I’m embarking on a new project to help developers, indie hackers, and entrepreneurs identify the most promising opportunities in the AI space. The goal is to create a data-driven AI Opportunity Leaderboard. This post outlines the proposal and methodology.

The Problem

The AI landscape is exploding. Every day, new tools, APIs, and models are released, creating a Cambrian explosion of new applications. For a developer looking to build a side project or even a startup, it’s difficult to see where the real opportunities are. Which ideas have traction? What are people actually willing to pay for? What niches are underserved?

The Solution: AI Opportunity Leaderboard

The AI Opportunity Leaderboard will be a dynamic, data-driven ranking of AI project ideas and niches. It will systematically gather and analyze data from across the web to calculate an “Opportunity Score” for various project categories.

Methodology

My approach will be based on three core steps:

  1. Information Gathering: I will aggregate data from a wide range of sources where new products and ideas are discussed. This includes:

    • Launch Platforms: Product Hunt, Beta List, Launching Next, Startup Stash.
    • Developer & Founder Communities: Hacker News, Indie Hackers, Reddit (e.g., r/SideProject, r/startups).
    • Project Aggregators & Inspiration: Graygrids, AI Graveyard (for learning from failures).
    • Industry & Hiring Needs: LinkedIn Jobs, BOSS Zhipin (to see what skills and solutions companies are hiring for).

  2. Classification: To structure the data, I will adopt the comprehensive category and sub-category taxonomy from Product Hunt. This provides a robust framework for classifying diverse AI products (e.g., “AI”, “Developer Tools”, “Marketing”, “Productivity”).

  3. Opportunity Scoring: I will develop an algorithm to calculate an “Opportunity Score” for each category. The score will be a weighted function of several factors, including:

    • Volume: Number of new projects launched in that category.
    • Traction: Social mentions, upvotes, comments, and user engagement.
    • Market Demand: Signals from hiring websites indicating a need for solutions in that space.
    • Saturation: A measure of how crowded a niche is, with a lower score for more saturated markets.

The Leaderboard (Prototype)

Below is a prototype of the leaderboard. It defaults to showing aggregated scores by category for yesterday. You can click on a category to drill down and see the details for the underlying subsets.

Opportunity Leaderboard

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Next Steps

The immediate next step is to begin developing the data gathering scripts. Concurrently, I will design the database schema to store this information, which will be hosted on Neon, a serverless Postgres provider that integrates well with platforms like Netlify.

Stay tuned for more updates as this project progresses!