Puzzle Replay and Time-Travel System

[Mkalbani]

Location: Backend API
Labels: backend, game-features, good first issue
Estimated Time: 7 hours
Difficulty: Medium

Description

Enable players to replay completed puzzles and review their solving process step-by-step for learning.

Tasks

• Design puzzle replay data structure
• Implement action recording during gameplay
• Create replay storage system
• Add replay retrieval endpoints
• Implement playback metadata (timestamps, actions)
• Create replay sharing functionality
• Add replay comparison (original vs new attempt)
• Write replay flow tests
• Implement replay compression
• Add replay analytics

Acceptance Criteria

• Actions recorded during puzzle solving
• Replays stored efficiently
• Playback data accurate
• Sharing works correctly
• Comparison shows differences
• Tests verify replay integrity

[drips-wave]

This issue has been added to the Stellar Wave Program and is worth 150 Points.

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[codewithzubair07]

@codewithzubair07 has applied to work on this issue as part of the Stellar Wave Program's 2nd wave.

Hi 👋

I’d like to work on this feature.

I’m Zubair — a backend-focused fullstack developer experienced in designing scalable APIs and structured data systems. I’ve built state-tracking systems and event-based workflows before, so implementing a replay + time-travel architecture fits well with my background.

GitHub: https://github.com/codwithzubair07

Proposed Approach

I’ll structure this in a clean, extensible way:

• Design a replay data model optimized for action logging + efficient storage
• Implement action recording middleware during gameplay
• Store replay data with compression to minimize payload size
• Add retrieval endpoints for replay playback
• Include metadata (timestamps, ordered actions, attempt IDs)
• Implement replay comparison logic (diff-based analysis between attempts)
• Add sharing support (tokenized replay IDs or public-safe view model)
• Implement analytics hooks for replay insights
• Write end-to-end tests to verify replay integrity and correctness

I’ll focus on:

• Storage efficiency
• Deterministic playback
• Clean API contracts
• Test coverage for replay accuracy and comparison logic

ETA: 7 hours from assignment.

I’ll break the work into clear commits and ensure all acceptance criteria are satisfied.

Looking forward to contributing 🚀

ℹ️ Repo Maintainers: To accept this application, review their application or assign @codewithzubair07 to this issue.

[YahKazo]

@YahKazo has applied to work on this issue as part of the Stellar Wave Program's 2nd wave.

I have reviewed this issue and I would love to work on this please I promise to deliver on time.

ℹ️ Repo Maintainers: To accept this application, review their application or assign @YahKazo to this issue.

[drips-wave]

Congratulations, @YahKazo! 🎉 Your application was accepted by the repo's maintainers, and the issue is due on February 26, 2026.

🧑‍💻 @YahKazo: Please resolve the issue such that the repo's maintainers have enough time to review your contribution before the due date. You'll earn Points for completing the issue on-time, which will make you eligible for a share of the Stellar Wave Program's reward pool.

Warning

When opening a PR, please link it to this issue to ensure it gets tracked accurately. Points are issued when this issue is marked as completed by the maintainer.

🤠 Repo maintainers: Please keep an eye on the contributor's progress and review their work before the due date. You can manage this issue, including adjusting its complexity and points, here.

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