If you're starting a new online business without an audience or an established name, start with this: 1. Look for people's problems on Reddit, Facebook groups, X etc. Reach out and say, “Vm planning to build a product that does X, Y, and Z.” Ask for their thoughts. At this stage, you're gathering feedback, not selling. 2. Use those responses to refine the problems your product will solve. 3. Develop a solution that addresses 3-5 problems and build a minimum viable product (MVP). 4. Start reaching out to strangers through cold outreach and selling your product. Contact at least 2,000 people. 5. Gather feedback about how your product helped people solve their problems. Turn those stories into content and ads. I've seen so many people waste effort, money, and years of their lives for the same reason. Wise people learn from the mistakes of others.

I’m proud to announce that I’ve successfully completed the HighLevel Admin Certification from HighLevel University! 🚀 This is another important milestone in my journey as a GoHighLevel professional. I’m continuously learning and improving my skills in: ✅ GoHighLevel CRM Setup & Management ✅ Workflow Automation ✅ Funnels & Landing Pages ✅ Pipelines & Lead Management ✅ Forms & Surveys ✅ Account Setup & Optimization My goal is to help agencies and businesses streamline their operations, automate processes, and grow more efficiently with GoHighLevel. A big thank you to the HighLevel team for providing this learning opportunity! Always learning. Always improving. Ready for new challenges! 💙 #GoHighLevel #HighLevelCertified #GHL #Automation #CRM #HighLevelUniversity

Build Showcase: AI-Powered Gmail Customer Support Automation 📩 I recently built an AI-powered customer support system using n8n. The goal was to automate repetitive customer email responses using a company's existing knowledge base. Here's the architecture: Part 1: Customer email automation 1. Gmail Trigger monitors incoming emails. 2. AI Agent processes the customer message. 3. Pinecone Vector Store retrieves relevant knowledge. 4. Groq Chat Model generates the response. 5. Gmail sends the reply. Part 2: Knowledge base ingestion 1. A customer support form accepts document uploads. 2. Default Data Loader processes the uploaded documents. 3. Google Gemini Embeddings converts the content into embeddings. 4. Pinecone stores the knowledge for future retrieval. Tech stack: - n8n – Workflow orchestration - Groq – LLM - Pinecone – Vector database - Google Gemini – Embeddings - Gmail – Email integration What I explored in this build: - AI Agents in n8n - Retrieval-Augmented Generation (RAG) - Vector databases - Document ingestion - Automated email responses I'd love feedback from other builders: 1. How would you improve the retrieval process? 2. Would you add a human approval step before sending AI-generated replies? 3. What error-handling approach would you use? I help small businesses save time on repetitive tasks using n8n and AI automation. Interested in making your business processes simpler with automation? Let's connect and explore what can be automated. 📅 Free 15-minute AI automation discovery call: https://calendly.com/mashfehaque/free-ai-automation-discovery-call

The least glamorous system I run is a naming convention, and it has saved me more time than most of my automations. Every working file gets the same shape: what it is, which project it belongs to, and a date or version — in that order, every time, no exceptions for "I'll remember this one." I won't remember. Three weeks later I am effectively a stranger to my own folders, and a stranger can only find things that were labelled for strangers. Three rules do most of the work: 1. The name describes the contents, not my feelings about them — "draft-final-v2" tells nobody anything; the topic goes in the name. 2. One canonical copy. Experiments get their own clearly-marked files; the moment two files both claim to be the real one, neither is. 3. If a file matters, its name says where it sits in the chain — source, working, or finished — so nobody (including me) publishes a working file by mistake. None of this is clever. That's the point. Search only works when the names were written for the searcher, and the searcher is always future me, in a hurry. What's your version of this — the boring convention you'd defend to the death because it keeps saving you?

🚀 I built a job alert automation for myself. Then a friend saw it and asked for a bigger one. My own version works on the job alert emails that land in my inbox. It reads them, filters the jobs, saves them in a Google Sheet and sends them on WhatsApp. 📲 A friend saw it working and said: “I want this too.” But their needs were bigger. Emails weren’t enough, because job alerts arrive late and miss a lot of jobs. They are looking for remote and part-time marketing jobs, from any country, with official apply links. So this time the automation doesn’t wait for emails. It goes and searches for jobs on its own. 🔎 🆚 My version vs their version 🅰️ My version 🔎 Source: Job alert emails ⏰ Trigger: When an email arrives 🌍 Coverage: Whatever the emails contain 🔗 Links: From the email 🧠 Matching: AI scores each job for me 🅱️ My friend’s version 🔎 Source: Searches job sites directly ⏰ Trigger: Twice a day, on schedule 🌍 Coverage: 20 roles, any country 🔗 Links: Filtered for official sources 🧠 Matching: Role and keyword filters 🎯 What it had to do ✅ Cover 20 marketing roles: SEO, Meta Ads, Google Ads, Performance Marketing, Social Media, Copywriting, Content Writing, Video Editing, Graphic Design, Web Development, CRM, Data Analytics and more ✅ Find remote and part-time jobs from any country ✅ Send only jobs with a valid, official apply link ✅ Never repeat a job ✅ Alert on WhatsApp, in the morning and evening ✅ Keep a record of every job in Google Sheets 🧰 The tools 🔧 n8n for the workflow 🔎 JSearch API (RapidAPI) to search jobs across job sites 📊 Google Sheets as the job database 💬 WhatsApp Cloud API for the alerts ⚙️ How it works, step by step 1️⃣ Schedule ⏰ The workflow starts on its own at 9 AM and 8 PM. 2️⃣ Build the search 🧩 Each run picks 2 roles from the list of 20 and rotates, so every role gets its turn without wasting API requests. 3️⃣ Search for jobs 🔎 The JSearch API returns fresh remote listings for those roles. 4️⃣ Clean and filter 🧹 Code checks every job and keeps only the ones that: