poker scientist

Poker Training SaaS — Nash Equilibrium Strategies Made Learnable for Human Players

Screenshots

Poker Scientist hero image showcasing dashboard with performance metrics and analytics

Poker Scientist - Key Features overview displaying hand analyzer and performance tracking

Poker Scientist - Mobile view with responsive design and touch-optimized interface

Poker Scientist - Settings page with preferences and configuration options

Poker Scientist - Reports section with performance trends and session analysis

Poker Scientist - Onboarding flow for connecting poker accounts and platform setup

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.

Outcome

Operated a profitable three-tier SaaS business ($29-$75/month) for six years. 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.

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.

Stack

Frontend

React

Next.js

TypeScript

Tailwind CSS

Backend

Node.js

Express

Database

PostgreSQL

Tools

Stripe

Redis

Docker

Vercel

**Built in One Year, Profitable for Six Years** We built Poker Scientist in one year and ran it profitably for six more — a SaaS platform at $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** — 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

Challenge

Solution

Outcome

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.