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15.9M views +10.8K subscribers $2,500 in 28 days This didn’t happen by luck it started with a simple decision. Same platform. Same opportunity. The only difference is who decides to take it seriously. If you keep waiting, nothing changes. If you start now, this could be your numbers next. Make the decision. Type “START” if you’re ready 👇

Most people chase views… I chase revenue. This video did 438.9K views → $7,156 with a $16.30 RPM. Same platform, same algorithm… different strategy. As a YouTube channel manager, I’ve learned one thing: Not all views are equal. If your niche, audience, and content structure aren’t aligned for high RPM… you’ll stay busy, not profitable. But only if you do it the smart way. If you were getting these numbers, would you focus on views… or revenue? Comment “START” if you want to understand how this works.

2.4M views, 113.6K watch hours, +8,000 subscribers, and $5.4K estimated revenue in just 28 days. This is what happens when the right niche, strategy, and monetization system come together. No guessing just data-driven YouTube growth. If you want results like this 👉 Check under the comments 👉 Join our Telegram channel 📥 Or DM me directly to get started

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?