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?

n8n Workflow attached below and in our community classroom Here’s the workflow: Step 1: You submit job details through a simple form. Step 2: OpenAI extracts + infers key details into a structured JSON object (client info, project name, scope, deliverables, exclusions, timeline estimate, pricing, deposit amount, and more). Step 3: n8n duplicates your Google Docs template so the original stays untouched. Step 4: The automation replaces every {{variable}} in the doc with the mapped JSON values (reliable, repeatable, and fast). Step 5: You get a finished proposal you can quickly review, tweak, and send. This setup is perfect if you constantly create proposals/contracts and want AI to do the heavy lifting—scope drafting, estimates, payment schedule (like 50/50), and all the repetitive template filling. If you want, the next step is sending the completed doc to a signing tool (like PandaDoc) for signature + deposit collection—so your onboarding is basically push-button. Youtube video (So sorry about the quality, Loom has been giving me a headache lately): https://youtu.be/ACSgwveCeaw?si=tQlws28YDW_H_9W5

Hey everyone! I'm giving away a copy of "React Native Notes for Professionals" — completely free. It's attached below. No email required. No catch. Why React Native? It's one of the fastest ways to build real mobile apps for both iOS and Android using a single codebase. Whether you're breaking into mobile development for the first time or expanding your skillset — this is a great place to start. What's inside: → Setup guides for Mac, Windows & Linux → Props, State & Component patterns → Styling with Flexbox (with visual examples) → Navigation best practices → HTTP requests & API integration → Push notifications setup → Firebase authentication → How to create a shareable APK → Unit testing with Jest → And much more... Who this is for: ✅ You're curious about mobile app development ✅ You're new to React Native and want a solid reference ✅ You've been building apps but want to level up ✅ You learn best from real code examples What makes this book different: It's compiled from Stack Overflow documentation. That means it's written by developers who actually solved these problems — not theory, but real solutions. 80+ pages. 32 chapters. Zero fluff. Happy building! 🚀

Never write another YouTube title or description from scratch again! In this tutorial, I'm showing you the exact n8n workflow I use to automate my entire YouTube upload prep. Just paste your video transcript into a simple form — and AI generates 3 viral title options, a fully optimized description (with all your links included), AND a custom thumbnail. Everything gets delivered straight to your inbox. This workflow combines GPT-5.2 for copywriting and OpenAI's image generation for thumbnails, all connected through n8n. Whether you're a solo creator or running a content team, this will save you hours every single week. Video: https://youtu.be/HnnU85bPH14?si=6FG_qs0f51hTdpet Workflow (and in our community classroom): https://drive.google.com/drive/folders/1D817PhVvlTT1ttgvN53UvIh_wPE7EL6m?usp=sharing

Want a REAL production-ready n8n deployment? In this video we break down the n8n-aiwithapex infrastructure stack and why it’s a massive upgrade over a “basic docker-compose n8n” setup. You’ll see how this project implements a full queue-mode architecture with: - n8n-main (Editor/API) separated from execution - Redis as the queue broker - Multiple n8n workers for horizontal scaling - External task runners (isolated JS/Python execution) for safer Code node workloads - PostgreSQL persistence with tuning + initialization - ngrok for quick secure access in WSL2/local dev We’ll also cover the “Ops” side that most tutorials ignore: - Comprehensive backups (Postgres + Redis + n8n exports + env backups) - Offsite sync + optional GPG encryption - Health checks, monitoring, queue depth, and log management scripts - Restore + disaster recovery testing so you can recover fast - Dual deployment paths: WSL2 for local + Coolify for cloud/production If you’re building automations for clients, running n8n for a team, or scaling AI workflows, this architecture is the blueprint: separation of concerns, isolation, scaling, and recoverability. Youtube video: https://youtu.be/HzgrId0kgfw?si=0bzdvDgJW4dLApfi Repo: https://github.com/moshehbenavraham/n8n-aiwithapex