🚀 *I think I just had my "holy sh*t" moment.* Repository: github.com/tamzrod/KDSE For the longest time, AI-assisted development looked like this: > Prompt → Code → Debug → Prompt Again Today I tried something completely different. Instead of asking the AI to write code... I asked it to engineer. Using my experimental methodology, KDSE (Knowledge-Driven Software Engineering), the workflow became:

Knowledge

↓

Architecture

↓

Implementation

↓

Verification

Then I gave it ONE engineering request. > "Build an Industrial Power Plant Monitoring Dashboard." That's it. No architecture prompt. No implementation prompt. No debugging prompt. No screenshot prompt. The runtime handled the engineering process. The result? ✅ Knowledge Base ✅ Architecture ✅ Backend ✅ Frontend ✅ Apache ECharts Dashboard ✅ Mock Telemetry Simulator ✅ Automated Verification ✅ Playwright Screenshots 🤯 From a single engineering request. This isn't about generating code anymore. It's about giving AI an engineering process to follow. I honestly think we're moving beyond prompt engineering. We're entering engineering runtime territory. This is still experimental, but today was the first time I felt like... > "I can build software by engineering the knowledge—not by micromanaging the code." I'm ridiculously excited to see where this goes. 🔥 #AI #SoftwareEngineering #OpenHands #LLM #ApacheECharts #KDSE

This is awesome! Thank you for sharing! @rapid-iron-54628... have you built any automations with agent canvas yet?

Not yet. So far I've been focusing on the KDSE engineering runtime itself. Agent Canvas automations are actually the next thing I want to explore—using triggers like GitHub issues or scheduled runs to invoke the KDSE engineering pipeline automatically.