,var(--tw-text-opacity)); }
@media (hover: hover) and (pointer: fine) {
.btn-danger-outline:hover { background-color: var(--main-surface-secondary); }
}
.btn-danger-outline.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(220,38,38,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-danger-outline:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(220,38,38,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-secondary { background-color: var(--main-surface-primary); border-color: var(--border-medium); border-width: 1px; color: var(--text-primary); font-size: 0.875rem; line-height: 1.25rem; }
@media (hover: hover) and (pointer: fine) {
.btn-secondary:hover { background-color: var(--main-surface-secondary); }
}
.btn-secondary.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(103,103,103,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-secondary:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(103,103,103,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
@media (hover: hover) and (pointer: fine) {
.btn-ghost:hover { background-color: rgba(0, 0, 0, 0.05); }
}
.btn-ghost.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-color: rgba(0,0,0,.05); box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); outline: transparent solid 2px; outline-offset: 2px; }
.btn-ghost:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-color: rgba(0,0,0,.05); box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); outline: transparent solid 2px; outline-offset: 2px; }
@media (hover: hover) and (pointer: fine) {
.btn-ghost:hover:is(.dark *) { background-color: rgba(255, 255, 255, 0.1); }
}
.btn-small { min-height: 30px; padding: 0.25rem 0.5rem; }
.ZnJ1aXRqdWljZQ .btn-small { padding-left: 0.75rem; padding-right: 0.75rem; }
.btn-giant, .btn-large { min-height: 46px; }
.btn-large { padding-bottom: 0.75rem; padding-top: 0.75rem; }
.ZnJ1aXRqdWljZQ .btn-large { padding-left: 1rem; padding-right: 1rem; }
.btn-giant { font-size: 1rem; font-weight: 600; line-height: 1.5rem; padding: 0.625rem 1.25rem; }
.ZnJ1aXRqdWljZQ .btn-giant { padding-left: 1.5rem; padding-right: 1.5rem; }
.divide-token-border-light > :not([hidden]) ~ :not([hidden]) { border-color: rgba(0, 0, 0, 0.1); }
.border-token-border-heavy { border-color: rgba(0, 0, 0, 0.2); }
.border-token-border-light { border-color: rgba(0, 0, 0, 0.1); }
.border-token-border-medium { border-color: rgba(0, 0, 0, 0.15); }
.border-token-border-xheavy { border-color: rgba(0, 0, 0, 0.25); }
.border-token-main-surface-primary { }
.border-token-main-surface-secondary { }
.border-token-main-surface-tertiary { }
.border-token-surface-error\/15 { }
.border-token-text-primary { }
.border-token-text-secondary { }
.border-token-text-tertiary { }
.border-y-token-border-medium { border-bottom-color: rgba(0, 0, 0, 0.15); border-top-color: rgba(0, 0, 0, 0.15); }
[dir="ltr"] .border-l-token-sidebar-surface-secondary { }
[dir="rtl"] .border-l-token-sidebar-surface-secondary { }
.\!bg-token-main-surface-primary { }
.\!bg-token-text-primary { }
.bg-token-border-light { background-color: rgba(0, 0, 0, 0.1); }
.bg-token-border-medium { background-color: rgba(0, 0, 0, 0.15); }
.bg-token-main-surface-primary { }
.bg-token-main-surface-secondary { }
.bg-token-main-surface-tertiary { }
.bg-token-sidebar-surface-primary { }
.bg-token-sidebar-surface-secondary { }
.bg-token-sidebar-surface-tertiary { }
.bg-token-surface-error { background-color: rgb(theme(colors.red.700)/var(--tw-bg-opacity)); }
.bg-token-text-primary { }
.bg-token-text-quaternary { }
.bg-token-text-secondary { }
.bg-token-text-tertiary { }
.fill-token-main-surface-primary { }
.stroke-token-main-surface-tertiary { }
.\!text-token-main-surface-primary { }
.\!text-token-text-primary { }
.text-token-link { color: rgb(41, 100, 170); }
.text-token-main-surface-primary { }
.text-token-main-surface-tertiary { }
.text-token-text-error { }
.text-token-text-primary { }
.text-token-text-quaternary { }
.text-token-text-secondary { }
.text-token-text-tertiary { }
.decoration-token-link { text-decoration-color: rgb(41, 100, 170); }
.decoration-token-text-primary { }
.caret-token-text-primary { }
body, html { background-color: var(--main-surface-primary); }
#__next, #root, body, html { height: 100%; }
.markdown { max-width: none; }
.markdown h1 { font-weight: 600; }
.markdown h1:first-child { margin-top: 0px; }
.markdown h2 { font-weight: 600; margin-bottom: 1rem; margin-top: 2rem; }
.markdown h2:first-child { margin-top: 0px; }
.markdown h3 { font-weight: 600; margin-bottom: 0.5rem; margin-top: 1rem; }
.markdown h3:first-child { margin-top: 0px; }
.markdown h4 { font-weight: 600; margin-bottom: 0.5rem; margin-top: 1rem; }
.markdown h4:first-child { margin-top: 0px; }
.markdown h5 { font-weight: 600; }
.markdown h5:first-child { margin-top: 0px; }
.markdown blockquote { --tw-border-opacity: 1; border-color: rgba(155,155,155,var(--tw-border-opacity)); line-height: 1.5rem; margin: 0px; padding-bottom: 0.5rem; padding-top: 0.5rem; }
[dir="ltr"] .markdown blockquote { border-left-width: 2px; padding-left: 1rem; }
[dir="rtl"] .markdown blockquote { border-right-width: 2px; padding-right: 1rem; }
.markdown blockquote > p { margin: 0px; }
.markdown blockquote > p::after, .markdown blockquote > p::before { display: none; }
.markdown table { --tw-border-spacing-x: 0px; --tw-border-spacing-y: 0px; border-collapse: separate; border-spacing: var(--tw-border-spacing-x) var(--tw-border-spacing-y); margin-bottom: 0.25rem; margin-top: 0.25rem; width: 100%; }
.markdown th { background-color: var(--border-light); border-bottom-width: 1px; border-color: var(--border-medium); border-top-width: 1px; padding: 0.25rem 0.75rem; }
[dir="ltr"] .markdown th { border-left-width: 1px; }
[dir="rtl"] .markdown th { border-right-width: 1px; }
[dir="ltr"] .markdown th:first-child { border-top-left-radius: 0.375rem; }
[dir="rtl"] .markdown th:first-child { border-top-right-radius: 0.375rem; }
[dir="ltr"] .markdown th:last-child { border-right-width: 1px; border-top-right-radius: 0.375rem; }
[dir="rtl"] .markdown th:last-child { border-left-width: 1px; border-top-left-radius: 0.375rem; }
.markdown td { border-bottom-width: 1px; border-color: var(--border-medium); padding: 0.25rem 0.75rem; }
[dir="ltr"] .markdown td { border-left-width: 1px; }
[dir="ltr"] .markdown td:last-child, [dir="rtl"] .markdown td { border-right-width: 1px; }
[dir="rtl"] .markdown td:last-child { border-left-width: 1px; }
[dir="ltr"] .markdown tbody tr:last-child td:first-child { border-bottom-left-radius: 0.375rem; }
[dir="ltr"] .markdown tbody tr:last-child td:last-child, [dir="rtl"] .markdown tbody tr:last-child td:first-child { border-bottom-right-radius: 0.375rem; }
[dir="rtl"] .markdown tbody tr:last-child td:last-child { border-bottom-left-radius: 0.375rem; }
.markdown a { color: var(--link); font-weight: 400; text-decoration-line: none; }
@media (hover: hover) and (pointer: fine) {
.markdown a:hover { color: var(--link-hover); }
}
.gizmo .markdown > :not(pre), .gizmo .markdown > :not(pre) > :not(a) { color: var(--text-primary); }
@keyframes blink {
100% { visibility: hidden; }
}
.result-streaming > :not(ol):not(ul):not(pre):last-child::after, .result-streaming > pre:last-child code::after { content: "●"; font-family: Circle, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif; line-height: normal; vertical-align: baseline; }
[dir="ltr"] .result-streaming > :not(ol):not(ul):not(pre):last-child::after, [dir="ltr"] .result-streaming > pre:last-child code::after { margin-left: 0.25rem; }
[dir="rtl"] .result-streaming > :not(ol):not(ul):not(pre):last-child::after, [dir="rtl"] .result-streaming > pre:last-child code::after { margin-right: 0.25rem; }
.result-thinking:empty:last-child::after { -webkit-font-smoothing: subpixel-antialiased; animation: 1.25s ease-in-out 0s infinite normal none running pulseSize; backface-visibility: hidden; background-color: var(--text-primary); border-radius: 50%; box-sizing: border-box; content: " "; display: block; height: 12px; position: absolute; top: 7px; transform: translateZ(0px); transform-origin: center center; width: 12px; will-change: transform; }
@supports (selector(:has(*))) {
.result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child::after, .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child > ol:last-child > li:last-child::after, .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child > ul:last-child > li:last-child::after, .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child::after, .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child > ol:last-child > li:last-child::after, .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child > ul:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child > ol:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child > ul:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child > ol:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child > ul:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child[*|\:not-has\(]::after { content: "●"; font-family: Circle, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif; line-height: normal; vertical-align: baseline; }
.result-streaming > ul:last-child > li:last-child:not(:has(* > li))::after { content: "●"; font-family: Circle, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif; line-height: normal; vertical-align: baseline; }
.result-streaming > ol:last-child > li:last-child[*|\:not-has\(]::after { content: "●"; font-family: Circle, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif; line-height: normal; vertical-align: baseline; }
.result-streaming > ol:last-child > li:last-child:not(:has(* > li))::after { content: "●"; font-family: Circle, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif; line-height: normal; vertical-align: baseline; }
[dir="ltr"] .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child[*|\:not-has\(]::after { margin-left: 0.25rem; }
[dir="ltr"] .result-streaming > ul:last-child > li:last-child:not(:has(* > li))::after { margin-left: 0.25rem; }
[dir="ltr"] .result-streaming > ol:last-child > li:last-child[*|\:not-has\(]::after { margin-left: 0.25rem; }
[dir="ltr"] .result-streaming > ol:last-child > li:last-child:not(:has(* > li))::after { margin-left: 0.25rem; }
[dir="rtl"] .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ol:last-child > li:last-child > ol:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ol:last-child > li:last-child > ul:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child > ol:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child > ul:last-child > li:last-child > ul:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child[*|\:not-has\(]::after { margin-right: 0.25rem; }
[dir="rtl"] .result-streaming > ul:last-child > li:last-child:not(:has(* > li))::after { margin-right: 0.25rem; }
[dir="rtl"] .result-streaming > ol:last-child > li:last-child[*|\:not-has\(]::after { margin-right: 0.25rem; }
[dir="rtl"] .result-streaming > ol:last-child > li:last-child:not(:has(* > li))::after { margin-right: 0.25rem; }
}
@supports not (selector(:has(*))) {
.result-streaming > ol:last-child > li:last-child::after, .result-streaming > ul:last-child > li:last-child::after { content: "●"; font-family: Circle, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif; line-height: normal; vertical-align: baseline; }
[dir="ltr"] .result-streaming > ol:last-child > li:last-child::after, [dir="ltr"] .result-streaming > ul:last-child > li:last-child::after { margin-left: 0.25rem; }
[dir="rtl"] .result-streaming > ol:last-child > li:last-child::after, [dir="rtl"] .result-streaming > ul:last-child > li:last-child::after { margin-right: 0.25rem; }
}
@keyframes pulseSize {
0%, 100% { transform: scaleX(1); }
50% { transform: scale3d(1.25, 1.25, 1); }
}
@keyframes toast-open {
0% { opacity: 0; transform: translateY(-100%); }
100% { transform: translateY(0px); }
}
@keyframes toast-close {
0% { opacity: 1; }
100% { opacity: 0; }
}
.toast-root { align-items: center; display: flex; flex-direction: column; height: 0px; transition: all 0.24s cubic-bezier(0, 0, 0.2, 1) 0s; }
.toast-root[data-state="entered"], .toast-root[data-state="entering"] { animation: 0.24s cubic-bezier(0.175, 0.885, 0.32, 1) 0s 1 normal both running toast-open; }
.toast-root[data-state="exiting"] { animation: 0.12s cubic-bezier(0.4, 0, 1, 1) 0s 1 normal both running toast-close; }
.toast-root .alert-root { box-shadow: rgba(67, 90, 111, 0.3) 0px 0px 1px, rgba(67, 90, 111, 0.3) 0px 5px 8px -4px; flex-shrink: 0; pointer-events: all; }
.title { font-feature-settings: normal; font-family: ui-sans-serif, system-ui, -apple-system, "Segoe UI", Roboto, Ubuntu, Cantarell, "Noto Sans", sans-serif, "Helvetica Neue", Arial, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; font-variation-settings: normal; }
.icon-xs { stroke-width: 1.5; flex-shrink: 0; height: 0.75rem; width: 0.75rem; }
.icon-sm { stroke-width: 2; }
.icon-sm, .icon-sm-heavy { flex-shrink: 0; height: 1rem; width: 1rem; }
.icon-sm-heavy { stroke-width: 2.5; }
.icon-md { stroke-width: 1.5; }
.icon-md, .icon-md-heavy { flex-shrink: 0; height: 18px; width: 18px; }
.icon-md-heavy { stroke-width: 2.5; }
.icon-lg { stroke-width: 1.5; flex-shrink: 0; height: 1.5rem; width: 1.5rem; }
.icon-lg-heavy { stroke-width: 2; flex-shrink: 0; height: 22px; width: 22px; }
.icon-xl { stroke-width: 1.5; flex-shrink: 0; height: 1.75rem; width: 1.75rem; }
.icon-xl-heavy { stroke-width: 2; flex-shrink: 0; height: 24px; width: 24px; }
.icon-2xl { stroke-width: 1.5; flex-shrink: 0; height: 2rem; width: 2rem; }