Project 20: Friend Slop Viral Prototype

A fast social prototype designed for shareability loops and measurable retention checkpoints.

Quick Reference

Attribute Value
Difficulty Level 2
Time Estimate Weekend
Main Programming Language C# (.NET 8) + MonoGame
Alternative Programming Languages F#, C++ (raylib), Godot C#
Coolness Level Level 4
Business Potential Level 2
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Loop hypothesis testing, Instrumentation first, Scope slicing

1. Learning Objectives

  1. Translate one concrete production question into a testable implementation plan.
  2. Implement and validate the feature in a MonoGame runtime context.
  3. Instrument success and failure paths with actionable diagnostics.
  4. Produce a repeatable demo artifact for portfolio or interview use.

2. All Theory Needed (Per-Concept Breakdown)

Loop hypothesis testing

Fundamentals Loop hypothesis testing is central to this project because it defines the non-negotiable behavioral contract for the feature. You should be able to describe valid inputs, legal state transitions, and expected outputs under normal and failure conditions.

Deep Dive into the concept Treat Loop hypothesis testing as a boundary-setting mechanism. Start by defining the smallest deterministic scenario that proves the feature works. Stress that scenario under altered timing, altered content inputs, and altered user actions. If behavior changes unexpectedly, document hidden coupling and sequence assumptions. Keep transitions explicit and observable via logs or debug panels. Connect each transition to an event record so regression analysis is possible after refactors.

Instrumentation first

Fundamentals Instrumentation first ensures the project scales from local prototype behavior to repeatable system behavior.

Deep Dive into the concept Use Instrumentation first to reason about data flow ownership and mutation timing. Document where writes occur, when validation runs, and how rollback behaves if a write fails.

Scope slicing

Fundamentals Scope slicing connects this project to shipping reality by forcing you to think about operational constraints early.

Deep Dive into the concept Define one production-like failure mode related to Scope slicing and build a mitigation checklist. The solution is complete when you can demonstrate both a golden path and a controlled failure path.

3. Project Specification

3.1 What You Will Build

A rapid social gameplay prototype optimized for repeatable share loops and first-session retention telemetry.

Visible game deliverable:

  • Fast playable prototype scene with instant restart
  • Share card generator panel with session summary
  • Telemetry mini-dashboard for invites and return sessions

3.2 Functional Requirements

  1. Implement a short gameplay loop restartable in under 5 seconds.
  2. Generate share artifact after each completed run.
  3. Track invite/send/return events with timestamps.
  4. Display retention-relevant metrics in prototype dashboard.

3.3 Non-Functional Requirements

  • Performance: Must remain inside project-appropriate frame budget.
  • Reliability: Must recover from at least one injected failure mode.
  • Usability: Outcome must be observable by a reviewer in under two minutes.

3.4 Example Usage / Output

[PROTO] run_complete time=4m12s share_card=created
[METRIC] invites_sent=4 returns_24h=1
[METRIC] median_session=6m12s

3.5 Data Formats / Schemas / Protocols

  • Event record: {timestamp, module, action, result}
  • Feature state snapshot: {version, state, counters, flags}

3.6 Edge Cases

  • Share artifact generation failure.
  • Duplicate invite events for same user/session.
  • Telemetry drop when prototype restarts rapidly.

3.7 Real World Outcome

This is a game-facing outcome you can see and play immediately.

What you will see in the game window:

  • Fast playable prototype scene with instant restart
  • Share card generator panel with session summary
  • Telemetry mini-dashboard for invites and return sessions

Project 20 Friend Slop Viral Prototype Window Mockup

How you interact:

  • Play loop quickly with Enter restart
  • S generate share card
  • I simulate invite event

3.7.1 How to Run (Copy/Paste)

$ dotnet restore
$ dotnet build
$ dotnet run --project src/Game -- --scene friend-slop-prototype

3.7.2 Golden Path Demo (Deterministic)

  1. Start the scene and confirm all HUD panels load.
  2. Perform the three core interactions listed above.
  3. Verify the success signal appears without warnings.

3.7.3 If CLI: exact transcript

$ dotnet run --project src/Game -- --scene friend-slop-prototype
[PROTO] run_complete time=4m12s share_card=created
[METRIC] invites_sent=4 returns_24h=1
[METRIC] median_session=6m12s

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| friend-slop-prototype                                   [F1 HUD] |
|------------------------------------------------------|
| PLAYFIELD: gameplay objects and interactions         |
| HUD: key metrics + status badges                    |
| STATUS: success/failure cues and prompts            |
+------------------------------------------------------+

4. Solution Architecture

4.1 High-Level Design

Short Gameplay Loop -> Completion Event -> Share Artifact -> Invite Event -> Retention Metrics

Short Gameplay Loop -> Completion Event -> Share Artifact -> Invite Event -> Retention Metrics

4.2 Key Components

Component Responsibility Key Decisions
PrototypeLoopController Keeps gameplay cycle short and resettable Sub-5s restart target
ShareArtifactBuilder Generates session share summaries Template-driven output for consistency
RetentionMetricsTracker Records invite and return signals Event deduplication by session id

4.4 Algorithm Overview

  1. Validate preconditions.
  2. Apply deterministic transition.
  3. Emit feedback and telemetry.
  4. Persist if required.

5. Implementation Guide

5.3 The Core Question You’re Answering

“How do you test viral hooks quickly without losing engineering discipline?”

5.4 Concepts You Must Understand First

  1. Loop hypothesis testing
  2. Instrumentation first
  3. Scope slicing

5.5 Questions to Guide Your Design

  1. What metric best signals social loop viability?
  2. How do you ensure fast restart without hidden stale state?
  3. Which events are required to measure day-1 return behavior?

5.6 Thinking Exercise

Trace one full success path and one failure path on paper before implementation.

5.7 The Interview Questions They’ll Ask

  1. Why did you pick this architecture boundary?
  2. Which failure mode did you prioritize first and why?
  3. How does your instrumentation accelerate debugging?
  4. How would you scale this feature to a larger game?

5.8 Hints in Layers

  • Hint 1: Stabilize one invariant before feature expansion.
  • Hint 2: Add diagnostics before optimization.
  • Hint 3: Keep platform calls at system boundaries.
  • Hint 4: Re-run deterministic scenario after each refactor.

5.9 Books That Will Help

Topic Book Chapter
Core concept “Sprint by Jake Knapp” Relevant concept chapter
Reliability “Release It!” Failure handling chapters
Architecture “Clean Architecture” Boundary and dependency chapters

6. Testing Strategy

  1. Golden path completes and emits success signal.
  2. Injected failure path recovers without crash.
  3. Re-run scenario after restart and confirm consistency.

7. Common Pitfalls & Debugging

  • Hidden initialization order coupling
  • Time-coupled behavior tied to render rate
  • Missing fallback behavior on platform call failure

8. Extensions & Challenges

  • Beginner: add one extra diagnostics panel metric.
  • Intermediate: add replay capture for event flow.
  • Advanced: add automated stress test harness.

9. Real-World Connections

This project mirrors shipping feature-module work in real indie and mid-size game teams.

10. Resources

  • Steamworks official docs
  • MonoGame docs
  • Gemini image generation docs (for asset-related projects)

11. Self-Assessment Checklist

  • I can explain the feature invariant and prove it in a demo.
  • I can trigger and handle one deterministic failure scenario.
  • I can describe tradeoffs and future scaling choices.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Feature works in deterministic golden path.
  • One controlled failure path is handled gracefully.
  • Core diagnostics are visible and documented.

Full Completion:

  • All minimum criteria plus edge-case coverage and regression checks.

Excellence:

  • Includes polished instrumentation and clear productionization notes.