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A September 2026 report by Listwright says a 30-day Hacker News Search API query returned 34,795,481 comments with exhaustiveNbHits: false. The author later reported 317,984 comments by adding the results of 30 one-day queries, each marked exhaustive. Those are the author’s measurements, not independently verified totals—and the author’s correction changes what the first number means.
What the two numbers represent
The figures come from Listwright’s September 2026 account of querying the Algolia-backed Hacker News Search API. They are not two measurements with the same scope and counting conditions.
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| Reported result | Query and method | Exhaustiveness and timing |
|---|---|---|
| 34,795,481 | Comments in one 30-day query | exhaustiveNbHits: false; reported for September 23, 2026 |
| 317,984 | Sum of 30 one-day comment queries covering the 30-day period | Each daily response reportedly had exhaustiveNbHits: true; measured September 23, 2026 |
| 171,753 | Comments in a rerun of the 30-day query | exhaustiveNbHits: false; reported roughly a day after the first measurement |
| 310,522 | Unfiltered comment query | Reported in the same later rerun; the article notes it is below the reported 317,984 for the recent 30-day subset |
The author says rerunning the 30 daily windows again returned the exhaustive flag. However, the article does not reproduce all 30 raw responses or daily values, so the 317,984 figure remains the author’s reported sum rather than a count readers can audit from the page.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhy the correction matters
The original explanation treated the enormous, non-exhaustive result as if it represented the whole index. Listwright’s correction withdraws that interpretation. The reported 30-day result shifted from 34,795,481 to 171,753 across runs, while the later unfiltered comment query returned 310,522. These figures do not support treating the non-exhaustive values as reliable, comparable exact counts.
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The narrower lesson in the correction is about the flag: when exhaustiveNbHits is false, the engine stopped counting before an exhaustive total was established. The returned nbHits value therefore should not be read as a dependable exact count. The article does not provide an official Algolia explanation for why its particular responses varied.
How to count comments across a date range
Listwright’s reported approach was to split the date range into daily windows, check the exhaustiveness flag for every result, and add the daily counts. In the author’s September 2026 account, all 30 one-day windows were marked exhaustive, and rerunning them returned the same flag status. That is a method reported for those queries, not a guarantee that daily windows will always be exhaustive.
- Choose the date range and comment query scope you need. Record the exact query and date boundaries so the daily results are comparable.
- Divide the range into one-day windows, making sure the windows cover the intended period without overlap or gaps.
- Run one query for each window and inspect
exhaustiveNbHitsin every response. - Only sum the returned counts for windows marked exhaustive. If a window is non-exhaustive, do not present its
nbHitsas an exact daily total. - Record when the requests were made and preserve the responses if readers need to reproduce or audit the total.
The September 2026 article provides the reported sum and flag status, but not the underlying 30 responses. That limits independent verification of the total and its exact query boundaries from the article alone.
What the result did—and did not—kill
Listwright also used public-text queries to test a product hypothesis. The author reports 495 comments matching phrases expressing demand. In the article’s September 2026 comparison, the author reports 1,104 Ask HN questions, 1,153 Stack Overflow questions, and 317,984 Hacker News comments for the period; the article says 278 of the Stack Overflow questions were closed, or 24.1%. For four product categories, the author reports buyer-vocabulary match counts of 1, 2, 5, and 1.
The author’s conclusion was that this small set of source queries and keyword predicates did not validate the product hypothesis. It is a conclusion about that approach and those results—not evidence that the broader market lacks demand. In particular, counts based on matching phrases depend on the chosen terms and do not by themselves establish how many people would buy a product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.HN Search is not the official Firebase API
Hacker News has a documented Firebase-backed v0 API and a separate Algolia-backed Search API. The official Hacker News API repository describes the v0 API, including item identifiers, story and comment types, timestamps, comment text, parent relationships, and a story or poll’s descendants count. Its documentation does not describe the Algolia Search API or verify Listwright’s search totals. The repository characterizes the v0 API this way: “The v0 API is essentially a dump of our in-memory data structures.”
That distinction matters when assessing evidence: the Firebase documentation explains the documented v0 data model, but it cannot be used to corroborate counts returned by a different search service. The reported Search API figures should remain attributed to the author unless the relevant queries and responses are independently available.
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