I used to spend 6+ hours every week just keeping my LinkedIn alive. Writing, formatting, hitting publish, waiting, checking engagement. It was the part of my business I hated most. So I built an n8n pipeline that now handles the entire posting workflow — from content research to scheduled publishing. It's been running for 4 months without a single missed post. Here's what actually changed: 1. Research became automated. Instead of scrolling for 30 minutes to find relevant talking points, a cheap LLM scans my niche and surfaces angles I'd actually want to write about. 2. Draft + publish got decoupled. I write when I'm inspired (usually Sunday evening). The system publishes Tuesday and Thursday at 8am when engagement peaks. No more posting at midnight because I just finished. 3. The consistency compound hit around week 6. Profile views went from ~200/week to ~900/week. Not because the content got better — but because I stopped ghosting the algorithm for weeks at a time. The biggest surprise? People noticed the consistency before they noticed any single post. My DMs warmed up because I was just... always there. Full demo video showing the setup: https://www.youtube.com/watch?v=SAAy2vUkQLQ I built this — https://inboundy.app/ — a LinkedIn automation tool for outreach + CRM integration. What does your current posting workflow look like? Are you still doing it manually or have you automated any part of it?

Most people think the hard part of LinkedIn automation is the sending. It is not. The hard part is what happens AFTER someone replies. I spent weeks building a Puppeteer-based outreach flow that worked beautifully — until I realized replies were piling up in my LinkedIn inbox with zero connection to my actual pipeline. So I rebuilt the whole thing in n8n with one rule: every inbound reply triggers a CRM update before I even see it. The setup is simple on paper:- LinkedIn reply webhook → n8n trigger- Parse the message with a cheap LLM (classification: interested / not interested / needs follow-up)- Push the result to the CRM with the contact's full context- If "interested" → auto-create a task for me with a 24h reminder The surprising part? Response handling took 3x longer to get right than the outreach itself. Classification accuracy was garbage at first — the model kept confusing "not interested right now" with "not interested ever." Took about 15 iterations of the prompt to get it to 90%+. But now the pipeline runs without me touching it. Last month: 340 replies processed, 47 flagged as warm leads, 0 missed follow-ups. Video demo (60 sec): [Watch the demo](https://www.youtube.com/watch?v=CNe_mxVe18Q) What is the bottleneck in your outreach right now — getting replies, or actually doing something with them?

Let’s be honest: most of us here want to spend our time building core systems, tweaking workflows, and handling clients—not staring at a blank screen trying to figure out what to post on LinkedIn. But organic visibility brings in clients. To solve my own frustration, I built a tool that takes over the heavy lifting. Instead of just spinning out generic, robotic templates, the AI behind it actually maps out context to craft human-sounding posts and coordinates everything in the background. In this quick 58-second video, I walk you through exactly how it handles the generation and how easily you can iterate on both the copy and the image prompts until it fits your exact branding. If you want to play around with it and save yourself some time, you can try it out for free here: https://inboundy.app/?utm_source=skool Let me know what you think of the generation quality or if you have any feature requests!

Everyone talks about "LinkedIn automation" like it's one thing — but the difference between getting banned in 2 weeks and running safely for months is HOW you automate. I spent the last year building a system that doesn't trigger LinkedIn's risk detection. Here's what I learned: ✅ Browser identity matters more than proxy rotation LinkedIn doesn't just check your IP — it fingerprints your entire browser. Same device, same timezone, same language every session. A random headless Chrome in a datacenter screams "bot." ✅ Speed is the #1 red flag Humans don't type at 1000 characters per second. They don't send 50 connection requests back to back. Delays between actions (1-3 seconds) and typing with human-like pacing make the difference. ✅ Spread actions across multiple time windows Sending everything at 9 AM = pattern. Sending in 6+ small batches throughout the day = human. LinkedIn's algorithms notice the shape of your activity, not just the volume. ✅ Conservative limits protect your account Just because LinkedIn ALLOWS 100+ connections/day doesn't mean your account should do it. Staying well under the hard limits keeps your account off their radar. The tools that ignore these things are the reason people think "LinkedIn automation = banned." It doesn't have to be that way. I ended up baking all these principles into inboundy.app — not pitching, just sharing what 12 months of trial and error taught me.

Most LinkedIn content problems aren’t writing problems — they’re systems problems. The bottleneck lives in the loop between research, writing, and publishing. Fix the loop and the writing gets easier on its own. Worked with a health & wellness company recently. Their team was spending a full day every week on LinkedIn — research, drafting, scheduling, the whole thing. They were good at it. They just had no time. So we stopped treating content as a creative task and started treating it like a system you could improve. Here’s what I learned: ✅ Research is its own phase, not a pre-task. Most teams skip it or rush through it. Treating research as a separate step changed everything downstream. ✅ Match the model to the task. Don’t burn an expensive call on summarizing an article. Cheaper models handle research just fine — the cost difference adds up fast. ✅ Decouple writing from publishing. Most teams rewrite because they have to schedule. Separate draft from schedule from publish, and you can review, batch, and adapt without throwing work away. ✅ Close the loop. The system should learn what topics get replies, not just what gets posted. That signal feeds the next research cycle, and the content gets sharper on its own. The CEO put it like this: “We save 8 hours per week on content creation alone. It’s like having a marketing assistant that never sleeps.” The part that surprised me wasn’t the time savings — it was the consistency. The posts just kept showing up, on brand, every week. If you’re doing this manually right now, the fix probably isn’t “hire another writer.” It’s build a loop. What part of your content workflow is the worst right now — research, writing, or distribution?