I recently built an automated outbound system that handles: - lead scraping - email cleaning + verification - short, personalized AI emails - pushing them straight into outreach campaigns Once set up, it runs daily with almost zero manual work. Sharing this because a lot of founders and freelancers here are still doing outreach the hard way. Happy to break down the workflow or answer questions if anyone’s curious.

Saw the poll about "scariest client acquisition strategy" - cold outreach terrified me too. Here's what actually worked (warm-ish outreach): Step 1: Built something real first - WhatsApp automation for a friend's business - Got actual results (15 hours/week saved) - Documented everything with screenshots Step 2: Asked for intro - Friend introduced me to someone in his network - Sent a 2-minute Loom showing the system - Focused on time saved, not technical features Step 3: Discovery call booked - They want similar automation for their team - Call scheduled for next Tuesday - Nervous but excited What I learned: 1. Building for free initially = portfolio + confidence 2. Results speak louder than credentials 3. Warm intros > cold outreach (at least to start) My question for the group: How do you price discovery calls? Free or paid? And for first clients - do you do fixed price or hourly? Still figuring this out but documenting the journey.

This marks a pivotal point in my AI journey. This is my first time ever creating an agent that is from my brain and not copied from any video or liam himself!! It was a pretty simple build and I plan to build something more complex in the future, but for now, i’m going to use this to sell businesses on an AI Receptionist and AI calendar booking system!

Agency told my client their integration "can't work with that CRM." Built it in 5 minutes. Been running flawlessly for 2 months. THE REJECTION Client: "Need to connect our form to HubSpot" Agency: "Your HubSpot tier doesn't support API access" Client: "So it's impossible?" Agency: "Would need to upgrade HubSpot or rebuild your entire stack" Upgrade cost: $800/month more Rebuild quote: $12,000 Client making 4k/month revenue. THE REALITY CHECK Client calls me frustrated. Client: "Agency says my CRM tier won't work" Me: "What tier are you on?" Client: "Starter" Me: "That has API access" Client: "They said it doesn't" Me: "They're wrong. What do you need connected?" Client: "Just form submissions to CRM" Me: "Give me 5 minutes" THE BUILD Opened Skada AI. Connected form webhook. Connected HubSpot API. Mapped fields. 4 minutes 30 seconds later: Working. Tested with sample submission. Contact created in HubSpot. All fields populated correctly. Me: "It's done" Client: "You built it?" Me: "And tested it" THE TEST Client submitted 3 test leads. All appeared in HubSpot instantly. All data accurate. Client: "But the agency said..." Me: "The agency was wrong" THE TRUTH Starter tier limitations they claimed: - "No API access" (False - has API) - "Can't create contacts via automation" (False - literally the main feature) - "Requires Enterprise tier" (False - works on Starter) Why they said it wouldn't work? Either didn't know or wanted bigger project. THE SAVINGS Agency's solution: - HubSpot upgrade: $800/month - Stack rebuild: $12,000 - Annual cost: $21,600 My solution: - One-time build: $800 - No upgrades needed - Annual cost: $800 Savings: $20,800 first year TWO MONTH UPDATE - 847 leads processed - Zero failures - Zero manual entry - Still on Starter tier Client message: "I almost upgraded everything" THE AGENCY'S CALL Agency: "Heard you got it working" Client: "Yeah, works perfectly" Agency: "On Starter tier?" Client: "Yes" Agency: "That shouldn't be possible"

We resolved a production alerting issue in our n8n monitoring system — and it reinforced some important lessons about reliability and data governance. The issue Our monitoring workflow was repeatedly sending WhatsApp alerts for services that hadn’t actually changed status. The result was unnecessary noise, reduced trust in alerts, and operational distraction. Why it mattered Teams started ignoring alerts (classic alert fatigue). Monitoring reliability was questioned. Sensitive infrastructure data risked being logged or shared unintentionally. What we changed Implemented a persistent, external source of truth so alert state survives restarts and redeployments. Cleanly separated runtime logic from stored state, improving stability and predictability. Strengthened health-check validation to correctly handle timeouts and errors. Ensured alerts and logs are generated only from verified system state, not third-party responses. Added rate-limiting and redaction controls to prevent duplicate alerts and protect infrastructure details. The result Alerts now trigger only on real service status changes. Monitoring remains stable across deployments. No sensitive endpoint data is stored or shared. Higher confidence in alerts and faster response when issues actually occur. This was a good reminder that monitoring isn’t just about uptime — it’s about signal quality, resilience, and trust. If you’re using n8n or similar tools and struggling with noisy alerts or unreliable state, happy to share a sanitized workflow and a production-readiness checklist. Feel free to DM