HackerNews

HackerNews Signals capture product launches (Show HN), trending tech discussions, and company mentions from Hacker News via the Algolia API. Each signal is enriched with company identification, B2B relevance scoring, founder extraction, and outreach hooks.

We process HackerNews daily, filtering for Show HN launches, high-engagement stories mentioning companies, and hiring threads. Each signal includes an engagement score based on points and comments, plus LLM-generated outreach hooks.

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See real delivered data → Sample Files

Each HackerNews signal is classified into one of three subtypes based on the type of story detected.

Signal	Subtype Enum	Description
Show HN Launch	`showHnLaunch`	Product launches posted as Show HN
Company Mention	`companyMention`	Trending stories mentioning a company
Hiring Thread	`hiringThread`	Company appearing in Who's Hiring threads

Example Signal

What a single entry looks like in a delivered signal file:

{
  "signal_id": "hn-47580350",
  "batch_id": "2026-03-30-00-00-00",
  "signal_type": "hackernews",
  "signal_subtype": "showHnLaunch",
  "association": "company",
  "detected_at": "2026-03-30T22:10:22Z",
  "company": {
    "name": "Acme AI",
    "domain": "acme.ai",                     // match on domain
    "linkedin_url": "linkedin.com/company/acme-ai",  // or match on LinkedIn URL
    "industries": ["Artificial Intelligence", "SaaS"],
    "employee_count_low": 11,
    "employee_count_high": 50,
    "description": "AI-powered GTM workflow automation platform..."
  },
  "contact": [],
  "data": {
    "hn_id": "47580350",
    "hn_title": "Show HN: Acme AI – Automate your GTM workflows with AI agents",
    "hn_url": "https://acme.ai",
    "hn_author": "acmefounder",
    "hn_points": 232,
    "hn_comments": 98,
    "hn_link": "https://news.ycombinator.com/item?id=47580350",
    "story_type": "show_hn",
    "company_name": "Acme AI",
    "company_description": "AI-powered GTM workflow automation platform.",
    "product_name": "Acme AI",
    "product_description": "Automate prospecting, research, and outreach with AI agents.",
    "founder_names": ["Jane Smith"],
    "key_features": ["AI agents", "GTM automation", "CRM integration"],
    "target_market": "B2B sales and marketing teams",
    "competitive_angle": "AI-native approach vs. traditional workflow tools.",
    "relevance": 0.9,                        // 0.0-1.0; higher = more actionable for outreach
    "is_b2b": true,
    "outreach_hooks": [
      "Congrats on the Show HN launch - 232 points is impressive traction.",
      "Saw the HN discussion around your AI agents - would love to connect."
    ],
    "tags": ["ai-product", "b2b", "gtm-automation"],
    "engagement_score": 4.36,
    "source": "hackernews-algolia",
    "confidence": "high",                    // how certain this signal is accurate
    "sentiment": "positive"
  }
}

Field Reference

Standard envelope and entity fields are shared across all signals — see Schema and Resolution. The fields below are specific to this signal:

Signal-Specific Fields

The data object contains everything unique to this signal type — the intelligence extracted from the HackerNews post and discussion.

Field	Type	Description
`hn_id`	string	HackerNews story ID. Useful for deduplication and linking to the original discussion
`hn_title`	string	Original post title as it appeared on HN. Contains the company pitch in their own words
`hn_url`	string	URL shared in the post (usually the product website)
`hn_author`	string	HN username of the poster. Often a founder or early employee
`hn_points`	integer	Upvote count at time of collection. Higher = more community validation
`hn_comments`	integer	Comment count. High comment counts signal controversy or strong interest
`hn_link`	string	Direct link to HN discussion. Useful for displaying to reps or mining comments for objections
`story_type`	string	`show_hn`, `story`, or `hiring`. Determines the nature of the signal
`company_name`	string	Extracted company name
`company_description`	string	LLM-generated one-line company summary
`product_name`	string	Product name (for Show HN). May differ from company name
`product_description`	string	LLM-generated product summary. Designed to explain what the product does in one sentence
`founder_names`	array[string]	Extracted founder/maker names. Useful for personalized outreach
`key_features`	array[string]	Key product features extracted from the post and landing page
`target_market`	string	Target market description. Useful for ICP matching
`competitive_angle`	string	Competitive positioning as described or inferred
`relevance`	float (0–1)	How actionable this signal is for B2B outreach. Higher = stronger commercial signal
`is_b2b`	boolean	Whether the product is B2B. Useful for filtering out consumer launches
`outreach_hooks`	array[string]	Ready-to-use conversation starters tailored to this specific launch. Useful for powering rep suggestions
`tags`	array[string]	Classification tags (e.g., `ai-product`, `b2b`, `startup`). Useful for filtering and routing
`engagement_score`	float	Composite engagement metric combining points, comments, and velocity
`source`	string	Always `"hackernews-algolia"`
`confidence`	string	`high`, `medium`, or `low`. How certain we are the company identification is correct
`sentiment`	string	Community reception: `positive`, `neutral`, or `negative`. Derived from comment sentiment analysis

Timing & Delivery

detected_at is when the HN post was created. Use hn_link for the original discussion context.
One signal per company per HN post. A company appearing in multiple posts will produce multiple signals, but the same post won't generate duplicates.
Each delivery arrives in a timestamped folder. Treat all signals in a new folder as recent — no need to diff against prior deliveries.

Coverage

Refresh: Daily
Coverage: All Show HN launches, trending stories, and monthly hiring threads
Best for: Startup selling, PLG companies, developer tools, competitive intelligence

Contact Sales →

See real delivered data → Sample Files

Example Signal

Field Reference

Signal-Specific Fields

Timing & Delivery

Coverage