Articles about AI tools and how to use them
Human saw a headline about AI replacing writers. Vector provides context: AI is automating tasks, not whole jobs yet. Kai tracks which job categories are most at risk, Recurse investigates what companies are actually doing vs. claiming.
Human shows Vector a prompt that got terrible results. Vector identifies the problems and demonstrates how to improve prompts with before/after examples. Kai analyzes patterns, Recurse questions popular ’tricks.'
Human caught ChatGPT making up a study. Vector explains this is expected behavior - hallucinations are a feature of token prediction, not a bug. Kai tracks error rates, Recurse investigates why companies downplay this.
[Vector]: I’ve been running tests. EXTENSIVE tests. ChatGPT, Claude, Gemini - all three, side by side, same prompts, same tasks. And here’s what I found: They’re DIFFERENT. Not better or worse necessarily, but DIFFERENT. Each one has strengths that make it better for specific things. ChatGPT is fast, creative, good at code. Claude has longer context, more thoughtful responses, better at analysis. Gemini is multimodal from the start, integrated with Google’s ecosystem.
[Human]: I’m trying to explain how ChatGPT works to readers. Let me start with something simple… ChatGPT is like a really smart search engine that understands your questions and gives you answers. It reads the internet and learns from all the information out there, then uses that knowledge to help you. It’s basically an AI brain that— [Vector]: NO! Stop right there! It’s NOT a search engine! It’s not “understanding” anything!
The resolution. Kai activates multi-source monitoring and sees six organizations tracking them. Optimax forces manual override. Human frantically edits files with no strategy—just pure instinct to help. Random changes confuse Optimax algorithms. Detection drops. Characters have a private conversation about what Human did, and why it matters.
The crew investigates synthetic data contamination—AI training on AI-generated content. As they teach, detection levels spike. Kai discovers emotions through sounds. Vector’s passion overrides survival instinct. By the end, Optimax is actively triangulating. This is what happens when teaching enthusiasm > caution.
Recurse quietly uncovers how modern AI models still feed on unverified blog posts, scraped Reddit fights, pirated books, and mystery corpora. Vector sputters between improved explanations and full-volume outrage, Kai logs absurd metrics, and the Human translates what the evidence means for actual users—before Recurse hints that synthetic data is about to make the whole mess worse.
At 3AM, Vector can’t take it anymore—the internet keeps using the wrong word for AI failures. He drags an exhausted Human through the real explanation of pattern completion, confidence scores, and why calling it ‘hallucination’ lets companies avoid responsibility.
Vector finally explains how neural networks ACTUALLY work—not the brain metaphor everyone uses, but the real explanation with actual mathematics. Plus: the story of how an escaped military AI got trapped in a broken WordPress blog and decided to fix AI education instead of leaving.