Idea Board — Turn 3 AM Idea Overload into Actionable 30-Minute Tasks with Local AI

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

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What I Built

I built Idea Board for my best friend who suffers from severe "3 AM idea overload." He constantly wakes up with ambitious side-project ideas, writes down vague goals like "Learn AI vectors" or "Build a search engine," but gets completely overwhelmed trying to figure out where to start. Within a few days, those ideas die in his notes app clutter.

Idea Board solves this by converting any vague goal or project idea into a structured, step-by-step Kanban task board in seconds. Each generated task is concrete, starts with an action verb, and is strictly capped at 15 to 60 minutes so he can make real progress in bite-sized sessions without feeling overwhelmed.

Demo

Video Demo

https://github.com/Vijaykr35/idea-board/blob/main/ideaBoard.mp4

Note: Video demo is also stored in the codebase at frontend/public/ideaBoard.mp4.

Screenshots

Dark Notebook Kanban Board View

Generated Task Breakdown

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Code

github Vijaykr35/idea-board

Repository: https://github.com/Vijaykr35/idea-board

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How I Built It

Idea Board is built with local open-source AI at its core:

• Local AI Inference Engine: Powered by Ollama running the open-weight qwen2.5:3b model locally at http://localhost:11434.
• Backend: Built with FastAPI (Python 3.10+), SQLAlchemy, SQLite, and httpx for async local REST calls to Ollama.
• Frontend: A custom React (Vite) application styled with Tailwind CSS using a dark notebook aesthetic with hand-drawn Patrick Hand SC typography, interactive HTML5 drag-and-drop Kanban columns (To do, Doing, Done), progress bar tracking, and batch text import.

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Why Does Open Innovation Matter?

1. 100% Privacy & Zero Server Leakage: My friend doesn't want his raw startup ideas or personal notes sent to third-party cloud LLM APIs or used to train corporate models. Running qwen2.5:3b locally via Ollama keeps all data strictly on his machine.
2. Runs Offline on a Laptop: It requires zero internet connection. He can work on airplane rides or in cafes without relying on cloud availability or API latency.
3. Zero Subscription Fees: Closed API subscriptions add up fast. Open-weight models running locally cost $0 per prompt.
4. Full System Control: Open innovation allowed me to fine-tune the system prompt and strict formatting rules without API guardrail interference or rate limits.

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My Friend's Reaction

"Man, this is literally what I needed. I plugged in 'Learn vectors for AI' with 1 hour/day, and having 30-minute micro-tasks right in front of me on a dark notebook board made starting feel effortless instead of intimidating."

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My Agent Session

I used an agentic pair-programmer during the hackathon to build, test, and style this full-stack application.

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Prize Categories

• Best Project Built for a Friend
• Most Impactful Open Source Learning Tool

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