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Building
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Building
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Achievements

Achievement: Pair Extraordinairex2Achievement: Pull Sharkx3Achievement: Starstruckx2Achievement: QuickdrawAchievement: YOLOAchievement: Arctic Code Vault Contributor

Achievements

Achievement: Pair Extraordinairex2Achievement: Pull Sharkx3Achievement: Starstruckx2Achievement: QuickdrawAchievement: YOLOAchievement: Arctic Code Vault Contributor

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adityak74/README.md

👋 Hi, I’m Aditya Karnam

Senior AI Systems Engineer · AI Innovator · Researcher

I build intelligent AI systems that combine strong engineering, modern LLM workflows, and real‑world usability.
My work focuses on context‑aware AI, LLM infrastructure, prompt and reasoning tools, and products that make complex workflows feel effortless.

🧠 Key projects & research:

  • 🧠 Universal embedding retrieval toolkit & agentic memory layer → embenx
    • unified API for 15+ vector backends with MCP support for Claude and autonomous agents
    • pip install embenx
  • 📘 Interoperable AI chat contexts → opencontext
  • 📚 Academic retrieval with MCP → mcp-scholarly
  • 🏥 Applied model fine‑tuning in healthcare → medfit-llm
  • ⚙️ Developer productivity automation → google-drive-upload-git-action
  • 🎨 Creativity‑focused Claude skills → cc-creativity-skills

💬 I share work and thoughts on AI systems, open‑source tools, prompt design, and intelligent automation:

🧪 Currently exploring how to make AI systems more interpretable without sacrificing performance.

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  1. embenx embenx Public

    🚀 The Agentic Memory Layer & Universal Retrieval Toolkit. Synthetic data generation, 15+ vector backends, hybrid search, and MCP native memory for AI agents.

    Python 4

  2. google-drive-upload-git-action google-drive-upload-git-action Public

    Forked from mathisve/gdrive-upload-action

    Github action that uploads files to Google Drive

    Go 104 24

  3. mcp-scholarly mcp-scholarly Public

    A MCP server to search for accurate academic articles.

    Python 178 25

  4. medfit-llm medfit-llm Public

    MEDFIT-LLM: Medical Enhancements through Domain-Focused Fine Tuning of Small Language Models