The AI Engineering Bookshelf: Five Books That Changed How I Think About Building AI Systems
Author(s): Hamza Khaled Mahmoud Originally published on Towards AI. The AI Engineering Bookshelf: Five Books That Changed How I Think About Building AI Systems Few engineers have the luxury of reading every technical book cover-to-cover. I certainly haven’t. But deep engagement with …
LLM Evaluation Is Broken: Why BLEU and ROUGE Don’t Measure Real Understanding
Author(s): Ayoub Nainia Originally published on Towards AI. Large Language Models can now summarize research papers, analyze data, and even draft academic arguments. Yet behind the flood of progress reports and leaderboard charts, one question remains stubbornly neglected: How do we actually …
Agent Engineering: How Agentic AI Is Redefining Software Development
Author(s): Sai Kumar Yava Originally published on Towards AI. Something fundamental is shifting in how we build software. For years, we’ve operated in a world of predictable inputs and deterministic outputs — write the code, test it thoroughly, ship when it passes …
State of AI 2025
Author(s): Igor Novikov Originally published on Towards AI. Image by the author The year is almost over, and it’s time to review the State of AI for this year and look at forecasts for the next. This overview is based on a …
What are AI Agents
Author(s): Nileka Samarasinghe Originally published on Towards AI. From not knowing what is an AI Agent to building Multi Agent AI Systems You open twitter (or X, whatever) and suddenly everyone is shouting this fancy words at you ‘agentic ai’, ‘large action …
Understanding XGBoost: A Deep Dive into the Algorithm
Author(s): Utkarsh Mittal Originally published on Towards AI. Introduction XGBoost (Extreme Gradient Boosting) has become the go-to algorithm for winning machine learning competitions and solving real-world prediction problems. But what makes it so powerful? In this comprehensive tutorial, we’ll unpack the mathematical …
I Built an AI That Understands My Team’s Emotions From Our Commits and Messages
Author(s): Manash Pratim Originally published on Towards AI. I Built an AI That Understands My Team’s Emotions From Our Commits and Messages I built an AI that analyzes commits, PR reviews, and Slack messages to detect emotional drift and burnout in engineering …
Deploying Agentic AI on GCP: A Deep Dive Into Building Data-Native, Scalable Intelligent Agents on Google Cloud
Author(s): Kyle knudson Originally published on Towards AI. Deploying Agentic AI on GCP: Building Data-Native Intelligent Agents If we look at the cloud landscape today, the distinctions are becoming clear. If AWS is the infrastructure powerhouse and Azure is the hub for …
Understanding L1 and L2 Regularization in Machine Learning
Author(s): VARUN MISHRA Originally published on Towards AI. Understanding L1 and L2 Regularization in Machine Learning Regularization is a fundamental technique in machine learning used to prevent overfitting, improve model generalization, and ensure that models perform well on unseen data. Two of …
Why the Future of GPU Architectures Will Redefine AI Strategy for Every Company
Author(s): Igor Voronin Originally published on Towards AI. Over the last few years, AI has moved faster than anyone expected. And the next chapter of AI isn’t being written in model papers or research labs. It’s being written inside the hardware that …
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