Articles

February 16, 2026 — 30 min read

A pragmatic guide to choosing your database in 2026 — with a four-layer decision framework, real-world case studies, and a 20-question interactive checklist

A research-backed framework for choosing between PostgreSQL, MySQL, MongoDB, MongoDB Atlas, and hyperscaler-managed databases (Aurora, AlloyDB, Cosmos DB). Covers a four-layer decision methodology, weighted scoring matrices, TCO analysis, seven anti-patterns, real-world case studies from Discord, Uber, Pinterest, Shopify, Airbnb, and Temenos, and a 20-question interactive checklist with decision hygiene guardrails.

February 13, 2026 — 14 min read

Part 4 of 4 — LLM Fundamentals

Data, objectives, and the trillion-token training runs that create general intelligence

Pre-training is the foundation of every LLM — a massive, expensive process where the model learns language from trillions of tokens. This article covers the training data pipeline, the loss function, the optimization loop, and the compute required to produce a frontier model.

February 12, 2026 — 16 min read

Part 3 of 4 — LLM Fundamentals

Attention, feed-forward layers, and the block that changed everything

The transformer is the engine inside every modern LLM. This article builds the architecture piece by piece — from self-attention to multi-head attention to the full decoder stack — with interactive diagrams that let you trace how information flows through the network.

February 11, 2026 — 12 min read

Part 2 of 4 — LLM Fundamentals

How LLMs turn raw text into numbers — and why the way you split matters

Before an LLM can process a single word, it has to convert text into tokens — subword pieces that balance vocabulary size against meaning. This article walks through character-level, word-level, and subword tokenization, shows how BPE works step by step, and explores why tokenization choices ripple through the entire system.

February 10, 2026 — 10 min read

Part 1 of 4 — LLM Fundamentals

The surprisingly simple idea behind the most powerful AI systems ever built

Every LLM — GPT, Claude, Llama — does one thing: predict the next token. This article strips away the hype and builds an intuition for what language models actually are, how they differ from traditional software, and why next-token prediction is far more powerful than it sounds.

February 8, 2026 — 14 min read

Part 3 of 3 — Vector Search Fundamentals

How retrieval-augmented generation turns your data into context an LLM can actually use

A hands-on guide to retrieval-augmented generation — from chunking and embedding to retrieval, prompt assembly, and advanced patterns like hybrid search and reranking. Interactive visuals at every step.

February 7, 2026 — 10 min read

Part 2 of 3 — Vector Search Fundamentals

A visual guide to the three distance metrics behind vector search

The same two vectors can look close or far apart depending on which distance metric you choose. This interactive guide visualises Euclidean distance, cosine similarity, and dot product — and shows you when to pick each one for vector search, RAG, and recommendations.

February 6, 2026 — 8 min read

Part 1 of 3 — Vector Search Fundamentals

A visual, from-scratch guide to the numbers behind semantic search, RAG, and recommendations

Vector embeddings turn words, images, and ideas into lists of numbers that capture meaning. This interactive guide walks through what each dimension encodes, why similar things cluster together, and how modern AI systems use embeddings for search, recommendations, and retrieval-augmented generation.