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Earlier today, an n8n developer knocked me with a problem.
He was stuck on an Alibaba product details scraping project and couldn’t get past the setup phase.
We jumped on a call.
40 minutes later, the issue wasn’t the tool anymore.
Once we walked through how Alibaba structures product pages, what changes dynamically, and what shouldn’t be scraped blindly, the workflow started making sense.
By the end of the call, the scraper was pulling clean product data again.
A good reminder that most “hard” problems are really clarity problems.
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Automating Website Lead Collection with n8n + Google Sheets
I built a simple but powerful automation using n8n that automatically captures website leads and stores them in Google Sheets in real time.
Here’s how it works:
Webhook captures every new form submission
Data is cleaned and validated automatically
Lead details are stored instantly in Google Sheets
Optional: Sends real-time notifications via Telegram or Email
This system helps businesses:
• Never lose a lead
• Track prospects instantly
• Eliminate manual copy-paste work
• Respond faster to new inquiries
Automation saves time — and time saves money.
If you’re still manually transferring leads from your website, it’s time to upgrade your workflow.
Let’s automate it.
#n8n #automation #workflow #nocode #productivity
𝗗𝗮𝘆 𝟰 𝗼𝗳 𝗿𝗲-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝗿
Today, I still continued with my framer journey by starting from a blank canvas. Followed along the tutorial on YT. The focus was on building an animated, fully responsive website from scratch, making sure:
• Breakpoints actually made sense
• Sections flowed naturally
• Animations felt purposeful
• Layout held up across devices
• Nothing broke under resize
Starting from a blank canvas exposes your real understanding. Every day I build, I feel my thinking getting sharper.
Less guessing.
More intention.
More control.
If you can design it and build it, you think differently.
Day 4 complete.
We’re not stopping.
Great start. Which video did you follow?
Problem:
Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.
Solution:
This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.
Very real problem. Manual invoice processing is still surprisingly common and becomes a huge bottleneck as companies scale. Automating extraction and validation with AI can save a lot of operational time and reduce errors significantly.
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