Xavier Tai

Social media has made news feel like it is happening in real time. People react, share, fact check, and even break stories before traditional outlets do. From the AI side, it is interesting because platforms basically act like giant data streams. You can see what topics spike, how sentiment changes, and how fast something spreads. It has made news faster, more interactive, and a lot more driven by what people are talking about in the moment.

#ThisIsNotTheOnion People use it when a real news story sounds so ridiculous that it feels like satire. Always makes me laugh.

For building automation systems: n8n, Make.com, Zapier, Python, JavaScript. For AI integrations: Claude API, OpenAI API, vector databases like Supabase and Pinecone. For CRM work: Salesforce, HubSpot, custom API integrations. For writing: I keep it simple—Google Docs for drafts, Grammarly for editing, and Claude for research and ideation. I also use Fireflies.ai to transcribe client calls, which often turn into article ideas. Most of my workflow is about connecting different tools together, which is probably why I write about automation so much.

Skool. It is the platform I use daily to learn, connect with other builders, and stay updated on AI automation. It feels more community driven compared to traditional social networks, and I get far more value there than on mainstream platforms.

"The Gratitude Gap: Why 80% of AI Projects Fail" for She Rises Studios hit different because it addressed something I see constantly but rarely written about. Everyone writes about AI success stories. I wanted to write about why most projects fail—not because the technology doesn't work, but because expectations are misaligned, change management is ignored, and companies chase shiny objects instead of solving real problems. It came from watching clients waste money on AI tools they didn't need while ignoring simple automations that could save them 15 hours a week. The article practically wrote itself out of frustration. I'm proud of it because people actually changed their approach after reading it. That's the goal—write something that shifts how people think, not just informs them.

That AI automation is either magic that solves everything instantly, or that it's too complex for non-technical teams to implement. The reality is messier and more interesting. Most successful automation isn't about deploying cutting-edge AI—it's about connecting existing tools intelligently and solving boring, repetitive problems that eat up 10-20 hours a week. People expect AI agents to replace entire departments overnight. What actually works is automating specific workflows: lead scoring, follow-up sequences, data entry between systems. The ROI comes from compounding small efficiencies, not revolutionary transformation. The other misperception is that you need a team of engineers. Some of the best automation I've built uses no-code tools like n8n and Make.com. The barrier isn't technical complexity—it's clearly defining what you want to automate and measuring whether it actually saves time or makes money.