Every single person following AI right now is overwhelmed. Including me. I make videos about this stuff for a living and I still feel the pressure. New model drops. New framework. New feature update. It feels like every single day. But after hearing a ton of you guys bring up "AI overwhelm" week after week, I realized this: → There's a HUGE difference between knowing the "what" and knowing the "how." Staying aware does not mean testing everything. Most new tools and features only need the "what." You see the title. You understand what it does. You move on. The "how" is reserved for the stuff that solves a problem you actually have right now. So when something new drops, I ask myself one question: Does this solve a specific pain point I'm currently dealing with? If yes, I test it in a real scenario. I test it against something that actually matters to me. If no, I save the link. I mentally file it away. And I keep walking. Because here's the thing. Your north star is probably very different from mine. Part of my job is to experiment, form opinions, and share what I think is useful. So naturally I test a lot of stuff. But if your north star is building a business or getting better at your craft, then every shiny new tool might just be a distraction. The number one mistake I see people make is they try to learn everything. They watch every video. They test every tool. They jump to the next thing before the last thing even had a chance to work. And if I've contributed to your overwhelm with my daily uploads, I apologize. hehe. But a lot of people think that this ties directly into how you measure your day. Productivity is not how many hours you worked. It's how many meaningful outputs you created that actually moved the needle towards your north star. Someone can work 12 hours one day and feel insanely productive, but they were just watching tutorials and playing around with new tools. Meanwhile someone else sits down for 5 hours, ships the one thing that actually matters, and makes more progress.

I am happy to share my Day4 challenge, a fully automated lead generation system that finds dental practice leads across 38 cities in Northern France and saves them directly into an Airtable CRM How it works Step 1: Scheduled Search (every Monday at 7am) Step 2: Parallel Contact Extraction Step 3 — Duplicate Check Step 4 — Save to Airtable CRM First run results - 284 dental practices found across 38 cities - Leads landing directly in Airtable, ready for outreach

Invoice Generation Skill I am happy to share with you my Day3 challenge, a fully automated invoice generation skill that pulls client data from Airtable CRM and produces an invoice .docx file in seconds. How it works 1. Trigger /invoice [Company Name] or a sentence such as "generate an invoice for company x" 2. Fetch sender info(address, SIRET, email, website) 3. Find the client. It searches the CRM for the matching company record (with Airtable MCP) 4. Locate the pending invoice with status "To generate" for that client 5. Grab the billing lines retrieves each line item (description, quantity, unit price, total) 6. Generate the .docx, fills a Word template 7. Returns the file path with a clean summary (invoice number, dates, total) This skill ca be replicated for any document generation workflow(contracts, proposals, reports),connected to any data source (Notion, Google Sheets, a REST API, etc.).

Web Scraper I am happy to share with you my Day2 challenge. It is a real web scraper to extract job listings from leboncoin.fr (a major French classifieds site) Claude Code reasoned like an expert. It identified that the site uses DataDome, a serious enterprise-grade anti-bot protection system. It chose the right proxy strategy: stealth from Firecrawl MCP to bypass DataDome detection without getting blocked. I learned that Web scraping isn't just about downloading a page. The MCP Firecrawl server handles a LOT of complexity under the hood Result 100 job listings from leboncoin.fr, exported as a clean CSV, ready to import directly into Google Sheets Thanks to Nate

Newsletter Automation I am happy to share with you my Day1 challenge. here is my workflow (based on the WAT framework) Step 1: Research with GPT Step 2: Writing with GPT Step 3: Visuals with GPT-image1 Step 5: Delivery with Brevo The next step is to put in the production Thanks to Nate @Nate Herk