This project automates completing the Bumble Daily Challenge on Android devices using a reliable and repeatable workflow engine. It removes the manual, repetitive effort of navigating the app and performing the required interactions through a controlled automation pipeline. The result is a hands-off system that boosts consistency, accuracy, and overall task throughput.
This automation system handles the repetitive sequence of actions needed to complete the Bumble Daily Challenge on Android. It navigates the UI, interacts with elements, logs outcomes, and can run on schedules or triggers. By removing manual tapping and timing guesswork, it provides consistent performance for users or businesses operating device farms.
- Eliminates manual navigation through Bumble’s challenge screens.
- Ensures consistent timing, gestures, and UI interactions for reliable results.
- Scales to large device fleets with coordinated scheduling.
- Adds logging, retry logic, and safety fallbacks to prevent stalled runs.
- Improves productivity for teams managing routine app-based tasks.
| Feature | Description |
|---|---|
| Automated Challenge Completion | Executes the required Bumble Daily Challenge sequence via UI automation. |
| Smart Element Detection | Identifies UI elements using Appilot/UI Automator for stable interaction. |
| Gesture & Tap Engine | Performs taps, swipes, and waits with adaptive timing logic. |
| ADB-less Interaction Mode | Allows interaction without direct ADB command dependencies. |
| Multi-Device Scheduling | Distributes tasks across multiple Android devices with coordinated timing. |
| Retry & Backoff Logic | Automatically retries failed steps with exponential backoff. |
| Structured Logging | Captures run data, performance metrics, and UI states. |
| Proxy & Network Manager | Handles network rotation and proxy assignment when needed. |
| Config-Driven Workflows | All behaviors controlled by YAML/ENV configurations. |
| Result Exporting | Outputs structured results to JSON and CSV for downstream use. |
- Input or Trigger — A scheduled job, CLI command, or queue message initiates a run.
- Core Logic — The automation worker launches the app, identifies key UI components, and performs the Bumble Daily Challenge steps.
- Output or Action — Results are logged, exported, and optionally sent to a monitoring service.
- Other Functionalities — Includes proxy rotation, adaptive timing, and fallback navigation.
- Safety Controls — Timeout detection, duplicate-run protection, and error recovery workflows.
Language: Python Frameworks: Appilot, UI Automator, lightweight orchestration libraries Tools: Scheduler, device manager, structured logger Infrastructure: Local or remote Android device farm, queue-based worker system
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
- Solo users automate completing Bumble Daily Challenge tasks so they can avoid repeating the same steps daily.
- Developers use it to test UI consistency and app behavior under repeated workflows.
- Growth or QA teams run it across device farms to validate challenge functionality at scale.
- Automation engineers integrate it into larger pipelines to orchestrate multiple app routines.
Does this require rooting the device? No, it uses standard Android automation frameworks.
Can it run on multiple devices simultaneously? Yes, via sharded workers and scheduled execution.
Is configuration required? Only minimal YAML/ENV setup for device IDs and timings.
Does it interact with personal user data? It only acts on UI elements defined in the workflow.
Can I extend the workflow? Yes, all tasks are modular and can be extended with new steps.
Execution Speed: Typically 25–35 actions per minute under standard device farm load. Success Rate: Averages ~93–94% over long-running jobs with automatic retries. Scalability: Supports 300–1,000 Android devices via queue sharding and horizontally scaled workers. Resource Efficiency: Each worker targets ~1 CPU core and 300–450MB RAM per active device. Error Handling: Implements structured logs, retry/backoff strategy, alert hooks, and state recovery after UI mismatches.