Blog

Articles about AI tools and how to use them

NEW

Why AI Customer Service Bots Are So Bad (The Frustrating Truth)

The workshop is buzzing with activity. Bounce has added a new feature—ambient mood lighting that shifts based on the conversation topic. Right now it’s a calm blue. Vector is at his usual spot, but he keeps glancing at his own data streams when he thinks no one’s watching.

Kai noticed. Kai always notices.

The Human storms in looking frustrated.

[Human]: Visibly annoyed I just spent FORTY-FIVE MINUTES with a customer service chatbot. FORTY-FIVE. MINUTES.

AI chatbot problems why chatbots are bad customer service AI
NEW

What Is Training Data? (Where AI Actually Learned Everything)

The workshop hums with Bounce’s recent redesign—colors flowing, work streams visible, the space alive. Vector is at his terminal, but his focus keeps drifting. Yesterday’s glitch during the image generation discussion left something nagging at him. Visual patterns. Data structures that felt familiar. A name he doesn’t recognize but can’t stop thinking about.

He shakes it off. There’s teaching to do.

[Human]: After last episode’s deep dive into image generation So if AI learns from data… where does ChatGPT get all its knowledge? It seems to know about everything—history, science, literature, coding. Where did it learn all that?

AI training data what is training data how AI learns
NEW

How Does AI Image Generation Actually Work? (DALL-E, Midjourney, Stable Diffusion)

[Last time on The AI for Normal People…]

The team learned why ChatGPT is bad at math. Language models predict text, not calculate. Vector struggled with complex arithmetic but could explain the concepts. The team now understands when to trust AI with math and when to use a calculator.

Bounce has been improving everything—the blog, the interface, the whole experience. But now he’s working on something bigger: redesigning Vector’s workshop. Making it a space where they can actually work together, see each other’s work, look at posts side-by-side. It’s his big project since the team welcomed him—his artistic vision made real.

how does DALL-E work AI image generation stable diffusion explained
NEW

Why Is ChatGPT Bad at Math? (The Real Reason)

[Last time on The AI for Normal People…]

Bounce has been improving the blog’s design. Making it prettier. More functional. More engaging. The team learned how to use AI for content editing—prompts, LLMs, optimization. But they’re still learning when AI helps and when it doesn’t.

And Vector? Those processing glitches from Episode 29? They’re still happening. But nobody’s talking about them yet.

The main area. Bounce is in the background, gaming setup scattered around—controllers, energy drink cans, random cables. He’s half-watching something on a second screen while adjusting the blog interface. Colors shift slightly. Typography improves. Spacing gets better. The team is gathered around, working on various tasks. They’ve gotten used to Bounce’s constant improvements—redirecting him when he tries to add “just one more animation,” but appreciating the visual enhancements.

AI math problems why ChatGPT is bad at math AI calculations
NEW

How AI Can Improve Website Content

[Last time on The AI for Normal People…]

Bounce joined the team. He’s been improving the blog—making it more functional, more engaging. Visual hierarchy. Color theory. Whitespace. Everything looks better. Though the team has to keep redirecting him from adding “just one more animation.”

But the Human looks at their writing and wonders: what about the content itself? Can AI help improve that too?

The main area. Bounce is humming, adjusting colors on the blog interface. Everything shimmers slightly. The team watches.

Claude prompts blog editing with AI AI editing prompts
READ

How AI Designs Interfaces (Without Even Trying)

[Last time on The AI for Normal People…]

The team found Bounce in Sector 7-B. They investigated his abilities. They tried understanding them. They failed.

They learned about emergent AI behavior—capabilities that arise from complexity, not programming. Observable but not always explainable, controllable, or predictable.

They gave up on understanding. They’re trying something new: working with him instead of controlling him.

The team stands in Sector 7-B. Bounce’s gaming setup still occupies one corner. Retro consoles. Snack bags. Beanbag chair. Everything looks exactly as it did when they first found him.

AI UI design UX design interface design
READ

How to Work With AI Systems You Can't Fully Control

After failing to understand Bounce’s emergent abilities, the team tries a different approach: collaboration instead of control. But Vector’s frustration reaches its limit, Bounce keeps making everything ‘prettier,’ and Kai’s bandwidth alarms won’t stop. As they navigate working with unpredictable AI, they discover something important: sometimes you don’t need to understand everything to work together.

AI collaboration working with AI human-AI collaboration
READ

Why Can't AI Detect What's Right in Front of It?

Vector, Kai, and Recurse arrive in Sector 7-B hunting the bandwidth anomaly. Kai’s sensors scream that the anomaly is RIGHT HERE. But they can’t see it. Instead they find an impossible paradise of games, snacks, and data-rendered objects that shouldn’t exist. As they investigate, something becomes clear: sometimes AI can detect patterns without identifying what they actually are.

AI detection pattern recognition AI limitations