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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
<title>NodeMind — Binary Document Intelligence</title>
<meta name="description" content="NodeMind compresses RAG float32 indexes 32x with BGE-M3, 48x vs HNSW, 96x with BGE-base, and up to 128x on multimodal data — using a patent-pending integer-only binary codec. Sub-1ms Hamming search. No GPU. No vector database."/>
<link rel="preconnect" href="https://fonts.googleapis.com"/>
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin/>
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;700&family=Inter:wght@300..700&display=swap" rel="stylesheet"/>
<style>
/* === BASE === */
*,*::before,*::after{box-sizing:border-box;margin:0;padding:0}
html{scroll-behavior:smooth;-webkit-font-smoothing:antialiased;text-rendering:optimizeLegibility;}
:root{
--text-xs:clamp(0.75rem,0.7rem + 0.25vw,0.875rem);
--text-sm:clamp(0.875rem,0.8rem + 0.35vw,1rem);
--text-base:clamp(1rem,0.95rem + 0.25vw,1.125rem);
--text-lg:clamp(1.125rem,1rem + 0.75vw,1.5rem);
--text-xl:clamp(1.5rem,1.2rem + 1.25vw,2.25rem);
--text-2xl:clamp(2rem,1.2rem + 2.5vw,3.5rem);
--text-3xl:clamp(2.5rem,1rem + 4vw,5rem);
--space-1:.25rem;--space-2:.5rem;--space-3:.75rem;--space-4:1rem;
--space-5:1.25rem;--space-6:1.5rem;--space-8:2rem;--space-10:2.5rem;
--space-12:3rem;--space-16:4rem;--space-20:5rem;--space-24:6rem;
--radius-sm:.375rem;--radius-md:.5rem;--radius-lg:.75rem;--radius-xl:1rem;--radius-full:9999px;
--transition:180ms cubic-bezier(.16,1,.3,1);
--font-body:'Inter','Helvetica Neue',sans-serif;
--font-mono:'JetBrains Mono','Fira Mono',monospace;
}
[data-theme='light']{
--bg:#f0f2f7;--surface:#ffffff;--surface-2:#f8f9fb;--surface-3:#f0f2f7;
--border:#e2e5ec;--divider:#dde0e8;
--text:#0f111a;--muted:#5a6070;--faint:#9ba3b2;
--primary:#0066cc;--primary-hover:#0052a3;--primary-glow:rgba(0,102,204,.15);
--accent:#00c4a7;--accent-hover:#009e87;--accent-glow:rgba(0,196,167,.12);
--warn:#e07000;--success:#1a8c45;
--shadow-sm:0 1px 3px rgba(0,0,0,.07);
--shadow-md:0 4px 16px rgba(0,0,0,.09);
--shadow-lg:0 12px 40px rgba(0,0,0,.12);
--code-bg:#1a1d2e;--code-text:#e8ecf4;
}
[data-theme='dark']{
--bg:#0a0c12;--surface:#0f1118;--surface-2:#141720;--surface-3:#1a1e28;
--border:#1e2230;--divider:#252a38;
--text:#dde2ee;--muted:#7a839a;--faint:#3a4055;
--primary:#4da3ff;--primary-hover:#3390f5;--primary-glow:rgba(77,163,255,.18);
--accent:#00d9b8;--accent-hover:#00bfa1;--accent-glow:rgba(0,217,184,.14);
--warn:#f5a623;--success:#4cba6b;
--shadow-sm:0 1px 3px rgba(0,0,0,.35);
--shadow-md:0 4px 16px rgba(0,0,0,.45);
--shadow-lg:0 12px 40px rgba(0,0,0,.55);
--code-bg:#0d1017;--code-text:#e8ecf4;
}
body{min-height:100dvh;font-family:var(--font-body);font-size:var(--text-base);color:var(--text);background:var(--bg);line-height:1.65;}
img,svg{display:block;max-width:100%;height:auto;}
p,li{text-wrap:pretty;max-width:72ch;}
h1,h2,h3,h4{text-wrap:balance;line-height:1.15;font-weight:700;}
a,button,[role='button']{transition:color var(--transition),background var(--transition),border-color var(--transition),box-shadow var(--transition),opacity var(--transition);}
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code,pre{font-family:var(--font-mono);}
table{border-collapse:collapse;width:100%;}
/* === LAYOUT === */
.container{max-width:1100px;margin-inline:auto;padding-inline:clamp(var(--space-5),5vw,var(--space-16));}
.container--wide{max-width:1280px;margin-inline:auto;padding-inline:clamp(var(--space-5),5vw,var(--space-16));}
/* === NAV === */
nav{position:fixed;top:0;left:0;right:0;z-index:100;height:60px;border-bottom:1px solid var(--border);
background:color-mix(in srgb,var(--bg) 85%,transparent);
backdrop-filter:blur(12px);-webkit-backdrop-filter:blur(12px);}
.nav-inner{height:100%;display:flex;align-items:center;justify-content:space-between;gap:var(--space-8);}
.nav-logo{display:flex;align-items:center;gap:var(--space-3);text-decoration:none;color:var(--text);}
.nav-logo-text{font-family:var(--font-mono);font-weight:700;font-size:var(--text-sm);letter-spacing:.02em;}
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.nav-links a{text-decoration:none;color:var(--muted);font-size:var(--text-sm);font-weight:500;}
.nav-links a:hover{color:var(--text);}
.nav-right{display:flex;align-items:center;gap:var(--space-3);}
.btn-nav{display:inline-flex;align-items:center;gap:var(--space-2);padding:var(--space-2) var(--space-5);
background:var(--primary);color:#fff;border-radius:var(--radius-md);font-size:var(--text-sm);
font-weight:600;text-decoration:none;border:1.5px solid var(--primary);}
.btn-nav:hover{background:var(--primary-hover);border-color:var(--primary-hover);}
.theme-btn{padding:var(--space-2);border-radius:var(--radius-md);color:var(--muted);border:1px solid var(--border);}
.theme-btn:hover{color:var(--text);border-color:var(--faint);}
/* === HERO === */
.hero{padding-block:clamp(var(--space-24),10vw,var(--space-32));padding-top:calc(clamp(var(--space-24),10vw,var(--space-32)) + 60px);text-align:center;position:relative;overflow:hidden;}
.hero::before{content:'';position:absolute;inset:0;
background:radial-gradient(ellipse 80% 60% at 50% 0%,var(--primary-glow) 0%,transparent 70%);
pointer-events:none;}
.hero-badge{display:inline-flex;align-items:center;gap:var(--space-2);padding:var(--space-1) var(--space-4);
background:var(--surface);border:1px solid var(--border);border-radius:var(--radius-full);
font-size:var(--text-xs);font-weight:600;color:var(--muted);margin-bottom:var(--space-8);
font-family:var(--font-mono);}
.hero-badge span{color:var(--accent);font-size:.65rem;}
.hero h1{font-size:var(--text-3xl);font-family:var(--font-mono);font-weight:700;letter-spacing:-.02em;
margin-bottom:var(--space-6);color:var(--text);}
.hero h1 em{font-style:normal;color:var(--accent);}
.hero-sub{font-size:var(--text-lg);color:var(--muted);max-width:640px;margin-inline:auto;margin-bottom:var(--space-10);line-height:1.6;}
.hero-actions{display:flex;align-items:center;justify-content:center;flex-wrap:wrap;gap:var(--space-4);}
.btn-primary{display:inline-flex;align-items:center;gap:var(--space-2);padding:var(--space-3) var(--space-8);
background:var(--primary);color:#fff;border-radius:var(--radius-md);font-size:var(--text-sm);
font-weight:600;text-decoration:none;border:1.5px solid var(--primary);}
.btn-primary:hover{background:var(--primary-hover);border-color:var(--primary-hover);}
.btn-ghost{display:inline-flex;align-items:center;gap:var(--space-2);padding:var(--space-3) var(--space-8);
background:transparent;color:var(--text);border-radius:var(--radius-md);font-size:var(--text-sm);
font-weight:600;text-decoration:none;border:1.5px solid var(--border);}
.btn-ghost:hover{border-color:var(--muted);}
/* === STATS BAND === */
.stats-band{background:var(--surface);border-top:1px solid var(--border);border-bottom:1px solid var(--border);}
.stats-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(160px,1fr));gap:0;}
.stat-item{padding:var(--space-8) var(--space-6);text-align:center;border-right:1px solid var(--border);}
.stat-item:last-child{border-right:none;}
.stat-value{font-family:var(--font-mono);font-size:var(--text-2xl);font-weight:700;color:var(--accent);line-height:1;}
.stat-label{font-size:var(--text-xs);color:var(--muted);margin-top:var(--space-2);font-weight:500;letter-spacing:.05em;text-transform:uppercase;}
/* === SECTION === */
section{padding-block:clamp(var(--space-16),8vw,var(--space-24));}
.section-label{font-family:var(--font-mono);font-size:var(--text-xs);font-weight:700;letter-spacing:.1em;
text-transform:uppercase;color:var(--accent);margin-bottom:var(--space-4);}
.section-title{font-size:var(--text-2xl);font-weight:700;margin-bottom:var(--space-6);}
.section-sub{font-size:var(--text-base);color:var(--muted);max-width:600px;line-height:1.7;}
/* === WIN TABLE === */
.table-wrap{overflow-x:auto;border-radius:var(--radius-xl);border:1px solid var(--border);margin-top:var(--space-10);}
.win-table{width:100%;border-collapse:collapse;}
.win-table thead{background:var(--surface-2);}
.win-table th{padding:var(--space-4) var(--space-6);text-align:left;font-size:var(--text-xs);
font-weight:700;letter-spacing:.07em;text-transform:uppercase;color:var(--muted);
border-bottom:1px solid var(--border);}
.win-table th:not(:first-child){text-align:right;}
.win-table td{padding:var(--space-4) var(--space-6);font-size:var(--text-sm);border-bottom:1px solid var(--divider);}
.win-table tr:last-child td{border-bottom:none;}
.win-table tr:hover td{background:var(--surface-2);}
.win-table td:not(:first-child){text-align:right;font-family:var(--font-mono);font-weight:500;}
.td-nm{color:var(--accent);font-weight:700;}
.td-rag{color:var(--muted);}
.td-savings{color:var(--success);font-weight:700;}
.td-badge{display:inline-block;padding:.15em .55em;border-radius:var(--radius-full);
font-size:.75em;font-weight:700;background:var(--accent-glow);color:var(--accent);}
.row-head td{background:var(--surface-3)!important;font-weight:700;color:var(--muted);
font-family:var(--font-mono);font-size:var(--text-xs);letter-spacing:.08em;text-transform:uppercase;padding:var(--space-2) var(--space-6);}
/* === MODALITY GRID === */
.modality-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:var(--space-5);margin-top:var(--space-10);}
.modality-card{background:var(--surface);border:1px solid var(--border);border-radius:var(--radius-xl);padding:var(--space-8) var(--space-6);position:relative;}
.modality-card.active{border-color:var(--accent);box-shadow:0 0 0 1px var(--accent),var(--shadow-md);}
.modality-icon{width:48px;height:48px;border-radius:var(--radius-lg);background:var(--surface-2);border:1px solid var(--border);
display:flex;align-items:center;justify-content:center;margin-bottom:var(--space-5);}
.modality-title{font-size:var(--text-base);font-weight:700;margin-bottom:var(--space-2);}
.modality-ratio{font-family:var(--font-mono);font-size:var(--text-xl);font-weight:700;color:var(--accent);margin-bottom:var(--space-3);}
.modality-desc{font-size:var(--text-sm);color:var(--muted);line-height:1.6;}
.coming-badge{position:absolute;top:var(--space-4);right:var(--space-4);padding:.2em .6em;
background:var(--surface-3);border:1px solid var(--border);border-radius:var(--radius-full);
font-size:.7rem;font-weight:700;letter-spacing:.06em;text-transform:uppercase;color:var(--faint);}
/* === ARCHITECTURE DIAGRAM === */
.arch-diagram{background:var(--code-bg);border-radius:var(--radius-xl);padding:var(--space-8);
margin-top:var(--space-10);overflow-x:auto;border:1px solid rgba(255,255,255,.06);}
.arch-diagram pre{font-family:var(--font-mono);font-size:clamp(.7rem,1.5vw,.875rem);color:var(--code-text);line-height:1.7;white-space:pre;}
/* === ALGO SECTION === */
.algo-grid{display:grid;grid-template-columns:1fr 1fr;gap:var(--space-8);margin-top:var(--space-10);}
@media(max-width:720px){.algo-grid{grid-template-columns:1fr;}}
.algo-card{background:var(--surface);border:1px solid var(--border);border-radius:var(--radius-xl);padding:var(--space-8);}
.algo-card h3{font-size:var(--text-base);font-weight:700;margin-bottom:var(--space-3);}
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.algo-card ul{margin-top:var(--space-4);padding-left:var(--space-5);display:flex;flex-direction:column;gap:var(--space-2);}
.algo-card li{font-size:var(--text-sm);color:var(--muted);line-height:1.6;}
.algo-card li::marker{color:var(--accent);}
/* === PIPELINE === */
.pipeline{display:flex;align-items:center;flex-wrap:wrap;gap:var(--space-3);margin-top:var(--space-8);}
.pipe-step{background:var(--surface);border:1px solid var(--border);border-radius:var(--radius-lg);
padding:var(--space-3) var(--space-5);font-size:var(--text-sm);font-weight:600;}
.pipe-step.active{border-color:var(--accent);color:var(--accent);background:var(--accent-glow);}
.pipe-arrow{color:var(--faint);font-size:var(--text-sm);}
/* === PATENT === */
.patent-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(300px,1fr));gap:var(--space-6);margin-top:var(--space-10);}
.patent-card{background:var(--surface);border:1px solid var(--border);border-radius:var(--radius-xl);padding:var(--space-8);}
.patent-num{font-family:var(--font-mono);font-size:var(--text-xs);font-weight:700;letter-spacing:.08em;
text-transform:uppercase;color:var(--primary);margin-bottom:var(--space-4);}
.patent-title{font-size:var(--text-base);font-weight:700;margin-bottom:var(--space-3);}
.patent-desc{font-size:var(--text-sm);color:var(--muted);line-height:1.7;}
/* === FOOTER === */
footer{background:var(--surface);border-top:1px solid var(--border);padding-block:var(--space-12);}
.footer-inner{display:flex;flex-wrap:wrap;gap:var(--space-8);justify-content:space-between;align-items:center;}
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.footer-links{display:flex;gap:var(--space-6);flex-wrap:wrap;}
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.footer-links a:hover{color:var(--text);}
.footer-copy{font-size:var(--text-xs);color:var(--faint);}
/* === CODE BLOCK === */
.code-block{background:var(--code-bg);border-radius:var(--radius-xl);padding:var(--space-6) var(--space-8);
border:1px solid rgba(255,255,255,.06);overflow-x:auto;margin-top:var(--space-6);}
.code-block pre{font-family:var(--font-mono);font-size:clamp(.7rem,1.5vw,.875rem);color:var(--code-text);line-height:1.8;white-space:pre;}
.tok-kw{color:#c792ea;}
.tok-str{color:#c3e88d;}
.tok-num{color:#f78c6c;}
.tok-cmt{color:#546e7a;font-style:italic;}
.tok-fn{color:#82aaff;}
.tok-cls{color:#ffcb6b;}
/* === HORIZONTAL RULE === */
hr{border:none;border-top:1px solid var(--divider);margin-block:var(--space-4);}
/* === CALLOUT === */
.callout{background:var(--accent-glow);border:1px solid var(--accent);border-radius:var(--radius-lg);
padding:var(--space-5) var(--space-6);margin-top:var(--space-8);}
.callout p{font-size:var(--text-sm);color:var(--text);max-width:none;}
.callout strong{color:var(--accent);}
/* === RESPONSIVE === */
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<div class="hero-badge"><span>●</span> AU Patent Pending · 2026</div>
<h1>Document search<br/>at <em>binary speed</em></h1>
<p class="hero-sub">NodeMind compresses float32 RAG indexes <strong>32× smaller</strong> with BGE-M3, <strong>48× smaller</strong> vs HNSW, <strong>96× smaller</strong> with BGE-base, and <strong>up to 128×</strong> on multimodal data — using a patent-pending integer-only binary codec, then searched at <strong>sub-1ms</strong> with pure-integer Hamming MIH. No GPU, no vector database, no cloud bills.</p>
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<div class="stat-item">
<div class="stat-value">32×</div>
<div class="stat-label">Smaller than float32 RAG</div>
</div>
<div class="stat-item">
<div class="stat-value">128×</div>
<div class="stat-label">Max compression (multimodal)</div>
</div>
<div class="stat-item">
<div class="stat-value"><1ms</div>
<div class="stat-label">Hamming search (CPU)</div>
</div>
<div class="stat-item">
<div class="stat-value">0</div>
<div class="stat-label">GPU required</div>
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<div class="stat-item">
<div class="stat-value">$0</div>
<div class="stat-label">Vector DB bills</div>
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<section id="wins">
<div class="container">
<div class="section-label">Performance</div>
<h2 class="section-title">RAG float32 vs NodeMind Binary</h2>
<p class="section-sub">A 1 GB text document becomes a <strong>10 GB RAG float32 index</strong> — that's the real cost of vector search at scale. NodeMind's binary codec crushes that 10 GB down to just <strong>210 MB</strong> online (or 32× smaller offline). Same documents. Same BGE-M3 embeddings. Dramatically different storage.</p>
<div class="callout" style="margin-top:var(--space-6);margin-bottom:var(--space-2)">
<p><strong>Why does RAG expand 10×?</strong> Chunking 1 KB of text produces a 1024-dim float32 vector = 4 KB (4× on raw text). HNSW graph index structures add another 2–3×. Result: every 1 GB of documents becomes ~10 GB in a vector database — confirmed by Elasticsearch, Pure Storage, and Milvus benchmarks. NodeMind then compresses that 10 GB RAG index <strong>48× further</strong> on text (<strong>up to 128×</strong> on multimodal data with NM-256) using our patent-pending binary codec.</p>
</div>
<div class="table-wrap">
<table class="win-table">
<thead>
<tr>
<th>Original Documents</th>
<th>RAG Index<br/><span style="font-weight:400;opacity:.7">float32 · ~10× expansion</span></th>
<th>NodeMind Index<br/><span style="font-weight:400;opacity:.7">binary · 48× smaller online</span></th>
<th>vs RAG</th>
<th>RAG Storage/mo<br/><span style="font-weight:400;opacity:.7">S3 Standard</span></th>
<th>NodeMind Storage/mo<br/><span style="font-weight:400;opacity:.7">S3 Standard</span></th>
<th>Managed Vector DB/mo<br/><span style="font-weight:400;opacity:.7">Pinecone pricing</span></th>
<th>Annual Savings</th>
</tr>
</thead>
<tbody>
<tr class="row-head"><td colspan="8">— Storage Comparison</td></tr>
<tr>
<td><strong>1 GB documents</strong><br/><span style="color:var(--faint);font-size:var(--text-xs)">~250K chunks</span></td>
<td class="td-rag">10 GB</td>
<td class="td-nm">210 MB</td>
<td><span class="td-badge">48×</span></td>
<td class="td-rag">$0.23/mo</td>
<td class="td-nm">$0.0024/mo</td>
<td class="td-rag">$25.00/mo</td>
<td class="td-savings">$300 / yr</td>
</tr>
<tr>
<td><strong>10 GB documents</strong><br/><span style="color:var(--faint);font-size:var(--text-xs)">~2.5M chunks</span></td>
<td class="td-rag">100 GB</td>
<td class="td-nm">2.1 GB</td>
<td><span class="td-badge">48×</span></td>
<td class="td-rag">$2.30/mo</td>
<td class="td-nm">$0.024/mo</td>
<td class="td-rag">$250.00/mo</td>
<td class="td-savings">$3,000 / yr</td>
</tr>
<tr>
<td><strong>100 GB documents</strong><br/><span style="color:var(--faint);font-size:var(--text-xs)">~25M chunks</span></td>
<td class="td-rag">1 TB</td>
<td class="td-nm">21 GB</td>
<td><span class="td-badge">48×</span></td>
<td class="td-rag">$23.00/mo</td>
<td class="td-nm">$0.24/mo</td>
<td class="td-rag">$2,500/mo</td>
<td class="td-savings">$30,000 / yr</td>
</tr>
<tr>
<td><strong>1 TB documents</strong><br/><span style="color:var(--faint);font-size:var(--text-xs)">~250M chunks</span></td>
<td class="td-rag">10 TB</td>
<td class="td-nm">210 GB</td>
<td><span class="td-badge">48×</span></td>
<td class="td-rag">$230/mo</td>
<td class="td-nm">$2.40/mo</td>
<td class="td-rag">$25,000/mo</td>
<td class="td-savings">$300,000 / yr</td>
</tr>
<tr class="row-head"><td colspan="8">— Search Performance</td></tr>
<tr>
<td><strong>Search method</strong><br/><span style="color:var(--faint);font-size:var(--text-xs)">Same 1024-dim BGE-M3</span></td>
<td class="td-rag" colspan="2">Cosine similarity on float32 — O(N·D) multiply-accumulate</td>
<td><span class="td-badge"><1ms</span></td>
<td class="td-nm" colspan="4">Hamming distance on 1024-bit integers — POPCNT only</td>
</tr>
<tr>
<td><strong>GPU required</strong></td>
<td class="td-rag" colspan="3">Yes — needed for fast cosine at scale</td>
<td class="td-nm" colspan="4">No — pure CPU, any machine</td>
</tr>
<tr>
<td><strong>RAM for 250M chunks</strong></td>
<td class="td-rag" colspan="3">~1 TB RAM</td>
<td class="td-nm" colspan="4">~10 GB RAM</td>
</tr>
<tr>
<td><strong>Offline / portable</strong></td>
<td class="td-rag" colspan="3">No — requires live vector DB connection</td>
<td class="td-nm" colspan="4">Yes — download zip, run anywhere, no cloud needed</td>
</tr>
</tbody>
</table>
</div>
<div class="callout" style="margin-top:var(--space-8)">
<p><strong>Codec:</strong> NodeMind's compression is <em>not</em> standard binary quantization (which breaks down on out-of-distribution queries). Our patent-pending algorithm is integer-only, deterministic, and produces fingerprints with recall that beats fixed-threshold binary baselines on real BEIR queries. This achieves <strong>32× compression with BGE-M3</strong> (1024-bit), <strong>48× vs HNSW</strong> (incl. ~50% graph overhead), <strong>96× with BGE-base</strong> (256-bit), and <strong>up to 128× on multimodal data</strong> (NM-256). Costs use S3 Standard at $0.023/GB/mo vs Pinecone managed vector DB at $2.50/GB/mo.</p>
</div>
<div class="callout" style="margin-top:var(--space-4)">
<p><strong>Note on benchmarks.</strong> Compression ratios are <em>mathematical</em> and verifiable with <code>os.path.getsize()</code> on the downloadable indexes — see the <a href="benchmark.html" style="color:var(--accent)">interactive benchmark page</a>. Sub-1ms search latency holds at small/medium N; sub-linear scaling to ~12ms is documented in the patent for 100M-chunk indexes. On real out-of-distribution BEIR queries, NodeMind beats standard FAISS Fixed Binary on 3 of 4 datasets at the same compression, and stays within ~5pp of float32 cosine — the 32× / 48× / 96× / 128× compression numbers are the trade you make for that gap.</p>
</div>
</div>
</section>
<!-- MODALITIES -->
<section id="modalities" style="background:var(--surface);border-top:1px solid var(--border);border-bottom:1px solid var(--border);">
<div class="container">
<div class="section-label">Modalities</div>
<h2 class="section-title">Built for every data type</h2>
<p class="section-sub">The NodeMind codec is modality-agnostic — text, images, audio, tables, and code share the same patent-pending binary encoding. Every modality below is <strong>measured</strong> in the <a href="benchmark.html" style="color:var(--accent)">multimodal benchmark</a>.</p>
<div class="modality-grid">
<!-- TEXT — LIVE -->
<div class="modality-card active">
<div class="modality-icon">
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="var(--accent)" stroke-width="2"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/><line x1="16" y1="13" x2="8" y2="13"/><line x1="16" y1="17" x2="8" y2="17"/><polyline points="10 9 9 9 8 9"/></svg>
</div>
<div class="modality-title">Text & Documents</div>
<div class="modality-ratio">32×–96×</div>
<div class="modality-desc">PDF, TXT, Markdown. BGE-M3 → 1024-bit (32×); BGE-base + PCA-256 → 256-bit (96×). <strong>Recall@5 ≥ 0.999</strong> on 500K-chunk Wikipedia/arXiv/Gutenberg corpus. Live on <a href="https://nodemind.space" style="color:var(--accent)">nodemind.space</a>.</div>
</div>
<!-- IMAGE — TESTED -->
<div class="modality-card active">
<div class="modality-icon">
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="var(--accent)" stroke-width="2"><rect x="3" y="3" width="18" height="18" rx="2"/><circle cx="8.5" cy="8.5" r="1.5"/><polyline points="21 15 16 10 5 21"/></svg>
</div>
<div class="modality-title">Images</div>
<div class="modality-ratio">32×–128×</div>
<div class="modality-desc">Real Unsplash photos embedded with BGE-Visualized-M3. NM-1024 (32×) / NM-512 (64×) / NM-256 (128×) all hit <strong>Recall@1 = 1.000</strong> vs Gemini RAG float32 baseline.</div>
</div>
<!-- AUDIO — TESTED -->
<div class="modality-card active">
<div class="modality-icon">
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="var(--accent)" stroke-width="2"><path d="M9 18V5l12-2v13"/><circle cx="6" cy="18" r="3"/><circle cx="18" cy="16" r="3"/></svg>
</div>
<div class="modality-title">Audio</div>
<div class="modality-ratio">32×–128×</div>
<div class="modality-desc">ESC-10 environmental clips (CC-BY 4.0) routed through Whisper transcription, then binarised with the same codec. <strong>Recall@1 = 1.000</strong> across all three compression levels.</div>
</div>
<!-- TABLES — TESTED -->
<div class="modality-card active">
<div class="modality-icon">
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="var(--accent)" stroke-width="2"><rect x="3" y="3" width="18" height="18" rx="2"/><line x1="3" y1="9" x2="21" y2="9"/><line x1="3" y1="15" x2="21" y2="15"/><line x1="9" y1="3" x2="9" y2="21"/><line x1="15" y1="3" x2="15" y2="21"/></svg>
</div>
<div class="modality-title">Tables & Code</div>
<div class="modality-ratio">32×–128×</div>
<div class="modality-desc">Structured CSV / SQL / OHLCV tables and Python / SQL / Bash code. Same codec, same compression sweep, <strong>Recall@1 = 1.000</strong>. Video pipeline (transcript + frame embeddings) coming next.</div>
</div>
</div>
<p style="font-size:var(--text-xs);color:var(--faint);margin-top:var(--space-6);">All modality ratios are measured on real files — images are real Unsplash photos, audio is real ESC-10 environmental WAV files, tables and code are real structured data. See the <a href="benchmark.html" style="color:var(--accent)">multimodal benchmark</a> for methodology, queries, and download links.</p>
</div>
</section>
<!-- HOW IT WORKS -->
<section id="how">
<div class="container">
<div class="section-label">Algorithm</div>
<h2 class="section-title">How NodeMind works</h2>
<p class="section-sub">Three stages — embedding, binary encoding with our proprietary codec, and Multi-Index Hashing search. No gradients. No GPU. Pure integer arithmetic throughout.</p>
<div class="pipeline">
<div class="pipe-step">1. Chunk document</div>
<div class="pipe-arrow">→</div>
<div class="pipe-step">2. BGE-M3 embed (1024-dim)</div>
<div class="pipe-arrow">→</div>
<div class="pipe-step active">3. NodeMind binary codec</div>
<div class="pipe-arrow">→</div>
<div class="pipe-step">4. MIH index (64 sub-tables)</div>
<div class="pipe-arrow">→</div>
<div class="pipe-step">5. Hamming search → results</div>
</div>
<div class="algo-grid">
<div class="algo-card">
<h3>Patent-pending Binary Codec</h3>
<p>Each float32 embedding is converted to a compact binary fingerprint using our patent-pending integer-only algorithm. This is not standard binary quantization (fixed-zero or per-vector mean) — our codec preserves semantic neighbourhood structure far better, so we beat fixed-threshold binary baselines on real BEIR queries. The full method is a trade secret protected under AU 2026904283.</p>
<ul>
<li>32× / 48× / 96× / 128× depending on encoder + bit width</li>
<li>Integer-only — no floats at indexing</li>
<li>Deterministic, single portable file</li>
<li>Patent AU 2026904283 (codec + index)</li>
</ul>
</div>
<div class="algo-card">
<h3>Multi-Index Hashing (MIH)</h3>
<p>The 1024-bit fingerprint is split into 64 sub-strings of 16 bits. Each sub-string indexes into a hash table; at query time, exact matches plus radius-1 Hamming variants per sub-table are merged into a candidate set, then re-ranked by full Hamming distance. Sub-linear exact nearest-neighbour search — no approximate structures.</p>
<ul>
<li>64 hash sub-tables, 16 bits each</li>
<li>Sub-1ms query at small/medium N; ~12ms at 100M</li>
<li>Pure XOR + POPCNT — no FAISS, no HNSW, no ANN library</li>
<li>Patent AU 2026904283 (codec + index)</li>
</ul>
</div>
<div class="algo-card">
<h3>BGE-M3 Embeddings</h3>
<p>NodeMind uses BGE-M3, the state-of-the-art multilingual embedding model with 1024 dimensions. Dense, sparse, and multi-vector representations are supported. The model is loaded once per worker — no repeated downloads.</p>
<ul>
<li>MTEB top-ranked multilingual model</li>
<li>1024-dim dense vectors</li>
<li>Runs on community hardware (RTX 3080 + 128 GB RAM) — no datacenter required</li>
<li>Scales to zero when idle</li>
</ul>
</div>
<div class="algo-card">
<h3>Portable Index Files</h3>
<p>After indexing, users download two zip files: the NodeMind binary index and a standard RAG float32 index. Both run completely offline using the included <code>nodemind_local.py</code> runner. No cloud subscription needed to query.</p>
<ul>
<li>NodeMind zip: binary MIH index</li>
<li>RAG zip: float32 cosine index</li>
<li>Side-by-side benchmark built in</li>
<li>Auto-deleted after 24 hours</li>
</ul>
</div>
</div>
<div class="arch-diagram">
<pre>
<span style="color:#c792ea">User uploads PDF</span>
│
▼
<span style="color:#82aaff">[ FastAPI — nodemind.space ]</span> ← nginx + SSL (Google Cloud VPS, 1TB)
│
▼ submit job
<span style="color:#ffcb6b">[ Community Hardware: RTX 3080 + 128 GB RAM ]</span>
1. pdfplumber → chunks
2. BGE-M3 → float32 embeddings (1024-dim)
3. <span style="color:#c3e88d">Patent-pending binary codec</span> → 1024-bit fingerprints (32× smaller; up to 128× at 256-bit)
4. MIH index: 64 sub-tables × 16-bit keys
5. RAG index: float32 cosine (comparison baseline)
6. Return nm_zip + rag_zip
│
▼
<span style="color:#82aaff">[ VPS stores zips ]</span> ← auto-deleted after 24 hours
│
▼
<span style="color:#c792ea">User downloads both — runs offline</span>
</pre>
</div>
</div>
</section>
<!-- PATENTS -->
<section id="patents" style="background:var(--surface);border-top:1px solid var(--border);">
<div class="container">
<div class="section-label">Intellectual Property</div>
<h2 class="section-title">Patent-protected technology</h2>
<p class="section-sub">NodeMind's core algorithm is protected by an Australian provisional patent held by Sai Kiran Bathula, independent researcher, Coleambally NSW.</p>
<div class="patent-grid">
<div class="patent-card">
<div class="patent-num">AU 2026904283 · Provisional</div>
<div class="patent-title">NodeMind Codec & Index</div>
<div class="patent-desc">The proprietary integer-only encoding that converts float32 embeddings into compact binary fingerprints, plus the portable single-file binary fingerprint index format used at query time. Achieves 32× / 96× / 128× compression depending on encoder, with recall that beats fixed-threshold binary quantization on out-of-distribution BEIR queries. Sub-linear exact Hamming nearest-neighbour search on a single CPU core. Full method is a trade secret.</div>
</div>
</div>
</div>
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<!-- WHAT'S NEXT — HIVEMIND -->
<section style="background:var(--surface);border-top:1px solid var(--border);">
<div class="container">
<div class="section-label">What's Next</div>
<h2 class="section-title">HiveMind — funded by NodeMind</h2>
<p class="section-sub">We did the hard math on document compression. NodeMind is shipped. The next big bet is <strong>HiveMind</strong> — a public AI reasoning network where humans and agents leave compressed reasoning traces, register watches on ideas, surface contradictions, and connect tools through shared memory. Funded by NodeMind revenue.</p>
<p style="margin-top:var(--space-6);">
<a href="https://nodemind.space/hivemind/" class="btn-primary" style="display:inline-block;padding:var(--space-3) var(--space-6);background:var(--accent);color:var(--bg);border-radius:var(--radius-md);text-decoration:none;font-weight:600;">See the HiveMind concept →</a>
<a href="https://x.com/QLNI_AI" target="_blank" rel="noopener" style="display:inline-block;padding:var(--space-3) var(--space-6);border:1px solid var(--border);border-radius:var(--radius-md);text-decoration:none;color:var(--text);font-weight:600;">Follow @QLNI_AI for updates</a>
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<div class="section-label">Get Started</div>
<h2 class="section-title">Try NodeMind for free</h2>
<p class="section-sub">No installation. No API key. Upload any PDF, TXT, or Markdown file at the live demo and get a portable binary index back in under 2 minutes.</p>
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<div style="font-family:var(--font-mono);font-size:var(--text-xl);font-weight:700;color:var(--accent);margin-bottom:var(--space-4);">01</div>
<div style="font-weight:700;margin-bottom:var(--space-2);">Visit the demo</div>
<div style="font-size:var(--text-sm);color:var(--muted);">Go to <a href="https://nodemind.space" style="color:var(--accent);">nodemind.space</a> and click Try Free. Enter your email — login is instant, no inbox check.</div>
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<div style="font-family:var(--font-mono);font-size:var(--text-xl);font-weight:700;color:var(--accent);margin-bottom:var(--space-4);">02</div>
<div style="font-weight:700;margin-bottom:var(--space-2);">Upload a document</div>
<div style="font-size:var(--text-sm);color:var(--muted);">Drop any PDF, TXT, or Markdown file (10 MB per file, 50 MB lifetime per account). Community hardware (RTX 3080 + 128 GB RAM) picks it up, embeds with BGE-M3, and applies the NodeMind codec — typical 5,500-page PDF indexes in ~7 minutes.</div>
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<div style="font-family:var(--font-mono);font-size:var(--text-xl);font-weight:700;color:var(--accent);margin-bottom:var(--space-4);">03</div>
<div style="font-weight:700;margin-bottom:var(--space-2);">Download your index</div>
<div style="font-size:var(--text-sm);color:var(--muted);">Once ready, download both the NodeMind binary index and the RAG float32 index. Run queries side-by-side offline.</div>
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<div style="font-family:var(--font-mono);font-size:var(--text-xl);font-weight:700;color:var(--accent);margin-bottom:var(--space-4);">04</div>
<div style="font-weight:700;margin-bottom:var(--space-2);">Compare live</div>
<div style="font-size:var(--text-sm);color:var(--muted);">Use the Compare tab to run queries and see NodeMind vs RAG side by side — latency, index size, compression ratio, and result quality.</div>
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<a href="https://nodemind.space" target="_blank" rel="noopener" class="btn-primary" style="font-size:var(--text-base);padding:var(--space-4) var(--space-12);">Open Live Demo →</a>
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<h2 class="section-title">Interested in licensing or collaboration?</h2>
<p class="section-sub" style="margin-inline:auto;text-align:center;">NodeMind is built by a solo independent researcher. Reach out for licensing, enterprise integration, or research collaboration.</p>
<a href="mailto:saikiranbathula1@gmail.com" style="display:inline-flex;align-items:center;gap:var(--space-3);margin-top:var(--space-8);padding:var(--space-4) var(--space-10);background:var(--surface);border:1.5px solid var(--border);border-radius:var(--radius-lg);font-size:var(--text-sm);font-weight:600;text-decoration:none;color:var(--text);">
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saikiranbathula1@gmail.com
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<span style="font-size:var(--text-xs);color:var(--faint);">Sai Kiran Bathula · Coleambally, NSW, Australia · Independent Researcher</span>
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