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
Building a RAG Pipeline with LangGraph: A Developer’s Tutorial
We’re building a RAG pipeline that actually handles messy PDFs — not the clean-text demos you see everywhere. In this tutorial, I’m going to walk through each step of building this system using LangGraph, a project that, honestly, has pretty lofty ambitions. With over 27,083 stars
LangChain vs Semantic Kernel: Which One for Side Projects?
LangChain boasts a staggering 130,504 stars on GitHub, while Microsoft’s Semantic Kernel lags behind with 27,522 stars. But let’s face it, stars alone don’t ship features, nor do they guarantee usability in real-world applications. This article compares LangChain and Semantic Kernel in detail, especially for those
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
Ollama vs TGI: Which One for Startups?
Ollama boasts 165,710 GitHub stars, while TGI (Text Generation Inference) has only 10,812. But, trust me, stars don’t always translate to production power, especially when you’re a startup racing against time and resources. In this showdown, I will break down both tools, showcasing which fits startups better, and
Production Deployment Checklist: 10 Things Before Going to Production
I’ve seen 5 production deployments fail this month. All 5 made the same 7 mistakes. That’s ridiculous and avoidable. If you’re a developer who’s serious about deployment quality, having a solid production deployment checklist is non-negotiable. Without it, you’re just asking for trouble.
The List
1.
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
Qdrant vs ChromaDB: Which One for Production?
Qdrant has 29,692 stars on GitHub while ChromaDB has 26,727. But more stars don’t mean it’s the best choice for your production needs. In today’s world of data-driven applications, the choice of vector database can significantly impact performance, scalability, and ease of use. This article will compare Qdrant
7 Multi-Agent Coordination Mistakes That Cost Real Money
I’ve seen 3 production agent deployments fail this month. All 3 made the same 5 mistakes. Multi-agent coordination is one of those buzz-worthy terms that sound impressive but when done poorly, it costs companies not just time and headache but serious cash.
1. Poor Communication Protocols
Why
After one year with ChromaDB, it’s handy for R&D but a pain in production.
In 2026, I’ve spent a solid year shuffling bits around with ChromaDB, using it primarily for building experimental machine learning models and handling vector embeddings in our products. Scale-wise, we tested it with datasets ranging from 10,000 to over a million