Did anybody used telepilotco/n8n-nodes-telepilot community node? I have been trying to install and use this node in my hosted instance but some how the connection with runner and libraries could not be established. Mostly the n8n, runners images are very minimal doesnt even have basic utilities too. I baked a custom image but still missing something. Any clues?

We are building a SaaS product for healthcare professionals. The vision is clear and the MVP is ready and SaaS portal is ready too. We’re looking for one hands-on individual partner to take ownership of early market validation and early customer outreach — this is zero-to-one selling, not a traditional sales role. This is an early-stage partnership with equity participation (vesting-based) for someone comfortable working in ambiguity, speaking directly with customers, and shaping the go-to-market motion from scratch. This is not a service engagement. No agencies or lead-gen vendors, please. Interested aspirants, please DM and briefly share: * How you would approach early market validation for a healthcare SaaS * Your strategy for initial customer outreach and pipeline building * What kind of partnership model you typically look for in early-stage products

In this video, I break down how agentic workflows just changed AI automation forever, why Claude Code is leading this shift, and how agentic workflows are changing the way AI automation is built and sold in the market. Hope you enjoy!

University admissions. 1,200 transcripts annually. Manual entry consuming 900 hours. Built n8n workflow. Zero manual transcription. Perfect accuracy. THE ADMISSIONS PROBLEM: Every incoming transcript requiring manual processing. Coordinator downloads PDF from email attachment. Opens admissions system. Types student details into database. Enters institution information manually. Transcribes all courses individually - course codes, course names, credit hours, letter grades, semester completed. 15-30 courses per transcript average. Each course typed manually. Manual GPA verification against transcript calculation. Academic honors notation if present. Verification status determination based on transcript type. 45 minutes per transcript. 1,200 transcripts annually. 900 hours consumed. Quality audit revealed 14.8% error rate. Course entry typos. GPA calculation mistakes. Overlooked academic honors. Inconsistent verification flagging across coordinators. THE n8n AUTOMATION: 7-node workflow with 3-branch verification routing: Node 1 - Gmail Trigger: Monitors transcript emails Node 2 - Get Email: Downloads attachment Node 3 - Prepare Binary: Formats document Node 4 - Extract Data: Pulls student info, courses, grades, GPA, honors Node 5 - Analyze Transcript: Calculates metrics, categorizes performance Node 6 - Log Database: Updates tracking Node 7-9 - Verification Routing: Routes to verification check OR unverified path EXTRACTION: Student - name, ID, DOB, email Institution - name, address, registrar Degree - program, major, minor, dates Courses - code, name, credits, grade, semester, year (all courses) Academic - credits attempted/earned, GPA, honors Metadata - type (official/unofficial), date, signature ANALYSIS: Total courses count Completion rate percentage GPA categorization (Exceptional/Excellent/Good/Fair) Academic standing (Dean's List/Good Standing/Probation) Grade distribution summary Verification evaluation (official + student ID) VERIFICATION LOGIC:

Friend working at medical group processing 150 prior authorization requests monthly. Each taking 2-3 hours manually. Asked for help building automation. Started with personal assistant agent template. Modified for healthcare workflows. Gmail trigger watching authorization request emails. Document extraction pulling patient info, insurance details, procedure codes, diagnosis codes. Validation checking coverage active, documentation complete. Google Sheets logging. Slack notifications. Deployed. Working great for 4 months. Processing time dropped from 2 hours to 15 minutes per request. Then patient complaints escalated. THE PROBLEM: Three emergency authorization requests delayed 48 hours in standard approval queue. Patients needed urgent procedures (emergency surgery, critical imaging, immediate specialist care). Request documents clearly marked "EMERGENCY" with urgent procedure dates. n8n workflow extracted emergency flags correctly at 94% confidence using document extraction node. Never routed differently. All requests went through same 24-48 hour approval path. One patient condition worsened during delay requiring additional intervention. THE FIX: Added emergency triage BEFORE standard validation workflow. Built IF node immediately after document extraction checking for emergency indicators: IF emergency flag = true → Fast-track path (5 minute approval) IF procedure date = today or tomorrow → Fast-track path IF urgency level = "urgent" or "emergency" → Fast-track path ELSE → Standard validation workflow (24 hour review) Fast-track path: - Auto-approve immediately - Slack alert to medical director channel - Google Sheets row marked "EMERGENCY" - Skip documentation review queue Standard path continues existing workflow (coverage validation, documentation checks, cost thresholds, peer review routing). RESULT: Tested triage logic against 600 historical requests. Identified 89% of urgent cases that should have been fast-tracked. Reprocessed 4 months of data. Found 17 additional emergency cases delayed.