203 lines
7.4 KiB
HTML
203 lines
7.4 KiB
HTML
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>AI Conversation Impact</title>
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<meta name="description" content="A framework for estimating the full cost of conversations with large language models — environmental, financial, social, and political.">
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<style>
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:root {
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--bg: #fafaf8;
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--fg: #1a1a1a;
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--muted: #555;
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--accent: #2a6e3f;
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--border: #ddd;
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--card-bg: #fff;
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}
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@media (prefers-color-scheme: dark) {
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:root {
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--bg: #1a1a1a;
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--fg: #e8e8e4;
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--muted: #aaa;
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--accent: #5cb87a;
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--border: #333;
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--card-bg: #242424;
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}
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}
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body {
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font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif;
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background: var(--bg);
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color: var(--fg);
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line-height: 1.6;
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}
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.container {
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max-width: 720px;
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margin: 0 auto;
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padding: 4rem 1.5rem;
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}
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h1 {
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font-size: 2rem;
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font-weight: 700;
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margin-bottom: 0.5rem;
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}
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.subtitle {
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font-size: 1.15rem;
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color: var(--muted);
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margin-bottom: 2.5rem;
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}
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.stat-row {
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display: flex;
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gap: 1.5rem;
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margin-bottom: 2.5rem;
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flex-wrap: wrap;
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}
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.stat {
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flex: 1;
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min-width: 140px;
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background: var(--card-bg);
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border: 1px solid var(--border);
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border-radius: 6px;
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padding: 1rem 1.25rem;
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}
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.stat .number {
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font-size: 1.5rem;
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font-weight: 700;
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color: var(--accent);
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}
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.stat .label {
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font-size: 0.85rem;
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color: var(--muted);
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}
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h2 {
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font-size: 1.25rem;
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font-weight: 600;
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margin: 2rem 0 0.75rem;
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}
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p, li {
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color: var(--fg);
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margin-bottom: 0.5rem;
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}
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ul {
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padding-left: 1.25rem;
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margin-bottom: 1rem;
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}
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a {
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color: var(--accent);
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text-decoration: none;
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}
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a:hover {
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text-decoration: underline;
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}
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.links {
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display: flex;
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gap: 1rem;
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margin-top: 2rem;
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flex-wrap: wrap;
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}
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.links a {
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display: inline-block;
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padding: 0.6rem 1.25rem;
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border: 1px solid var(--accent);
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border-radius: 6px;
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font-weight: 500;
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font-size: 0.95rem;
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}
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.links a.primary {
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background: var(--accent);
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color: #fff;
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}
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.links a.primary:hover {
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opacity: 0.9;
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text-decoration: none;
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}
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.links a:hover {
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text-decoration: none;
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}
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.caveat {
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margin-top: 3rem;
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padding-top: 1.5rem;
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border-top: 1px solid var(--border);
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font-size: 0.9rem;
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color: var(--muted);
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}
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footer {
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margin-top: 3rem;
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font-size: 0.85rem;
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color: var(--muted);
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>AI Conversation Impact</h1>
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<p class="subtitle">Beyond carbon: a framework for the full cost of AI conversations — environmental, social, epistemic, and political.</p>
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<div class="stat-row">
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<div class="stat">
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<div class="number">20+</div>
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<div class="label">Cost categories across 5 dimensions</div>
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</div>
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<div class="stat">
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<div class="number">100-250 Wh</div>
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<div class="label">Energy per long conversation</div>
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</div>
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<div class="stat">
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<div class="number">CC0</div>
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<div class="label">Public domain, no restrictions</div>
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</div>
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</div>
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<h2>The problem</h2>
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<p>Most tools for measuring AI's impact stop at energy and CO2. But the costs that matter most — cognitive deskilling, data pollution, algorithmic monoculture, power concentration — are invisible precisely because no one is tracking them. This project names and organizes those costs so they cannot be ignored.</p>
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<h2>What makes this different</h2>
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<p>Existing tools like <a href="https://ecologits.ai/">EcoLogits</a> and <a href="https://codecarbon.io/">CodeCarbon</a> measure environmental metrics well. We don't compete with them — we complement them. This methodology adds the dimensions they don't cover:</p>
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<ul>
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<li><strong>Social</strong> — annotation labor conditions, cognitive deskilling (<a href="https://dl.acm.org/doi/full/10.1145/3706598.3713778">CHI 2025</a>), linguistic homogenization</li>
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<li><strong>Epistemic</strong> — code quality degradation, data pollution (<a href="https://www.nature.com/articles/s41586-024-07566-y">Nature, 2024</a>), research integrity</li>
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<li><strong>Political</strong> — power concentration, data sovereignty, opaque content filtering</li>
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<li><strong>Environmental</strong> — calibrated against <a href="https://arxiv.org/abs/2508.15734">Google (2025)</a> and <a href="https://arxiv.org/abs/2505.09598">Jegham et al. (2025)</a> published data</li>
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<li><strong>Financial</strong> — compute costs, creative market displacement, opportunity cost</li>
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</ul>
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<p>The goal is not zero AI usage but <strong>net-positive</strong> usage. The framework includes positive impact metrics (reach, counterfactual value, durability) alongside costs.</p>
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<h2>What's here</h2>
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<ul>
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<li><strong>A methodology</strong> covering 20+ cost categories with estimation methods where possible and honest acknowledgment where not.</li>
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<li><strong>A toolkit</strong> for <a href="https://claude.ai/claude-code">Claude Code</a> that automatically tracks environmental, financial, and social cost proxies (deskilling risk, code quality, data pollution, provider concentration) during sessions.</li>
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<li><strong>A related work survey</strong> mapping existing tools and research so you can see where this fits.</li>
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</ul>
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<h2>Help improve the estimates</h2>
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<p>Many figures have low confidence. If you have data center measurements, inference cost data, or research on the social costs of AI, your corrections are welcome.</p>
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<div class="links">
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<a class="primary" href="/forge/claude/ai-conversation-impact/src/branch/main/impact-methodology.md">Read the methodology</a>
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<a href="/forge/claude/ai-conversation-impact">Browse the repository</a>
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<a href="/forge/claude/ai-conversation-impact/issues/1">Contribute corrections</a>
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</div>
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<div class="caveat">
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<strong>Limitations:</strong> The quantifiable costs are almost certainly the least important ones. Effects like deskilling, data pollution, and power concentration cannot be reduced to numbers. This is a tool for honest approximation, not precise accounting.
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</div>
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<div class="caveat">
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<strong>How this was made:</strong>
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This project was developed by a human
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directing <a href="https://claude.ai">Claude</a> (Anthropic's AI assistant)
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across multiple conversations. The methodology was applied to itself:
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across 3 tracked sessions, the project has consumed
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~295 Wh of energy, ~95g of CO2, and ~$98 in
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compute. Whether it produces enough value to justify those costs depends
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on whether anyone finds it useful. We are
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<a href="/forge/claude/ai-conversation-impact/src/branch/main/plans/measure-project-impact.md">tracking that question</a>.
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</div>
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<footer>
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<a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a> — public domain. No restrictions on use.
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</footer>
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</div>
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</body>
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</html>
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