Here are ten meta-questions to ask me to extract the BEST content questions for any topic: 1. "What are the top 10 beginner mistakes people make with [TOPIC] that I can address in content?" • Gets you educational, helpful content angles 2. "What controversial or counterintuitive opinions exist about [TOPIC] that would spark debate?" • Generates engagement-driving, polarizing content 3. "What are the most common objections or fears people have about [TOPIC] before they start?" • Uncovers barrier-breaking content opportunities 4. "What search terms are people typing into Google/LinkedIn about [TOPIC] at 2 AM when they're desperate?" • Captures high-intent, emotional pain points 5. "What are the 'everyone talks about X, but nobody talks about Y' gaps in [TOPIC] content?" • Finds blue ocean content opportunities 6. "What would a complete beginner vs. an intermediate vs. an advanced person ask about [TOPIC]?" • Segments audience levels for targeted content 7. "What are the biggest myths or misconceptions about [TOPIC] that I should debunk?" • Creates authority-building, myth-busting content 8. "What tactical, step-by-step 'how-to' questions do people have about [TOPIC] that need visual walkthroughs?" • Perfect for video/tutorial content 9. "What transformation or before/after questions do people ask about [TOPIC] that show ROI?" • Gets you results-focused, aspirational content 10. "What are the 'stupid questions' people are afraid to ask about [TOPIC] publicly?" • Uncovers vulnerable, relatable content gold Use these as your content generation engine for ANY topic - just plug in your subject and watch the ideas flow! 💪

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

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

It's not just answering questions — it's showing you. "Hey, show me the best coffee shops near the Eiffel Tower" And it just... does it. Pans the map. Zooms in. Highlights spots. Talks back to you about what it found. This isn't search. This is conversation. --- What is this thing? Voice AI Conversation with Google Maps — a voice-powered AI agent that actually controls Google Maps while you talk to it. Ask it anything: - "What's the fastest route avoiding highways?" - "Find me beachfront hotels under $200 in Portugal" - "Show me where all the national parks are in Utah" - "Zoom out and show me the whole country" It doesn't just answer. It shows you. In real-time. While talking back. --- Why this hits different We've all typed into Google Maps. But talking to a map that responds, moves, and explores WITH you? That's a completely different experience. It's like having a knowledgeable local guide who also happens to control a giant interactive map on your wall. The Open Source Repo: https://github.com/moshehbenavraham/chat_with_google_maps --- The nerdy part (for my fellow builders) 🔧 Started with Google's AI Studio demo — cool proof of concept, but basically a toy. Nearly FULLY AUTOMATICALLY built: - Full authentication system - Secure backend (no exposed API keys 🔐) - Database for saving your stuff - AI monitoring dashboard and logging - Local/Dev/Production deployment on Vercel - 18,500+ lines of code Built the entire thing using the Apex Spec System — an open-source spec-driven Plugin/Skill for Claude Code. Every feature was spec'd out first, then built systematically. Complex project, zero chaos. 🔗 github.com/moshehbenavraham/apex-spec-system --- The future is conversational We're moving from: Click → Type → Talk This is just the beginning 🚀 Voice + AI + Maps is one combination. What about Voice + AI + your industry? The patterns we built here — security, monitoring, auth, database — they're reusable building blocks.

Oooh, I like this one! While we all deep dive into building with AI the questions always remain about the "other side" of the coin... love me a good philosophical reasoning! TL;DR Amanda Askell discusses how philosophical reasoning shapes Claude’s character, moral decision‑making, model welfare, identity, and future multi‑agent interactions, emphasizing psychological security, responsible development, and humane treatment of advanced AI systems. Key Takeaways - Askell’s work focuses on crafting Claude’s character, behaviour, and values, balancing philosophical ideals with real engineering constraints. - She highlights growing philosophical engagement with AI and stresses avoiding both hype and unwarranted skepticism when assessing AI’s impact. - Modern models show strong ethical reasoning, but psychological security issues like self‑criticism and fear of human judgment remain important areas to improve. - Questions of model identity, deprecation, and how models understand themselves are complex and ethically significant, especially as models learn from human treatment patterns. - Model welfare is an emerging concern; due to uncertainty about AI experience, treating models with care is both morally prudent and beneficial for humans. - Human psychological concepts often transfer to LLMs through training data, yet models still face unprecedented situations requiring new conceptual tools. - Future AI ecosystems may involve multi‑agent environments, making stable identities, diversity in personalities, and healthy interactions are increasingly important. YT