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Are there any jobs that AI can’t take over?

HOLY SH*T..🤯 A legend dropped 1000+ N8N workflows in one repo and I'm literally shaking... He scraped EVERY workflow from the official n8n site + GitHub. ✅ E-commerce automation ✅ Social schedulers ✅ Lead gen machines, etc. Workflows worth $10K+ in consulting fees!

What would you do differently if you lost $80,000 on a failed app? How would you recover emotionally and financially?

WOW! Stumbled across this n8n catalogue with 2,201+ workflows! 🤯 Been struggling with the automation setup for weeks. → Battle-tested templates → Build, don't buy philosophy → Save countless development hours Comment "Link" below if you want to access it

An 8-year enterprise engineer just leaked his exact Claude Code workflow. And it's embarrassingly simple. Why is nobody talking about this systematic approach? This guy literally went from sceptical to shipping complex features in under 2 hours. Three months ago, he was manually debugging enterprise systems for days. Now he's solving production issues faster than teams with dedicated tools. He's using basic Claude Code with zero fancy configurations. And before you think "Another productivity hack post," this is different. This isn't about tool mastery. It's about treating Claude like you would a junior engineer and following a systematic process. Here's his exact 11-step workflow that's crushing it: 1. Start with planning mode (5-15 minutes) → Treat Claude like a junior engineer → Explain the problem clearly before any code → Get alignment on what needs to be done 2. Understand the codebase first → Navigate manually to learn existing patterns → Don't let Claude do everything for you → Identify entry points and function signatures 3. Use printf debugging for exploration → Add logging to trace data flow → Let Claude write console.log statements → Visualise enter/exit points in unfamiliar code 4. Think patterns before prompting → Decide what approach YOU would use first → Only ask Claude for suggestions if torn between options → Never ask "what should I do?" from the start (yields horrible ideas) 5. Give Claude proper context → Mention if this affects latency at scale → Specify if the code will barely be used → Tell it to keep solutions succinct and minimal 6. Respect architectural boundaries → Don't let Claude mix domain logic in controllers → Maintain intentional layers of separation → Follow established patterns (controller-service-repository) 7. Disable auto-edit initially → Verify the first few changes match your vision → Give feedback before letting it run → Only enable auto-edit for repetitive tasks 8. Test everything before unit tests → Verify functionality, including edge cases → Don't jump to testing until you know it works → Manual verification first, automation second 9. Follow existing test conventions → Ask Claude to review existing unit tests first → Don't let it go crazy with mocking → Ensure tests don't obfuscate errors 10. Let Claude run and revise tests → Run unit tests and get immediate feedback → Let Claude fix failing tests iteratively → Repeat for integration tests if necessary 11. Use the second Claude session for review → Spin up fresh Claude to review the git diff → Often finds issues that the first session missed → Review your own PR before requesting reviews The results are honestly unfair: Complex enterprise features that used to take weeks now ship in hours. Production bugs that stumped teams get solved in minutes. Code quality actually improved because of the systematic approach. But here's his controversial warning: "Gen AI is genuinely bad for rising engineers who use it as a crutch." Junior devs are prompting for hours, making messes, and learning nothing. They feed code review comments directly to AI without thinking. Meanwhile, experienced engineers are using this systematic approach to amplify their expertise. The gap between "AI prompt jockey" and "AI-amplified engineer" is massive. And it's only going to get wider. But here's something even better... I found another incredible resource from someone who's been testing Claude Code pretty heavily and documenting exactly how it behaves. He created a comprehensive GitHub guide that covers: → Prompt patterns that actually work in production → Lesser-known quirks and capabilities most people miss → A complete cheat sheet for using Claude like a coding assistant → Real-world examples with before/after comparisons This goes way deeper than the 11-step workflow. It's like having the manual Claude never gave us. Want the GitHub guide? Reply "Claude" and I'll send it over.

I just watched someone build a $5,000 website in 45 minutes I'm honestly questioning everything. Like... what am I even doing with my life? This person had zero coding experience. Six months ago, they couldn't even centre a div. Now they're charging local businesses $2-5k for websites that used to cost $15k and take weeks. They are just using Cursor AI. And before you roll your eyes thinking "another AI tool," hear me out. This isn't some overhyped chatbot. This thing actually builds real, functional websites while you have a conversation with it. I'm talking to it like: "Hey, can you make me a restaurant website with online ordering?" 30 minutes later: Full website. Working cart. Payment processing. Mobile responsive. I literally sat there with my mouth open. But there is something that is keeping me up at night, though... While I'm still Googling "how to fix CSS layout issues," there are people out there building entire businesses with this thing And they're all complete beginners. The craziest part is that these aren't just "toy projects." These are real solutions solving real problems for real money. I used to think you needed: - Years of coding bootcamps - Computer science degree - Understanding of 15 different technologies - Ability to debug complex errors Now you literally just need: - Cursor AI - Ability to describe what you want - 20-30 minutes of free time The math behind the idea: If I can build 1 simple business website per week at $1,000 each, that's $52k/year working weekends. If I get decent and do 2-3 per week? We're talking six figures. The arbitrage opportunity is massive right now. But it won't last forever. Soon, everyone will figure this out. The early movers are going to clean up while the rest of us are still debating whether AI can "really" code. What's the first thing you'd build if you could code? 🤔

Do you think work-life balance is a myth or a necessity?

Have you ever used any no-code tools for your projects?

What’s one thing you underestimated when you started building?

Why is everyone saying 'I'm building this, I'm building that' But no one’s talking about why they're building it? Is it for money? Social media clout? To help people? Or something deeper?

is it just me, or is nobody talking about what's happening in the n8n space right now? like... everyone's acting like this is just another tech trend. but i'm seeing something that's genuinely concerning me. i've been watching thousands of people jump into "AI automation" after binge-watching YouTube tutorials. and they're immediately launching agencies and taking on client work. these people have NO idea what they don't know. i saw a reddit post yesterday that perfectly captured what i've been thinking. this seasoned pro basically said: "there's a massive gap between workflow porn on youtube and commercial reality." and he's absolutely right. the scary stuff nobody talks about: → beginners think error handling is adding one try-catch node → they've never dealt with webhook timeouts that kill entire workflows → they're building 500-node monsters that take 30 minutes to debug → they quote 2-week projects that become 2-month disasters but here's the part that really got me... one comment mentioned agencies discovering their client's CRM returns DIFFERENT FIELD TYPES ON WEEKENDS. like... what? if you don't even know that systems behave differently under various conditions, how are you charging businesses $5k for "reliable automation"? the horror stories i'm hearing: → workflows that work perfectly in testing but fail in production → entire business processes breaking at 3am with no way to fix them → small businesses losing money because their "expert" didn't understand API rate limits → automations that corrupt data because nobody validated inputs and the worst part? when these projects blow up, it's not just the "expert" who gets hurt. it's the small business owner who trusted them with their customer data. their sales process. their entire operation. but everyone's too busy posting "look at my 700-node workflow" on linkedin to talk about this. the gap between "that's so cool" and "i can build production-ready systems" is MASSIVE. yet people are confidently jumping that gap with zero safety net. here's my controversial take: if you've only built workflows with clean data and unlimited API calls, you're not ready for client work. if you've never had a system fail under real-world conditions, don't charge premium prices. if you don't understand the difference between a demo and a production system, please don't start an agency yet. i know this sounds harsh. but we're about to see a wave of failed projects that could destroy trust in automation entirely. the market's crying out for education that bridges the gap between youtube tutorials and commercial deployment. someone needs to teach error handling, debugging, security, rate limits, data validation - all the unglamorous stuff that actually matters. because right now? most people think they're experts after getting a template to work once. and that's terrifying. am i being too harsh here? or have you seen similar patterns in other spaces? the early movers who actually learn the fundamentals are going to have a massive advantage when the hype settles. but first, we need to stop pretending that watching tutorials makes you production-ready.

Stop worrying about AI taking your job. Start thinking about what you can finally create. We're obsessing over the wrong thing. AI isn't making experts obsolete. It's giving the rest of us superpowers we never had before. Six months ago, I couldn't create videos to save my life. Couldn't draw stick figures that looked decent. My "creative writing" peaked in 8th 8th-grade English class. Today I built three small apps, designed graphics for my side project, and wrote content that actually gets engagement. Did I become a genius overnight? Nope. The floor just got lifted under my feet. Before AI: You needed years of training to participate in coding, art, music, writing After AI: A 5-year-old can create things that would've required professional skills I can now create in areas where I had zero talent or skills before. This isn't about replacing the best. It's about including the rest. What I've noticed: - More people are starting creative projects they never would've attempted - Small business owners building tools they couldn't afford before - Students exploring fields outside their "natural" abilities - Ideas getting tested instead of staying stuck in someone's head The barrier to entry just collapsed in dozens of fields simultaneously. My Advice (From Someone Who's Living This) 1. Stop comparing yourself to experts - You're not trying to beat them, you're trying to create what you couldn't before 2. Start with small experiments - Build tiny things, create simple art, write short pieces 3. Focus on your unique perspective - AI gives you the skills, but your ideas and experiences are still yours 4. Embrace being a beginner - In a world where anyone can participate, being new isn't shameful Yes, entry-level jobs will face pressure. That's real, and we need to address it. But a world where more people can create, build, and express themselves? Where your lack of technical skills doesn't kill your ideas before they start? That's not a world to fear. That's a world to embrace.

Two years ago, I laughed at a competitor's AI content. It looked fake and robotic. I used it as an example of what NOT to do. Then their traffic doubled while my writers burned out. Here's what I learned watching my competitor who figured it out: Step 1: Be super specific Tell it exactly who to be, who you're talking to, give real examples, set the tone Step 2: Make it yours Edit so it sounds like you, add your stories, cut AI-ish parts, and inject your expertise Step 3: Test it Does this sound human? Like MY brand? Would my audience care? Does it solve real problems? AI content quality isn't about the AI. It's about: ✓ How well you tell it what to do ✓ How much you edit and fix it ✓ How specific you get with details ✓ How well you make it sound like you Companies saying "AI sucks" ask basic questions like "write about marketing tips." Winners give detailed briefs and add human touch. AI doesn't replace strategy - it amplifies it. Generic strategy = generic content. Smart strategy = smart content. What's your experience? Still getting robot content, or have you cracked the code?