Hacker News Keyword Mention Monitor

Monitor Hacker News stories and comments for brand, competitor, product, domain, and technology mentions. Use it to find customer pain points, competitor comparisons, launch chatter, pricing debates, and integration requests from a technical audience.

The Actor uses the public HN Algolia API, so it does not need a login, proxy, browser, or GitHub/Product Hunt token.

Who should use this

• Founders tracking how HN talks about their product, category, or competitors
• Product marketers looking for authentic technical pain points and comparison language
• DevRel teams monitoring developer tool mentions and integration requests
• VC, accelerator, and research teams watching AI/devtool/startup trends
• Indie hackers who want a lightweight competitor and audience-monitoring feed

What it does

• Searches HN stories and comments for multiple keywords in one run
• Returns structured mention records with HN URL, external URL, author, points, comments, snippet, intent, and signals
• Classifies mentions as comparison, pain point, launch, hiring, pricing, integration, or general mention
• Supports strict keyword matching for clean monitoring and broad Algolia matching for maximum recall
• Optionally sends a JSON notification to Slack, Discord, Zapier, Make, n8n, or your own webhook
• Produces a Markdown summary in the key-value store under SUMMARY

Best first run

Track a few AI developer tools across recent HN stories and comments:

{
  "keywords": ["OpenAI", "Claude", "Cursor", "Codex"],
  "maxItemsPerKeyword": 25,
  "searchIn": "all",
  "matchMode": "strict",
  "webhookUrl": "",
  "includeText": true
}

For brand monitoring, keep matchMode as strict. If you are doing exploratory research and want wider recall, use broad.

Scheduled monitoring examples

Run the Actor on an Apify schedule to turn it into a lightweight alerting workflow.

Daily brand and competitor monitor

{
  "keywords": ["yourproduct.com", "Your Product", "Competitor A", "Competitor B"],
  "maxItemsPerKeyword": 25,
  "searchIn": "all",
  "matchMode": "strict",
  "webhookUrl": "https://hooks.zapier.com/hooks/catch/...",
  "failOnWebhookError": false,
  "includeText": false
}

Suggested schedule: every morning.

Weekly AI devtools digest

{
  "keywords": ["Claude Code", "Codex", "Cursor", "Windsurf", "OpenCode"],
  "maxItemsPerKeyword": 100,
  "searchIn": "all",
  "matchMode": "strict",
  "minPoints": 0,
  "includeText": true
}

Suggested schedule: weekly, then export the dataset to CSV or send the summary to a webhook.

Product pain-point research

{
  "keywords": ["authentication", "billing", "observability", "vector database"],
  "maxItemsPerKeyword": 100,
  "searchIn": "comments",
  "matchMode": "broad",
  "includeText": true
}

Suggested use: read rows where intent is pain-point, pricing, comparison, or integration.

Optional webhook alerts

Set webhookUrl to send the run summary and top mentions to an automation tool. The payload is generic JSON and includes a Slack-compatible text field.

Works with:

• Slack incoming webhooks
• Discord webhooks
• Zapier Catch Hook
• Make custom webhook
• n8n webhook trigger
• Any HTTP endpoint that accepts JSON

The Actor stores webhook status in WEBHOOK_RESULT. By default, webhook failures do not fail the run. Set failOnWebhookError to true if you want strict alert delivery.

Sample output

{
  "keyword": "Claude",
  "matchType": "comment",
  "title": "Ask HN: What is your (AI) dev tech stack / workflow?",
  "hnUrl": "https://news.ycombinator.com/item?id=48415368",
  "storyUrl": "https://news.ycombinator.com/item?id=48413629",
  "author": "example_user",
  "createdAt": "2026-06-05T17:06:34Z",
  "snippet": "I use Claude Code, Codex, and terminal-first workflows...",
  "exactMatch": true,
  "intent": "integration",
  "signals": {
    "competitorComparison": false,
    "pricing": false,
    "painPoint": false,
    "launch": false,
    "hiring": false,
    "integration": true,
    "ai": true
  },
  "relevanceScore": 11
}

Example input

{
  "keywords": ["OpenAI", "Claude", "Cursor", "Codex"],
  "maxItemsPerKeyword": 50,
  "searchIn": "all",
  "matchMode": "strict",
  "createdAfter": "2026-01-01",
  "minPoints": 0,
  "webhookUrl": "https://hooks.zapier.com/hooks/catch/...",
  "failOnWebhookError": false,
  "includeText": true
}

Output fields

• keyword - keyword that matched
• matchType - story or comment
• title - story title or parent story title
• hnUrl - direct HN item URL
• storyUrl - parent story URL when the match is a comment
• url and domain - external story URL when available
• author, points, commentCount, createdAt
• snippet - short context around the keyword
• exactMatch - whether the keyword appears directly in title, text, or URL
• intent - simple intent classification
• signals - booleans for competitor comparison, pricing, pain point, launch, hiring, integration, and AI
• relevanceScore - heuristic score combining title/text matches and HN traction

Use cases

• Brand monitoring for tools, products, or domains
• Competitor monitoring across HN discussions
• Finding unhappy users and integration requests
• Tracking launch mentions and pricing debates
• Building Slack, email, or spreadsheet alerts from Apify datasets

Why this Actor instead of a generic HN scraper

Generic HN scrapers collect stories and comments. This Actor is tuned for monitoring:

• Multi-keyword input in one run
• Strict keyword filtering to reduce noisy search matches
• Intent labels for pain points, pricing, comparisons, launches, and integrations
• Relevance scoring so you can sort the most important mentions first
• Optional webhook alerts for scheduled monitoring workflows
• Markdown summary saved alongside the dataset

If you only need raw HN stories, use a general HN scraper. If you want recurring product, competitor, or market-intelligence monitoring, use this Actor.

Practical workflow

1. Start with 3-10 keywords.
2. Keep matchMode as strict for clean brand or competitor monitoring.
3. Add an Apify schedule.
4. Add webhookUrl if you want alerts in Slack, Discord, Zapier, Make, or n8n.
5. Review SUMMARY first, then inspect the dataset rows with the highest relevanceScore.