DEV Community: mamangwarkop

How to detect if a website runs Meta Pixel, TikTok Pixel or Google Tag Manager with plain Python

mamangwarkop — Wed, 21 Oct 2026 11:27:10 +0000

Agencies and freelancers lose hours opening DevTools on prospect sites. You can automate it in 20 lines because every pixel leaves a fingerprint in the HTML.

import urllib.request

SIGNS = {
    "Meta Pixel": ("connect.facebook.net", "fbq("),
    "TikTok Pixel": ("analytics.tiktok.com", "ttq."),
    "Google Tag Manager": ("googletagmanager.com/gtm.js",),
    "GA4": ("gtag(", "google-analytics.com"),
}

def audit(url):
    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
    html = urllib.request.urlopen(req, timeout=10).read().decode("utf-8", "replace").lower()
    return {name: any(s in html for s in sigs) for name, sigs in SIGNS.items()}

print(audit("https://example.com"))

Limits to know

Pixels injected only after consent or via server-side tagging will not show up in raw HTML. Treat "missing" as "not detected", not as proof.
Single-page apps may load tags late. For those, use a headless browser.

Doing it for 1,000 domains

Run this with concurrency and add email, WhatsApp and social extraction, plus a 0-100 lead score and a ready pitch sentence. I packaged exactly that as an Apify Actor: https://apify.com/mamang_warkop/b2b-website-contact-and-pixel-lead-enricher (open-source repo: https://github.com/mamangwarkop/b2b-website-contact-and-pixel-lead-enricher).

I pulled 40 freshly launched startups with contact emails into a free CSV (and the script to repeat it)

mamangwarkop — Tue, 20 Oct 2026 16:46:57 +0000

Every week hundreds of startups launch on Hacker News with a "Show HN" post. Their founders are online, reading replies, and open to partnerships, tools and services. The problem: nobody hands you their contact info in a clean file.

So I built a small script that:

Reads the open Hacker News Algolia API for show_hn posts
Visits each project's website
Extracts public contact emails, X/LinkedIn links
Checks which marketing pixels are installed (Meta, Google Analytics/GTM, TikTok)
Scores each lead 0-100 and writes an outreach hook

Get the data

Free CSV (about 40 rows, roughly 40% with a contact email): https://github.com/mamangwarkop/startup-leads-free-sample
The script is in the same repo. One command:

python lead_engine.py --keywords AI,saas --max 100 --out leads.csv

Why the `missing` column matters

If a startup has traffic from a launch but no Meta Pixel, you have a concrete, honest reason to write to them: "your launch visitors can't be retargeted." That beats a generic "can I pick your brain" message.

Need a list for your niche instead?

I build done-for-you lists (100 leads from 15 USD, free 10-row sample first). Details in the repo README, or run the cloud version with no setup: https://apify.com/mamang_warkop/product-hunt-launches-and-maker-leads-scraper

Only public business data is used. Please follow GDPR / CAN-SPAM when you email.

How to Build an Automated Startup Lead Generation Engine from Product Hunt Launches

mamangwarkop — Tue, 20 Oct 2026 16:22:09 +0000

How to Build an Automated Startup Lead Generation Engine from Product Hunt Launches

If you run a B2B agency, freelance design/development studio, or SaaS tool, standard cold outreach is becoming increasingly saturated. Cold emailing established companies often results in low open rates because their inboxes are overwhelmed.

However, there is a golden window of opportunity: Day-1 Startup Launches on Product Hunt.

When founders launch on Product Hunt:

They are sitting in front of their computers all day monitoring incoming traffic and comments.
They have urgent needs: converting traffic, setting up retargeting, fixing onboarding bugs, and acquiring early paying users.
Relevant outreach sent on launch day gets 25%–40% response rates because it references their live announcement.

In this guide, we'll build a lightweight Python automation that monitors daily Product Hunt launches, discovers the startup's real website and contact emails, and audits their marketing pixel setup.

1. Monitoring Product Hunt without Heavy Scraping

Instead of scraping complex JavaScript single-page applications with headless browsers, Product Hunt provides an official Atom RSS feed that updates in real-time:
https://www.producthunt.com/feed

Here is how you parse it with pure Python:

import urllib.request
import xml.etree.ElementTree as ET
import re

def get_latest_launches():
    url = "https://www.producthunt.com/feed"
    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})

    with urllib.request.urlopen(req, timeout=10) as resp:
        xml_data = resp.read().decode("utf-8")

    root = ET.fromstring(xml_data)
    ns = {'atom': 'http://www.w3.org/2005/Atom'}

    launches = []
    for entry in root.findall('atom:entry', ns):
        title = entry.find('atom:title', ns).text
        author = entry.find('atom:author/atom:name', ns)
        author_name = author.text if author is not None else "Founder"

        content = entry.find('atom:content', ns).text or ""
        # Extract tagline and direct website redirect link
        tagline = re.search(r"<p>\s*([^<]+?)\s*</p>", content)
        redirect = re.search(r'href="(https?://(?:www\.)?producthunt\.com/r/p/[^"]+)"', content)

        launches.append({
            "title": title,
            "maker": author_name,
            "tagline": tagline.group(1).strip() if tagline else "",
            "redirect_url": redirect.group(1) if redirect else None
        })
    return launches

# print(get_latest_launches()[:3])

2. Resolving the Destination Website & Auditing Marketing Pixels

Product Hunt uses clean 301 Moved Permanently headers for external product links. By querying the redirect URL with allow_redirects=False, you can instantly read the destination domain from the Location header without getting rate-limited.

Once you have the homepage HTML, you can scan for marketing tags:

Meta Pixel: Check for connect.facebook.net or fbq(
Google Analytics / GTM: Check for googletagmanager.com
TikTok Pixel: Check for analytics.tiktok.com or ttq.

If a startup is driving thousands of launch visitors from Product Hunt but has no Meta or TikTok retargeting pixel, that is the highest-converting outreach hook possible:

"Hey [Maker], saw your launch today on Product Hunt ('[Tagline]')! Congratulations on reaching the top rankings! Noticed you have high-volume launch traffic landing on your site right now, but don't have Meta Pixel active yet—retargeting those visitors before they bounce could easily 2x your day-1 signups."

3. Production Cloud Tool & Open Source Repository

If you want this pipeline running on a scheduled cron in the cloud or piped into your CRM/Slack:

Apify Store Actor: Product Hunt Launches & Maker Leads Enricher Extracts 50 enriched startup leads with founder emails, socials, and automated agency pitch angles for \$0.002 per lead.
Open-Source GitHub Repository: github.com/mamangwarkop/product-hunt-launches-and-maker-leads-scraper
Central Portfolio Hub: mamangwarkop.github.io

Happy hunting and prospecting!

How to Extract Shopify Competitor Product Catalogs & Real Discount Margins with Python

mamangwarkop — Tue, 20 Oct 2026 16:05:05 +0000

How to Extract Shopify Competitor Product Catalogs & Real Discount Margins with Python

Whether you run an e-commerce brand, a dropshipping store, or a digital marketing agency, competitor price monitoring is often tedious. Manually clicking through hundreds of category pages or paying \$200/month for specialized e-commerce intelligence tools is both slow and expensive.

Fortunately, Shopify stores have a standardized architecture that makes extraction straightforward.

In this tutorial, you'll learn:

How Shopify's hidden JSON endpoints work.
How to extract product titles, variants, and live inventory status.
How to calculate actual discount percentages programmatically.
How to detect installed marketing pixels (Klaviyo, Meta, TikTok) on any store.

1. The Power of Shopify's Standardized API

Almost 99% of Shopify merchants leave their public catalog endpoints exposed under standard routing:

Catalog Endpoint: https://[store-domain].com/products.json?limit=250&page=1
Collections Endpoint: https://[store-domain].com/collections.json

This means you don't need heavy headless browsers (like Selenium or Puppeteer) just to read product data. Pure HTTP requests with JSON deserialization work instantly.

2. Python Script to Scrape and Calculate Discount Margins

Here is an example in Python that fetches products, resolves all SKU variants, and detects real promotional markdowns:

import urllib.request
import json

def fetch_shopify_products(store_url: str, limit: int = 50):
    endpoint = f"{store_url.rstrip('/')}/products.json?limit={limit}"
    req = urllib.request.Request(
        endpoint,
        headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
    )

    with urllib.request.urlopen(req, timeout=15) as resp:
        data = json.loads(resp.read().decode("utf-8"))

    enriched_products = []
    for item in data.get("products", []):
        variants = item.get("variants", [])
        for v in variants:
            price = float(v.get("price") or 0.0)
            compare_price = float(v.get("compare_at_price") or 0.0) if v.get("compare_at_price") else None

            discount_pct = 0.0
            if compare_price and compare_price > price:
                discount_pct = round(((compare_price - price) / compare_price) * 100, 2)

            enriched_products.append({
                "productTitle": item.get("title"),
                "variantTitle": v.get("title"),
                "sku": v.get("sku"),
                "currentPrice": price,
                "originalPrice": compare_price,
                "discountPercent": discount_pct,
                "inStock": v.get("available", False)
            })

    return enriched_products

# Example test:
# products = fetch_shopify_products("https://allbirds.com", limit=10)
# print(products[:2])

3. Detecting Marketing Stack (Pixels & CRM)

If you are an agency pitching e-commerce brands, knowing their tech stack gives you immediate leverage. By fetching the homepage HTML and scanning script tags, you can check:

Klaviyo: Look for klaviyo.com/onsite/js
Meta Pixel: Look for connect.facebook.net/en_US/fbevents.js
TikTok Pixel: Look for analytics.tiktok.com/i18n/pixel

If a merchant has high-volume traffic but is missing TikTok Pixel or Klaviyo, that's your exact \$2,000/month pitch right there.

4. Ready-to-Use Cloud Tool & n8n Blueprint

If you want an automated tool that handles pagination, export to CSV/JSON, and store lead qualification without having to write or host the code yourself:

Apify Store Actor: Shopify Products & Store Leads Scraper Supports extracting products, store leads, and collections at \$0.001 per item (runs inside Apify's \$5 free monthly tier).
Open-Source GitHub Repository: github.com/mamangwarkop/shopify-products-and-store-leads-scraper
Automated n8n Blueprint: Download Shopify B2B Lead Enricher Workflow

Happy building, and feel free to connect or drop suggestions below!

How to Bulk Check Google Ads Spend & Verify B2B Leads using Python and n8n (Zero-Budget Guide)

mamangwarkop — Tue, 20 Oct 2026 16:04:23 +0000

How to Bulk Check Google Ads Spend & Verify B2B Leads using Python and n8n

If you run outbound sales, marketing consultancy, or lead generation workflows, you know that pitching paid media services to a business that doesn't run ads (or pitching organic SEO to someone with a \$50k/month paid ads budget) ruins your conversion rates.

The key to hyper-targeted cold outreach is prospect qualification via advertising transparency data.

In this guide, we'll break down:

Why Google Ads Transparency Center is a goldmine for B2B intelligence.
How to extract active ad counts and verified advertiser credentials programmatically.
How to automate the entire lead enrichment pipeline using n8n and Python.

1. The Challenge with Manual Ad Research

Google launched the Ads Transparency Center to provide public visibility into all active ad creatives across Search, YouTube, and Display.

However, agencies face two major bottlenecks:

No Native Bulk Export: The official UI only supports looking up one single advertiser domain at a time.
Enterprise Software Costs: Tools like Semrush or SpyFu charge \$150 to \$300/month for ad intelligence subscriptions.

By directly querying the underlying gRPC/JSON endpoints, you can evaluate 1,000+ prospect domains in under 2 minutes.

2. The Core Architecture

Here is the data pipeline:

[ Domain List / CSV / CRM ]
          │
          ▼
[ Google Ads Transparency Scraper ]
          │
          ├──> Resolves Verified Advertiser Name & Country
          ├──> Counts Active Search, YouTube & Display Creatives
          └──> Computes adSpendTier (Enterprise / Growth / Testing / None)
          │
          ▼
[ n8n Automation Engine ]
          │
          ├──> Enriches with Emails & WhatsApp (wa.me)
          └──> Generates Personalized Cold Pitch Hook

Categorization Rules for High-Conversion Outbound:

Ad Count	adSpendTier	Outreach Angle / Pitch Hook
50+ Ads	`Enterprise`	Creative fatigue testing, video ad scaling, landing page CRO.
10 - 49 Ads	`Growth`	Multi-channel retargeting (Meta/TikTok pixel parity).
1 - 9 Ads	`Testing`	Ad copy optimization, negative keyword audits.
0 Ads	`None`	Competitor conquesting, high-intent Google Search setup.

3. Implementing with Python

Here is a lightweight Python snippet demonstrating how to query Google Ads Transparency:

import urllib.request
import json

def check_domain_ads(domain: str):
    url = "https://adstransparency.google.com/anji/_/rpc/SearchService/SearchSuggestions"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
        "Content-Type": "application/x-www-form-urlencoded"
    }
    # Form data request with domain query
    payload = f"f.req=%5B%22{domain}%22%2Cnull%2Cnull%2Cnull%5D"
    req = urllib.request.Request(url, data=payload.encode("utf-8"), headers=headers, method="POST")

    with urllib.request.urlopen(req, timeout=10) as resp:
        content = resp.read().decode("utf-8")
        # Google prefixes RPC responses with ')]}\' to prevent JSON hijacking
        clean_json = content.split('\n', 1)[-1]
        data = json.loads(clean_json)
        return data

# Example usage
# res = check_domain_ads("shopify.com")
# print(res)

4. Plug-and-Play Cloud Solution & Free n8n Template

If you don't want to maintain proxies, headless browsers, or session cookies on your own infrastructure, we've packaged the complete production-grade pipeline:

Production Cloud Actor on Apify Store:
- Run in the cloud or integrate via REST API: Google Ads Transparency Scraper on Apify
- Handles automatic retry, pagination, and multi-country geographic targeting.
- Apify provides \$5 free monthly credits, which is more than enough to test 5,000+ domain lookups.
Open-Source GitHub Repository:
- Full Python source code, Dockerfile, and CLI tools: github.com/mamangwarkop/google-ads-transparency-scraper
Free n8n Workflow Blueprint:
- Pre-configured webhook and enrichment nodes: Download the JSON file from mamangwarkop.github.io.

Conclusion & Next Steps

Targeting leads based on verified ad spend transforms outbound response rates from the standard 1-2% up to 8-12%. Prospects appreciate outreach that references their actual live campaigns and creative choices rather than generic cookie-cutter templates.

Feel free to clone the repository or fork the actor. Happy automating!

DEV Community: mamangwarkop

How to detect if a website runs Meta Pixel, TikTok Pixel or Google Tag Manager with plain Python

Limits to know

Doing it for 1,000 domains

I pulled 40 freshly launched startups with contact emails into a free CSV (and the script to repeat it)

Get the data

Why the missing column matters

Need a list for your niche instead?

How to Build an Automated Startup Lead Generation Engine from Product Hunt Launches

How to Build an Automated Startup Lead Generation Engine from Product Hunt Launches

1. Monitoring Product Hunt without Heavy Scraping

2. Resolving the Destination Website & Auditing Marketing Pixels

3. Production Cloud Tool & Open Source Repository

How to Extract Shopify Competitor Product Catalogs & Real Discount Margins with Python

How to Extract Shopify Competitor Product Catalogs & Real Discount Margins with Python

1. The Power of Shopify's Standardized API

2. Python Script to Scrape and Calculate Discount Margins

3. Detecting Marketing Stack (Pixels & CRM)

4. Ready-to-Use Cloud Tool & n8n Blueprint

How to Bulk Check Google Ads Spend & Verify B2B Leads using Python and n8n (Zero-Budget Guide)

How to Bulk Check Google Ads Spend & Verify B2B Leads using Python and n8n

1. The Challenge with Manual Ad Research

2. The Core Architecture

Categorization Rules for High-Conversion Outbound:

3. Implementing with Python

4. Plug-and-Play Cloud Solution & Free n8n Template

Conclusion & Next Steps

Why the `missing` column matters