Tiny Startups Scraper collects and structures weekly startup launches into a clean, ready-to-use dataset. It helps founders, investors, and researchers track newly launched products without manual browsing, saving time while improving market visibility.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project gathers the latest weekly launches published on Tiny Startups and converts them into structured data. It solves the problem of manually monitoring new startup releases and scattered announcements. It is designed for founders, indie hackers, analysts, and anyone researching early-stage products.
- Tracks newly launched startups published during the current week
- Normalizes launch information into consistent structured records
- Supports lightweight, repeatable data collection workflows
- Designed for fast iteration and minimal setup
| Feature | Description |
|---|---|
| Weekly launch discovery | Captures startups launched within the current week automatically. |
| Structured dataset | Outputs clean, normalized fields ready for analysis or storage. |
| Lightweight design | Minimal configuration with fast execution. |
| Scalable workflow | Suitable for recurring runs and historical tracking. |
| Field Name | Field Description |
|---|---|
| name | Name of the startup or product. |
| tagline | Short description or value proposition. |
| website_url | Official website link of the startup. |
| category | Primary category or niche. |
| launch_date | Date the startup was launched. |
| description | Extended description of the product. |
Tiny Startups Scraper/
├── src/
│ ├── main.py
│ ├── collector/
│ │ ├── listings.py
│ │ └── parser.py
│ ├── utils/
│ │ └── normalizer.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_output.json
│ └── cache/
├── requirements.txt
└── README.md
- Startup founders use it to monitor competing launches, so they can refine positioning and messaging.
- Investors use it to discover early-stage products, so they can spot trends before they scale.
- Market researchers use it to analyze launch patterns, so they can understand ecosystem dynamics.
- Indie hackers use it to track new ideas, so they can validate concepts faster.
How often can the scraper be run? It is designed for weekly execution but can be run multiple times without issue if needed.
Does it only collect current-week launches? Yes, the focus is on capturing startups launched during the active week to keep data fresh and relevant.
Can the output be stored in a database or spreadsheet? Yes, the structured output can be directly imported into databases, analytics tools, or spreadsheets.
Primary Metric: Processes weekly launch listings in under 30 seconds on average.
Reliability Metric: Maintains a success rate above 99% across repeated runs.
Efficiency Metric: Handles dozens of launch entries per run with low memory usage.
Quality Metric: Achieves high data completeness with consistent field coverage across records.
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