Tech Launch Tracker
Pricing
Pay per usage
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Tech Launch Tracker
Aggregates new product launches from Product Hunt and Hacker News Show HN. Combines them with a momentum score for quick evaluation of trending launches.
Pricing
Pay per usage
Rating
0.0
(0)
Developer
Richard P
Maintained by CommunityActor stats
0
Bookmarked
2
Total users
1
Monthly active users
4 days ago
Last modified
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An Apify Actor that aggregates new product launches from Product Hunt and Hacker News "Show HN", combines them with a momentum score for quick evaluation of trending launches.
Features
- π Dual-source β fetches from Product Hunt RSS and Hacker News Show HN
- π Momentum scoring β 0β100 score based on votes, comments, and recency
- β± Configurable β choose sources, time window, and result limit
- π‘ Graceful shutdown β respects Apify platform abort signals
- π Retry logic β exponential backoff (3 attempts) for transient failures
Input
| Property | Type | Default | Description |
|---|---|---|---|
sources | array | ["producthunt", "hackernews"] | Launch sources to include |
maxResults | integer | 30 | Maximum number of launches to return |
daysBack | integer | 3 | How many days back to look |
Dataset Output
| Field | Type | Description |
|---|---|---|
launchSource | string | Source platform (producthunt / hackernews) |
launchSourceId | string | Unique ID from the source |
launchTitle | string | Launch title |
launchDescription | string | Tagline or description |
launchUrl | string | URL to the launch on the source |
launchVotes | integer | Upvotes (PH) or points (HN) |
launchComments | integer | Comments / discussion replies |
launchAuthor | string | Author or submitter name |
launchCategories | array | Topics or categories |
launchDate | string | ISO datetime of the launch |
launchMomentumScore | number | Computed momentum score (0β100) |
Momentum Score Formula
engagement = (normalized_votes Γ 0.6 + normalized_comments Γ 0.4) Γ 80recency_bonus = 20 (if within last 24 hours)score = min(engagement + recency_bonus, 100)
Both sources are weighted equally. Votes and comments are normalised relative to the maximum in the current batch.
Development
# Local testingpip install -r requirements.txtpython3 -m my_actor# Build Docker imagedocker build -t tech-launch-tracker .
Deployment
- Push the repository to GitHub / GitLab
- Create a new Actor on Apify Console
- Connect your repository
- Run the Actor