// Shared content + placeholders for Amey Ranaware's portfolio.
// One source of truth so we can iterate copy in one place.

const PROFILE = {
  name: 'Amey',
  nameAlt: 'Ranaware Amey',
  employer: 'Pune Institute of Computer Technology',
  role: 'Software Engineer',
  location: 'Pune, MH',
  email: 'ranaware.amey@gmail.com',
  blurb: [
    "Backend engineer. AI agent builder. Loop finder.",
    "Currently studying at Pune Institute of Computer Technology (PICT), specializing in backend systems, Gen AI engineering, and autonomous agent workflows."
  ],
  excited: "scalable backend systems, autonomous AI agents, and finding loops in products to make them highly optimized and profitable",
  cta: "Get in touch with me if you love building.",
  links: [
    { label: 'Email', href: 'mailto:ranaware.amey@gmail.com' },
    { label: 'GitHub', href: 'https://github.com/ameysr' },
    { label: 'X', href: 'https://x.com/internetDevv' },
    { label: 'LinkedIn', href: 'https://www.linkedin.com/in/amey-ranaware-8537062a0' },
  ]
};

const EXPERIENCE = [
  {
    role: 'Open Source Contributor',
    company: 'traceroot.ai (YC S25)',
    companyLink: 'https://traceroot.ai/',
    date: 'Jun 2026',
    points: [
      <span>Designed a <strong>two phase trace loading system</strong> (<strong>FastAPI + React Query</strong>), cutting payloads <strong>~24 MB to ~39 KB (99.8%)</strong> and latency <strong>~7s to ~28ms (250x)</strong>, see <a href="https://github.com/traceroot-ai/traceroot/pull/1063#issuecomment-4651247666" target="_blank" rel="noreferrer">PR #1063</a>.</span>,
      <span>Integrated pricing for <strong>Zhipu AI's GLM 5.2 model</strong> across the catalog, provider adapters, and automated tests, see <a href="https://github.com/traceroot-ai/traceroot/pull/1251" target="_blank" rel="noreferrer">PR #1251</a>.</span>
    ]
  },
  {
    role: 'Open Source Contributor',
    company: 'mem0.ai (YC S24)',
    companyLink: 'https://mem0.ai/',
    date: 'Jul 2026',
    points: [
      <span>Added <strong>Elasticsearch vector store provider</strong> to the TypeScript SDK (<strong>@elastic/elasticsearch</strong> v9), supporting Elastic Cloud and self-hosted with kNN search, metadata filtering, and 22 unit tests, see <a href="https://github.com/mem0ai/mem0/pull/5866" target="_blank" rel="noreferrer">PR #5866</a>.</span>,
    ]
  },
  {
    role: 'Backend Development Intern',
    company: 'Strikes',
    companyLink: 'https://strikes.in/',
    date: 'Oct 2025 – Nov 2025',
    points: [
      <span>Resolved <strong>YouTube API quota exhaustion</strong> by implementing a <strong>background job queue and scheduled nightly sync</strong>, enabling the platform to reliably support <strong>50K+ users</strong>.</span>,
      <span>Optimized <strong>Judge0 API</strong> usage by redesigning the execution flow so failed submissions consume only <strong>1 token instead of 10+</strong>, cutting per failure cost by <strong>~90%</strong>.</span>,
      <span>Built an <strong>AI powered evaluation agent</strong> using <strong>Playwright</strong> that automatically analyzes <strong>30+ hackathon submissions</strong> and generates detailed performance reports.</span>
    ]
  }
];

const METRICS = [
  { number: '250x', label: 'Trace Load Speedup' },
  { number: '99.8%', label: 'Payload Size Cut' },
  { number: '50K+', label: 'Users Supported' },
  { number: '2000+', label: 'Submissions' }
];

const PROJECTS = [
  {
    title: 'SAP GraphIQ',
    subtitle: 'Graph Intelligence for SAP',
    desc: 'NL to Cypher agent over Neo4j with 85 hardcoded query functions, deterministic semantic routing, and a live Schema Discovery Agent.',
    features: [
      <span><strong>85 hardcoded functions</strong>, LLM selects which to call, never generates raw SQL. <strong>Zero hallucination, zero injection</strong>.</span>,
      <span><strong>Schema Discovery Agent</strong> auto-introspects <strong>Neo4j</strong> on startup. Zero hardcoded schema.</span>,
      <span><strong>92% useful answer rate, 40% exact match</strong> on benchmark queries.</span>,
    ],
    tech: ['TypeScript', 'LangGraph', 'Neo4j', 'Redis'],
    links: [
      { label: 'Live Demo', href: 'https://dodge-ai-task-lilac.vercel.app' },
      { label: 'GitHub', href: 'https://github.com/Ameysr/DodgeAI_Task' }
    ]
  },
  {
    title: 'IntentMap',
    subtitle: 'ML Pipeline for Customer Intent Intelligence',
    desc: 'End to end pipeline that turns raw customer support messages into clustered intent insights with sentiment scores, fully local, zero API cost.',
    features: [
      <span>PII scrubbing, <strong>384 dim embeddings</strong> via <strong>all-MiniLM-L6-v2</strong>, fully local. <strong>No API cost, no latency</strong>.</span>,
      <span><strong>UMAP</strong> (384 to 5 dims) + <strong>HDBSCAN</strong> clustering. No guess-K-upfront, auto-falls back to KMeans.</span>,
      <span>Concurrent labeling via <strong>AsyncGroq</strong>, 30s of work in under <strong>5s at 500+ tok/s</strong> on the free tier.</span>,
    ],
    tech: ['Python', 'HDBSCAN', 'UMAP', 'Groq', 'all-MiniLM-L6-v2'],
    links: [
      { label: 'GitHub', href: 'https://github.com/Ameysr/IntentMap' }
    ]
  },
  {
    title: 'PeerPro',
    subtitle: 'Connect and Stay Consistent',
    desc: 'Peer learning platform where groups watch YouTube playlists together, track progress, and compete on leaderboards.',
    features: [
      <span><strong>3-shard MongoDB</strong> architecture with hash-based distribution. Replaced <strong>Mongoose .populate()</strong> entirely.</span>,
      <span>Optimistic real-time chat via <strong>Socket.io</strong>, client-side ID assignment, <strong>10 msg/s rate limiting</strong>.</span>,
      <span><strong>Redis cached leaderboards</strong> with cross-shard aggregation, weighted scoring, and <strong>5 min TTL</strong>.</span>,
    ],
    tech: ['React.js', 'Node.js', 'MongoDB', 'Redis', 'Socket.io'],
    links: [
      { label: 'Live Demo', href: 'https://peerpro.in' }
    ]
  }
];

const SKILLS = [
  { category: 'Tech Stack', items: ['React.js', 'Node.js', 'Python', 'TypeScript', 'WebSocket', 'Express.js'] },
  { category: 'Gen AI', items: ['RAG', 'LangGraph', 'LangChain', 'Vector Databases', 'Graph Databases'] },
  { category: 'Database', items: ['MongoDB', 'PostgreSQL', 'ClickHouse', 'Redis', 'Neo4j', 'Pinecone'] },
  { category: 'Infrastructure', items: ['AWS (EC2)', 'Docker', 'Render', 'Vercel'] },
  { category: 'Tools', items: ['Git', 'GitHub', 'Postman', 'Playwright'] }
];

const ACHIEVEMENTS = [
  <span>Product strategy idea shared with <strong>Dhruv Rathee</strong> implemented in his AI product; garnering 11K+ views on <a href="https://x.com/internetDevv/status/1978104569696505868" target="_blank" rel="noreferrer">X</a>.</span>,
  <span>Proposed an emotional voice note engagement strategy for AI companion apps on <a href="https://x.com/internetDevv/status/2035607018766229947" target="_blank" rel="noreferrer">X</a>, later adopted and implemented by <strong>Rumik AI</strong>.</span>,
  <span>Achieved <strong>16th rank</strong> in the Nexus Fullstack Hackathon among hundreds of participants.</span>,
  <span>Secured <strong>973rd rank</strong> in MSBTE examination, earning admission to PICT, one of Maharashtra's top engineering colleges.</span>,
];

const OSS = [
  { org: 'mem0ai', label: 'mem0' },
  { org: 'traceroot-ai', label: 'traceroot' },
  { org: 'Tracer-Cloud', label: 'Tracer-Cloud' }
];

Object.assign(window, { PROFILE, EXPERIENCE, METRICS, PROJECTS, SKILLS, ACHIEVEMENTS, OSS });