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Rules and Guidelines 🛠 — Rahul Joshi’s AI Automation Club
1) 🚫 No Promotions→ No selling, “DM me for…,” or “Comment ‘Automation’” posts. 2) 🔗 No Self‑Promo Links→ Don’t link your own communities/courses/YouTube. 3) 🏷️ Title Clearly→ Name the tool + workflow + outcome. 4) 🔍 Search First→ Use the search bar; add to existing threads when relevant. 5) 🙌 Be Respectful→ Constructive, kind, professional. 6) 🧹 Enforced Clean‑Up→ Violations may be removed without warning. Need help? Share context, stack, what you tried, a minimal repro, and the desired output. Let’s learn and build together. 🚀
n8n hosting
Hello, community Please advise on the best hosting service for n8n. Options I am considering include Hostinger, Bluehost, or any other recommendations you may have.
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The $99 AI That Replaces HR Onboarding
Still paying someone to manually chase HR paperwork every time you hire? Stop. This AI Onboarding Compliance Agent does the job of a full-time HR coordinator — for a fraction of the cost. ✅ Auto-checks every new hire's documents (ID, Tax Form, Policy, NDA) ✅ Catches missing or invalid submissions instantly using AI ✅ Alerts you on Telegram in real time ✅ Auto-escalates until it's resolved — no human follow-up needed Built in n8n. Runs 24/7. Never forgets, never gets tired, never needs a raise. If you're hiring at any scale, this pays for itself in the first week. 💰 Get the full automation for $99 — comment "ONBOARD" and I'll send you the details.
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Most of your "production" n8n workflows would die at 2,000 users. Here's the fix.
Here's a quick gut check: has your n8n workflow ever been hit by 2,000 users at once? If not, you don't know it's production-ready. You just know it hasn't failed yet. By default, n8n runs everything — UI, triggers, execution — through one main instance. Fine with 20 users. At 2,000, that instance chokes: requests pile up, executions time out, the editor lags. The fix is Queue Mode: → Main instance receives the trigger, doesn't execute it → Job goes into Redis (the queue) → Workers pull jobs and run them in parallel → Results get written to a shared PostgreSQL database If a worker crashes mid-job, another one picks it up. Main instance stays untouched, so your editor and webhooks never freeze. One thing most people miss: scaling isn't just "add more workers." It's worker count × concurrency. Get concurrency wrong, and more workers can strain your database faster than they add capacity. Full visual breakdown attached — Regular Mode vs Queue Mode, side by side. Running n8n in production and still on Regular Mode? Worth testing before your users force the question. Drop a comment if you want to walk through your setup.
Most of your "production" n8n workflows would die at 2,000 users. Here's the fix.
The n8n roadmap I wish I had when I started
When I started with n8n, I had no idea where to even begin — nodes, AI agents, workflows, all of it felt scattered. Found this starter guide a while back and it's genuinely one of the clearest beginner-to-intermediate breakdowns I've seen — attaching it below for anyone in the same spot. If you want something more tailored to your specific domain (pharma, IT, manufacturing, whatever you're in), drop a comment — happy to point you in the right direction.
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