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Achievement: QuickdrawAchievement: StarstruckAchievement: Pair ExtraordinaireAchievement: Pull Shark

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kiyeonjeon21/README.md

Hi, I'm Kiyeon

AI Engineer — started in statistical ML, now building with agents.

I've worked across the full data & AI stack as an ML Engineer, Data Engineer, Data Scientist, and AI Engineer.

What I'm focused on

  • Agentic AI — orchestration, harness engineering, and building autonomous agents
  • Native AI Development — building services with Claude Code
  • Container Orchestration — Docker, Kubernetes
  • Data Lakehouse — modern data engineering architectures

Currently

Building AI-native services and exploring what's possible with agentic workflows.

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  1. graphrag-lab graphrag-lab Public

    Benchmark 9 GraphRAG frameworks (Microsoft, LightRAG, nano, fast, Neo4j, DataStax, Cognee, Graphiti, RAG-Anything) with cross-model LLM-as-judge evaluation

    Python

  2. latent-lab latent-lab Public

    ML/DL/LLM experimentation lab for Apple Silicon — one CLI for classical ML, deep learning, LLM fine-tuning, and RAG

    Python

  3. data-stack-lab data-stack-lab Public

    Docker-based Modern Data Stack sandbox — 26+ tools (Airflow, dbt, Kafka, ClickHouse, Trino, Spark, Flink, Iceberg, MLflow, and more) with one-command recipes for batch ELT, streaming, lakehouse, an…

    Jupyter Notebook

  4. agentops-learn agentops-learn Public

    Hands-on tutorials comparing LLM observability tools — Langfuse, AgentOps.ai, Braintrust, Helicone, Arize Phoenix

    Jupyter Notebook

  5. dart-fss-cli dart-fss-cli Public

    CLI for Korea DART Open API — 83 endpoints for corporate filings, financials, and disclosures. Built for humans and AI agents.

    TypeScript

  6. naver-cli naver-cli Public

    Agent-native CLI for Naver Open APIs

    TypeScript