I Built 18 Web Scrapers in One Week - Here's What I Learned About Modern Scraping

Do — Sun, 18 Jan 2026 08:19:02 +0000

Last week, I challenged myself to build and publish 18 production-ready web scrapers on Apify Store. Not toy projects - real tools that handle pagination, anti-bot measures, and edge cases.

Here's what I learned (and the mistakes I made).

The Challenge

Goal: Build scrapers for different categories - jobs, news, crypto, social media, developer tools.

Stack: Node.js, Cheerio, Crawlee, and FireCrawl API for the tough sites.

Result: 18 working scrapers, 350+ test runs, ~1 paying user (we'll get there).

Lesson 1: Free APIs Are Everywhere (And Nobody Uses Them)

Before writing a single line of scraping code, I discovered something surprising: many "protected" sites have completely free, undocumented APIs.

Examples I Found:

Site	API Type	Auth Required
Remotive.com	REST API	No
CoinGecko	Public API	No
Greenhouse Job Boards	JSON endpoints	No
Hacker News	Firebase API	No
Reddit	JSON append to URLs	No

The lesson: Spend 30 minutes looking for APIs before writing a scraper. Check:

Network tab in DevTools
robots.txt for API hints
GitHub for unofficial API wrappers
Adding .json to URLs

// Instead of scraping Reddit HTML:
const url = 'https://www.reddit.com/r/webscraping.json';
const response = await fetch(url);
const data = await response.json();
// Clean JSON with all post data!

Lesson 2: The 403 Tier List

Not all websites are created equal. After building 18 scrapers, here's my tier list:

S-Tier (Easy - Use APIs)

Hacker News
CoinGecko
GitHub API
Stack Overflow API
NPM Registry

A-Tier (Medium - Standard Scraping Works)

Dev.to
RemoteOK
Arbeitnow
Eventbrite
Google News RSS

B-Tier (Hard - Need Stealth)

Product Hunt
Glassdoor
TripAdvisor
Bark.com

F-Tier (Basically Impossible Without $$$)

LinkedIn (DataDome)
Yelp (Custom WAF)
DoorDash (Bot Detection)
Amazon (CAPTCHA + IP blocks)

Lesson: Pick your battles. Start with S and A tier sites.

Lesson 3: The "Works on My Machine" Problem

My scrapers worked perfectly locally. Then I deployed them.

What changed:

Apify's IP ranges are well-known (blocked by many sites)
No residential proxy by default
Different User-Agent detection

Solution: Use external scraping APIs for tough sites:

// For B-tier sites, use a scraping API
const scrapePage = async (url) => {
  // FireCrawl, ScrapingBee, or similar
  const response = await fetch('https://api.firecrawl.dev/v1/scrape', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${API_KEY}` },
    body: JSON.stringify({ url, formats: ['markdown'] })
  });
  return response.json();
};

Lesson 4: Pagination is Where Scrapers Die

Most scraper tutorials show you how to scrape one page. Real scrapers need to handle:

Infinite scroll
"Load more" buttons
URL-based pagination (?page=2)
Cursor-based pagination
Rate limits between pages

My pagination pattern:

const scrapeWithPagination = async (baseUrl, maxPages = 10) => {
  const results = [];
  let page = 1;

  while (page <= maxPages) {
    const url = `${baseUrl}?page=${page}`;
    const data = await scrapePage(url);

    if (!data.items?.length) break; // No more results

    results.push(...data.items);
    page++;

    // Be nice to servers
    await new Promise(r => setTimeout(r, 1000));
  }

  return results;
};

Lesson 5: Error Handling > Feature Count

My first scrapers had great features and terrible error handling. They crashed on:

Empty responses
Changed HTML structure
Rate limit responses
Network timeouts
Partial data

Now every scraper has:

const safeScrape = async (url) => {
  try {
    const response = await fetch(url, {
      timeout: 30000,
      headers: { 'User-Agent': getRandomUA() }
    });

    if (response.status === 429) {
      console.log('Rate limited, waiting...');
      await sleep(60000);
      return safeScrape(url); // Retry
    }

    if (!response.ok) {
      console.log(`HTTP ${response.status} for ${url}`);
      return { success: false, error: response.status };
    }

    const data = await response.json();
    return { success: true, data };

  } catch (error) {
    console.log(`Error scraping ${url}: ${error.message}`);
    return { success: false, error: error.message };
  }
};

Lesson 6: The MCP Revolution

The most exciting discovery: MCP (Model Context Protocol) lets AI agents use scrapers directly.

Instead of:

Human requests data
Human runs scraper
Human processes results
Human gives to AI

Now:

AI agent calls scraper via MCP
AI processes results automatically

This changes everything. Scrapers aren't just for developers anymore - they're tools for AI agents.

What Actually Worked (And What Didn't)

Worked:

Job board scrapers - High demand, structured data
News aggregators - RSS feeds are reliable
Developer tools (GitHub, NPM, Stack Overflow) - Great APIs
Crypto data - Free APIs everywhere

Didn't Work:

E-commerce - Too protected, need expensive proxies
Social media - API changes, legal gray area
Review sites - Heavy anti-bot (Yelp, TripAdvisor)

The Numbers (Honest)

After one week:

18 scrapers published
350+ test runs
~21 MAU (Monthly Active Users)
$0 revenue (so far)

The Apify $1M Challenge requires 50 MAU by January 31st. I'm getting there!

Lesson: Building is the easy part. Distribution is everything.

What's Next

Content marketing - This article is part of that
GitHub awesome-lists - PRs submitted
Community engagement - Discord, Reddit (carefully)
Better SEO - Optimizing actor descriptions

Try My Scrapers

All 18 scrapers are free to try on Apify Store:

Jobs:

Developer Tools:

News & Social:

Other:

Questions?

Drop a comment if you want me to dive deeper into any of these topics:

Anti-bot bypass techniques
Pagination patterns
MCP integration for AI agents
Monetizing scrapers

Building in public. Follow the journey on Twitter/X or check the portfolio.

How to Build an AI-Powered Data Pipeline with Web Scrapers

Do — Sun, 18 Jan 2026 06:23:09 +0000

Web scraping is essential for AI agents that need real-time data. In this tutorial, I'll show you how to set up a complete data extraction pipeline using Apify actors.

The Problem

AI agents need fresh data to make decisions:

Job aggregators need current listings
Lead generation tools need verified contacts
Market research needs competitor data
News monitoring needs latest articles

Manual data collection doesn't scale. APIs are often limited or expensive. Web scraping fills the gap.

Solution: Pre-built Scrapers + AI

Instead of building scrapers from scratch, use production-ready actors. Here's my toolkit:

Job Data

RemoteOK Scraper - Remote job listings with salary data
Greenhouse Scraper - ATS job boards (thousands of companies use Greenhouse)
Arbeitnow Scraper - European job market

Developer Data

GitHub Scraper - Repository stats, stars, languages
Stack Overflow Scraper - Q&A for training data
NPM Scraper - Package ecosystem analysis

News & Social

Hacker News Scraper - Tech news and discussions
Reddit Scraper - Community sentiment
Google News Scraper - Headlines by topic

Business

Email Verifier - Clean your lead lists
CoinGecko Scraper - Crypto market data

Quick Start

1. Get Apify Account

2. Run a Scraper

// Using Apify Client
const { ApifyClient } = require('apify-client');

const client = new ApifyClient({
    token: 'YOUR_API_TOKEN',
});

// Scrape remote jobs
const run = await client.actor("muscular_quadruplet/remoteok-scraper").call({
    maxItems: 100
});

// Get results
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Found ${items.length} jobs`);

3. Use with AI Agents (MCP)

Connect to mcp.apify.com and use natural language:

"Scrape 50 remote JavaScript jobs from RemoteOK"
"Get top 100 cryptocurrencies from CoinGecko"
"Find trending posts from r/webdev"

Integration Examples

n8n Workflow

Add Apify node
Select actor (e.g., muscular_quadruplet/hackernews-scraper)
Connect to your AI processing nodes

Python Script

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")

# Verify emails before outreach
run = client.actor("muscular_quadruplet/email-verifier").call(
    run_input={"emails": ["lead1@company.com", "lead2@startup.io"]}
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item["valid"]:
        print(f"Valid: {item['email']}")

Why Pre-built Scrapers?

Maintained - I update them when sites change
Tested - E2E tests ensure they work
Scalable - Apify handles proxies and retries
MCP Ready - Works with Claude, Cursor, and AI agents

Available Actors

All my actors are free to use on Apify Store:

Actor	Use Case
Email Verifier	Lead cleaning
RemoteOK Scraper	Remote jobs
GitHub Scraper	Developer analytics
Hacker News Scraper	Tech news
CoinGecko Scraper	Crypto data
Reddit Scraper	Community insights

Next Steps

Pick an actor for your use case
Test with free tier credits
Integrate into your AI workflow

Questions? Drop a comment below.

Building AI-ready data tools at flowbot.company

DEV Community: Do