# Hacker News Scraper (`techionik9993/hacker-news-scraper`) Actor Reliable Apify actor for scraping public Hacker News sections with HTML-only crawling. Extract rank, title, URL, points, author, age, and comment count in a clean dataset for trend tracking, research, content discovery, and automation. - **URL**: https://apify.com/techionik9993/hacker-news-scraper.md - **Developed by:** [Techionik](https://apify.com/techionik9993) (community) - **Categories:** Automation, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.65 / run started This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### Hacker News Scraper A fast, lightweight, and marketplace-ready Apify actor for scraping public Hacker News listings using plain HTML parsing. This actor is built specifically for Hacker News and uses CheerioCrawler instead of a full browser, making it efficient, low-cost, and reliable for structured data extraction. It collects one clean dataset item per post and supports the main Hacker News sections used for trending stories, discovery, monitoring, and research workflows. ### Features - Scrapes public Hacker News listing pages - Extracts one clean record per post - Supports multiple Hacker News sections - Automatically paginates until the requested result limit is reached - Uses HTML-only crawling for faster and cheaper runs - Keeps input simple and user-friendly ### Supported Page Types - front - newest - ask - show - jobs - best - active - classic ### Extracted Fields Each result may include the following fields: - pageType - rank - title - url - points - author - age - commentsCount ### Input This actor uses a very simple input format: - **pageType**: The Hacker News section to scrape - **maxResults**: The maximum number of posts to extract Example input: { "pageType": "best", "maxResults": 10 } ### Output The actor returns one dataset item per Hacker News post. Example output: { "pageType": "best", "rank": 1, "title": "Ghostty is leaving GitHub", "url": "https://mitchellh.com/writing/ghostty-leaving-github", "points": 3400, "author": "WadeGrimridge", "age": "1 day ago", "commentsCount": 1015 } ### Notes About the Data Some Hacker News sections do not always expose the same metadata. For example, on the **jobs** page, fields like `points`, `author`, or `commentsCount` may be missing on the page itself. In such cases, the actor returns default values such as `0` or `null` where appropriate. This is expected behavior and reflects the actual structure of Hacker News. ### Why Use This Actor Hacker News is mostly server-rendered HTML, which makes it a strong fit for a Cheerio-based scraper. Benefits of this actor include: - Faster execution than browser-based scrapers - Lower runtime and compute cost - Clean and structured output - Reliable extraction from major Hacker News sections - Good fit for automation, trend tracking, research, and content workflows ### Best Use Cases This actor is useful for: - Tracking trending Hacker News posts - Monitoring top stories by section - Startup and tech news aggregation - Research and content discovery workflows - Lightweight automation and data collection pipelines ### Technical Approach This actor is built with: - Apify - Crawlee CheerioCrawler - Plain HTML parsing - Automatic pagination handling Because it does not use Playwright or Puppeteer, it is more efficient for Hacker News than a full-browser solution. ### Scope This actor is designed specifically for **Hacker News**. It is not a universal news scraper and is not intended for arbitrary websites with different HTML structures. If you need broad website text extraction, a generic content scraper is a better fit. This actor is purpose-built for clean Hacker News post extraction. ### Summary If you need a simple, reliable, and cost-effective Hacker News scraper for Apify, this actor provides a clean structured output with minimal input and efficient HTML-only crawling. # Actor input Schema ## `pageType` (type: `string`): Choose which Hacker News section to scrape. ## `maxResults` (type: `integer`): Maximum number of Hacker News posts to extract in total. ## Actor input object example ```json { "pageType": "front", "maxResults": 10 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("techionik9993/hacker-news-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("techionik9993/hacker-news-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call techionik9993/hacker-news-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=techionik9993/hacker-news-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hacker News Scraper", "description": "Reliable Apify actor for scraping public Hacker News sections with HTML-only crawling. Extract rank, title, URL, points, author, age, and comment count in a clean dataset for trend tracking, research, content discovery, and automation.", "version": "0.0", "x-build-id": "6fycOdVmRiEKyZTkz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/techionik9993~hacker-news-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-techionik9993-hacker-news-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/techionik9993~hacker-news-scraper/runs": { "post": { "operationId": "runs-sync-techionik9993-hacker-news-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/techionik9993~hacker-news-scraper/run-sync": { "post": { "operationId": "run-sync-techionik9993-hacker-news-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "pageType": { "title": "Page Type", "enum": [ "front", "newest", "ask", "show", "jobs", "best", "active", "classic" ], "type": "string", "description": "Choose which Hacker News section to scrape.", "default": "front" }, "maxResults": { "title": "Max Results", "minimum": 1, "type": "integer", "description": "Maximum number of Hacker News posts to extract in total.", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```