Let's get to know each other! Comment below sharing where you are in the world, a career goal you have, and something you like to do for fun. 😊

Quick share. I trained an AI on more than 10,000 viral LinkedIn posts. At first, it was just an experiment.I spent a long time trying to dissect the structure of viral posts to build a system that could reproduce them. Mixed results. Then I thought: maybe with enough data, there’s no need to explain the rules at all. Result: an AI that applies viral patterns without needing them to be explicitly described. I called it ViralGPT. So what does it actually do? You can tell it:“Create a post from my notes:” + a voice dictation of your idea. In a few seconds, it generates a post: → with a real hook (not something lukewarm) → a smooth, effortless reading rhythm → emotions that actually grab attention → and an ending that makes people want to act Not “a nice piece of writing.” A post designed like the ones that get 100k+ views. Are the posts it generates always viral? Obviously not. There are too many factors involved: - Number of followers and size of your active audience - Whether other big creators comment on your posts (and whether you comment on theirs) - Your community outside the platform (it is intented for LinkedIn, but can be used for X or Threads) - The topic itself - Timing But what is certain is that everyone who’s used it has seen their posts drive more reach. And most importantly, it removes everything that blocks you from publishing. You can literally take a YouTube video you’re interested in, grab the transcript, feed it to the GPT, and generate a post with your own take on the topic. In 30 seconds, you’ve got a solid foundattion. You can finally focus on impact, not commas. If you want to try it yourself, it’s here: 👉 https://chatgpt.com/g/g-6974b135a1b88191b37464224fe98fee-viralgpt-viral-post-creator And for context: I’m Paul Irolla from France. I work on automation, AI, and time-saving systems. With code, I’m 100% full-stack ;) Curious to hear your feedback - and hope it’s useful.

It’s about systems that think before you do. Do you agree? The people winning right now aren’t “AI experts” —they’re builders who know how to:• connect no-code tools• design smart workflows• turn chaos into leverage That’s exactly what we’re doing. If you’re building (or want to build) automations that actually run businesses let me know in the comments I would love to hear. Future belongs to the Automator's.

I just built a multi-agent AI system for Instagram that automates the entire customer journey from first DM to confirmed booking. Not a chatbot. An operations system. The problem: Brands are flooded with Instagram DMs: bookings, voice notes, support, promos. Humans can’t keep up, context gets lost, and revenue leaks. What I built: A multi-agent AI orchestration system that:• Handles text + voice DMs• Transcribes and understands voice notes• Classifies intent in real time• Routes messages to specialized AI agents• Maintains full conversation memory Agents • Booking • Support • Marketing • Retention All sharing context and handing off seamlessly. What’s live • Instagram DM automation (text + voice) • End-to-end booking + support workflows What’s next • Comment-to-DM automation • WhatsApp chatbot using the same agents and memory Results • 24/7 replies • <30s response time • ~80% less manual support • Built to scale 10× Built with OpenAI agents + Make / n8n + APIs. If you’re dealing with DM overload or building automation systems, happy to share details. @Nate Herk Check this out. I have also applied for the job also

Something I’m starting to notice: Most AI projects don’t fail because of the tech — they fail because of a hidden cost that shows up later. From your experience, what’s the most underestimated cost when building or deploying AI automations / agents? Could be: - Maintenance & edge cases - Monitoring & incident response - Client education / trust - Prompt drift or model changes - Over-engineering too early Curious what ended up costing you more time or money than expected.