Skip to content
View kchia's full-sized avatar
77 followers · 4 following

Achievements

Achievement: YOLOAchievement: Pair ExtraordinaireAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3

Achievements

Achievement: YOLOAchievement: Pair ExtraordinaireAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3

Highlights

  • Pro

Organizations

@Boundless-Avocado @CulturedCheese

Block or report kchia

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
kchia/README.md

Hi, I’m Hou 👋

I’m a senior full-stack engineer with 10+ years of experience building and operating secure, scalable, high-reliability web platforms. My background spans healthcare and large-scale consumer products, where correctness, uptime, and operational discipline mattered.

I’m passionate about the shift toward AI-driven products. To stay ahead of that curve, I’ve been intentionally evolving my full-stack toolkit to include LLM integration, and I’m eager to apply these skills to solve real-world problems. I approach AI the same way I approach any complex dependency: with guardrails, observability, cost awareness, and clearly understood failure modes. I’m particularly interested in where AI should and should not be used in real product workflows.

📝 I share notes and experiments from this work at
https://prompt-deploy.beehiiv.com and https://www.youtube.com/@promptdeploy

What I Work On

  • Full-Stack Applications
    End-to-end systems built with TypeScript, React, Next.js, Node.js, Python, and FastAPI, designed to scale, be observable, and be owned long-term. Some projects use LangChain/LangGraph for orchestration.

  • AI-Powered Product Features
    Applying LLM capabilities to specific product workflows where they add leverage, with guardrails, evaluation, and clear degradation paths.

  • Data Retrieval & Search Patterns
    Structured retrieval pipelines (including vector-based approaches) used to support AI features without compromising reliability or cost.

  • Operational Scaffolding
    Error handling, monitoring, evaluation harnesses, and CI/CD that make complex systems — including AI-assisted ones — debuggable and supportable.

Tech I Use

Languages

TypeScript JavaScript Python

Frontend

React Next.js

Backend & APIs

Node.js FastAPI REST GraphQL

Data & Storage

PostgreSQL MongoDB Redis

Infrastructure & Operations

AWS Docker CI/CD Kubernetes

AI-Enabled Features (Project Work)

OpenAI Anthropic RAG Qdrant

Background

Before working on AI-enabled features, I spent over a decade as a full-stack engineer shipping code at companies including Allergan Aesthetics (AbbVie), Zocdoc, and GameStop. That experience shaped how I think about system design, on-call ownership, and long-term maintainability.

I founded Kappa Innovation LLC - a solo software development and tech training consultancy. I designed and delivered software engineering training — including AI-related curricula — for enterprise and bootcamp programs, training over 500 engineers live. Teaching reinforced my bias toward clarity, fundamentals, and avoiding unnecessary complexity.

What I’m Looking For

Senior full-stack engineering roles working on AI-powered products.

Project Spotlight: ComponentForge

View Repository

An AI-powered design-to-code pipeline that transforms visual inputs (screenshots and Figma) into accessible, production-ready React components.

Key Engineering Pillars:

  • Design System Integrity: Uses a multi-agent RAG architecture to ensure generated code strictly follows shadcn/ui patterns and local design system tokens.
  • Structured Orchestration: Moves beyond "one-shot" generation by using specialized agents to handle visual analysis, architectural mapping, and accessibility validation.
  • Operational Reliability: Built with the same discipline as a standard full-stack dependency, focusing on observability and predictable component output rather than just creative generation.

🤝 Connect

Pinned Loading

  1. component-forge component-forge Public

    Multi-agent RAG pipeline transforming UI designs into production-ready React components with design-system integrity.

    Python

  2. gaming-research-agent-ai gaming-research-agent-ai Public

    A production-oriented research agent utilizing RAG and semantic search to navigate industry-specific data. Features an agentic workflow designed for verifiable outputs and structured information re…

    Jupyter Notebook

  3. paperflow-ai paperflow-ai Public

    A simulation of complex business logic automation using a multi-agent architecture. Demonstrates how to handle stateful, long-running operations and task handoffs in a structured environment.

    Python

  4. project-management-agentic-workflow project-management-agentic-workflow Public

    Automated planning engine that decomposes high-level goals into actionable task graphs. Explores agentic error-handling and recursive task refinement for project management workflows.

    Python

  5. ai-engineering-starter ai-engineering-starter Public template

    A comprehensive 'Day One' template for AI products. Integrates TypeScript/Python with observability, auth, and CI/CD, providing the operational scaffolding needed for reliable LLM applications.

    Makefile

  6. langgraph-research-agent langgraph-research-agent Public

    An implementation of cyclic agentic workflows using LangGraph. Demonstrates precise state management and iterative reasoning loops for complex, open-ended research tasks.

    TypeScript