Hey everyone, Morgan here from aidebug.net!
I don’t know about you, but lately, it feels like my life is a never-ending series of debugging sessions. And honestly, I wouldn’t have it any other way. It’s the thrill of the chase, the “aha!” moment, the pure satisfaction of seeing that red error message finally disappear. But
Hey everyone, Morgan here from aidebug.net! Today, I want to dive into something that’s probably given every single one of you (and definitely me) a headache at 3 AM: the dreaded, the mysterious, the utterly frustrating AI error. Specifically, I want to talk about an issue that’s become increasingly common with the rise of complex
Hey everyone, Morgan here, back with another deep dive into the messy, often frustrating, but ultimately rewarding world of AI debugging. Today, I want to talk about something that’s been rattling around in my brain for a while, especially after a particularly hairy week with a client’s LLM fine-tuning project: the silent killer. No, I’m
Hey everyone, Morgan here from aidebug.net! Today, I want to dive into a topic that keeps so many of us up at night: those sneaky, frustrating, sometimes downright baffling AI errors. Specifically, I want to talk about the often-overlooked art of debugging when your shiny new AI model starts giving you… well, not what you
Hey everyone, Morgan here, back with another dive into the nitty-gritty of AI development. Today, we’re talking about the ‘F’ word – no, not that one. I mean Fix. Specifically, fixing those maddening, elusive errors that pop up in our AI models when we least expect them. It’s 2026, and while AI has made incredible
Hey everyone, Morgan here, back at aidebug.net! Today, I want to talk about something that probably keeps most of us up at night, staring at a screen full of red squiggly lines and cryptic error messages: the dreaded AI error. Specifically, I want to dive into a particular type of error that’s been haunting my
Learn why AI systems fail silently and how to catch bugs with data drift detection, confidence thresholds, and observable pipelines.
Hey everyone, Morgan here, back with another deep dive into the messy, glorious world of AI debugging. Today, I want to talk about something that hits close to home for anyone building AI, something that often feels like a punch to the gut: the dreaded “silent error.”
You know the one. Your model is running,
Practical strategies for debugging AI systems, from data validation to LLM error handling, with code examples you can use today.
March 23, 2026
Alright, so you’re working on a side project, probably juggling APIs, integrations, or building some AI-powered mojo. You stumble upon two popular frameworks: LangGraph and Semantic Kernel. Both promise to simplify working with large language models and AI agents, but which one is actually