AI Without the Fog

A thread you can test

Debunking the Hype

10 notes move from the word to a real choice at work — understand it first, then decide whether to use it.

READING THREADOPEN
10notes
HOW TO READStart where you are stuck, then follow the evidence and trade-offs

Each note stands alone, or becomes the next step in this thread.

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THE QUESTION THIS PAGE ANSWERS

ANSWER FIRST

What is Debunking the Hype, and which AI decisions does it change?

Separate prompt changes, retrieval, fine-tuning, and pretraining by what they change, what they cost, and what evidence they leave. Clear language makes technical claims and budgets easier to evaluate. This page keeps the related concepts, common mistakes, and practical notes in one reading thread.

DECISION RULE

First decide whether you are blocked by a definition, a choice, or verification; then choose the closest of the 10 notes below.

TRY NEXT

Start with “Be precise when you say “we trained a model”,” then restate the conclusion using your own task.

WATCH FOR

Do not treat every method in a topic as interchangeable. The answer changes with the input, risk, and acceptance bar.

THIS QUESTION THREAD

Put the word back inside the choice it changes.

10 notes
Interactive

Be precise when you say “we trained a model”

Separate prompt changes, retrieval, fine-tuning, and pretraining by what they change, what they cost, and what evidence they leave. Clear language makes technical claims and budgets easier to evaluate.

AI Without the Fog 9 min →
Interactive

AI Jargon Translator

In-house, wrapper, digital employee, empower… tap a launch-event line for the plain-language version, plus a gold-content rating and three follow-ups that get the real story on the spot

AI Without the Fog 7 min →
Interactive

Does an “Open-Source Model” Mean It's Free?

Weights, data, method — a three-piece check of what mainstream models actually open; full-size vs distilled, and what you run locally is usually the small one

AI Without the Fog 6 min →
Interactive

Why Does the "#1 on the Leaderboard" Model Feel Worse in Real Use?

A reversal demo of leaderboard score vs real usefulness + three reasons: gaming the board, overfitting the question bank, scenario mismatch; and which boards you can actually trust

AI Without the Fog 6 min →
Interactive

The More We Chat, the Better It Knows Me — Is It Learning?

You think the model is growing; it's a little notebook stuffed back into the chat — split-screen animation + a new-chat "memory wipe" demo

AI Without the Fog 6 min →
Interactive

When an AI Detector Says "This Was Written by AI," Can You Trust It?

Guess how the detector will call six passages, and feel the classic false-positive moments yourself; why it can't work in principle, and what to do if you're wrongly accused

AI Without the Fog 6 min →
Interactive

Are "Secret Prompt Playbooks" Worth Buying?

Tear through the paid-course talking points one by one: what this site already teaches free, what's just common sense, what's pure packaging. The skeleton is free — the work is using it

AI Without the Fog 5 min →
Interactive

Why Is the Answer Different Every Time?

Ask the same question three times and get three answers, plus a "next word" probability-dice animation; it's design, not a bug — and when you need stable output

AI Without the Fog 5 min →
Interactive

Why Is AI Customer Service So Dumb?

Send the same complaint to a chat AI and a support bot — the gap is obvious; three reasons: a cheap small model, guardrails locked tight, old tech wearing an AI sticker

AI Without the Fog 6 min →
Interactive

Are Siri and ChatGPT the Same Thing?

Path animation of the same sentence through two generations of assistant: command matching apologizes when it doesn't understand; generative AI can pick up any phrasing

AI Without the Fog 5 min →