arsam
c83e31d28a
Import the existing local project snapshot. This commit organizes existing files; it does not represent newly developed functionality.
279 lines
14 KiB
JavaScript
279 lines
14 KiB
JavaScript
import { ART_EN1_CONTENT } from './article-contents-en-1.js';
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import { ART_EN2_CONTENT } from './article-contents-en-2.js';
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import { ART_EN3_CONTENT } from './article-contents-en-3.js';
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import { ART_EN4_CONTENT } from './article-contents-en-4.js';
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import { ART_EN5_CONTENT } from './article-contents-en-5.js';
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import { ART_EN6_CONTENT } from './article-contents-en-6.js';
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import { ART_EN7_CONTENT } from './article-contents-en-7.js';
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import { ART_EN8_CONTENT } from './article-contents-en-8.js';
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import { ART_EN9_CONTENT } from './article-contents-en-9.js';
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import { ART_EN10_CONTENT } from './article-contents-en-10.js';
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export const authors = [
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{
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id: 'a1',
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name: 'Arsam Sabbagh',
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slug: 'arsam-sabbagh',
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username: 'arsam-sabbagh',
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role: 'Editor-in-Chief',
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specialty: 'AI Infrastructure & Software Engineering',
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bio: 'Arsam covers AI infrastructure, developer tooling, and software systems at Teknav. He focuses on the engineering decisions that shape how AI is built and deployed at scale — from training data quality to inference cost to agentic architectures.',
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expertise: ['AI Engineering', 'Software Architecture', 'Data Infrastructure', 'Developer Tools'],
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initials: 'AS',
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color: '#0F6B73',
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verifiedExpert: true,
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articleCount: 52,
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joinedDate: '2025-01-01',
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social: { twitter: 'https://x.com/teknavir', linkedin: 'https://linkedin.com/company/teknav', github: '#' },
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},
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{
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id: 'a2',
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name: 'Ahmadreza Mahjoub',
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slug: 'ahmadreza-mahjoub',
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username: 'ahmadreza-mahjoub',
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role: 'Security Editor',
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specialty: 'Cybersecurity & AI Security',
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bio: 'Ahmadreza covers cybersecurity, AI security, and the legal landscape around technology risk. His writing focuses on threat analysis, vulnerability disclosure, and the intersection of AI capability with security policy.',
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expertise: ['AI Security', 'Supply Chain Security', 'Legal Tech', 'Cryptography'],
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initials: 'AM',
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color: '#2F8F6B',
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verifiedExpert: true,
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articleCount: 10,
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joinedDate: '2025-03-01',
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social: { twitter: 'https://x.com/teknavir', linkedin: 'https://linkedin.com/company/teknav', github: '#' },
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},
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];
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export const categories = [
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{ id: 'ai', name: 'AI Engineering', color: '#0F6B73', description: 'Production AI systems, agents, RAG, inference, and model infrastructure.' },
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{ id: 'security', name: 'Security', color: '#2F8F6B', description: 'AI security, supply chain risk, cryptography, and vulnerability analysis.' },
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{ id: 'software', name: 'Software Architecture', color: '#0F6B73', description: 'System design, developer tools, frameworks, and engineering decisions.' },
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{ id: 'hardware', name: 'Hardware & Compute', color: '#5F6B6D', description: 'GPUs, custom chips, edge computing, and local AI compute.' },
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{ id: 'data', name: 'Data Infrastructure', color: '#D49A2A', description: 'Data quality, vector search, pipelines, and analytics.' },
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{ id: 'policy', name: 'AI Policy', color: '#C76D4A', description: 'Regulation, geopolitics, liability, and governance of AI systems.' },
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{ id: 'research', name: 'Research Notes', color: '#5F6B6D', description: 'Frontier science, mathematics, and AI in academic discovery.' },
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];
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export const articles = [
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{
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id: 'en1',
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slug: 'clean-data-ai-success',
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title: 'Clean Data Is the Real AI Moat: Why Model Quality Starts Before Training',
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subtitle: 'The shift from model-centric to data-centric AI — and what it means for teams building production systems',
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category: 'ai',
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author: 'a1',
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date: 'June 3, 2026',
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dateEn: '2026-06-03',
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readTime: 12,
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photo: '/images/articles/ai-clean-data.jpg',
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metaTitle: 'Clean Data and AI Success: The Data-Centric AI Guide',
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metaDescription: 'Why clean, well-structured training data matters more than model architecture. A technical guide to data-centric AI for production teams.',
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tags: ['data quality', 'training data', 'data-centric AI', 'MLOps'],
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diagram: 'pipeline',
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persianSlug: 'ai-clean-data',
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persianUrl: 'https://www.teknav.ir/article/ai-clean-data',
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content: ART_EN1_CONTENT,
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},
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{
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id: 'en2',
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slug: 'custom-ai-chips-architecture-guide',
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title: 'Beyond the GPU: How Custom AI Silicon Is Reshaping Inference Economics',
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subtitle: 'A technical breakdown of TPUs, NPUs, and purpose-built inference chips — and when they beat general-purpose GPUs',
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category: 'hardware',
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author: 'a1',
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date: 'June 4, 2026',
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dateEn: '2026-06-04',
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readTime: 13,
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photo: '/images/articles/custom-ai-chip.jpg',
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metaTitle: 'Custom AI Chips vs GPUs: Architecture and Inference Cost Guide',
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metaDescription: 'How TPUs, NPUs, and custom AI silicon work, when they outperform GPUs, and what this means for AI infrastructure costs in 2026.',
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tags: ['AI hardware', 'TPU', 'NPU', 'inference', 'GPU'],
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diagram: 'chip',
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persianSlug: 'custom-ai-chips',
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persianUrl: 'https://www.teknav.ir/article/custom-ai-chips',
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content: ART_EN2_CONTENT,
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},
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{
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id: 'en3',
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slug: 'software-supply-chain-security-guide',
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title: 'Software Supply Chain Security: The Attack Surface Hidden in Your Dependencies',
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subtitle: 'How SolarWinds, XZ Utils, and npm package attacks changed the threat model — and what defensive engineering looks like now',
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category: 'security',
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author: 'a2',
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date: 'June 4, 2026',
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dateEn: '2026-06-04',
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readTime: 11,
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photo: '/images/articles/software-supply-chain.jpg',
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metaTitle: 'Software Supply Chain Security: A Technical Guide for Engineering Teams',
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metaDescription: 'How supply chain attacks work, real-world cases from SolarWinds to XZ Utils, and the defensive engineering practices that reduce risk.',
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tags: ['supply chain security', 'SBOM', 'dependency security', 'DevSecOps'],
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diagram: 'cyber',
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persianSlug: 'software-supply-chain',
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persianUrl: 'https://www.teknav.ir/article/software-supply-chain',
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content: ART_EN3_CONTENT,
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},
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{
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id: 'en4',
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slug: 'small-language-models-production-guide',
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title: 'Small Language Models Are Winning in Production: Here\'s Why',
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subtitle: 'When SLMs outperform LLMs on cost, latency, and privacy — a practical evaluation framework for choosing the right model size',
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category: 'ai',
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author: 'a1',
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date: 'June 5, 2026',
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dateEn: '2026-06-05',
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readTime: 10,
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photo: '/images/articles/small-llms.jpg',
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metaTitle: 'Small Language Models (SLMs) vs LLMs: When Smaller Models Win',
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metaDescription: 'A practical guide to when small language models outperform LLMs in production — on cost, latency, accuracy, and data privacy.',
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tags: ['SLM', 'LLM', 'model selection', 'AI inference', 'on-device AI'],
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diagram: 'neural',
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persianSlug: 'small-llms',
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persianUrl: 'https://www.teknav.ir/article/small-llms',
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content: ART_EN4_CONTENT,
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},
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{
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id: 'en5',
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slug: 'vibe-coding-production-failures',
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title: 'Vibe Coding in Production: A Post-Mortem',
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subtitle: 'Why AI-generated code without structure collapses under real-world load — and what engineering discipline still prevents that',
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category: 'software',
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author: 'a1',
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date: 'June 5, 2026',
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dateEn: '2026-06-05',
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readTime: 11,
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photo: '/images/articles/agentic-ai-production-2026.jpg',
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metaTitle: 'Vibe Coding Fails in Production: Post-Mortem Analysis',
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metaDescription: 'Real post-mortem from vibe coding failures in production. What goes wrong when AI-generated code lacks structure, testing, and review.',
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tags: ['vibe coding', 'AI code generation', 'production failures', 'software engineering'],
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diagram: 'arch',
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persianSlug: 'vibe-coding-production-failures',
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persianUrl: 'https://www.teknav.ir/article/vibe-coding-production-failures',
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content: ART_EN5_CONTENT,
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},
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{
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id: 'en6',
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slug: 'openclaw-self-hosted-agent-architecture',
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title: 'OpenClaw\'s Architecture: How a Self-Hosted AI Agent Framework Reached 145K Stars',
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subtitle: 'Inside the technical decisions that make OpenClaw deployable on a VPS — and why self-hosting AI agents is becoming the default for security-conscious teams',
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category: 'software',
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author: 'a1',
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date: 'June 6, 2026',
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dateEn: '2026-06-06',
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readTime: 13,
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photo: '/images/articles/openclaw-architecture.jpg',
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metaTitle: 'OpenClaw Self-Hosted AI Agent Architecture Explained',
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metaDescription: 'How OpenClaw\'s self-hosted AI agent architecture works, why it reached 145K GitHub stars, and how to evaluate it for production use.',
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tags: ['AI agents', 'self-hosted', 'OpenClaw', 'open source AI'],
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diagram: 'arch',
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persianSlug: 'openclaw-self-hosted-architecture',
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persianUrl: 'https://www.teknav.ir/article/openclaw-self-hosted-architecture',
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content: ART_EN6_CONTENT,
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},
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{
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id: 'en7',
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slug: 'ai-ceo-personal-liability-legal-analysis',
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title: 'Piercing the AI Corporate Veil: Florida\'s Push for CEO Personal Liability',
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subtitle: 'How product liability law is being adapted to hold AI executives personally accountable — and what this precedent means for the industry',
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category: 'policy',
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author: 'a2',
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date: 'June 6, 2026',
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dateEn: '2026-06-06',
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readTime: 10,
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photo: '/images/articles/ai-legal-liability.jpg',
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metaTitle: 'AI CEO Personal Liability: Florida Lawsuit Analysis',
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metaDescription: 'Florida\'s lawsuit to pierce the corporate veil in AI product liability cases. Legal analysis of what CEO personal liability means for AI companies.',
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tags: ['AI liability', 'product liability', 'AI law', 'corporate law'],
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diagram: 'legal',
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persianSlug: 'florida-openai-ceo-personal-liability',
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persianUrl: 'https://www.teknav.ir/article/florida-openai-ceo-personal-liability',
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content: ART_EN7_CONTENT,
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},
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{
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id: 'en8',
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slug: 'ladybird-browser-rust-memory-safety',
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title: 'Ladybird\'s Rust Mandate: What It Means When a Browser Bets on Memory Safety',
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subtitle: 'The independent browser project\'s decision to require Rust for all new code — and why the browser industry is converging on memory-safe languages',
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category: 'software',
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author: 'a1',
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date: 'June 7, 2026',
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dateEn: '2026-06-07',
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readTime: 10,
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photo: '/images/articles/ladybird-rust-memory-safety.jpg',
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metaTitle: 'Ladybird Browser Rust Mandate: Memory Safety in Practice',
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metaDescription: 'Why Ladybird requires Rust for all new browser code, how memory safety prevents real vulnerabilities, and what this signals for C++ in systems software.',
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tags: ['Rust', 'memory safety', 'Ladybird', 'browser security', 'systems programming'],
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diagram: 'arch',
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persianSlug: 'ladybird-rust-mandate-memory-safety',
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persianUrl: 'https://www.teknav.ir/article/ladybird-rust-mandate-memory-safety',
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content: ART_EN8_CONTENT,
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},
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{
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id: 'en9',
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slug: 'ai-disproves-erdos-unit-distance-conjecture',
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title: 'AI Disproves an 80-Year Math Conjecture: Inside the Erdős Unit Distance Proof',
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subtitle: 'How Gemini Deep Think produced a 125-page proof that human mathematicians missed — and what it reveals about AI as a scientific collaborator',
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category: 'research',
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author: 'a1',
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date: 'June 7, 2026',
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dateEn: '2026-06-07',
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readTime: 12,
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photo: '/images/articles/erdos-unit-distance-conjecture-disproved-photo-1.jpg',
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metaTitle: 'AI Disproves Erdős Unit Distance Conjecture: Full Analysis',
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metaDescription: 'Gemini Deep Think disproved the Erdős unit distance conjecture after 80 years. A technical breakdown of the proof, the AI methodology, and what it means for mathematics.',
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tags: ['AI in mathematics', 'Erdős conjecture', 'Gemini', 'AI discovery', 'graph theory'],
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diagram: 'math',
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persianSlug: 'erdos-unit-distance-conjecture-disproved',
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persianUrl: 'https://www.teknav.ir/article/erdos-unit-distance-conjecture-disproved',
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content: ART_EN9_CONTENT,
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},
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{
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id: 'en10',
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slug: 'github-copilot-token-billing-guide',
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title: 'GitHub Copilot\'s Token Billing: What Every Developer Needs to Know',
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subtitle: 'How per-token pricing changes the economics of AI-assisted coding — and practical strategies to control your bill',
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category: 'software',
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author: 'a1',
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date: 'June 8, 2026',
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dateEn: '2026-06-08',
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readTime: 10,
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photo: '/images/articles/copilot-token-billing.jpg',
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metaTitle: 'GitHub Copilot Token Billing: Developer Cost Guide 2026',
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metaDescription: 'How GitHub Copilot\'s token-based billing works, why your bill jumped, and practical techniques to reduce AI coding costs without losing productivity.',
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tags: ['GitHub Copilot', 'AI billing', 'developer tools', 'token pricing'],
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diagram: 'billing',
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persianSlug: 'copilot-token-billing-2026',
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persianUrl: 'https://www.teknav.ir/article/copilot-token-billing-2026',
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content: ART_EN10_CONTENT,
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},
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];
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export function getArticleBySlug(slug) {
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return articles.find(a => a.slug === slug) || null;
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}
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export function getAuthorById(id) {
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return authors.find(a => a.id === id) || null;
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}
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export function getAuthorBySlug(slug) {
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return authors.find(a => a.slug === slug) || null;
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}
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export function getArticlesByCategory(cat) {
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return articles.filter(a => a.category === cat);
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}
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export function getArticlesByAuthor(authorId) {
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return articles.filter(a => a.author === authorId);
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}
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export const siteConfig = {
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name: 'Teknav',
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tagline: 'Deep technology analysis for builders',
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url: 'https://www.teknavglobal.com',
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persianUrl: 'https://www.teknav.ir',
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description: 'Teknav publishes deep technical analysis on AI engineering, security, software architecture, and emerging compute — written for engineers, researchers, and technical founders.',
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twitterHandle: '@teknavir',
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ogImage: '/images/og/default.jpg',
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};
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