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Every piece of content on Hacker News—whether it's a story, comment, poll, or job posting—has a unique numeric identifier called an item ID. These IDs are sequential integers assigned when content is created, and they serve as permanent references to specific items. When you need to access a particular story or comment directly, querying by item ID is the most precise and efficient method available.
To retrieve a specific item, simply ask your AI assistant for the content using the item's ID number. For example, you might say "Get me Hacker News item 8863" or "Show me the details for item 37602663." The server will fetch the complete details for that specific item, including its type, content, metadata, and relationships to other items.
The beauty of ID-based queries is their specificity—you'll always receive exactly the item you requested, with no ambiguity. This makes ID queries particularly valuable when you're following up on previously retrieved content, tracking specific discussions over time, or working with external references that cite particular Hacker News items.
When you query an item by ID, the server returns comprehensive information about that item:
Querying by ID becomes invaluable when you want to revisit a specific comment someone mentioned, check whether a story has received new comments since you last viewed it, or analyze particular discussions in depth. If you're monitoring threads about specific topics, saving the item IDs allows you to efficiently check back for updates.
You can also chain ID queries together—for instance, retrieving a story by ID, then fetching specific comments from its "kids" array to explore the most interesting parts of the discussion without loading the entire thread.
Item IDs are permanent and never change, making them reliable bookmarks for Hacker News content. However, note that deleted or dead items may return minimal information. When exploring comment threads, you'll often work with multiple IDs as you traverse the conversation tree, using parent and kids relationships to navigate the discussion structure.
The Polar Signals integration lets you query and analyze continuous profiling data to identify performance bottlenecks, investigate CPU and memory usage patterns, and optimize your application's resource consumption directly from your workflow.
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