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Manual data extraction doesn’t scale.
This Python project automates web scraping, validates documents, runs AI-based analysis, and generates structured reports — all via a simple Streamlit interface.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Just finished building a small Python tool for monitoring product prices on e-commerce websites.
The idea was simple:
enter a product URL → extract price and availability → save results to CSV.
It’s a small project, but it’s a good example of how Python can automate repetitive data collection tasks.
I’ve been building more tools like this lately — automation scripts, web scraping utilities, and small desktop apps to simplify workflows.
Curious what kind of automation tools other Python developers are working on lately.
Great
Built a Multimodal RAG system live on stream. From zero to working CLI in one session.
The goal: a terminal-based RAG pipeline using Haystack, Gemini Multimodal Embeddings, and Flash Lite, querying complex research PDFs through a conversational loop.
Three real engineering lessons from the build:
Chunking isn't optional. Raw PDFs exhaust token limits instantly. 6-page splits solved it.
InMemoryDocumentStore works for prototyping, but re-vectorizes on every launch. Persistent DBs like Weaviate are the next step.
Haystack's pipeline is strict. Mis-wiring a retriever to a generator crashes the loop immediately. API contracts matter.
Result: a CLI that maps user queries to the top 4 semantic chunks in RAM and returns grounded, non-hallucinated answers from your own documents.
Every mistake, every fix, on camera.
"Ugurcan Uzunkaya this is seriously impressive! 🔥 Building a full RAG pipeline live on stream in one session is no joke!
That chunking lesson is so real — token limits hit different when you're working with raw PDFs.
And the Haystack pipeline point about API contracts is...
AI-Powered Outreach Automation (n8n)
Built a workflow that turns outreach into a structured pipeline.
The system:
• Pulls verified leads from Supabase
• Uses AI to generate personalized emails
• Sends outreach via Gmail
• Monitors replies and analyzes sentiment
• Routes responses automatically (book meeting, review, or archive)
• Sends follow-ups if there’s no reply after 48 hours
Result: a fully automated pipeline that moves leads → conversations → booked meetings.
Designed for creators, agencies, and teams running outbound outreach at scale.
Nice workflow. Using AI to personalize outreach and track replies in one pipeline is really useful.
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