Hey Everyone, Initially, this was my idea of what an Agent Dashboard would be. I put a lot of thought into it, multiple agents working and seeing their progress in a nice flashy dashboard. I actually used the theme from the video game MARATHON, and loved the design concept. However, as I was building it, I kept noticing how much time it took to make changes, and I felt I could have used that time more in building real Ai projects than just messing with my own dashboard so it looks flashy. Excited to try out the agentic OS, but this was an interesting learning path I went down for the last 2 months.

Instead of manually prompting AI, you design systems that think, act, learn, and repeat. Triggers → Actions → Feedback → Value → Reinforcement. This open-source project breaks down the mindset, patterns, and tools needed to build real AI loops — from state handling to cost awareness and reusable workflows. If you’re building with AI agents and want leverage, not babysitting, this is worth exploring 👇 🌐 https://github.com/cobusgreyling/loop-engineering

They fail because people try to automate everything too early. What usually goes wrong: - No clear real world problem being solved - Too many tools stacked together - Over automation before understanding the workflow - No human checkpoint in the process - Building complexity instead of clarity In my experience, the simplest setups tend to work the best in real environments. Start with one problem. One workflow. One outcome. Then expand only when there’s actual proof it works. 𝗠𝘆 𝘁𝗮𝗸𝗲: 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗯𝗲𝗮𝘁𝘀 “𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱” 𝘀𝗲𝘁𝘂𝗽𝘀 𝗮𝗹𝗺𝗼𝘀𝘁 𝗲𝘃𝗲𝗿𝘆 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀. What do you think is the biggest reason AI setups fail in practice?

The weather in Europe is hot . Was wondering what are doing to keep your head cool ?

Is anyone out there using AI tools for editing or have an idea of best tools regarding this!! Also I would like to know is there a possibility to automate multiple video editings using n8n??