poker scientist
Built in One Year, Profitable for Six — 500 Concurrent Paying Users
Challenge
Professional poker players knew optimal strategies existed mathematically but couldn't access them in practice. Calculating Nash equilibria for poker requires processing near-infinite game states—every bet size creates exponential branches in already complex decision trees. The computational complexity made brute-force approaches completely impractical. Existing solutions were academic prototypes that couldn't handle real-world scenarios.
Solution
We built the first user-facing GTO platform by solving two problems simultaneously: computational efficiency and cognitive accessibility. We built custom abstraction algorithms that reduced near-infinite situations to tractable calculations, then designed clustering visualizations that revealed strategic patterns humans could actually learn and apply.
Led frontend architecture migration from Angular to Next.js, prioritizing long-term stability and developer velocity. Designed card matrix UI that displays equity distributions, monetary values, and probabilities in formats poker players can scan mid-session. Connected the C++ Nash solver to a Node.js API layer, optimizing for both calculation speed and user experience. Built adaptive visualization components that maintained clarity while presenting complex multi-dimensional data.
Built in One Year, Profitable for Six — 500 Concurrent Paying Users
We built Poker Scientist in one year and ran it profitably for six more — a SaaS platform with 500 concurrent paying users ($29-75/month). As Co-founder and Technical Lead, I tackled what the poker industry considered computationally impractical: making Nash equilibrium strategies understandable for human players.
The Problem: Optimal Strategies Existed but Were Inaccessible
Professional poker players knew optimal strategies existed mathematically but couldn't access them in practice. Calculating Nash equilibria requires processing near-infinite game states—every bet size creates exponential branches in already complex decision trees. Brute-force approaches were completely impractical. Existing solutions were academic prototypes that couldn't handle real-world scenarios.
The Solution: Computational Efficiency Meets Cognitive Accessibility
We built the first user-facing GTO platform by solving two problems simultaneously:
1. Computational Efficiency — Custom abstraction algorithms reduced near-infinite situations to tractable calculations running in real-time 2. Cognitive Accessibility — Clustering visualizations revealed strategic patterns humans could actually learn and apply at the tables
This required understanding how poker players think, what they need to see, and how to present complex data in ways that improve real-game decision-making.
Founder Journey: One Year to Build, Six Years to Run
Over 7 years (2018-2025), I led Poker Scientist from concept to sustainable business:
- Co-founder & Technical Lead — Full business responsibility from product strategy to technical architecture
- Product-Market Fit — Achieved in a specialized vertical requiring both technical sophistication and deep domain expertise
- Profitable Operations — Built in one year, operated profitably for six in a competitive market with three-tier SaaS pricing ($29-$75/month)
- User Impact — 500 concurrent paying users who applied strategies to improve their real-game win rates
Technical Leadership
As Technical Lead and Frontend Architect:
- Angular → Next.js Migration — Improved performance 30%+ while reducing technical debt
- C++ Integration — Connected the C++ Nash solver to a Node.js API layer for optimal computational speed
- Data Visualization — Designed card matrix UI displaying equity distributions, monetary values, and probabilities in scannable formats
- Full-Stack Architecture — From payment infrastructure (Stripe & Chargebee) to database design (PostgreSQL for millions of game states)
Technologies & Skills Demonstrated: Full-stack development, SaaS architecture, payment integration, frontend framework migration, cross-language system design (C++/Node.js), data visualization, API design, PostgreSQL, technical leadership, product-market fit validation
Timeline: 2018-2025 | Role: Co-Founder & Technical Lead
Screenshots
Frontend
Backend
Tools & Services
Database
Impact
Scaled to 500 concurrent paying users with three-tier SaaS model ($29-$75/month). Achieved product-market fit in a specialized vertical requiring both technical sophistication and domain expertise. Built in one year, operated profitably for six (2018-2025). Players applied the strategies to improve their real-game win rates.
Key Learnings
- Founder perspective: Poker players paid $29-75/month for six years because we solved THEIR specific problem, not a generic one. Specialized verticals reward depth over breadth.
- Product-market fit: Sustainable business came from deep domain expertise, not broad appeal. We understood how poker players think before we wrote a line of code.
- Technical leadership: Angular → Next.js migration improved performance 30%+ while reducing technical debt.