What Story Data Tells You

Every story on Hacker News comes with several pieces of metadata that help you understand its reception and context within the community. When you retrieve stories through the server, you'll encounter three primary data points: scores, timestamps, and additional metadata fields.

Understanding Scores

The score represents the number of upvotes a story has received from the Hacker News community. A higher score indicates that more users found the content valuable or interesting enough to vote for it. Scores are crucial indicators of community engagement and content quality.

When analyzing stories, consider that scores accumulate over time. A story posted hours ago with 100 points demonstrates stronger momentum than a week-old story with the same score. Top stories typically range from dozens to hundreds of points, with exceptional content occasionally reaching over a thousand.

Scores reflect not just quality but also timing and topic relevance. Breaking news, significant technical breakthroughs, and controversial discussions tend to accumulate points rapidly. The score gives you immediate insight into what's resonating with the tech community.

Interpreting Timestamps

Timestamps indicate when a story was submitted to Hacker News. This data is typically provided in Unix epoch format (seconds since January 1, 1970), though your AI assistant will usually convert this into human-readable dates and times.

Timestamps are essential for understanding content freshness and analyzing trends over time. A story's age combined with its score reveals its trajectory—recent stories with high scores are trending, while older stories with substantial scores have demonstrated lasting value.

When monitoring specific topics or conducting research, timestamps allow you to filter content by time periods, track when discussions peaked, or identify how quickly the community responded to particular news.

Additional Metadata Fields

Beyond scores and timestamps, story data includes several other useful fields:

Title and URL: The story's headline and link to the original content, giving you immediate context about what's being discussed.

Author (by): The username of the person who submitted the story, useful for tracking prolific contributors or identifying submissions from notable community members.

Descendants: The total number of comments in the discussion thread, indicating how much conversation the story has generated. High comment counts often signal controversial or deeply interesting topics.

Type: Identifies whether the item is a story, comment, or other content type, helping you filter results appropriately.

Practical Applications

Use this metadata combination to identify trending stories (high scores with recent timestamps), find evergreen content (older stories maintaining high scores), or discover controversial discussions (many comments relative to score). Understanding these data points transforms raw Hacker News information into actionable insights about what matters in the tech community.