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Lwg78/README.md

Hi there, I'm Wen Gio 👋

Bridging Retail Operations & Artificial Intelligence.

I am a Retail Ops Professional transitioning into AI Engineering. I build practical systems that solve real-world problems—from automating stocktake variances to decoding genomic sequences.

🛠️ Technical Arsenal

  • Languages: Python (Pandas, PyTorch, NumPy)
  • Engineering: Docker, GitHub Actions (CI/CD), FastAPI, Streamlit
  • Focus: Process Automation, Deep Learning, Data Engineering

📂 Portfolio Projects

🧠 Cognitive AI & Edge Engineering

Engineering intelligent systems that run efficiently on constrained hardware.

Project Description Tech Stack
Memory Bear (Legacy Edge) 🐻 Cognitive Agent. A local AI agent running on a 2017 MacBook Air (Intel). Implements Ebbinghaus Forgetting Curves to dynamically manage context window limits. Features Quantized Inference and a biologically inspired Memory Graph. Python, Llama.cpp, ChromaDB, NetworkX, Phi-3

🛒 Retail Operations & Supply Chain AI

My core focus: Bringing engineering rigor to supermarket logistics.

Project Description Tech Stack
FreshGuard V2 (Retail Waste) 🚀 Flagship. Production-grade forecasting engine reducing perishable waste. Features Docker, CI/CD, and Streamlit. The engineered evolution of V1. Python, Docker, Pytest, Holt-Winters
Stocktake Variance Reporter Automation Tool. A full-stack utility designed to cut stocktake reporting time by 99%. Includes "Theft Detection" logic and a web UI. FastAPI, Docker, Pandas
Enterprise Retail Solution Advanced R&D. A predictive analytics experiment utilizing Armstrong Cycle Transformers to forecast complex sales demand patterns. Time-Series, PyTorch Transformers
Retail Waste System (V1) Prototype. My initial menu-driven application for inventory tracking. Focuses on core CRUD operations and basic analytics. Python, Matplotlib, Pandas

🧠 AI Engineering & Search Systems

Building efficient, modular systems for real-world constraints.

Project Description Tech Stack
Silver Retriever Offline RAG System. A modular search engine designed for legacy hardware (No GPUs). Features a Plugin Architecture ("The Brain") to detect user intent (Deadlines, Tasks) using TF-IDF instead of heavy LLMs. Includes CI/CD and Smart Chunking. Python, Streamlit, Scikit-Learn, GitHub Actions

🧬 Bio-Informatics & Deep Learning

Applying AI to decode complex genomic sequences.

Project Description Tech Stack
Genomic Decoder V2 Advanced Pipeline. Refined Deep Learning architecture for DNA sequencing. Focuses on modular code structure and improved inference performance over the original. PyTorch, BioPython, CI/CD
Genomic Decoder (FlyOS) Research Implementation. An end-to-end pipeline that reads raw DNA sequences to predict gene expression. Features O(1) lazy-loading for massive datasets. PyTorch, Transformers

🎓 Early Projects

Project Description Tech Stack
O-Level Predictor First App. My very first attempt to convert a Python calculation script into an interactive web app using Streamlit. Python, Streamlit

📫 Connect with me

Pinned Loading

  1. memory-bear-legacy-edge memory-bear-legacy-edge Public

    Cognitive AI Agent implementing Ebbinghaus Decay on Legacy Intel Mac hardware.

    Jupyter Notebook

  2. Silver-Retriever Silver-Retriever Public

    A lightweight, modular RAG system designed for offline document retrieval on low-resource hardware. Powered by TF-IDF and dynamic plugins instead of heavy LLMs.

    Jupyter Notebook

  3. retail-waste-management-v2 retail-waste-management-v2 Public

    Production-grade retail demand forecasting engine. Reduces perishable waste using Holt-Winters smoothing. Upgraded from V1 with Streamlit, Docker, and CI/CD pipelines.

    Jupyter Notebook

  4. genomic-decoder-fly genomic-decoder-fly Public

    End-to-end Deep Learning pipeline (PyTorch/Transformers) that reads raw DNA to predict gene expression. Features O(1) lazy-loading for massive datasets.

    Python

  5. Stocktake-Variance-Reporter Stocktake-Variance-Reporter Public

    Full-stack automation tool for Retail Ops. Features Docker, GitHub Actions (CI/CD), and automated variance detection logic.

    Python

  6. Enterprise-Solution-for-Supermarket-style-retail Enterprise-Solution-for-Supermarket-style-retail Public

    Sales forecasting model utilizing Armstrong Cycle Transformers to predict supermarket demand patterns.

    Jupyter Notebook