Bumble Custom Notification Sounds

This project automates the process of assigning, updating, and maintaining custom alert tones for Bumble. It removes the guesswork and repetitive configuration steps users face when juggling multiple chat threads. With Bumble Custom Notification Sounds automation, you get consistent, personalized notifications across devices or environments.

Introduction

This automation streamlines notification sound customization for Bumble by detecting new chats, classifying message types, and applying user-defined tone rules. It replaces manual app navigation and settings adjustments with a fully automated workflow. Businesses or power users benefit from uniform configurations, reduced setup time, and improved alert clarity when handling high volumes of conversations.

Automated Notification Sound Mapping

• Dynamically reads Bumble’s notification events and maps them to custom tones.
• Eliminates repetitive manual settings changes across devices or app reinstalls.
• Ensures reliable tone syncing during profile migrations or multi-device use.
• Applies logic-based rules for chat type, priority, or sender category.

Core Features

Feature	Description
Event Listener Engine	Monitors Bumble notifications and triggers sound-assignment workflows.
Smart Tone Mapper	Automatically selects the correct tone based on user-defined rules.
UI Automator Integration	Interacts with Bumble settings screens where direct APIs are unavailable.
Device Profile Sync	Syncs custom sound preferences across multiple Android devices.
ADB-less Automation	Executes all tasks without requiring direct ADB connections.
Retry & Backoff Handling	Recovers from misfires or UI delays with structured retries.
Sound Asset Loader	Imports custom WAV/MP3 tones and validates compatibility.
Category-Based Alerts	Assigns unique tones for matches, messages, or priority chats.
Scheduled Refresh	Periodically verifies that Bumble settings haven't reverted.
Analytics & Logging	Captures sound assignments, errors, and workflow execution timelines.

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How It Works

Explain the technical flow in 3–5 steps: Input or Trigger — A new Bumble notification or scheduled verification interval. Core Logic — Parses event details, applies tone rules, and updates settings via UI automation. Output or Action — The correct custom sound is assigned and confirmed. Other Functionalities — Syncing preferences, importing sound files, and validating device profiles. Safety Controls — Retry logic, fallbacks, and guarded access to sound files and app settings.

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Tech Stack

List core technologies used: Language: Python Frameworks: UI Automator, Appium Tools: Appilot scheduler, sound processors Infrastructure: Local devices, device farms, containerized workers

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Directory Structure

automation-bot/
├── src/
│   ├── main.py
│   ├── automation/
│   │   ├── tasks.py
│   │   ├── scheduler.py
│   │   └── utils/
│   │       ├── logger.py
│   │       ├── proxy_manager.py
│   │       └── config_loader.py
├── config/
│   ├── settings.yaml
│   ├── credentials.env
├── logs/
│   └── activity.log
├── output/
│   ├── results.json
│   └── report.csv
├── requirements.txt
└── README.md

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Use Cases

• Power users use it to manage personalized Bumble alerts, so they can instantly identify priority messages.
• Customer support teams use it to maintain uniform notification settings across multiple devices, so they can reduce onboarding time.
• Automation engineers use it to ensure stable alert behavior in device farms, so they can run consistent tests.
• Digital creators use it to manage multiple profiles efficiently, so they can stay organized across campaigns.

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FAQs

Does this modify the Bumble app? No, it automates user-accessible settings via standard Android interactions.

Can I use my own sound files? Yes, supported formats are automatically validated before assignment.

Does it work on non-rooted devices? Yes, the workflow is built for standard non-rooted Android setups.

What happens if Bumble changes its UI? Selectors and flows can be updated through the config rules without rewriting the entire automation.

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Performance & Reliability Benchmarks

Execution Speed: Typically 35–45 automated actions per minute on mid-range device farms. Success Rate: ~94% across long-running jobs with automatic retries. Scalability: Handles 300–1,000 Android devices through sharded queues and distributed workers. Resource Efficiency: A single worker targets 1 vCPU and 350–450MB RAM per device. Error Handling: Implements retry queues, exponential backoff, structured logs, notifications, and automated recovery routines.