“Keep It Simple” is old advice. It got interesting again the moment anyone could generate 500 lines of code in 30 seconds. KISS says systems and tasks should be as straightforward as possible. No unnecessary complexity. Fewer moving parts, fewer things to break, fewer things to hold in your head. It was good advice when […]
Read More →A plain-English reference guide covering the jargon that shows up every time a new language model drops, from parameter counts to quantization methods. Contents 01 · Architecture & Model Design — Transformer · Dense Model · Mixture of Experts · Active Parameters · Feed-Forward Network · Layers · Hidden Dimension · Attention Heads 02 · Attention Mechanisms — Multi-Head Attention · Multi-Query Attention · Grouped-Query Attention · KV Cache · Sliding Window Attention · RoPE · RoPE Theta 03 · Sizing, Scale & Counting — Parameters · Embedding Parameters · Non-Embedding […]
Read More →Google DeepMind released Gemma 4 on April 2, 2026 under Apache 2.0. It’s their fourth-generation open model family, and it runs locally with surprisingly little friction. Here are three ways to get it going, depending on what hardware you have in front of you. Table of contents Option 1: On your phone No account, no […]
Read More →Most people blame Claude for strict limits. The blame is justified to an extent. Until Anthropic eases its usage limits, users are better off optimizing token usage. All you need to do is use tokens wisely, but not everyone knows how to do that and ends up losing a lot of tokens and money as […]
Read More →A format designed for bloggers in 2004 now sits at the center of how AI systems read, write, and think. Table of contents How we got here If you work with LLMs at all, you’ve probably noticed something: Markdown is everywhere. Ask Claude a question, you get Markdown back. Ask GPT-4, same thing. Feed a […]
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