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Jan 30 •
$20k/month in document automation - here are all my templates
📂 All my workflow templates are now in one place These are the exact automations I use to earn ~$20k/month from document processing clients. Finally organized everything into one repo: 👉 https://github.com/khanhduyvt0101/workflows Templates for n8n, Make, and Zapier. All free. No signup. Just grab what you need. Will keep adding more as I build them.
Pinned
Sep '25 •
Your Complete 30-Day Roadmap to Landing Your First $1,000+ Client
Welcome founding member! You're literally one of the first people here, and that's exactly where you want to be. The Promise: Follow this exact roadmap for 30 days. Land your first automation client. Or I'll personally help you until you do. WEEK 1: Build Your Authority (Even Starting from Zero) Day 1-2: The Foundation Setup Download these templates: Github Awesome Templates Day 3-4: Your Tech Stack Here's exactly what I use (most are free): - Automation: n8n, Zapier, or Make (pick ONE) - PDF Processing: Any tool you want (my suggestion: PDF Vector - go-to for reliability - free tier handles 100 pages) - Communication: Loom for demos, Calendly for bookings - Contracts: HelloSign or PandaDoc free tier Day 5-7: Your First "Proof" Create ONE simple automation that shows value: - Invoice extractor (Gmail → Spreadsheet) - Document organizer (Dropbox → Organized folders) - Research compiler (Web → Summary report) Assignment: Post your automation in comments. Get feedback from everyone. WEEK 2: Book Your First 5 Discovery Calls The 3-Message Method That Actually Works: Message 1: The Observation "Hey [Name], noticed you mentioned struggling with [specific problem]. Mind if I share something that might help?" Message 2: The Value "I built a simple automation that handles exactly this. Takes about 10 minutes to set up. Want me to show you how it works?" Message 3: The Close "I can jump on a quick call Tuesday or Thursday to walk through it. Which works better?" Where to Send These: - Your existing LinkedIn connections - Facebook groups you're already in - Local business owners you know - Previous colleagues or clients Goal: 5 calls booked by end of week 2 WEEK 3: Demo and Close Your First Deal The Problem Calculator Framework: Step 1: "How many hours per week does your team spend on [manual task]?" Step 2: "What's the average hourly rate?" Step 3: "So that's costing you $[amount] per month..."
3h •
🔮🚀🔜💡 For the future 🔮🚀🔜💡
Today, following our discussion on LLM Orchestration, we are specifically introducing the RAG Pipeline. For satisfactory processing, the RAG (Retrieval-Augmented Generation) pipeline is a key element in building AI systems that provide successful and context-aware answers. This pipeline combines the powerful capabilities of language models with document-related search functions, ensuring that AI responses are based on user data rather than relying solely on prior knowledge. The following is a subsequent diagram illustrating the RAG pipeline. It shows how data is retrieved, processed, and used to generate high-quality, powerful answers. This approach not only enables excellent answers but also allows for the integration of features through added content. We welcome any questions related to software, including issues encountered during the learning and development process. Our goal is ```for the future```.
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3h •
current metrics to meet and to beat:
curious to learn what metrics everyone is getting in 2026, here are mine: 1. cold 30k leads (3 days, email, company name, title, full name, address, social media links) 1$ 2. 10min ai video generation 10cents in 720p 3. ai cold calling in and outbound system 1cent per minute. 4. ~500 emails to one sale (90% are ai automated) 5. ~200 calls to one sale (90% are ai automated) 6. ~14 contacts to one sale 7. 40 hours per week at 10$USD per hour for freelancers. revenue to company $8-10K per month (EBITDA per Employee = EBITDA /# of employees/contractors/freelancers) 8. estimated hours saved thx to automation and ai each week per freelancer: ~500-600 hours (Time Saved per Employee=(Time Before−Time After)×Task Frequency)
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10h •
Your n8n Workflow is probably breaking (Here’s How to Fix It)
Most people build n8n workflows… but they break the moment something goes wrong. And the worst part?You don’t even realise it failed until it’s too late. 😅 If you're building automations (or AI agents),error handling is what separates hobby projects from real systems. Here are 5 simple ways I use to make my n8n workflows reliable: 1. Retry On Fail APIs fail all the time.Just retry automatically instead of letting everything crash. 2. Continue On Fail Not every step matters.Skip the failure, log it, move on. 3. Split Error Route This is underrated 👇Send success one way, errors another → super powerful for notifications & fallback logic. 4. AI Agent Fallbacks LLMs fail randomly.Always keep a backup model ready. 5. Global Error Workflow Game changer One central workflow to catch all errors → send alerts + execution link. 💡 Biggest lesson: Don’t try to avoid errors. Design your system to handle them gracefully. Curious — how are you guys handling errors right now? Are you just letting workflows fail or doing something smarter? 👇
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