# Hacker News Search API (https://www.socialcrawl.dev/platforms/hackernews/search)

> Searches Hacker News stories, comments, and front-page items via the Algolia HN API. Defaults to story-only results sorted by relevance. The HN Algolia index only exposes `created_at_i` for numeric filtering, so use `numericFilters` for date windows (e.g. `created_at_i>1700000000`). Results come back in the unified Post shape under `data.items[]`, NOT as raw Algolia hits: each item is `{ post: { id, url, content, author, engagement, flags, published_at }, computed }`. `post.url` is the Hacker News discussion permalink; the submitted article link is at `post.content.media_urls`; `post.engagement.likes` is the HN points score and `post.engagement.comments` is `num_comments`.

TL;DR: `GET /v1/hackernews/search` costs 1 credit per page. Relevance against your query (by default, or with relevance=) is free. Adding relevant_to= holds 4 extra credits and refunds down to 1 credit per started 25 rows that were newly judged: rows already judged for the same topic are free, a page where nothing could be judged refunds the whole extra, and a cached page is free. The default labels (sponsored, intent, niche) are free. label=mention, quality, ai_style, ai_written, injection, or label=intent with offer=, holds 4 extra credits and refunds down to 1 credit per started 25 posts that were newly judged: posts already labelled are free, a page where nothing could be judged refunds the whole extra, and a cached page is free. With `max_pages`, `max_items` or `max_credits`, each page walked is billed at this price (a cached page is free), and `max_credits` caps the total. and returns SocialCrawl's unified JSON schema. Single x-api-key auth, 100 free credits on signup.

## Parameters

| Parameter | Required | Description |
| --- | --- | --- |
| `query` | yes | Free-text search term. |
| `tags` | no | Algolia tag filter: comma-separated. Common values: "story", "comment", "poll", "show_hn", "ask_hn", "front_page", "author_<username>". Defaults to "story". |
| `numericFilters` | no | Algolia numeric filter expression on `created_at_i` (the only filterable numeric attribute): e.g. "created_at_i>1700000000". Combine with commas for AND. No filter is applied by default. |
| `hitsPerPage` | no | Hits per page (1-1000). Defaults to 30. |
| `page` | no | 0-indexed page number for pagination. |
| `relevance` | no | Optional. Without this param every row already carries computed.relevance against your query, free ({ p, sense, depth, spam }: is this row about what your query means, or a different thing that shares its words?), and nothing is dropped or reordered. score asks for it explicitly and waits for every row; filter also drops the rows that are not about your query, and lists their ids in data.relevance.dropped_ids. Your query is the topic; nothing to configure. A row that could not be judged is never dropped and carries relevance: null. Pagination is unchanged, so a filtered page can hold fewer rows. Free with your query as the topic; with relevant_to it adds 1 credit per started 25 newly judged rows (rows already judged for the same topic are free, and so is a cached page). (score \| filter) |
| `relevance_threshold` | no | Optional, only with relevance. The probability (0 to 1) a row must reach to be kept by relevance=filter. Default 0.5. Lower keeps more rows, higher keeps fewer. |
| `relevant_to` | no | Optional, only with relevance. Up to 200 characters describing what you mean, used as the topic instead of the query. Use it when the query is ambiguous, for example query=cleopatra with relevant_to=Cleopatra, the IGT slot game. |
| `judgments` | no | Optional, on (the default) or off. By default every row gains free SocialCrawl judgments (computed.labels, and computed.relevance on search endpoints), reported in data.labels (mode default) and data.relevance (origin default), each with a status (complete, partial or skipped) and pending: the rows still being judged when the page was sent, which carry null now and are filled on your next call or cached read. Default judgments never add credits, never change an existing field, and never drop or reorder a row. off returns the page exactly as before, with none of those keys. label=none does the same. (on \| off) |
| `dry_run` | no | Optional. When 1, return a cost preview for this labelled or relevance-filtered request without fetching the page or judging any row. data.estimate reports rows_expected, rows_cached, label_credits_min, label_credits_max and base_credits. 0 credits charged. (1) |
| `fit` | no | Optional. When goal, keep the rows and fields needed for the goal you pass in goal= (plus any that are uncertain, and the first and last), and replace the rest with a stub. data.held_back lists the held ids and a recall id that re-reads the full page from cache at no extra charge. Without this param the page is unchanged. (goal) |
| `goal` | no | Required by fit=goal. What you are trying to do, in your own words, up to 300 characters. |
| `fit_tokens` | no | Optional, only with fit=goal. Soft cap on how much of the page to keep, in tokens. Uncertain blocks and the first and last block are kept even if they exceed it. |
| `label` | no | Optional CSV of SocialCrawl labels to add to every post. Without this param every page already carries sponsored, intent and niche, free; label= adds the labels you name to them (the defaults keep running). sponsored, intent and niche are free when asked for too; mention, quality, ai_style, intent with offer=, and injection add 1 credit per started 25 newly judged posts. judgments=off (or label=none) turns the default labels off. sponsored: is the post a paid or gifted promotion (p, 0 to 1), did it carry a disclosure marker such as #ad or 광고 (disclosed), is it likely paid with no marker (undisclosed), and which of the accounts it mentions does it promote (brand, or null). These are signals to review, never a finding. mention (needs brand=): is the post about that brand rather than something that shares its name (about_brand, 0 to 1), how it feels about the brand on five levels (sentiment_level 0 to 4 and sentiment_score 0 to 1, null when the post is not about the brand), is it sarcastic, which aspect it talks about (taste_or_quality, price_or_value, availability_or_delivery, health_or_safety, advertising_or_campaign, customer_service, none), and did the author buy or use it (first_hand). intent: what the author is mainly doing (label: asking_for_recommendation, comparing_options, switching_away, complaining, promoting, news_or_discussion, other, or null when unsure, with confidence), whether they read as a potential buyer rather than a seller (buyer, seller), how pressing the need is (urgency, 0 to 3), and, when you pass offer=, whether your offer would plausibly help them (fits_offer). niche: which of the 33 niches of the published taxonomy sc-niche-v1 the caption belongs to, or personal_no_niche, or other (label, confidence, taxonomy), with label null when the caption is too thin to tell or the pick is unsure. It reads the caption only, not the video. quality: how much checkable detail the caption carries (fact_density 0 to 3), whether it mainly asks for likes, replies, shares, follows or tags (engagement_bait, 0 to 1), whether it is written to provoke anger as a way to get engagement (rage_bait, 0 to 1; about the writing, never the side taken), whether it only repeats someone else's news or view (secondhand, 0 to 1), and what the post is mainly doing (post_aim: inform, opinion, sell, entertain, provoke, other). exclude=engagement_bait drops posts whose engagement_bait is 0.8 or higher. ai_style: writing patterns common in AI-generated text, judged on the text alone. It does not say who wrote the post, and a person who writes in this style is flagged too. Six patterns are judged, each a probability (0 to 1): binary_contrast ("This is not X. It's Y."), faux_insight ("Here's what nobody tells you" and throat-clearing openers), colon_reveal ("The best part: it learns."), profound_kicker (ends on an aphorism or mic-drop line), puffery ("marks a pivotal moment", "highlighting the importance of") and rhetorical_setup ("What if I told you", a question the post answers itself, "Not a X. Not a Y. A Z."). Four are counted in the text: slop_words (words and phrases such as delve, leverage, robust, game changer, at the end of the day), em_dashes, emoji_bullets (lines that start with an emoji or a symbol bullet) and one_line_paragraphs (paragraphs that are one short line). pattern_count is how many patterns are present (a judged pattern at 0.8 or higher, slop_words 1 or more, em_dashes 2 or more, emoji_bullets 2 or more, one_line_paragraphs 5 or more) and markers lists their names, comma-separated. A post with less than about eight words of text is only counted, with every judged pattern 0. The judged patterns read the first 400 and the last 150 characters of a long post, and the counts read the whole post. exclude=ai_style drops posts with 2 or more patterns. ai_written: how likely the post was written by an AI model, from SocialCrawl's own text classifier, reading the opening of the post (about 1,000 characters): p (0 to 1, calibrated), level (likely (p 0.95 or higher), possible (p 0.71 or higher) or unlikely) and model (the model name; a new name means a new scale). A post under 8 words is too short to read: p null, level too_short. It is a probability, not proof: a person can be flagged and an AI-written post can pass, and the shorter the post the less certain it is. exclude=ai_written drops likely posts. injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. A post that could not be judged carries labels: null. Posts already labelled for anyone are free, and so is a cached page. data.labels reports what was judged and billed. |
| `exclude` | no | Optional CSV (engagement_bait, ai_style, ai_written). engagement_bait, only with label=quality, drops posts whose engagement_bait probability is 0.8 or higher. ai_style, only with label=ai_style, drops posts that show 2 or more of its writing patterns (pattern_count 2 or higher). ai_written, only with label=ai_written, drops posts whose level is likely (p 0.95 or higher), or whose p reaches your ai_written_threshold; a post too short to read is never dropped. Dropped posts are listed in data.labels.dropped_ids. A post that could not be judged is never dropped. |
| `ai_written_threshold` | no | Optional, only with label=ai_written and exclude=ai_written. The p (0 to 1) at which exclude=ai_written drops a post. Default 0.95 (level likely). 0.71 also drops possible posts: more AI posts go, and more posts by people with them. |
| `brand` | no | Required by label=mention, ignored otherwise. The brand or product the posts are judged against, up to 300 characters, for example brand=Buldak. Without it label=mention is skipped with the warning label_mention_needs_brand and is not billed. |
| `brand_description` | no | Optional, with label=mention. One plain sentence saying what the brand is, for example brand_description=Samyang's spicy instant noodle brand. Use it when the name is also an ordinary word or another company's name. |
| `offer` | no | Optional, with label=intent. One or two plain sentences saying what you sell, up to 300 characters, for example offer=A web design agency that builds websites for small businesses. Adds fits_offer to every post; without it fits_offer is null. |
| `label_evidence` | no | Optional, only with label=. When 1, every labelled row also carries computed.labels_evidence.<preset> = { quote, sentence_index }: the sentence in the row that most clearly shows the label, copied verbatim. Absent or null when no single sentence shows it. (1) |
| `since` | no | Only rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off each page. This list is not in date order, so the walk does not stop at the date: keep following `next_cursor`, and each page costs what a page costs even when every row on it is filtered out. A row with no date is kept. `published_after`, `start_date`, `from_date`, `publish_time` (`yesterday`, `this-week`, `this-month`, `last-3-months`, `last-6-months`) and `max_age_days` are accepted and applied as `since`. |
| `until` | no | Only rows published on or before this date: YYYY-MM-DD (that whole UTC day is included) or an ISO 8601 timestamp. Newer rows are left off each page and the walk does not stop, so older pages follow. A row with no date is kept. `published_before`, `end_date` and `to_date` are accepted and applied as `until`. |
| `max_pages` | no | 1 to 50 (default 1, or 50 when `max_items` or `max_credits` is sent). Walk up to this many pages in one call and return the rows from all of them. Each page walked is billed exactly as one call to this endpoint (a cached page is free) and counts against your rate limit; its charge and any refund are recorded under the page's own request id, listed in `data.walk.request_ids`. The walk stops early at the last page, or when the request's time budget runs low; `data.walk.stopped` says why (`end`, `max_pages`, `max_items`, `max_credits`, `time_budget`, `page_error`) and `pagination.next_cursor` continues from where it stopped. |
| `max_items` | no | 1 to 5000. Get more in one call: keep walking pages until the response holds at least this many rows, then stop with `data.walk.stopped: "max_items"`. The last page is returned whole (it was billed), so `data.items` can hold up to one page more than this, and `pagination.next_cursor` resumes right after it without skipping a row. Walks up to 50 pages unless `max_pages` says fewer; each page is billed as one call (a cached page is free). Add `max_credits` to cap the spend. A long walk can stop on the time budget first (`"time_budget"`): send the returned cursor with the same params to continue. |
| `max_credits` | no | 1 to 100000. The most this call may spend, across every page it walks. A page starts only while the credits already spent plus that page's price (or, when higher, the most any page of this walk has cost) still fit, so the call never goes past it; the walk then stops with `data.walk.stopped: "max_credits"` and the cursor to resume. Sent alone, it walks as far as the budget goes (up to 50 pages). A value below the price of one page is a free 400 whose `error.details.page_credits` names that price. Credits a row join (`include=`) adds are known only once a page has run, so page 1 is checked against its own price. |

## Code example

```bash
curl -G "https://www.socialcrawl.dev/v1/hackernews/search" \
  --data-urlencode "query=claude code" \
  -H "x-api-key: sc_YOUR_API_KEY"
```

## FAQ

### How do I search Hacker News with an API?

Send a GET request to /v1/hackernews/search with your terms in the query parameter. SocialCrawl returns matching HN stories and comments with title, URL, points, author, comment count, and item ID, normalized into the unified data.items schema.

### What filters does the Hacker News Search API support?

Pass tags to restrict to story, comment, show_hn, ask_hn, or a specific author, and numericFilters to bound by points, number of comments, or created_at timestamp. Combine them to pull, for example, Show HN posts above 100 points from the last week.

### How do I paginate Hacker News search results?

Use hitsPerPage to set the page size and page to step through results, starting at page 0. Increment page until you stop receiving hits to walk the full result set for a query beyond the first page.

### Can I monitor tech trends and launches on Hacker News?

Yes. Run the search endpoint on a cron with your topic, product, or domain as the query, scope with tags=show_hn for launches, and ingest the hits. It is built for tech-trend monitoring and dev-sentiment pipelines alongside your Reddit and GitHub sources.

### How much does the Hacker News Search API cost?

Each search request costs 1 credit on the standard tier, regardless of how many hits come back. New accounts get 100 free credits with no credit card required, enough to run around 100 searches while you build your pipeline.

See the full Hacker News API: https://www.socialcrawl.dev/platforms/hackernews

## Pricing

- Standard endpoints: 1 credit per call
- Advanced endpoints: 5 credits per call
- Premium endpoints: 10 credits per call
- 100 free credits on signup, no credit card required. Cached responses cost 0 credits. Credit packs never expire.
- Full pricing: https://www.socialcrawl.dev/pricing

## Explore with AI

Questions this API answers, phrased for an AI assistant:

- How to search Hacker News stories and comments with an API
- What does the SocialCrawl Hacker News Search API return?