Hacker News Mentions Scraper - Stories, Comments & Evidence

SOURCE COVERAGE & PRACTICAL LIMITS

Full product guide

Documentation captured from build 1.0.4. Example outputs retain their original sample context; they are not live results.

Mentions you can trace to the text

Build a repeatable brand, product or topic research watchlist from the public Hacker News Algolia index. Each saved story or comment includes literal match evidence and a discussion link.

Open this Actor in Console · First run · Results · Cost · Full guide

Try this Actor

1. Choose a small scope. Use the existing OpenAI Mentions in Hacker News Comments example below: ten saved comments at most, drawn from a candidate window of one hundred.
2. Run and review. Open the dataset and inspect matchedTerms to see which source text qualified. The run's key-value-store SUMMARY separates inspected candidates, saved matches and source failures.
3. Use the results. Export JSON for matching excerpts and saved comparisons, or CSV for a discussion list. Follow the item or thread link when reviewing context.

Read this before using the data: Literal matches do not establish sentiment, purchase intent or the truth of a post. The search index can lag Hacker News, and its bounded result window is not exhaustive history. Missing later results do not prove deletion.

OpenAI Mentions in Hacker News Comments — copy this input into the JSON editor in Console.

{
  "queries": [
    "OpenAI"
  ],
  "itemType": "comment",
  "maxResults": 10,
  "maxCandidatesPerQuery": 100
}

Result preview

The dataset view highlights these columns:

• Mention ID · mentionId
• Story or comment · itemType
• Item or thread title · title
• Text · text
• Public author handle · author
• Indexed publication time · publishedAt

Use the original source links and the field definitions below when interpreting the results. CSV works well for flat columns; JSON preserves nested values.

Usage and pricing

Base result rate on the FREE tier: $1 per 1,000 mention observations. The Pricing tab shows your plan's result rate and any Actor Start charge. Set a small result limit for the first run; the requested limit is not a guarantee of that many results.

Full guide

Find literal brand, product and topic mentions in public Hacker News stories and comments. Get matching excerpts, canonical discussion links and saved comparisons for a repeatable research watchlist.

The Actor uses the documented public Hacker News Search API by Algolia. No API key, account, proxy or browser is required. It does not crawl linked articles or infer sentiment, purchase intent or personal profiles.

Quick start

Run with {} to save up to 20 recent indexed story/comment mentions of OpenAI. The Console prefill uses the same phrase.

{
  "queries": ["OpenAI"],
  "itemType": "comment",
  "maxResults": 20,
  "maxCandidatesPerQuery": 100
}

Pricing is $0.001 per saved mention ($1 per 1,000). A mention is one unique story or comment ID. Duplicate matches across phrases, rejected fuzzy matches, filtered records, unchanged observations skipped by onlyChanges, and error diagnostics are not saved as paid rows. Spending limits may stop a run before the requested window is finished.

Literal evidence, not inferred relevance

Every saved item must match the current phrase in actual source text:

• A story may match its title, self-text or linked URL.
• A comment must match its own text. A parent story title alone does not qualify it.

Matching is a case-insensitive literal substring, so AI also matches those adjacent characters inside longer words. Use a more specific phrase when that is too broad. Algolia typo/prefix matching is disabled where supported, and an independent local check rejects fuzzy results. Each row's matchedTerms contains matching fields and short excerpts for all configured phrases found in that item.

The service is an index, not a live verification of the underlying item. Edits, deletion and metrics may lag Hacker News's official item API. A missing item in a later search does not prove deletion.

Inputs

Output

Bounded search coverage

Algolia exposes at most about 1,000 hits per search, even when nbHits is much larger. Each query's SUMMARY.sourceCoverage separates reported hits/pages from inspected candidates, fetched pages and saved results. Requested candidate/result caps are successful bounded checks, not full-history claims. Narrow dates or phrases for deeper targeted research.

Deleted/dead indexed items are excluded when flagged. Unexpectedly empty pages, repeated pages, invalid envelopes and unusable records are reported explicitly. A candidate window with no literal match can legitimately save zero rows.

Repeat runs and saved history

Enable comparePrevious to return new, changed or unchanged; otherwise status is not_requested. onlyChanges saves new/changed observations only. Changes include title, full text hash, linked URL, ancestry, author, points and comment count. Thus discussion metric updates can be meaningful changes even when text is unchanged. Index update and observation timestamps do not trigger changes.

The first comparison run warns that matching observations establish history. A row marked new reports the comparison result; SUMMARY.historyUpdated confirms whether history was committed. An unchanged rerun can succeed with zero saved rows.

The named store maydit-hacker-news-mention-history is scoped by snapshotName, sorted queries, type, story tag, author and date filters. Only confirmed saved rows advance per-item history. Limits, filters, failures and missing search results do not remove prior observations. History is capped at 20,000 identities per scope.

Use one concurrent writer per history scope. A final reread detects some concurrent changes, but this is not a transactional lock. Malformed/incompatible history fails clearly; a new snapshotName starts fresh.

Failure and limit handling

SUMMARY in the default key-value store records partial source failures, unusable hits, filters, duplicates, unchanged rows, candidate windows and time/spending stops. When all sources fail and no usable observations are saved, the run fails after writing its summary. Saved rows survive a later-page outage, with status partial. Errors are never written as mention rows.

All requests use the public API, a 25-second request deadline, the overall run budget and bounded retries. No login, rate-limit or challenge bypass is attempted. This workflow provides public evidence for research; it does not establish a person's intent or the truth of user-generated claims.

Ready-to-run examples

The following exact inputs also appear in examples.json.

OpenAI Mentions in Hacker News Comments

Find 10 recent indexed comments with a literal OpenAI mention in the comment itself, with evidence and canonical thread links.

Use the copyable first-run input above.

PostgreSQL Stories on Hacker News

Read 10 recent indexed stories whose title, self-text or linked URL literally mentions PostgreSQL. Includes public scores and discussion links.

{
  "queries": [
    "PostgreSQL"
  ],
  "itemType": "story",
  "maxResults": 10,
  "maxCandidatesPerQuery": 100
}

Show HN AI Stories - Saved Observation Watch

Compare 10 indexed Show HN stories with literal AI text against prior saved observations. AI is a substring phrase, not an inferred category.

{
  "queries": [
    "AI"
  ],
  "itemType": "story",
  "storyTag": "show_hn",
  "maxResults": 10,
  "maxCandidatesPerQuery": 100,
  "comparePrevious": true,
  "snapshotName": "show-hn-ai-demo"
}

Help with your run

Open the Issues tab on this Actor with the run URL, the input field that caused trouble and what you expected to receive. Remove API keys and private information before posting.

Explore more Mayd-It Actors

Understand your run

Open Run report in the run’s Output tab for saved-record counts, reported source issues and limits, and suggested next steps. Use CSV export to take the saved results into a spreadsheet. The existing JSON results and detailed machine-readable summary remain available.

The report reflects this run’s recorded coverage. A successful run or a saved result does not establish that every possible source record was collected.