DEV Community: Fyruz

Stop Building Web Scrapers From Scratch — 10 Production-Ready Python Scripts

Fyruz — Sun, 28 Jun 2026 08:50:22 +0000

Stop Building Web Scrapers From Scratch — 10 Production-Ready Python Scripts

5 min read | Tags: #python #webscraping #datascience #automation #tutorial

The Scraper Struggle Is Real

Every Python developer has been here:

You need data from a website
You spend 2 hours writing boilerplate: requests, BeautifulSoup, error handling, rate limiting, CSV export
You test it, it breaks on the 3rd page
You fix the pagination bug
You realize you forgot User-Agent rotation
4 hours later, you have ONE scraper

After years of freelance scraping work, I standardized my 10 most-used Python scrapers into a clean, documented pack. Stop building from scratch every time.

All 10 Scrapers Included

#	Scraper	What It Extracts	Real Use Case
1	🛒 Product Scraper	E-commerce product details, prices, variants	Dropshipping research
2	💼 Job Scraper	Job listings with titles, descriptions, salaries	Recruitment automation
3	💰 Price Monitor	Competitor pricing with change detection	SaaS competitive intel
4	📰 News Aggregator	Articles with headlines, dates, summaries	Industry monitoring
5	🎯 Lead Generator	Business contacts from directories	Sales prospecting
6	🏠 Real Estate Parser	Property listings, prices, specs	Market analysis
7	🌤 Weather Extractor	Forecast and historical weather data	Data science projects
8	📊 Finance Scraper	Stock data, financial reports	Trading research
9	📂 Public Data Tools	Government and open data portals	Research datasets
10	🖼 Image Scraper	Download and deduplicate images	ML training data

Production-Ready Features

Every scraper includes:

✅ CLI arguments — configure target, output path, limits from terminal
✅ Error handling — graceful retry with exponential backoff
✅ Rate limiting — respect robots.txt, configurable delays
✅ CSV export — structured output ready for pandas/Excel
✅ Type hints — full Python 3.9+ type annotations
✅ Standalone — each scraper is one self-contained file

Tech Stack

Python 3.9+ | requests | BeautifulSoup4 | Selenium (JS-heavy targets)

No API keys needed. No external services. Just pip install and run.

What Each Scraper Taught Me

After scraping professionally for years, each target type forced me to learn something new:

E-commerce: Handle pagination + variant SKUs with generator pattern
Job boards: Infinite scroll → intercept XHR calls instead of DOM parsing
Price monitoring: Session persistence + cookie jar for login-walled sites
Lead gen: Rotate User-Agents + respect robots.txt religiously
Real estate: Nested JSON in script tags is a goldmine for structured data

All these battle-tested patterns are baked into the scripts.

Quick Start

# Example: Scrape product listings
python product_scraper.py --url "https://example-store.com/products" --pages 5 --output products.csv

# Example: Monitor competitor prices
python price_monitor.py --competitors competitors.txt --output prices.csv

👉 Get the Web Scraper Pack — $25

Who This Is For

Data analysts needing fresh datasets without manual collection
Growth hackers monitoring competitors and generating leads
Marketers tracking industry trends and pricing
Recruiters sourcing candidates from multiple platforms
Real estate investors analyzing property markets
Python developers who value their time

Stop spending 4 hours per scraper. Get 10 battle-tested scripts for the price of one coffee meeting.

🔧 Complete your stack: n8n Workflow Templates | AI Prompt Pack

The Developer AI Shortcut: 510 Prompts for Python, Web Scraping and DevOps

Fyruz — Sun, 28 Jun 2026 08:50:21 +0000

The Developer's AI Shortcut: 510 Prompts That Write Code, Scrape Sites, and Debug Faster Than You Can Google

5 min read | Tags: #ai #promptengineering #chatgpt #developertools #productivity

The Problem With AI Coding

You've tried ChatGPT for coding. You got back a hallucinated library that doesn't exist and a regex that matches everything.

The problem isn't AI — it's your prompts.

The difference between garbage output and production-ready code is one well-crafted prompt. After months of trial and error, I compiled 510 battle-tested prompts across 10 categories specifically for developers.

What's Inside: 10 Categories

Category	Count	What You'll Generate
🐍 Python Development	80	Code, debug, optimize scripts
🕷️ Web Scraping	60	Extract data from any site
📊 Data Analysis	50	Pandas, SQL, visualizations
🔌 API Development	50	FastAPI, auth, testing
🐳 DevOps & CI/CD	40	Docker, GitHub Actions
🤖 AI/ML Engineering	50	RAG, fine-tuning, LLMs
🎯 Prompt Engineering	50	Meta-prompts for better AI output
⚡ n8n Automation	50	Workflow automation
🐛 Debugging & Testing	40	Pytest, error diagnosis
💼 Freelancing	40	Proposals, pricing, client work

Works With ALL Major AI Models

ChatGPT, Claude, Gemini, DeepSeek — no platform lock-in. Every prompt is tested across multiple models.

Free Sample Prompt

Here's one from the Python section:

"You are a senior Python developer. Write a FastAPI endpoint that accepts JSON with 'name' and 'email' fields, validates both, stores in PostgreSQL using SQLAlchemy async, and returns the created user with a 201 status. Include error handling for duplicate emails and input validation. Use type hints throughout."

This single prompt generates a complete, working endpoint in seconds instead of 30 minutes of boilerplate writing.

How to Use

Open prompts.md
Find your category
Copy a prompt
Replace placeholders with your details
Paste into any AI
Get results

Format: ZIP with 510 prompts in organized markdown. 82KB of pure prompt gold.

👉 Get the AI Prompt Pack — $15

Why Developers Love This

Multi-model compatible — not locked into one AI provider
Developer-specific — no generic "write a blog post" prompts
Copy-paste ready — zero prompt engineering needed
10 categories — covers the entire dev workflow

Stop fighting with AI. Start shipping with it.

🔧 Also check out: n8n Workflow Templates | Web Scraper Pack

I Automated My Freelance Ops with n8n — 12 hrs/week to 2 hrs (Templates Included)

Fyruz — Sun, 28 Jun 2026 08:42:13 +0000

I Built an Automation Empire with n8n (and Packaged It So You Don't Have To)

8 min read | Tags: #automation #n8n #devops #productivity #webdev

The Problem No One Talks About

Every side project, freelance gig, and SaaS comes with an invisible tax: operational glue work.

A user signs up → you manually add them to your CRM
Your SaaS needs competitor pricing → you copy-paste from 5 websites weekly
A client asks for a status update → you scramble through 4 tools to compile it
Content needs to go out → you post manually across platforms

I tracked my time for two weeks. The result was embarrassing:

12 hours per week spent on tasks that a Python script or n8n workflow could handle. That's 624 hours per year. Over a month of full-time work — burned on glue.

I'm a developer. I knew I could automate this. So I did.

Why n8n (and Not Zapier, Make, or Custom Scripts)

I evaluated four approaches:

Approach	Cost/Month	Self-Hosted	Code Extensibility	Best For
Zapier	$30–$800	❌	Limited JS	Non-technical teams
Make.com	$10–$150	❌	Limited	Visual builders
Custom scripts	Server cost	✅	Full	One-off automations
n8n	$0 (self-hosted)	✅	Full JS/Python nodes	Developers who ship

The killer feature: Code nodes. When a low-code node doesn't cover my edge case, I drop into JavaScript or Python — right inside the workflow. No external Lambda. No separate microservice.

I deployed n8n on a $6/month Hetzner VPS via Docker. Total monthly automation cost: $6.

The 5 Highest-Impact Workflows I Built

1. User Signup → CRM → Welcome Sequence

Trigger: New row in Supabase (user signup)

Webhook catches the signup event
Enrich user data (IP → geolocation, email → Clearbit)
Create/update record in Notion CRM
Add to ConvertKit with "free trial" tag
Send personalized Slack notification to #new-users

Time saved: ~45 mins per signup → 0 seconds.

2. AI-Powered Competitor Price Monitor

Trigger: Cron — every Monday 8 AM

HTTP Request to 5 competitor pricing pages
HTML Extract parses the price elements (CSS selectors)
Code node normalizes to USD and detects changes vs. last week
GPT node generates a "Price Intel Brief" — 3 bullet summary
Sends Slack message with the brief + links

Time saved: ~2 hours/week.

3. Content Factory: Idea → Draft → Publish

Trigger: Manual (or cron weekly)

RSS Feed pulls trending dev articles from 3 sources
GPT generates 5 content ideas based on trending topics
I pick one via a Slack interactive message
GPT generates a full draft with the chosen angle
WordPress node creates the draft post
Social media nodes queue tweets/LinkedIn posts

Time saved: ~3 hours per article. Went from 1 article/month → 1/week.

4. Client Reporting Engine

Trigger: Cron — 1st of each month

Pull Stripe revenue data
Pull Google Analytics traffic data
Pull GitHub commit stats
Code node merges into a single JSON report
GPT node generates natural-language summary
HTML template renders a client-facing PDF
Gmail sends the PDF to the client

Time saved: ~3 hours/month per client. With 5 clients = 15 hours/month.

5. Database Backup with Verification

Trigger: Cron — daily at 3 AM

PostgreSQL node runs pg_dump equivalent
Compresses with gzip
Uploads to S3-compatible storage (Backblaze B2 — $6/TB)
Verifies file size > 0 and integrity
Sends Slack confirmation or alert on failure
Auto-deletes backups older than 30 days

Time saved: Peace of mind. No more "did I remember to backup?" anxiety.

The Template Pack: Skip the Build Phase

Building these workflows took about 40 hours total — testing edge cases, debugging node connections, writing the custom code snippets.

I packaged all 20+ workflows (including the 5 above + 15 more for social media, email automation, data pipelines, and AI orchestration) into a ready-to-import JSON pack.

What you get:

20+ .json workflow files — drag-and-drop into n8n
Credential setup guide per workflow
Custom code snippets included
Tested on n8n v1.x (self-hosted and cloud compatible)

👉 Get the n8n Workflow Templates — $20

No config hell. No "why is this node red" debugging at 1 AM. Just working automations.

What I'd Do Differently

Start with one workflow. I tried building all 20 at once. Build → test → productionize one at a time.
Version your workflow JSONs in Git. n8n has a history feature, but Git gives you diff visibility and rollbacks.
Use environment variables for all credentials. Never hardcode API keys in code nodes. n8n supports $env variables natively.
Set up error webhooks early. Connect failed executions to Slack/Telegram. Silent failures are the enemy.

The Bottom Line

I now spend under 2 hours/week on ops — down from 12. That's an 83% reduction. The workflows paid for themselves in the first week.

If you're a developer grinding through repetitive tasks, the ROI on automation isn't theoretical. It's measurable. And with n8n, it's never been cheaper to get started.

Complete Developer Automation Stack:

🔧 n8n Workflow Templates — 20+ workflows, ready to import ($20)
💬 AI Prompt Pack — 510 prompts for coding, scraping, DevOps ($15)
🕷️ Web Scraper Pack — 10 Python scrapers, production-ready ($25)

How much time do you spend on glue work? Drop a comment — I bet we can automate it.

Test - I Automated My n8n Ops

Fyruz — Sun, 28 Jun 2026 08:41:57 +0000

Testing the Dev.to API integration.