class MLEngineer:
    def __init__(self):
        self.name        = "haihpse150218"
        self.role        = "ML / Data Science learner · MSE @ FSB (AI & Business Analytics)"
        self.philosophy  = "data > model — clean signal beats big nets"
        self.approach    = ["understand the fundamentals",
                            "engineer the data",
                            "reuse & fine-tune pretrained models"]
        self.note_system = "a structured Second Brain of ML study notes"

    def currently(self) -> str:
        return "shipping LLM-powered tooling and learning in public"

Languages & Core

ML / Data Science & Tooling

LLM IaC Drift Detection Service

An LLM-powered Infrastructure-as-Code drift detection service, built for the VPBank "Senior Stack" hackathon. It reasons over IaC definitions and live infrastructure to surface configuration drift, turning raw state diffs into human-readable, actionable insight.

📚 Currently Learning Core ML fundamentals — bias / variance, cross-validation, evaluation metrics Tree ensembles — Decision Trees, Random Forest, AdaBoost, XGBoost Hyperparameter tuning & model selection Data-centric workflows — cleaning, feature engineering, leakage hunting

🎯 Where I'm Heading Fine-tuning & adapting pretrained models (transformers, LLMs) Retrieval-augmented & LLM-powered services Productionizing ML — from notebook to deployable service Documenting everything in a structured Second Brain

I write about building and breaking things — AI, ML, and coding agents.

Best way to reach me is through my work — explore the code, open an issue, or start a discussion.