Pixel Punch - 2-day sprint to build an arcade fighting game

 Tech stack

Project: Pixel Punch — 2-day sprint to build a Retro 2D Fighter Engine

Role: Solo Developer / Product Manager

Context: Personal Technical Spike (Exploring AI-Orchestrated Development Pipelines)

Tools Used: Cursor IDE (Composer 2.5), Gemini (Image Generation & Multi-modal Context), Paint.NET, Suno AI.

📌 Project Overview

Pixel Punch is a functional 2D retro-style fighting game built to explore the limits of modern "vibe coding" workflows. The goal was to scaffold a robust gameplay engine from a blank workspace and establish a repeatable asset pipeline—combining code generation, image asset synthesis, and procedural audio—into a cohesive, playable build.

🕹️ The Development & Iteration Process

Phase 1: Architectural Scaffolding & Engine Mechanics

The project began by leveraging Cursor Auto Mode to build out the foundational game loops, collision boxes, and input handling.

• Iterative Progression: Once the basic physics were stable, I incrementally prompted the addition of themed fighting stages, active player special attacks, and reactive background elements that respond to match events.

Phase 2: Graphic Asset Pipeline & Sprite Parsing

To achieve visual consistency without a dedicated art team, I utilized Gemini Image Generation to createa character and background sprite sheets.

• Asset Ingestion: Rough generated layouts were cleaned using Paint.NET.

• IDE Capability: Upon importing the multi-frame sheets back into Cursor, the IDE successfully parsed and split the image into individual, key-framed active animation frames for the player states (idle, walk, attack).

• Cinematics: Gemini was further utilized to generate a cinematic intro sequence, referencing the completed sprite sheets to maintain visual context.

Phase 3: Audio Polish & Immersion

To round out the user experience, I integrated Suno AI to generate procedural, context-appropriate background tracks for specific fighting stages, validating a low-friction approach to indie audio design.

📉 Technical Challenges & Optimization: Managing Token Costs

Transitioning the project into a premium Cursor Pro tier shifted the focus from pure feature velocity to resource management. I had to actively manage model context windows to avoid token bloat.

• The Problem: Large, multi-file codebases cause exponential token growth when the IDE passes whole files into the LLM on every prompt.

• The Solution: I established a strict pipeline hygiene protocol: initiating fresh, isolated chat threads for distinct features, clearing out accumulated conversation context, and using hyper-targeted @file references to keep token burn low and stay highly efficient within the Pro credit pool.

💡 Key Takeaways

1. Tool Orchestration over Single Solutions: Current LLMs cannot build a polished digital product end-to-end solo. Success requires acting as an orchestrator—managing a diverse suite of specialized tools (Cursor for logic, Gemini for multi-modal context, Suno for audio) and stitching their outputs together.

2. Context Window Discipline: Managing token infrastructure costs is a core skill in modern AI development. Budgeting compute context is just as important as writing clean architecture.

🖥️ Project Media & Demos

[Insert your trimmed video clip showing gameplay, the intro sequence, and the Suno backing track here]