The Evolution of Todo Project

This project, "The Evolution of Todo," is designed to foster mastery in Spec-Driven Development and Cloud-Native AI by iteratively building a Todo application. Starting from a simple in-memory console application, the project evolves into a sophisticated, cloud-native AI chatbot deployed on Kubernetes.

What You Will Learn

You will gain hands-on experience and master key areas in modern software development:

• Spec-Driven Development: Utilizing Claude Code and Spec-Kit Plus for defining and implementing features.
• Reusable Intelligence: Developing Agents, Skills, and Subagents.
• Full-Stack Development: Building with Next.js, FastAPI, SQLModel, and Neon Serverless Database.
• AI Agent Development: Leveraging OpenAI Agents SDK and Official MCP SDK.
• Cloud-Native Deployment: Docker, Kubernetes (Minikube, DOKS), Helm Charts, Kafka, and Dapr.
• AIOps: Using tools like kubectl-ai and kagent.
• Cloud-Native Blueprints: Developing blueprints for spec-driven deployment.

Project Phases Overview

The project is structured into five progressive phases, each building upon the last:

Phase I: Todo In-Memory Python Console App

• Objective: Build a command-line todo application that stores tasks in memory.
• Functionality: Add, Delete, Update, View, Mark Complete tasks.
• Technology Stack: Python 3.13+, UV, Claude Code, Spec-Kit Plus.
• Development Approach: Strictly spec-driven; no manual coding allowed, Claude Code generates implementation from refined specs.

Phase II: Todo Full-Stack Web Application

• Objective: Transform the console app into a modern multi-user web application with persistent storage.
• Functionality: All Basic Level features as a web application, RESTful API, responsive frontend, persistent storage.
• Technology Stack: Frontend (Next.js 16+ App Router), Backend (Python FastAPI), ORM (SQLModel), Database (Neon Serverless PostgreSQL), Authentication (Better Auth with JWT).

Phase III: Todo AI Chatbot

• Objective: Create an AI-powered chatbot interface for managing todos through natural language.
• Functionality: Conversational interface for all Basic Level features, AI agents use MCP tools to manage tasks, stateless chat endpoint persisting conversation state to database.
• Technology Stack: Frontend (OpenAI ChatKit), Backend (Python FastAPI), AI Framework (OpenAI Agents SDK), MCP Server (Official MCP SDK), ORM (SQLModel), Database (Neon Serverless PostgreSQL), Authentication (Better Auth).

Phase IV: Local Kubernetes Deployment

• Objective: Deploy the Todo Chatbot on a local Kubernetes cluster using Minikube and Helm Charts.
• Functionality: Containerize frontend and backend (using Docker AI Agent - Gordon if available), create Helm charts, deploy on Minikube.
• Technology Stack: Containerization (Docker), Docker AI (Gordon), Orchestration (Kubernetes - Minikube), Package Manager (Helm Charts), AI DevOps (kubectl-ai, kagent).

Phase V: Advanced Cloud Deployment

• Objective: Implement advanced features and deploy to a production-grade Kubernetes cluster on Azure (AKS)/Google Cloud (GKE)/Oracle.
• Functionality: Implement Advanced Level (Recurring Tasks, Due Dates & Reminders) and Intermediate Level (Priorities, Tags, Search, Filter, Sort) features, add event-driven architecture with Kafka, implement Dapr for distributed application runtime, CI/CD, monitoring, and logging.
• Technology Stack: Azure/Google Cloud/Oracle Kubernetes, Kafka (Confluent/Redpanda Cloud or Self-hosted Strimzi), Dapr, GitHub Actions.

Core Principles

This project emphasizes two main principles:

• Spec-Driven Development (SDD): No code is written until the specification is complete and approved. This workflow follows a strict cycle: Specify → Plan → Tasks → Implement.
• Agentic Dev Stack: This integrates AGENTS.md (the "Constitution" for agent behavior), Spec-KitPlus (the "Architect" for managing spec artifacts), and Claude Code (the "Executor" that reads project memory and executes Spec-Kit tools via MCP).

Submission Requirements

For each phase, developers are required to submit:

1. A Public GitHub Repository with all source code, specification files, CLAUDE.md, and a comprehensive README.md.
2. Deployed Application Links (Vercel/frontend URL, Backend API URL, Chatbot URL, Minikube setup instructions, DigitalOcean deployment URL, depending on the phase).
3. A Demo Video (maximum 90 seconds).
4. A WhatsApp number for presentation invitations.

This project provides a comprehensive journey through modern software engineering, from basic application development to advanced cloud-native, AI-powered systems.