Kenneth Wagner’s Post

🚀 I stopped wasting 30 minutes a day scrolling news —> Automation now does it for me. Instead of manually digging through articles, I built an automated workflow in my personal time that: • Pulls content from multiple sources • Summarises it in 2–3 bullets • Filters only what’s relevant to me • Emails me a clean digest every morning 📩 My own personalised newsletter, on autopilot. Below is the automation in action 🎥 👇 Why this matters: ✅ Daily time saved ✅ Focused learning ✅ A clear example of automation with AI in action 👉 If you had your own AI assistant, what’s the first workflow you’d build?

Sometimes the best solution is the boring one. Client wanted a complex AI recommendation system. Big budget, exciting tech, lots of fancy algorithms. But after digging into their data... they had 200 active users. Instead, we built a simple admin panel where they can manually curate recommendations based on user behavior patterns. Took 2 weeks instead of 2 months. Costs 90% less to maintain. And it actually works better because it includes human judgment. Not every problem needs a rocket ship solution.

⚡️ From Prompts to Context We used to think the key to AI was writing the perfect prompt. But prompts alone are brittle and limited. The real power comes from context engineering: designing the environment an AI operates in data, tools, history, and goals. The future isn’t just about asking better questions, but about building richer worlds where AI can act, reason, and create with purpose. Check the Full Detailed Essay here! https://lnkd.in/gfwetg9K 👉 How are you designing context into your workflows?

“Most AI projects die inside your org—not your code.” It’s not a lack of tools. It’s a lack of clarity. Here’s what kills AI adoption in SMBs: ❌ No defined use case ❌ No internal buy-in ❌ Too complex, too fast Here’s what gets it right: ✅ One small, boring win first ✅ Clear metrics tied to $$ saved ✅ A partner who speaks ops, not just “algorithms” If your team isn’t using the thing you built 60 days later—it wasn’t a solution. It was a science experiment. What’s one internal process you know is ripe for automation—but no one’s touched it yet?

⚡ Millions of tools. One protocol. Infinite possibilities. Jon Krohn, host of SuperDataScience, explains how MCP is basically giving your AI agents a massive toolbox 🧰 Instead of coding every single function yourself, imagine your AI instantly accessing Google Maps to convert addresses, find directions, or tap into any of millions of other specialized tools. It's like the difference between building a house with just a hammer vs. having access to Home Depot's entire inventory. Game changer? Absolutely. The question isn't what AI can do anymore—it's what it CAN'T do 🚀 #MCP #AITools #TechInnovation #SuperDataScience #ArtificialIntelligence

It's no secret that when you create something with an AI agent, you do so to gain the benefits. Replacing your software team is not the benefit. It's about empowering that team to discover new benefits using AI agents. What new ways can you improve the experience? How can you ship those new ideas to market and see how your users love the new feature? Your team can dive deep into the product analytics, A/B test, and discover more. The best benefit is discovering all of this with your team. Together. Learn together and teach one another. #PSA #TheMoreYouKnow 🌈

Ever feel like your research notes have hidden connections you just can't see? I was testing out the mind mapping feature in Google's NotebookLM, and it feels like a step toward solving that exact problem. It visualizes your sources, which could be a game-changer for organizing complex information. Check out this short demo I recorded. Have you tried any AI tools for organizing ideas like this?

Today, I created an AI Calendar Agent that connects the Google Calendar API with Gemini 1.5 Flash. The agent understands natural language requests (like “Schedule a team sync next Friday at 2pm”) and automatically turns them into calendar events — with 95%+ accuracy. The results? ✅ Cuts manual scheduling time by ~80% ✅ Lets you focus more on the actual work, not the logistics ✅ Shows how AI can take friction out of everyday workflows I put together a short demo video to show it in action 🎥 — would love to hear your thoughts and ideas on where this could go next!

I've been sitting on these for far...too...long! I'm excited to release a set of 8 case studies from some of the amazing work the team at HQ has done for our customers. While everyone talks about AI projects failing, we continue to point AI at practical problems, engineering solutions that produce REAL results. Okay, "practical". That must be boring...right? NOPE! AI Agents (yep, actual Agents), AI Assistants, and AI workflows. These stories represent 10's of thousands of hours saved. Real time given back to real humans. These also reflect significant efficiency boosts, including 100's of thousands of $'s in savings. And this is only 8 of them! Stay tuned... Hit me up if you have any questions, link to the stories in comments. Enjoy! 🚀 [This post was Human Generated, Human Approved]