You're doing the same thing manually. Every. Single. Day. Copy-pasting data. Checking prices. Mon...You're doing the same thing manually. Every. Single. Day. Copy-pasting data. Checking prices. Mon...
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You're doing the same thing manually. Every. Single. Day.
Copy-pasting data. Checking prices. Monitoring websites. Sending the same reports. Watching for alerts that never come at the right time.
That's not work. That's just noise.
I write Python scripts that eliminate the noise.
→ Web scrapers that pull exactly the data you need → Telegram bots that alert you the moment something changes → Automation pipelines that run while you sleep → APIs that connect your tools so they talk to each other
No bloated software. No subscriptions. Just clean Python code that does one thing — solves your specific problem.
I've built a 11-module Web3 analytics bot running 24/7 on a Linux server, a price comparison scraper across two e-commerce platforms, and a URL shortener API with real-time GeoIP tracking.
If you have a repetitive problem, I probably have a Python solution for it.
Message me. Let's kill the manual work.
Stan's avatar
The price comparison scraper across two platforms is the one I'd want details on — normalizing product matching across sites without a shared SKU is usually the hard part, not the scraping itself. How are you matching listings between the two?
RIZAL's avatar
Haha yeah that's the real challenge honestly.
For this one I went with normalized name matching — lowercase, strip punctuation, remove common brand suffixes — then fuzzy matched with token sort ratio for the edge cases where names were too different.
No shared SKU so it's never...
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