I built this system for a locksmith company that needed a better way to handle incoming emergency calls and assign jobs to technicians automatically. Here’s what the system does: • AI answers incoming calls • Collects customer details (name, address, issue, phone number) • Creates a job record in the database • Sends the job to technicians one by one • Includes Accept / Reject buttons inside the email • Waits for a response • If ignored → automatically escalates to the next technician • Stops immediately once someone accepts The main challenge was building the loop logic in n8n so it doesn’t notify everyone at once, and making sure the workflow instantly stops once the job status changes to “accepted.” Result: Replaces manual dispatching, prevents double assignments, reduces missed calls, and ensures technicians are assigned faster without a human coordinating everything. I recorded a Loom walkthrough showing: – A live AI call test – The n8n workflow logic – How accept/reject works – How the escalation system is structured If anyone’s curious about the backend setup, here’s the full breakdown: https://youtu.be/fG83tKiWcgA Would love to hear how you’d improve the routing or escalation logic.

A lot of people talk about “doing outbound consistently,” but what usually breaks isn’t effort it’s friction. Recently, I built a LinkedIn outreach system to remove that friction completely. What the system does: - Scrapes targeted LinkedIn leads - Researches each profile - Writes a personalized connection message per lead - Sends the connection request - Automatically sends the 1st and 2nd follow-up What this removes from the process: - Searching and opening profiles - Copy-pasting profile data - Writing messages one by one - Manually remembering and sending follow-ups Realistic time impact:~20 minutes saved per lead. At ~30 leads/week → ~10 hours saved every week. The bigger outcome: Outbound runs in the background. Messages stay personalized.Follow-ups never fall through. If you’re curious and want to see the exact workflow or explore setting something similar up for yourself, feel free to DM me and we can walk through it on a quick call.

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So I got a bunch of Tavily API credits for completing their course AND I wanted to show how to use a Boilerplate template to start apps. Combined this with a system (Claude Code Plugin) I've been developing the past couple of months I'm calling 'Apex Spec System' and I made a pretty awesome and good looking app. Complete open-source here: https://github.com/moshehbenavraham/tavily-app How it works: - Phases → major feature groups - Sessions → focused implementation units - Specs → detailed requirements per session - Task checklists → 15-30 items to complete - Validation gates → quality checks before moving on The result: - 15 sessions across 3 phases - FastAPI backend + React frontend + PostgreSQL - Auth, CRUD, 4 Tavily operations, save results with metadata - ~15K lines of production-ready code - 2 afternoons The key insight: AI doesn't drift when it has clear scope, explicit constraints, and traceable progress. It's not magic—it's just structured prompting at the project level. Video below! Curious if anyone else is experimenting with structured AI dev workflows such as BMAD, Github Spec Kit, etc. What's working for you?

📺 244K Subscribers. Millions of Views. Fully Monetized. This is the power of YouTube Automation. Notice how this channel doesn’t need a face or fancy production just animated content, a smart niche, and a proven system. 💡 The result? Consistent viral videos, thousands of dollars in ad revenue, and a growing digital asset that earns 24/7. #youtubeautomation #FacelessYouTube #trendingpost #facelessyoutube