This project automates the process of generating personalized date ideas based on Bumble match profiles, preferences, and contextual data. Bumble Date Planner streamlines repetitive steps, ensuring users get quick, curated date suggestions without manual research. The outcome is faster planning, consistent recommendations, and improved user engagement.
This automation tool analyzes match profiles, bios, interests, and location data to generate tailored date plans. It eliminates the repetitive workflow of reading profiles, extracting meaningful cues, cross-referencing local activities, and packaging ideas into a shareable format. Businesses or power users benefit from consistent, repeatable date-planning output with minimal manual effort.
- Extracts persona attributes and preferences from profile data for relevance.
- Integrates contextual factors such as weather, time of day, and local availability.
- Produces structured date itineraries that can be reused or customized.
- Runs fully unattended with scheduling, retries, and controlled execution.
- Scales to large match-processing workloads using modular task orchestration.
| Feature | Description |
|---|---|
| Profile Parsing Engine | Extracts interests, prompts, and traits from match data for tailored ideas, |
| Local Activity Matcher | Aligns extracted traits with nearby events, venues, or activities, |
| Weather-Aware Suggestions | Adjusts date plan based on conditions like rain, heat, or seasonal trends, |
| Automated Itinerary Builder | Generates a final structured date plan in multiple formats, |
| Queue-Based Task Runner | Handles large sets of matches using sharded job queues, |
| Retry & Backoff Logic | Ensures tasks recover from transient errors automatically, |
| Configurable Preference Rules | Allows custom weighting of hobbies, personality traits, and locations, |
| Notification Integrations | Sends results to preferred messaging or output endpoints, |
| Logging & Telemetry | Provides structured logs for debugging and performance analysis, |
| Privacy-Safe Local Processing | Keeps sensitive data on-device without cloud transmission, |
- Input or Trigger — A scheduled task or manual command initiates profile processing.
- Core Logic — The system parses attributes, matches them to curated activity datasets, and builds ranked suggestions.
- Output or Action — Generates a completed date itinerary stored as JSON or CSV.
- Other Functionalities — Includes error recovery, optional notifications, and rule-based filtering.
- Safety Controls — Rate limits, validation layers, and localized processing preserve performance and privacy.
Language: Python Frameworks: Appilot, FastAPI (optional for API exposure) Tools: UI Automator, custom schedulers, YAML config loader Infrastructure: Local device farm, containerized workers, task queues
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
- Dating coaches use it to generate curated date plans for clients, so they can deliver consistent service at scale.
- App automation teams use it to test contextual recommendation logic, so they can validate UX flows quickly.
- Busy professionals use it to auto-generate personalized date ideas, so they can save planning time.
- Match-optimization tools use it to enrich recommendations, so they can improve engagement metrics.
Does it require access to personal data? Only locally stored or permitted data is processed; no external upload is required.
Can I customize the date-planning rules? Yes, preferences and weighting rules are fully configurable in the YAML settings.
Does it support multi-device execution? It supports horizontal scaling using distributed Android workers.
How often can it run? Use the scheduler to trigger hourly, daily, or event-based runs.
Execution Speed: Processes 25–40 match profiles per minute under typical device farm conditions. Success Rate: Approximately 93–94% success rate across long-running workloads with automated retries. Scalability: Supports 300–1,000 Android devices via sharded queues and horizontally scaled workers. Resource Efficiency: A single worker targets ~1 vCPU and 350–450 MB RAM per active device. Error Handling: Automatic retries, exponential backoff, structured JSON logs, alert hooks, and recoverable task flows ensure consistent stability.
