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Hello everyone! I'm new here. Nice to meet you all. Looking forward to connecting, learning, and growing together in this awesome community.
I'm a Certified AI Engineer and Full Stack Developer. If you have any questions about AI, LLMs, AI agents, automation, or full-stack development, feel free to reach out. I'd be happy to help however I can.
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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Excited to officially start offering my services on Contra!
I’m a Full-Stack & AI Engineer with 6+ years of experience, helping startups and businesses turn ideas into scalable, production-ready products.
I specialize in building:
🤖 Custom AI Web Applications
⚡ AI Agents & Automation
🚀 AI SaaS & SaaS Products
💬 AI Chatbots & RAG Applications
🌐 Full-Stack Web Applications
🔗 APIs & Backend Systems
📊 Custom Business Dashboards
My core stack includes Python, FastAPI, Django, React, Next.js, Node.js, MERN, LLMs, Machine Learning, and modern AI technologies.
I also run a YouTube channel with 38K+ subscribers, where I teach AI, Python, Machine Learning, and software development.
I’ve just launched my first Contra service: Custom AI Web Apps & SaaS Development.
Have an AI or SaaS idea you want to turn into a real product? Let’s build it. 🚀
#AI #FullStackDevelopment #SaaS #AIDevelopment #Python #FastAPI #NextJS #ArtificialIntelligence
A production-ready LangGraph agent that discovers, audits, scores, enriches, and qualifies B2B prospects automatically.
I built an AI-powered n8n automation that generates personalized ice breakers for cold emails.
It takes a company’s website URL, analyzes the business, extracts key information, and uses AI to create a unique opening line for outreach.
Instead of manually researching every prospect, this workflow helps personalize cold emails at scale while saving hours of repetitive work.
Built with n8n + AI + web data extraction.
Strong use case. The quality control matters as much as the generation: I’d keep the source snippet and URL beside every ice breaker, then use a fallback when the evidence is thin instead of letting the model infer. Have you added a similarity check so repeat prospects don’t receive nearly identical openings?