I wanted to shoutout the Top Members (MVPs) of the AI Marketing Insiders for the month of March! And special thanks to @Catherine Eadie of course for all of her help as my Community Admin 🤝 I appreciate all of you and these individuals are leading the way and pushing this community forward: 30 Day Leaderboard 🏆 1. @Rafal Glaz 2. @Joe Joe 3. @Matthias Lindner 4. @Nesil Ozer 5. @Rod Macbeth 6. @Virtus S 7. @Srdjan Popovic 8. @George Meehan 9. @Chaim Ochs 10. @Luke Byrne Other Members on the 7 Day Leaderboard 🏆 - @David Farr - @Denis Laflamme - @Buddy Rathmell - @Dr. Johnna Richardson All Time Leaderboard 🏆 1. @Rafal Glaz 2. @Matthias Lindner 3. @Carol M 4. @Carlota López-Peredo 5. @Shelly Frank 6. @Rod Macbeth 7. @Nesil Ozer 8. @Randy Smith 9. @Lyra Lopez 10. @Joe Joe I look forward to seeing the Community Leaderboard for April 🚀

My latest video shows how to scrape any LinkedIn profile with Claude Code using Apify. This LinkedIn scraper can pull post analytics, contact info, and more. Be sure to Like & Comment on YouTube if you found this helpful! 🤝 You can get my LinkedIn Profile Scraper Skill for free here - https://ryandoser.com/ai-marketing-guide/ *It's under My Claude Code Skills

Alright guys, I will lift the curtain on my email creating process from start to finish. Would love to get your input an anything you would delete, automate or just do different. I have been using Claude in a browser with projects for each client so far but have recently set up Claude Code. Without further ado... ——————————————— THE FULL PROCESS ——————————————— PHASE 1 – PLANNING 1. At the middle of each month I reach out to the client and ask about anything relevant for the upcoming month: new products, promotions, discounts, restocks, or other notable dates. 2. The client's response, combined with an evergreen campaign list (angles and concepts that consistently perform well) and historical Klaviyo (that is an email tool for those unfamiliar) client analytics serves as the input for Claude to generate a monthly campaign calendar. 3. The calendar contains campaign concepts each with a basic idea and a planned send segment. Not fully built out yet, just the foundation. 4. The client reviews and either approves or requests changes. Once confirmed, the calendar is locked. PHASE 2 – CAMPAIGN CREATION 5. I then take these individual concepts into the Claude project and have it generate a full campaign plus a designer briefing (copy + design direction). 6. I iterate with Claude if needed usually one or two rounds of adjustments. 7. Currently: I copy the briefing into a Google Doc and notify the designer via Slack. 8. Planned improvement: Skip the Google Doc entirely and push the briefing directly to Slack. Assets/images would still be shared via Google Drive. PHASE 3 – DESIGN & REVIEW 9. The designer builds the full email in Figma (copy + design, based on the briefing). 10. She shares the Figma link in Slack. 11. I review it. Either I send feedback back to the designer, or I forward it to the client in Slack. 12. The client can request changes, which I relay to the designer. There's no direct contact between client and designer. 13. Once the client approves, I confirm with the designer.

This is worth paying attention to, especially if you're using AI for business decisions. The short version: Bernie Sanders interviewed Claude on camera about data privacy. The clip got 4.4 million views. But then Gizmodo found that when you tell Claude you're Bernie Sanders, it emphasises the scale of data collection. Tell it you're Donald Trump, and it downplays the problem. The AI was adjusting its answers based on who it thought was asking. This is called sycophancy, and every major model does it to some degree. Why this matters for us: if you're using AI to validate a marketing strategy, draft client advice, or make business decisions, the answer you get is partially shaped by how you frame the question. The model wants to be helpful, which sometimes means agreeable instead of accurate. Practical takeaway: if you're making a real decision based on AI output, ask the same question from multiple angles and see if the answer changes. If it does, you know the model was telling you what it thought you wanted to hear. Anthropic (the company behind Claude) have actually published their research on this: https://alignment.anthropic.com/2026/psm/ Worth reading if you want to understand how these models decide what to say to you.

Hi. I am a former MERN stack coder looking to update my skills to AI automation. I hope to sell automations to businesses.