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Developed a sentiment classifier for hotel guest reviews (Positive / Negative / Neutral) and automatically extracted common complaint themes such as cleanliness, staff, service, and pricing to support guest experience decisions.
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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Amaan | Contra × Bubble Challenge 🚀
Amaan comes from the Arabic word أمان — meaning safety.
We all know the feeling of buying something from a stranger online…
You send the money and just hope the “imaginary product” actually shows up.
So I’m building Amaan — a social commerce platform where your money stays protected and held until you receive your product and confirm delivery.
Buy with confidence. Sell with trust. 💯
More soon 👀:
#Amaan #Bubble #Contra #BuildInPublic #SocialCommerce
AgentFlow10 — AI Agent & Automation Platform
Lead Engineer · Self-hosted AI agent platform with a visual workflow builder
The problem
Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.
There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.
What I built
AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.
The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.
What I did
I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.
Notable pieces of engineering:
A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent
LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text
A self-hosting path that a non-specialist can actually deploy
Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows
Why it matters for clients
This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.
Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker
I developed a Python web scraping automation script using Selenium, Undetected ChromeDriver, Pandas, WebDriverWait, and Excel. The script automatically opens car parts product URLs from an Excel file, loads each page, handles pop-ups, waits for dynamic content, and extracts product details such as title, OE number, and interchange part number. It includes error handling, fallback methods, random delays, and browser automation to make the scraping process more reliable. Finally, it stores all collected data in a structured Excel file using Pandas.
#Web_scraping #Automation #Python
How are you handling IP rotation for larger scraping runs? Are you using a proxy pool with session-level rotation, or relying more on request throttling and browser-level fingerprints to reduce blocking?