ProductHunt Scraper

Extract product launches, upvotes, makers, and more from ProductHunt — no API key required.

What does this Actor do?

This Actor scrapes public data from ProductHunt, giving you structured JSON output for:

• Daily launches — today's or any past day's products
• Weekly leaderboard — top products of the week
• Category pages — e.g. Developer Tools, AI, Productivity

Output fields

Each product record includes:

Field	Description
name	Product name
tagline	One-line description
url	ProductHunt post URL
upvotes	Number of upvotes
comments	Number of comments
tags	List of topic tags
thumbnail	Product thumbnail image URL
description	Full description (detail mode only)
makers	List of maker names (detail mode only)
website	External website URL (detail mode only)
launch_date	ISO launch date (detail mode only)

Example output

{
  "name": "Notion AI",
  "tagline": "Your connected AI assistant in Notion",
  "url": "https://www.producthunt.com/posts/notion-ai",
  "upvotes": 2847,
  "comments": 312,
  "tags": ["Productivity", "Artificial Intelligence"],
  "thumbnail": "https://ph-files.imgix.net/...",
  "description": "Notion AI lets you write, edit, summarize...",
  "makers": ["Ivan Zhao", "Simon Last"],
  "website": "https://notion.so",
  "launch_date": "2023-11-16T00:00:00Z"
}

How to use

1. Select Scrape Type: Daily, Weekly, or Category
2. (Optional) Enter a date (format: YYYY/M/D) for a specific day
3. (Optional) Enter a category slug like developer-tools or artificial-intelligence
4. Set Max Pages — each page contains ~20 products
5. Enable Fetch Full Details to get descriptions, makers, and website URLs (slower)
6. Click Run and download your data as JSON or CSV

Use cases

• Competitive research — track what's launching in your niche
• Lead generation — find newly launched SaaS tools and their makers
• Market research — spot trends in upvotes and categories
• Investment research — monitor traction of early-stage products
• Content creation — aggregate product data for newsletters or blogs

Notes

• Fetching full details visits each product page individually and uses more compute credits
• For large date ranges, increase Max Pages or run multiple times with different dates
• Data reflects publicly visible information only