Publishing Stack
WordPress powers somewhere north of 40% of the web. That number gets cited in every WordPress article ever written, usually as proof that WordPress is fine. But market share is a lagging indicator — it tells you what people chose five years ago, not what they'd choose today. In
Publishing Stack
This is the last article in the series, and if you've read the rest, you already know the individual tools. Ghost, WordPress, Substack, Kit, Beehiiv — each has a detailed breakdown. What's left is the part most people get wrong: making the actual decision. Not "which
Publishing Stack
Ghost is an open-source publishing platform built on Node.js that handles content, newsletters, and memberships in one package. It positions itself as the "professional publishing" alternative to WordPress and Substack — clean, focused, no plugin chaos. The honest take: Ghost delivers on that promise if you understand what
Prompting
There's a moment in every serious LLM user's journey where they hit a wall. The output is wrong — not wrong in a way that a clearer prompt would fix, but wrong in a way that reflects something the model fundamentally cannot do. You refine the prompt.
Prompting
Prompting an image model is not the same skill as prompting a text model. If you've spent months getting good at writing instructions for Claude or GPT and you assume that transfers to Midjourney or DALL-E, you're going to have a frustrating first week. Text models
Prompting
Most prompt engineering advice has a dirty secret — the elaborate frameworks, the 15-constraint mega-prompts, the emotional manipulation tactics — they don't meaningfully outperform a clear, direct request on the vast majority of tasks. The gap between a prompt that works and a prompt that's been "optimized&
Prompting
LLMs produce natural language by default. They want to write paragraphs. Getting them to produce structured data — clean JSON, consistent markdown tables, valid CSV, properly nested YAML — requires either explicit prompt-level constraints or API-level enforcement features. Both approaches work. Neither is foolproof. The gap between "the model returned something
Prompting
Temperature is the one generation parameter most people have heard of, and it's also the one most people adjust without understanding what it does. The short version: temperature controls how random the model's word choices are. Low temperature means predictable, high-probability outputs. High temperature means more
Prompting
Chain of thought prompting — asking the model to reason through intermediate steps before giving a final answer — is the second most evidence-backed prompting technique after few-shot. The original research showed dramatic improvements on math and reasoning tasks. The catch is that "dramatic improvements on math and reasoning" got
Prompting
Few-shot prompting is the single most reliably effective prompting technique available across every major LLM. The concept is simple — you give the model 2-5 examples of the task done correctly before asking it to do the task — and the results are consistently better than trying to describe what you want
Prompting
A system prompt is text that goes at the beginning of a conversation, before any user message, telling the model how to behave. Every major LLM platform supports them — Anthropic calls them system prompts, OpenAI calls them system messages (or custom instructions in ChatGPT), Google calls them system instructions. They&
Prompting
Prompt engineering is the practice of communicating with a language model effectively enough that it produces the output you need. That's it. Not secret syntax. Not magic words. Not a 47-step framework with an acronym. You're writing instructions for a system that predicts text — and the