Since leaving home, I’ve made it a habit to call my grandma every week. But let’s be honest—when one of you spends all day working and the other is just relaxing at home, finding new things to talk about isn’t easy. She’s already on Duolingo, crushing it with 2,000 XP daily, but I wanted to do something more interactive to keep our calls fun and her mind sharp. 💡 That’s when I got the idea: why not recreate “Who Wants to Be a Millionaire?” just for her? Growing up, we used to watch the show as a family and even played it on our old DVD player version. So, I built my own version using Lovable—in just two messages. Now, before every call, I generate a fresh set of trivia questions on any topic she loves, and we play together. 💰 And of course, we won the $1,000,000 on our first game—with all the questions about 19th-century writers she adores. 📌 Check out our guide on Lovable if you missed it—learn how to build your own solutions for small everyday needs

I’d like to take a moment to introduce myself. My name is Olek, and I am a web designer and web marketer and passionate about AI, creating and building my own digital agency. SO excited to be part of this group!

UPDATE: I made a video guide and template based on this case that you can find in Classroom - link to guide 2025 is shaping up to be the year of the solo entrepreneur. Why? Because with AI automations and agents, you can operate an entire company by yourself. Let me show you how. Last week, I worked on a real case that’s a perfect example of this. A client needed to hire for multiple positions for their clients but didn’t want to hire an HR team. Instead, they had: 1. A list of jobs with specific requirements. 2. A Gmail account receiving hundreds of CVs for these positions. After just a few days, they had over 500 emails sitting in their inbox. Manually processing all of them would take a full week of non-stop work. So we built an AI automation system that handled everything in one day. Here’s how we did it: 1️⃣ Email Processing: Every 5 minutes, our automation fetched new emails. It downloaded attached CVs in PDF format. If the document was in a different format (like .doc or image-based PDFs), we sent a message asking the candidate to resend their CV as a single, readable PDF. For better parsing, we could have added LlamaParse, but the standard n8n node worked for this case. 2️⃣ Filtering Non-CVs: If an email wasn’t a CV, the system marked it as read and left it in the inbox. No manual intervention needed. 3️⃣ Job Matching: Each CV was automatically analyzed to find the best matching job. If no match was found, the email was marked as read, saving time on irrelevant applications. 4️⃣ Scoring Candidates: Once a CV was matched to a job, we applied a custom scoring system tailored to the job requirements. Different positions required different scoring logic, so we built a separate AI-powered scenario for this step. 5️⃣ Personalized Feedback: • If a candidate didn’t meet the required score, the system generated a personalized rejection email, including constructive feedback and highlighting their strengths.