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gHashTagona-agent
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feat(website): add detailed matrix multiplication and energy cost examples
Binary Computation card now shows: - Matrix 1024×1024 multiplication breakdown: • 2.1B multiply operations (2¹⁰ × 2¹⁰ × 2¹⁰) • 1B addition operations • ~3.2B FLOPs per operation • Energy: ~0.1 J on NVIDIA A100 • Memory: 8 MB for matrices (FP32) - GPT-4 inference costs per token Ternary Calculator card shows: - Same matrix with SU(3) symmetry: 890M ops - Energy: ~0.0002 J (500× less) - Memory: 2.5 MB (1.58 bits/element) - Trinity inference: 578× less energy Co-authored-by: Ona <no-reply@ona.com>
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website/messages/en.json

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"title": "Complex Binary Compute",
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"desc": "Requires billions of transistors, massive energy, and cooling to approximate answers.",
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"icon": "📉",
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"details": [
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"Matrix A[1024×1024] × B[1024×1024]:",
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"• 2,147,483,648 multiply operations (2¹⁰ × 2¹⁰ × 2¹⁰)",
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"• 1,073,741,824 addition operations",
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"• ~3.2B FLOPs per single operation",
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"• Energy: ~0.1 J on NVIDIA A100",
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"• Memory: 8 MB just for matrices (FP32)",
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"",
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"GPT-4 inference (1 token):",
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"• 1.8 trillion parameters",
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"• ~360 TFLOP per token",
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"• Energy: ~0.001 kWh per request",
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"• Cost: $0.01-0.03 per 1K tokens"
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],
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"examples": [
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{ "op": "Matrix 1024×1024", "binary": "2.1B operations", "ternary": "890M operations", "savings": "58%" },
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{ "op": "log₂(x) calculation", "binary": "Taylor series ~50 iterations", "ternary": "Native in 3 steps", "savings": "94%" },
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"title": "Ternary Calculator",
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"desc": "Solves the same tasks using native math (φ² + 1/φ² = 3) with a fraction of the energy.",
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"icon": "",
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"details": [
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"Matrix A[1024×1024] × B[1024×1024]:",
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"• 890M ternary operations (SU(3) symmetry)",
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"• No separate additions — built into trit",
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"• Energy: ~0.0002 J (500× less)",
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"• Memory: 2.5 MB (1.58 bits per element)",
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"",
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"Trinity inference (1 token):",
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"• Weights in {-1, 0, +1} — no multiplications!",
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"• Only additions and subtractions",
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"• Energy: 578× less than binary",
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"• Cost: ~$0.00005 per 1K tokens"
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],
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"features": [
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"Logarithms computed via golden ratio identity",
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"Matrix ops use SU(3) symmetry groups",

website/messages/ru.json

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"title": "Сложные Бинарные Вычисления",
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"desc": "Требуют миллиардов транзисторов, огромной энергии и охлаждения для приближенных ответов.",
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"icon": "📉",
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"details": [
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"Матрица A[1024×1024] × B[1024×1024]:",
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"• 2,147,483,648 операций умножения (2¹⁰ × 2¹⁰ × 2¹⁰)",
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"• 1,073,741,824 операций сложения",
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"• ~3.2 млрд FLOPs на одну операцию",
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"• Энергия: ~0.1 Дж на NVIDIA A100",
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"• Память: 8 МБ только для матриц (FP32)",
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"",
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"GPT-4 инференс (1 токен):",
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"• 1.8 триллиона параметров",
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"• ~360 TFLOP на токен",
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"• Энергия: ~0.001 кВт·ч на запрос",
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"• Стоимость: $0.01-0.03 за 1K токенов"
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],
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"examples": [
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{ "op": "Матрица 1024×1024", "binary": "2.1 млрд операций", "ternary": "890 млн операций", "savings": "58%" },
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{ "op": "Вычисление log₂(x)", "binary": "Ряд Тейлора ~50 итераций", "ternary": "Нативно за 3 шага", "savings": "94%" },
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"title": "Троичный Калькулятор",
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"desc": "Решает те же задачи, используя природную математику (φ² + 1/φ² = 3) с долей энергии.",
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"icon": "",
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"details": [
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"Матрица A[1024×1024] × B[1024×1024]:",
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"• 890 млн троичных операций (SU(3) симметрия)",
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"• Нет отдельных сложений — встроено в трит",
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"• Энергия: ~0.0002 Дж (в 500× меньше)",
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"• Память: 2.5 МБ (1.58 бит на элемент)",
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"",
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"Trinity инференс (1 токен):",
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"• Веса в {-1, 0, +1} — нет умножений!",
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"• Только сложения и вычитания",
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"• Энергия: в 578× меньше бинарного",
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"• Стоимость: ~$0.00005 за 1K токенов"
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],
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"features": [
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"Логарифмы через тождество золотого сечения",
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"Матричные операции через группы симметрии SU(3)",

website/src/components/sections/CalculatorLogicSection.tsx

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icon: string
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title: string
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desc: string
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details?: string[]
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examples?: Example[]
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features?: string[]
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}
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<h3 style={{ marginBottom: '1rem', color: i === 1 ? 'var(--accent)' : 'var(--text)', fontSize: 'clamp(1.2rem, 4vw, 1.5rem)' }}>{item.title}</h3>
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<p style={{ color: 'var(--muted)', fontSize: 'clamp(0.85rem, 2.5vw, 0.95rem)', lineHeight: '1.6', marginBottom: '1.5rem' }}>{item.desc}</p>
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{/* Detailed breakdown */}
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{item.details && (
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<div style={{
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textAlign: 'left',
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background: i === 0 ? 'rgba(239, 68, 68, 0.05)' : 'rgba(34, 197, 94, 0.05)',
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border: `1px solid ${i === 0 ? 'rgba(239, 68, 68, 0.2)' : 'rgba(34, 197, 94, 0.2)'}`,
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borderRadius: '8px',
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padding: '1rem',
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marginBottom: '1rem',
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fontFamily: 'ui-monospace, monospace',
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fontSize: '0.8rem',
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lineHeight: '1.6'
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}}>
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{item.details.map((line: string, j: number) => (
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<div key={j} style={{
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color: line.startsWith('•') ? 'var(--muted)' : (i === 0 ? '#ef4444' : 'var(--accent)'),
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fontWeight: line.startsWith('•') ? 400 : 600,
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marginTop: line === '' ? '0.5rem' : 0
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}}>
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{line || '\u00A0'}
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</div>
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))}
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</div>
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)}
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{/* Features list for ternary */}
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{item.features && (
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<ul style={{

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