Command-driven moves
Every player action is encapsulated, making undo and analytics consistent and safe.
Case study
Solitaire Candy World
A LiveOps-ready solitaire with modular gameplay systems and production tooling for steady content updates.
Overview
Building a solitaire that supports live content updates.
The goal was to launch a solitaire loop that could support events, economy tuning, and content drops without slowing iteration.
Goals
- Ship a stable solitaire loop with reliable undo and modular mechanics.
- Support LiveOps experiments and events with a maintainable architecture.
- Reduce draw calls and loading overhead across UI-heavy flows.
My role
- Built gameplay systems, architecture patterns, and feature integrations.
- Built tooling and asset pipeline improvements for content speed.
- Partnered with design to tune retention, economy, and progression.
Gameplay systems
Modular mechanics with reliable undo.
Systems were built with Factory and Command patterns to enable fast feature additions and stable undo flows.
Factory-based rules
Game rules and table setups are data-driven for rapid event reskins and variations.
State + persistence
Session recovery and save state handle long play sessions and offline continuity.
Retention + LiveOps
Retention features with LiveOps hooks.
Progression, economy, and event hooks were built to support iteration and cohort testing.
Retention features
- Daily rewards, streaks, and session goals tied to progression.
- Event hooks that can be toggled per cohort.
- Reward pacing aligned with progression milestones.
Metrics
42%
D1 retention
12%
D7 retention
50K+
downloads
Editor tools
Tools that keep content moving.
Custom editor tooling helped designers iterate faster on content drops.
Economy tuning bot
- Ran tens of thousands of simulated rounds in minutes to validate economy balance and RNG.
- Flagged outliers in reward pacing and sink balance.
- Exported tuning reports for designers to iterate without code changes.
Designer-friendly tooling
- Inspector helpers and view bridges for faster tuning.
- Debug surfaces for economy, progression, and table layouts.
- Automation for test sessions and analytics validation.
AI-assisted tooling
Image-to-level seeding for faster iteration.
Integrated an internal model that converts reference images into base layouts so designers can iterate in Unity.
What it does
- Converts image inputs into a rough level layout.
- Outputs editable data so designers can refine and balance.
- Fits into existing editor workflows to keep ownership in design.
Why it helps
- Speeds up early level creation and iteration.
- Provides consistent starting points for variations.
- Reduces repetitive setup work for new content drops.
Contact
Open to Unity gameplay and tools roles.
Happy to discuss roles, collaborations, or consulting work.
Phone
+91 96336 22377GitHub
github.com/GhanasyamX