@Nate Herk @Nick Coppola @Muskan Ahlawat @Chad Samuel @Yash Chauhan @Kelvin G @Avneesh J @Fathia Fx @Omar Dahabi @Pierre Jamet-Fournier @Sujith Sl @Bill Hazelton @Andi Vee

@Zaid Feras

@Frank van Bokhorst This is such a solid breakdown — especially the point about silent failures. Those are brutal because everything looks “fine” until it’s not. The token expiry + rate limit combo feels like the most dangerous because it usually shows up only after success and scale, not during testing. Curious how others handle this part 👇 Do you lean more on platform-native alerts, custom monitoring, or periodic health-check workflows to catch these failures early? Also love the callout that “boring infrastructure” breaks more than the AI logic — that feels like a universal truth.

@Zaid Feras

@Nate Herk @Nick Coppola @Muskan Ahlawat @Chad Samuel @Hicham Char @Yash Chauhan @Kelvin G @Avneesh J @Fathia Fx @Omar Dahabi @Pierre Jamet-Fournier @Sujith Sl @Bill Hazelton @Andi Vee

@Hicham Char That’s a great example — lead intake + faster response time is a very visible win. When you set that up in n8n, what part delivered the most value:the GPT summary itself, or the Slack ping at the right moment? Feels like a lot of teams underestimate how much speed alone improves outcomes.

@Nick Coppola This is a great way to frame it — closing the loop end-to-end changes how you learn. I like the idea that the real education comes from watching it behave in the wild, not the initial build. If you had to define a “done enough” signal for that first loop — what would it be? Response time? Fewer dropped leads? Clearer handoffs? Feels like that checkpoint is what helps people avoid endless tinkering.

@Ahmad AI Dude 100% agree!