Articles

Multimodal AI and Grounding Challenges

February 16, 2026

Explore the biggest grounding challenges in multimodal AI, including visual hallucinations, weak spatial reasoning, dataset bias,...

Context Window Limits: Why Your LLM Still Hallucinates

February 13, 2026

Learn why LLMs hallucinate even with large context windows, how token limits impact reasoning, and what...

How to Generate Better Embeddings for Vector Search

February 12, 2026

Learn how to generate higher-quality embeddings for vector search by improving text preprocessing, chunking strategies, embedding...

Building Real-Time Chatbot Memory with Vector Databases + LLMs

February 12, 2026

A complete guide to building real-time chatbot memory using vector databases and LLMs, including architecture, chunking...

Why Most RAG Systems Fail in Production

February 10, 2026

A practical production-focused guide explaining why Retrieval-Augmented Generation (RAG) systems often fail and how to fix...

A Beginner’s Guide to Building AI Safety Filters

February 10, 2026

Learn how AI safety filters work, why they’re important, and how to implement them responsibly.

Airflow vs Prefect for ML Pipelines

February 08, 2026

A comprehensive guide comparing Airflow and Prefect for ML pipelines, covering architecture, scheduling, monitoring, deployment, and...

How OpenAI Builds and Maintains ChatGPT

January 27, 2026

An in-depth look at how OpenAI builds, updates, and maintains ChatGPT, including RLHF, safety, deployment, and...

Vector DB Comparison: Pinecone vs Weaviate vs Qdrant

January 10, 2026

Compare Pinecone, Weaviate, and Qdrant in terms of features, performance, scalability, and practical tips for deployment....

A Beginner’s Guide to CI/CD for ML Models (GitHub Actions + Docker + Kubernetes)

January 10, 2026

A detailed beginner-friendly guide to building CI/CD pipelines for machine learning models using GitHub Actions, Docker,...

Best Open-Source LLMs in 2026

January 08, 2026

A detailed guide to the best open-source LLMs in 2026, comparing model families like Llama, Mistral,...

How Netflix Builds Recommender Systems

December 18, 2025

An overview of how Netflix builds and scales its recommender systems, including candidate generation, ranking models,...

How to Monitor ML Drift in Real Deployments

December 05, 2025

A practical guide to monitoring machine learning drift in real-world deployments, covering data drift, concept drift,...

Feature Engineering: Making Data Understandable for Machines

November 25, 2025

A user-friendly guide to feature engineering, explaining why features matter, how to design them thoughtfully, and...

Metrics Beyond Accuracy: Measuring What Actually Matters

November 23, 2025

A practical, real-world guide to machine learning evaluation metrics, explaining why accuracy and R² are often...

Why Overfitting Is the Real Enemy of Machine Learning

November 17, 2025

An in-depth, intuitive explanation of overfitting in machine learning, exploring why high accuracy can be misleading,...

Why AI Models Fail in the Real World

November 14, 2025

An in-depth, practical discussion of why machine learning models that perform well in development often fail...

Agentic AI: From Passive Models to Autonomous Systems

November 10, 2025

An educational overview of Agentic AI, explaining how autonomous AI systems differ from traditional models, how...

A Beginner’s Guide to Agentic AI

November 09, 2025

An informative introduction to agentic AI, explaining its architecture, core components, and how autonomous AI systems...

Medical AI: Models, Data, and Evaluation in High-Risk Systems

November 06, 2025

A structured overview of medical AI systems, covering model types, data challenges, evaluation metrics, and the...

Why Governments Care About AI: Compute, Data, and Talent

November 06, 2025

An explanatory look at why AI has become a national priority, focusing on the strategic importance...

K-Nearest Neighbors (KNN) — Part 1: Classification

October 05, 2025

Introductory guide to K-Nearest Neighbors for classification with code and intuition.

A Beginner’s Guide to Elastic Net Regression (L1 + L2 Regularization)

October 04, 2025

Elastic Net combines L1 and L2 penalties. Learn what Elastic Net is, why it helps, and...

A Beginner’s Guide to Lasso Regression (L1 Regularization)

October 03, 2025

Learn what Lasso Regression is, why it matters, and how it works step by step with...

A Beginner’s Guide to Ridge Regression (L2 Regularization)

October 02, 2025

Learn Ridge Regression (L2 regularization) step by step with beginner-friendly explanations, manual calculations, derivations, Python code,...

A Beginner’s Guide to Residual Sum of Squares (RSS)

October 02, 2025

Learn what Residual Sum of Squares is, how it measures the difference between predicted and actual...

A Beginner’s Guide to Mean Absolute Error (MAE)

October 01, 2025

Learn Mean Absolute Error (MAE) with beginner-friendly guide to understand error metrics in machine learning and...

A Beginner’s Guide to R-Squared (R-Squared)

October 01, 2025

Understand R Squared, the coefficient of determination, and discover how it explains the proportion of variance...

A Beginner’s Guide to Root Mean Squared Error (RMSE)

September 29, 2025

Explore Root Mean Squared Error, its formula and interpretation, and see why it is one of...

Multiple Linear Regression Model

August 26, 2024

A step by step explanation of multiple linear regression, covering the method, interpretation of coefficients, and...

Simple Linear Regression Model

August 25, 2024

An introduction to simple linear regression that explains how the model works, its assumptions, and how...