Wolf Tickets AI
"An AI-powered UFC fight prediction platform that provides detailed analysis of upcoming bouts, with public past results and private predictions for future events behind a subscription."
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$500/mo (explicitly stated by creator: 'I've finally made it to the $500/month mark')
wolftickets.ai
Maker:
wolftickets
$500/mo (explicitly stated by creator: 'I've finally made it to the $500/month mark')
Marketing Channels
Primary
Word of mouth / Community
Creator gets feedback and ideas from members without having to do advertising, suggesting organic community-driven growth.
Secondary
Hacker News
Shared milestone and project in the HN yearly projects thread.
Growth Levers
- Publish public track record and accuracy stats to build credibility and attract new subscribers
- Expand to other combat sports (boxing, Bellator/PFL) to broaden addressable market
- Create social media content around fight predictions to drive organic traffic before major events
- Partner with MMA podcasts and content creators for cross-promotion
- Offer a free trial around major PPV events to convert curious fans into paying subscribers
First Customer Strategy
The creator built at the intersection of two personal passions — AI/ML and combat sports — and grew organically through community engagement, getting feedback and ideas from members without paid advertising.
Pricing Insight
Freemium model: all past prediction results are public (building trust and transparency), while predictions for upcoming events are gated behind a subscription.
Key Takeaways
- • Building at the intersection of personal passions (AI/ML + combat sports) sustains motivation and domain expertise
- • A transparent public track record of past predictions builds trust and serves as a free marketing asset
- • Community-driven growth without advertising is viable when the product delivers measurable value to a niche audience
- • The $500/mo milestone demonstrates that niche AI prediction products can generate meaningful side income
- • Iterating on models and approaches keeps the product differentiated in a space where static predictions lose value
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