Archives
Practitioners say models are only as good as their data — and they mean it literally. Here's what features and labels actually are, why feature choice often beats algorithm choice, and the data problems (bias, leakage, imbalance) that quietly wreck models.
June 17, 2026
Machine learning isn't magic and it isn't ordinary programming. Instead of writing rules, you show a model examples and it discovers the rules itself. Here's what 'learning from data' really means — in plain English.
A working ML model is the result of a whole lifecycle — framing the problem, gathering data, training, evaluating, deploying, and monitoring for drift. Here's the end-to-end journey in plain English, and why the model itself is the smallest part.
A model that scores perfectly on its own study material can still be useless. This is the most important idea in machine learning: training vs. testing, overfitting vs. underfitting, and why a model that memorises is a model that fails.
Almost all machine learning comes in three styles, defined by what kind of examples the model learns from. Here's supervised, unsupervised, and reinforcement learning explained in plain English — what each one is for, with everyday examples and no math.
Generative AI is genuinely powerful — and genuinely limited. An honest, no-hype look at what large language models are still bad at, why those weaknesses exist, and how to use AI well by working with the grain instead of against it.
June 15, 2026
Why does AI lose track of long conversations, miscount letters, or 'forget' what you said earlier? It comes down to tokens and the context window. A clear, no-math explanation of how AI reads — and why it has a memory limit.
June 14, 2026
AI hallucinations aren't random glitches — they're a direct, predictable consequence of how language models work. Here's why models confidently make things up, when it's most likely, and how to reduce it.
June 13, 2026
What is a model, really? A no-math look inside a large language model — what parameters are, how training actually works, what 'billions of parameters' means, and why size matters (but isn't everything).
June 12, 2026
How did we get from autocomplete to AI that writes essays and code? A clear, non-technical history of large language models — the Transformer, the GPT series, the scaling race, and the ChatGPT moment that changed everything.
June 11, 2026
Generative AI sounds mysterious, but underneath it is one repeated trick: predicting the next token. Here's what that means, why it produces such convincing text, and what it tells you about how these models really work.
June 10, 2026