Slashdot Scraper

Scrape public Slashdot section pages, archive searches, and direct story URLs into structured story records.

Extracted fields

Each record includes the Slashdot story ID, title, story URL, linked source URL/domain, Slashdot author, posted time, department, topic, comment count, visible summary/body, tags, input section/keyword, and scrape timestamp.

Input examples

{
  "startUrls": [{ "url": "https://tech.slashdot.org/" }],
  "maxResults": 25,
  "maxPages": 3
}

For a section feed, omit startUrls and set sections to values such as tech, news, yro, or science. For the public story archive, use searchKeywords, for example ["artificial intelligence"]. Direct slashdot.org/story/... URLs are supported.

Pricing

Event	Price
Actor start	$0.00005
Slashdot story	$0.005 per unique story record

A 25-story run costs up to about $0.12505 in event charges, excluding any platform usage or proxy costs that may apply to the account.

Reliability and limits

The Actor uses direct public HTML with bounded concurrency, retries, page limits, and a configurable delay. It does not bypass login walls, CAPTCHAs, or access controls. An empty result is reported as an empty run, while request failures are included in the OUTPUT key-value record and terminal status.

Compliance

Use this Actor only with public pages and in accordance with Slashdot's terms, robots guidance, and applicable law. Review source-site rights before storing or republishing article text.

Use cases

• Run a one-off research job and export the structured result as JSON, CSV, Excel, XML, or RSS from Apify.
• Schedule the same input to monitor changes over time and send completed datasets to a webhook or integration.
• Feed schema-shaped records into a database, spreadsheet, BI tool, or AI workflow with the source URL retained for verification.

Output example

{
  "storyId": "185056314",
  "title": "Amazon's Drones Will Soon Deliver to Nearly 500 US Cities and Towns",
  "url": "https://tech.slashdot.org/story/26/08/19/1950233/example",
  "sourceUrl": "https://example.com/article",
  "sourceDomain": "example.com",
  "author": "BeauHD",
  "postedAt": "on Wednesday August 19, 2026 @05:00PM",
  "department": "air-drop",
  "topic": "Transportation",
  "commentCount": 32,
  "summary": "Slashdot's visible story summary.",
  "storyBody": "Slashdot's visible story body when enabled."
}

The exact fields depend on the selected input and what the public source exposes. Use the dataset schema as the machine-readable contract and retain source URLs for verification.

Run Slashdot Scraper with the Apify API

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('muhammadafzal/slashdot-scraper').call({
  "startUrls": [
    {
      "url": "https://tech.slashdot.org/"
    }
  ],
  "sections": [
    "tech"
  ],
  "maxResults": 50,
  "maxPages": 5,
  "includeStoryBody": true,
  "requestDelayMs": 350
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

You can also run the Actor from Apify Console, schedules, webhooks, the REST API, Make, Zapier, n8n, or the hosted Apify MCP server.

Frequently asked questions

Can I schedule Slashdot Scraper?

Yes. Use an Apify schedule to run the same saved input at a chosen interval, then connect a webhook or integration to process the dataset when the run finishes.

How should I test a new input?

Begin with the prefilled example or a small limit. Confirm that the output fields, source coverage, runtime, and live charges match your workflow before increasing the scope.

How do I export the results?

Open the run's default dataset in Apify Console and export JSON, CSV, Excel, XML, or RSS. Applications can retrieve the same records through the Apify API client or REST dataset endpoint.

Can an AI agent call this Actor?

Yes. Add muhammadafzal/slashdot-scraper through the hosted Apify MCP server or call it through the API. The Actor's input and dataset schemas help agents construct valid requests and interpret returned records.

Recommended workflow

1. Define the smallest useful scope. Choose a representative public URL, query, identifier, or filter and keep the first result limit low.
2. Run and inspect. Check the run log, dataset item count, field coverage, source URLs, and live event or usage charges.
3. Validate downstream assumptions. Confirm nullable fields, deduplication keys, timestamps, and any locale-specific formats before importing records into another system.
4. Scale gradually. Increase limits or scheduling frequency only after the small run behaves as expected. Use Apify's maximum-cost and timeout controls to bound large jobs.
5. Monitor changes. Keep a small known-good input as a canary. If the source layout or API changes, compare the new dataset with a previously validated run and report the run ID when requesting support.

For recurring workflows, store the exact Actor input with your pipeline configuration. This makes runs reproducible and helps distinguish a source-data change from an input change.