Bumble Customizable Filters

This project automates the process of applying, adjusting, and optimizing Bumble Customizable Filters to streamline matching efficiency. It solves repetitive swiping and filter-tuning workflows by providing a fully automated Android interaction layer. With this system, users can consistently target ideal profiles while maintaining a hands-off experience.

Introduction

This automation tool interacts with the Bumble Android app to configure and maintain dynamic profile filters, monitor match types, and fine-tune search preferences. It eliminates repetitive manual adjustments and ensures consistent filtering strategies for improved match relevance. Businesses and hobbyists benefit from predictable, scalable automation that reduces manual overhead.

Adaptive Filter Optimization Engine

• Continuously applies and updates Bumble filters using Appilot or UI Automator interactions.
• Supports profile scanning logic to adapt filter parameters in real time.
• Reduces repetitive manual tasks tied to location, interests, age, and preference adjustments.
• Provides structured logs and metrics for filter performance insights.
• Scales across multiple Android devices for parallelized optimization.

Core Features

Feature	Description
Automated Filter Application	Applies Bumble Customizable Filters directly through Android UI automation.
Adaptive Filter Tuning	Adjusts filter ranges and categories based on profile analysis patterns.
UI State Recognition	Detects visible elements to ensure correct filter selection and page transitions.
Multi-Device Scaling	Runs parallel sessions across many Android devices with isolated workers.
Match Quality Scoring	Evaluates discovered profiles and adjusts filters accordingly.
Retry & Backoff Engine	Handles UI failures with structured retries, timing adjustments, and safe recovery.
Session Scheduling	Automates recurring filter updates using a configurable scheduler.
Anti-Stall Logic	Detects inactivity and resets the app or session when needed.
Secure Config Loading	Loads sensitive credentials and configuration via encrypted or environment-based input.
Metrics & Logging	Tracks performance, filter effectiveness, and interaction success in logs and reports.

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How It Works

1. Input or Trigger — Device worker receives a filter-update request or scheduled task event.
2. Core Logic — Automation navigates to Bumble settings, applies filters, and verifies results.
3. Output or Action — Updated filters, logs, and optional scoring data are stored for analysis.
4. Other Functionalities — Profile sampling, UI recovery routines, and environment-specific overrides.
5. Safety Controls — Rate limits, timeout handlers, and fail-safe restarts maintain reliability.

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Tech Stack

Language: Python Frameworks: Appilot, UI Automator, FastAPI (optional control layer) Tools: ADB-less interaction modules, schedulers, structured logging, proxy utilities Infrastructure: Local devices, cloud 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

• Solo users use it to automate Bumble filter adjustments so they can maintain consistent match quality.
• Growth operators use it to tune filters across multiple devices to optimize engagement segments.
• QA teams use it to test Bumble filter functionality repeatedly without manual effort.
• Automation engineers use it to benchmark UI automation reliability under complex flows.

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FAQs

Does this tool modify Bumble’s backend? No, it only interacts with the Android UI layer.

Can I run this on multiple phones? Yes, it supports scalable multi-device orchestration.

Do I need root access? No, standard Android automation APIs are sufficient.

Is personal data handled securely? Credentials are loaded through environment variables or encrypted configurations.

Can I extend filter logic? Yes, the tasks and scheduler modules are fully extensible.

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Performance & Reliability Benchmarks

Execution Speed: Approximately 18–25 filter adjustments per minute under typical device-farm latency. Success Rate: ~93–94% across long-running sessions with automated retries enabled. Scalability: Supports 300–1,000 Android devices using sharded queues and horizontally scaled workers. Resource Efficiency: ~1 vCPU and 350–450MB RAM per worker for stable multi-device execution. Error Handling: Built-in retries, exponential backoff, structured logs, crash recovery, and alert hooks.