HR team was drowning in resumes. Manually reviewing 50 applications took 8 hours. Built automated screening workflow. Now takes 10 minutes. THE PROBLEM: Startup hiring for 3 positions. Received 150 applications in one week. Each resume needed: skills extracted, experience calculated, fit assessment written. Manual process: 10 minutes per resume × 150 = 25 hours of work. THE SOLUTION (N8N WORKFLOW): Manual Trigger → Start batch processing Google Drive node → Retrieves resume files from folder PDF Vector Parse node → Extracts all candidate information Function node → Calculates years of experience per skill PDF Vector AI node → Evaluates candidate fit and seniority HTTP Request node → Posts to Airtable candidate database Slack node → Notifies team with top candidates Total workflow: 6 nodes. Processes any resume format - PDF, Word, even phone photos. WHAT IT EXTRACTS: Personal info: name, email, phone, location, LinkedIn Work history: companies, dates, technologies used per role Education: degrees, institutions, graduation dates Skills: technical and soft skills with experience levels Certifications: current and relevant to role Calculated metrics: total experience, skill proficiency scores THE SCORING LOGIC: Built custom scoring in Function node: ```javascript // Calculate experience score per skill const skillScores = {}; workHistory.forEach(job => { job.technologies.forEach(tech => { if (!skillScores[tech]) skillScores[tech] = 0; skillScores[tech] += job.durationYears; }); }); // Weighted ranking const rankingScore = (totalYears * 0.3) + (skillCount * 0.2) + (certCount * 0.1) + (requirementMatch * 0.4); ``` Assigns tier: A (interview immediately), B (strong candidate), C (consider), D (pass). THE AI ASSESSMENT: PDF Vector AI node writes custom assessment for each candidate: "Evaluates technical depth, leadership experience, culture fit. Determines if candidate is junior, mid-level, senior, or lead material based on scope of past projects."

**Morning Post (Before 9 AM)** Day 71 of 30 Goal: Get everything for the RE process done, tested, ready for pilot user until Thu (+1 day). Blocker: - Need: -

**Morning Post (Before 9 AM)** Day 70 of 30 Goal: Enhance RE process according to feedback. Get everything done, tested, ready for pilot user until Thu (+2 days). Blocker: - Need: -

"30 days to first client" sounds good. Reality? 47 days for me. And that's fast. Here's the actual timeline with real expectations. THE HONEST JOURNEY: WEEK 1-2: Learning phase Built demo workflows. Joined communities. Figured out tools. Zero clients. Normal. WEEK 3-4: Outreach begins Started DMing. Lots of rejection. No responses. Discouraging. Normal. WEEK 5-6: First conversations Got 3 discovery calls. 2 ghosted after. 1 said "too expensive." Frustrating. Normal. WEEK 7: First close Finally found right fit. Proposal sent Friday. Signed Monday. $1,200 deposit. Finally. 47 days total. Not 30. Still faster than most. REALISTIC EXPECTATIONS: Fast: 30-45 days to first client Average: 45-75 days to first client Slow: 90+ days (usually because stopped trying weeks 4-6) Most quit around day 30-40. Right before breakthrough. THE COMPOUND EFFECT: Month 1: Learning + outreach = 0 clients Month 2: First client + keep outreach = 1 client Month 3: Deliver + keep outreach = 2 more clients Month 4: 3 active clients + pipeline full Momentum takes time to build. WHAT ACTUALLY MATTERS: Not how fast you get first client. But whether you KEEP GOING when it feels slow. THE NUMBERS: My first 90 days: - 73 LinkedIn DMs sent - 18 responses - 9 discovery calls - 3 clients closed That's 24 DMs per client. 3 calls per client. 30 days per client. Your numbers will vary. But volume produces results. WHEN TO WORRY: If you've sent 100+ messages and zero responses - messaging is bad If you've had 10+ calls and zero closes - pitch is weak If you haven't sent messages at all - motivation is the issue Most problems are volume problems. REALISTIC MONTH 1 GOALS: Join 15 communities where clients hang out Send 50 DMs to prospects Get 3 discovery calls booked Build 1 working demo Send 1 proposal If you hit those numbers, client will come. THE TRUTH: This isn't get-rich-quick. It's build-sustainable-business. First client proves it works. Second proves it wasn't luck. Third proves it's a system.

Hi everyone, I'm exploring the idea of using a Raspberry Pi (v4 or v5) as a central monitoring and instruction-capturing system for my N8N setup. The goal is for it to delegate tasks to a cluster of 8 computing nodes. Before I dive in, I was hoping to draw on the community's wisdom. Has anyone here had experience installing and running the latest version of N8N on a Raspberry Pi for a similar purpose? I'd be incredibly grateful for any advice, insights, or potential pitfalls you could share. If not an RPi, any other common SBC would be fine also.