Alright, let me just get this off my chest first—RAG systems, or Reasoning and Generation systems, are not the golden goose everyone seems to think they are. Yeah, I’ve been tinkering with these for a while now, and to be honest, they’re more often a wild goose
Alright folks, Alex Petrov here, back at agntai.net. Today, I want to talk about something that’s been rattling around in my head for a while, especially after spending way too many late nights debugging an agent’s “understanding” of a simple task. We’re all building these AI agents, right? Autonomous systems, trying to get things done
A practical guide to moving ML models from notebooks to production, covering architecture choices, training pipelines, and deployment.
When a Cool Prototype Becomes a Total Disaster
So there I was, sipping my third coffee for the day, trying to untangle why our ML model was making the worst predictions possible. It’s a classic case: everything works great in the lab, then you throw it into production and BAM—chaos. If you’ve ever been here,
Agent Evaluation: Cutting Through the Noise
Just the other day, I was knee-deep in debugging yet another agent system when I realized how often we all skip proper evaluation. It’s like people are actively allergic to real feedback loops and thorough assessments! I’m sick of seeing releases where the agent is barely more intelligent than
Hey everyone, Alex here from agntai.net! It’s March 2026, and I’ve been spending way too much time lately thinking about how we build AI agents. Specifically, I’ve been wrestling with the “glue code” – the stuff that connects all the fancy LLM outputs, tool calls, and state management. We’ve all seen the impressive demos, right?
Understanding and Mitigating Convolutional Neural Network Bias
As machine learning engineers, we frequently deploy Convolutional Neural Networks (CNNs) for critical tasks like image recognition, medical diagnosis, and autonomous driving. While powerful, CNNs are not immune to bias. **Convolutional neural network bias** is a significant concern, impacting fairness, accuracy, and reliability. This article, written from the
Unlocking Potential: Deep Reinforcement Learning at Texas A&M (TAMU)
As an ML engineer, I’ve seen firsthand the power of deep reinforcement learning (DRL) to tackle complex problems. It’s a field that’s rapidly evolving, and universities like Texas A&M (TAMU) are at the forefront of this innovation. If you’re looking to understand practical applications, research opportunities,
Understanding and Fixing ModuleNotFoundError: No Module Named ‘transformers.modeling_layers’
Hello, I’m Alex Petrov, an ML engineer, and I’ve spent a fair amount of time debugging Python environments. One common issue that pops up for users working with the `transformers` library, especially when dealing with older models, custom implementations, or specific library versions, is the `ModuleNotFoundError: No
US Navy Submarine AI and Machine Learning: Practical Applications
By Alex Petrov, ML Engineer
The US Navy is actively integrating artificial intelligence (AI) and machine learning (ML) into its submarine fleet. This isn’t about science fiction; it’s about practical applications that enhance safety, improve operational efficiency, and provide a tactical advantage. From autonomous navigation to advanced