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I created KIBA as an AI-assisted operations command center for managing projects, business knowledge, decisions, schedules, and delegated work from a unified desktop experience.
KIBA converts verified project signals into actionable matters, preserves links to their original sources, and provides citation-backed knowledge retrieval instead of treating AI output as unquestioned truth.
Natural-language requests can become structured action proposals, but consequential actions remain behind explicit human review and confirmation.
The system also brings together project monitoring, calendar information, operational history, delegation, and follow-up states.
Result: an operational workspace that combines AI assistance with deterministic business rules, human approval, source traceability, and a purpose-built desktop interface.
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Lost 2 days building a complex n8n workflow. Here's what I learned.✌️
The project: A 12-node AI Receptionist automation for a luxury resort in Bangladesh. Multi-channel (WhatsApp, Telegram, Voice), 9 Google Sheets, Gemini API, payment automation, customer CRM.
What went wrong: Added a new voice integration node. Accidentally deleted the entire workflow.
Why it happened:
No JSON exports after milestones
No version control (Git)
Testing changes LIVE on production workflow
No documentation
Building locally with zero redundancy
The fix (now implemented):
✅ JSON backups after every 2-3 nodes
✅ GitHub versioning
✅ Clone workflows for testing
✅ Modular design
✅ Complete documentation
For automation professionals: You're not just builders—you're engineers.
Treat workflows like software:
Documentation
Version control
Backup strategy
Modular testing
Client delays = lost revenue + credibility damage.
Make the systems bulletproof.
How do you prevent data loss in your automation builds? 👇
#n8n #Automation #WorkflowAutomation #NoCode #LessonsLearned #make #AIAutomation #DataAnalyst #MachineLearning
I'd try restoring one of those JSON backups into a cloned workflow before the next handoff. That way you know it can actually get you running again.
Making components with an AI agent for my new Framer project. So far, I’ve made a background with adjustable square sizes, gaps, spacing, and colors, plus an animated text box, put them together in a little shot. I’m enjoying experimenting with small animations that don’t make the screen feel too busy.
The dotted gradient stays quiet while the coral on “recurring work” carries the hero. That small text motion feels like a system, not extra noise.
Two ways to build the same fitness app.
A: AI-first. No tabs, no dashboards. The app is one conversation with a coach that already knows you slept six hours and squatted heavy on Tuesday. You log sets by talking. The plan rewrites itself mid-workout.
B: The modern standard. Home, today's workout, set-by-set logging, class booking, progress. Everything a member expects, done properly.
Same brand, same components, same build quality. The only real difference is how much you're asking the user to unlearn.
Most gyms should ship B. A few should bet on A.
Which one would you open tomorrow morning?
1 voted
25%
3 voted
75%
4 votes
Closed
Going with A here, the idea of the plan rewriting itself mid-workout instead of you scrolling a dashboard feels like where fitness apps eventually land. Would you actually ship the AI-first version to real users right now, or is it more of a thought experiment at this stage?