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Real Estate Agent Data Scraping & Lead Generation Automation
I developed a powerful automated web scraping solution for collecting real estate agent data from Compass and other similar websites across multiple cities and pages.
Using Python, Selenium, BeautifulSoup, and Pandas, the scraper automatically visits listing pages, collects agent profile URLs, opens each profile, and extracts valuable information including agent names, job titles, emails, phone numbers, LinkedIn, Facebook, Instagram, and other available profile links.
The system supports multi-page and multi-city scraping and automatically exports all collected data into a clean, organized Excel file. This type of automation is ideal for lead generation, market research, real estate databases, and business outreach.
I can build custom web scraping and data extraction solutions for many different websites based on client requirements.
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We've been building something for a while, and it's finally time to share it.
Meet Zupa, an AI tool that helps you write LinkedIn and social media posts that actually sound like you.
Most people know they should post more. The hard part is sitting down and turning a rough idea into something worth reading. Zupa takes that idea and shapes it into a post built around what makes people stop scrolling and engage.
It's made for founders, marketers, agencies, and anyone building a personal brand who doesn't have hours to spend writing.
Here's a quick look at what we've built 👇
We're opening early access now, and it's free to try. If you want a look, check out https://www.zupapost.ai/
Honest feedback is welcome too. That's how we'll make it better.
Really like the idea of using psychology to help people rewrite their posts! The UI feels thoughtful and fits the concept really well. It’s a focused tool I could see being useful in everyday writing, especially for making posts more engaging. How long did it take you to go from the initial idea to the finished website?
An experimental AI-agent application for researching business opportunities using web search, webpage extraction, evidence collection and structured analysis.
The evidence collection step is where I would look first. When two pages disagree, do you keep both source links so someone can see why the analysis chose one?
I run my company on an AI chief of staff I built. It is called Coulson.
Every morning it puts the day on one page before I open email: the three revenue priorities, every commitment with its date, and one list of what is waiting on my approval. Behind it, a team of AI agents drafts replies, prepares meetings, chases follow ups and sends an end of day report at 7 PM. Nothing goes out without a yes.
Now I build the same system for other companies. The case study, with real screens on sample data: hqlabz.com/work/coulson
