Want to tap into TechCrunch's goldmine of tech news without manually clicking through hundreds of articles? You're in the right spot. This guide walks you through building a Python scraper that pulls headlines, summaries, author info, and more—all while dodging the usual web scraping headaches.

So here's the thing about TechCrunch. It's basically the pulse of the tech world—startups getting funded, new AI tools dropping, companies pivoting, all that stuff. If you're tracking tech trends, doing market research, or just trying to stay ahead of what's happening in your industry, having programmatic access to this data is pretty huge.

But TechCrunch isn't just sitting there waiting to hand over its content. Like most major sites these days, it's got anti-scraping measures in place. Nothing personal—they just want to make sure their servers aren't getting hammered by bots.

That's where this tutorial comes in. We're going to build a scraper using Python and BeautifulSoup (the classic combo), and we'll handle those anti-bot measures using a tool that does the heavy lifting for us.

What You'll Walk Away With

By the end of this, you'll have a working scraper that:

• Extracts article titles, URLs, summaries, authors, dates, and categories from TechCrunch

• Saves everything into a clean CSV file you can actually use

• Bypasses anti-scraping protections without breaking a sweat

Here's the full code if you want to skip ahead and tinker:

python
from bs4 import BeautifulSoup
import requests
import csv

Define the TechCrunch URL

news_url = "https://techcrunch.com"

Set up Scraper API parameters

payload = {'api_key': 'YOUR_API_KEY', 'url': news_url, 'render': 'true'}

Request via Scraper API

response = requests.get('https://api.scraperapi.com', params=payload)

Parse HTML with BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')
articles = soup.find_all('article', {"class": "post-block post-block--image post-block--unread"})

Open CSV file

with open('techcrunch_news.csv', 'a', newline='', encoding='utf-8') as csvfile:
csv_writer = csv.writer(csvfile)
csv_writer.writerow(['Title', 'Author', 'Publication Date', 'Summary', "URL", "Category"])

if articles:

    # Iterate over articles

    for article in articles:

        title = article.find("a", attrs={"class": "post-block__title__link"}).text

        url = article.find("a", attrs={"class": "post-block__title__link"})['href']

        complete_url = news_url + url

        summary = article.find("div", attrs={"class": "post-block__content"}).text

        date = article.find("time", attrs={"class": "river-byline__full-date-time"}).text

        author_span = article.find("span", attrs={"class": "river-byline__authors"})

        author = author_span.find("a").text if author_span else None

        category = article.find("a", attrs={"class":"article__primary-category__link gradient-text gradient-text--green-gradient"}).text

        # Write row to CSV

        csv_writer.writerow([title, author, date, summary, complete_url, category])

else:

    print("No article information found!")

Just swap in your API key and you're good to go. No key yet? Grab a free account and get 5,000 API credits to play with for a week.

But if you want to understand what's actually happening under the hood, stick around.

Why Bother Scraping Tech News?

Fair question. Why not just visit TechCrunch like a normal person?

Well, if you're doing any kind of serious analysis—competitive intelligence, trend spotting, content strategy, investment research—manually collecting this data doesn't scale. You need it structured, consistent, and automated.

Here's what scraping tech news gets you:

• Real-time trend tracking: See what topics are heating up before they hit mainstream

• Competitive landscape mapping: Know what your competitors or adjacent companies are up to

• Content planning: Align your own content with what's actually trending

• Investment signals: Spot funding rounds, pivots, and growth indicators early

The catch? TechCrunch's site structure is sophisticated. It uses JavaScript rendering, has dynamic class names, and probably has some rate limiting going on behind the scenes. If you're managing IP rotation, handling CAPTCHAs, and dealing with headers manually, you're going to have a bad time.

That's why tools exist to handle this stuff. When you're scraping data from major tech news sites or conducting web scraping for market research, you want something reliable that won't block you halfway through pulling a dataset. 👉 Skip the headache and let automation handle anti-bot tech while you focus on the data—it's honestly the difference between spending your afternoon debugging connection errors versus actually analyzing insights.

Alright, let's build this thing.

Building the TechCrunch Scraper

We're targeting the homepage of TechCrunch—specifically, the latest articles. Our script will loop through each article, pull out the relevant data, and dump it into a CSV file. Clean, simple, repeatable.

What You'll Need

Before you start coding, make sure you've got:

• Python installed (3.7 or newer works great)

• BeautifulSoup library: Install with pip install beautifulsoup4

• Requests library: Install with pip install requests

• A ScraperAPI account: Grab a free trial to get your API key

Set up a new Python file (call it techcrunch_scraper.py or whatever), and let's go.

Understanding the TechCrunch Page Layout

Here's where developer tools become your best friend. Right-click on the TechCrunch homepage, hit "Inspect," and you'll see the HTML structure.

What we're looking for:

• Article containers: Each article lives in an <article> tag with the class post-block post-block--image post-block--unread

• Titles and URLs: Inside an <a> tag with class post-block__title__link

• Author info: In a <span> with class river-byline__authors, containing another <a> tag

• Summary text: A <div> with class post-block__content

• Publication date: A <time> tag with class river-byline__full-date-time

• Category: An <a> tag with class article__primary-category__link gradient-text gradient-text--green-gradient

Once you know where everything lives, extracting it is straightforward.

Step 1: Import Your Libraries

Start with the basics:

python
from bs4 import BeautifulSoup
import requests
import csv

news_url = "https://techcrunch.com"

Nothing fancy. Just setting up the tools we need and defining our target URL.

Step 2: Route Your Request Through ScraperAPI

Here's where things get interesting. Instead of sending a direct request to TechCrunch (which might get blocked), we're routing it through ScraperAPI. This handles IP rotation, headers, CAPTCHAs, and all the annoying stuff automatically.

python
payload = {'api_key': 'YOUR_API_KEY', 'url': news_url, 'render': 'true'}
response = requests.get('https://api.scraperapi.com', params=payload)

Notice the 'render': 'true' parameter? That tells the API to execute JavaScript before returning the HTML. TechCrunch uses client-side rendering for some content, so this ensures we get the full page.

Quick tip: If you skip rendering, you'll still get most of the data, but you'll miss things like article categories and might need to adjust your CSS selectors. Save yourself the trouble—just render it.

Step 3: Parse the HTML with BeautifulSoup

Now we turn that HTML into something we can work with:

python
soup = BeautifulSoup(response.content, 'html.parser')
articles = soup.find_all('article', {"class": "post-block post-block--image post-block--unread"})

This gives us a list of all article containers on the page. Everything we need is nested inside these.

Step 4: Set Up Your CSV File

Time to prepare where we're storing this data:

python
with open('techcrunch_news.csv', 'a', newline='', encoding='utf-8') as csvfile:
csv_writer = csv.writer(csvfile)
csv_writer.writerow(['Title', 'Author', 'Publication Date', 'Summary', "URL", "Category"])

We're opening (or creating) a CSV file, setting up a writer, and adding column headers. The 'a' mode means we're appending, so if you run this multiple times, it won't overwrite your previous data.

Step 5: Loop Through and Extract Article Data

Now for the main event—actually pulling out the information:

python
if articles:
for article in articles:
title = article.find("a", attrs={"class": "post-block__title__link"}).text
url = article.find("a", attrs={"class": "post-block__title__link"})['href']
complete_url = news_url + url
summary = article.find("div", attrs={"class": "post-block__content"}).text

   date = article.find("time", attrs={"class": "river-byline__full-date-time"}).text

    author_span = article.find("span", attrs={"class": "river-byline__authors"})

    author = author_span.find("a").text if author_span else None

    category = article.find("a", attrs={"class":"article__primary-category__link gradient-text gradient-text--green-gradient"}).text

    csv_writer.writerow([title, author, date, summary, complete_url, category])

else:
print("No article information found!")

We're looping through each article, finding the elements we identified earlier, pulling out the text (or href for URLs), and writing each row to our CSV. The if author_span check handles cases where an author might not be listed (better safe than sorry).

The else statement at the end? That's just good error handling. If something goes wrong and we don't find any articles, we'll know immediately instead of staring at an empty CSV wondering what happened.

You're Done. Now What?

Run that script, and you should see a techcrunch_news.csv file pop up with all your scraped data. From here, you can:

• Import it into Excel or Google Sheets for quick analysis

• Feed it into a database for long-term tracking

• Use it as training data for NLP projects

• Build automated reports or dashboards

The beauty of having this in CSV format is you can plug it into basically any tool you want. Whether you're doing competitive analysis, tracking specific topics over time, or just building a personal tech news archive, you've got the infrastructure now.

And look, we kept this simple on purpose. Once you understand the basics here—making requests through an API service, parsing HTML with BeautifulSoup, handling data extraction with some basic error checking—you can adapt this to scrape just about any news site or content platform. The same principles apply whether you're pulling from TechCrunch, a competitor's blog, or your own internal data sources.

The hardest part about web scraping isn't writing the code. It's dealing with all the obstacles sites throw at you to prevent it. That's why having solid infrastructure matters—whether that's robust error handling in your code or reliable services that manage the anti-bot stuff. Once you've got that foundation, the rest is just pointing your scraper at new targets and tweaking selectors.

Happy scraping.