LLM & AI Agent Applications with LangChain and LangGraph — Part 2: What is a machine learning model and what makes LLMs special?
Author(s): Michalzarnecki Originally published on Towards AI. Welcome in next chapter in the series about LLMs-based application development. In this part I want to clarify two things that appear constantly in any discussion about AI: what a machine learning model actually is, …
LLM & AI Agent Applications with LangChain and LangGraph — Part 1: How LLMs become so important in modern app development
Author(s): Michalzarnecki Originally published on Towards AI. Welcome to the first part of this series. In this part I want to take a step back from LangChain, LangGraph and coding, and focus on the foundations. We will look at the main ideas …
Deep Compression, 2015: How Much More Can We Squeeze in 2025?
Author(s): Vasyl Rakivnenko Originally published on Towards AI. Image generated with ChatGPT-5.2 It may be hard to believe, but compression of Neural Networks was already an important topic more than 25 years ago. Yann LeCun, in his paper Optimal Brain Damage, published …
Cross Entropy
Author(s): Anjali Kakde Originally published on Towards AI. The Loss Function That Punishes, for Being Confident and Wrong For years, we were told that training a model meant minimizing error. Get the prediction right. Push accuracy higher. Reduce the loss.But cross entropy …
The AI Industry Is Eating Itself: Nvidia’s $20B Power Play, the End of Scaling, and the $2 Trillion Question
Author(s): Zoom In AI Originally published on Towards AI. Two stories from the past few weeks reveal an AI ecosystem at war with itself. The chips are winning. The economics might not. The Setup: What Just Happened On Christmas Eve 2025, while …
Simplifying LLM Conditional Workflows Using Structured Output
Author(s): Nachiket Mehendale Originally published on Towards AI. Some LangGraph LLM workflows simply cannot work without a Structured Output class. Imagine a pizza store collecting online customer feedback. Some reviews are happy, some unhappy. The store wants to automate responses: thank happy …
Understanding Retrieval in RAG Systems: Why Chunk Size Matters
Author(s): Sarah Lea Originally published on Towards AI. A step-by-step retrieval guide using sentence transformers, chunk size and similarity scores. These are exactly the kinds of answers we expect today from retrieval-augmented generation systems. You upload a PDF, ask a few questions, …
I Tested 12 Quantization Methods: The Winner Surprised Me (2-Bit vs 4-Bit)
Author(s): Manash Pratim Originally published on Towards AI. Small LLM Engineering #7 Everyone says 4-bit quantization is the practical limit. Image generated using AIThis article explores the implications of different quantization methods for machine learning models, particularly focusing on 2-bit and 4-bit …
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 …
Learn Python by Doing: Part 9
Author(s): Rashmi Originally published on Towards AI. Visualization with Python libraries Seaborn and Matplotlib Matplotlib is low-level, flexible, and base for Seaborn. First content image in the article.This article explores the essential Python visualization libraries, Matplotlib and Seaborn, outlining their unique features …
Learn Python by Doing: Part 8
Author(s): Rashmi Originally published on Towards AI. GenAI Data Pipelines — Must-Know Q&A Learn Python by Doing: Part 8 GenAI data pipelines (Python code Q&A)This article presents a comprehensive Q&A on GenAI data pipelines, covering important topics such as text chunking for …
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 Healthcare Taught Me About Churn (Hint: Classification Is the Wrong Tool)
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. Using survival analysis to predict when customers leave — and avoid a common data leakage trap Most churn models answer the wrong question: They tell you who might leave but not when. Observed …
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