What it is. Who makes it. How to use it.
Classical software is explicit: a programmer encodes rules. ML inverts this. You define an objective, feed in data, and an optimization algorithm — gradient descent — adjusts millions of internal parameters (weights) until the model minimizes its error.
No rules are written. Behavior emerges from optimization. The same core method — define a loss function, backpropagate gradients, update weights — underlies every major AI system in use today.
Modern AI didn't come from one invention. It came from two distinct advances — one in architecture, one in alignment — that together turned raw prediction into something that feels like intelligence.
Before the Transformer, AI read text sequentially — word by word, left to right. The breakthrough was attention: letting the model look at all words simultaneously and learn which ones matter most to each other.
In the sentence "The animal didn't cross the street because it was too tired" — attention is what tells the model that "it" refers to "animal," not "street." That ability to track relationships across context is what makes LLMs feel like they understand.
A trained model is technically just a very good text predictor. It doesn't yet know how to be helpful or safe. Reinforcement Learning from Human Feedback is the fine-tuning step that shapes it into an assistant.
Human raters compare pairs of model responses and pick the better one. Those preferences become a reward signal, and the model learns to produce outputs humans rate highly. This is what turns a raw predictor into something that follows instructions, avoids harm, and feels like it has a personality.
The same optimization principle underlies completely different model families — each with a different architecture, training signal, and class of problems it solves well.
Most AI tools you use today combine more than one. ChatGPT and Claude are LLMs fine-tuned with RL. DALL-E is a diffusion model guided by a text encoder. Sora combines both.
The tools you actively choose to use are just the tip of the iceberg.
Conversation, writing, analysis, research, advice.
Type a description, get a picture. Any style.
Text or images turned into moving clips.
Full songs, voice cloning, voiceovers.
Write, fix, and build entire apps.
Meetings, emails, files, tasks — automated.
Smarter opponents, generated worlds, lifelike NPCs.
Reads scans, folds proteins, discovers drugs.
Self-driving cars, drones, and physical robots.
AI-powered search with real-time cited answers.
Detects threats, flags fraud, protects systems.
Real-time translation, subtitles, multilingual content.
Trading, forecasting, risk analysis, and financial research.
Contract review, case research, and legal workflow automation.
Ad copy, trend forecasting, and AI-powered styling.
The tools on the previous slide are the ones you consciously choose to use. But AI has been quietly running in the background of your daily life for years — you just didn't call it that.
What's changed isn't that AI arrived. It's that it became visible, conversational, and creative — and now anyone can use it, not just engineers.
"AI won't replace you. But the version of you that knows how to use AI will be dramatically more capable than the version that doesn't."
Understanding these three types helps you know what any AI tool can — and can't — do for you.
Most tools today combine all three. Claude is generative when you chat with it, and agentic when you use Claude Code. The lines are blurring fast.
Coined by Andrej Karpathy — one of the founders of OpenAI — in early 2025 to describe a new way of building software that anyone can do.
Instead of writing code yourself, you simply describe what you want in plain English — and the AI builds it. You don't need to know syntax, frameworks, or computer science. You just need to know what you want.
"You describe it. Claude builds it. You take the credit."
Learn Python. Study HTML. Understand databases. Take courses for months. Write hundreds of lines of code. Debug errors. Start over.
Open Claude Code. Type "Build me a web app that tracks my family's weekly schedule and sends reminders." Watch it appear.
The skill isn't coding anymore — it's knowing what to ask for and recognizing when it's right. That's a skill you already have.
Claude Code used to be a tool only developers could use — living in the command line, requiring installs and technical setup. That's changed.
Anthropic now offers a Desktop App that gives you the full power of Claude Code through a regular window — just download and open it like any other app on your computer.
The terminal and the desktop app are just two different doors into the same room. Same AI, same power — the desktop app is just the friendlier entrance.
Create a free account with your email address.
$20/month — this unlocks Claude Code. The free plan doesn't include it.
Available for Mac and Windows. Install it like any normal app.
Describe what you want to build in plain English. That's it.
No terminal. No VS Code. No coding experience required. Just a conversation.
Whether you use Claude in the browser, the desktop app, or the terminal, you're talking to one of three underlying AI models. Think of them as different settings on the same engine.
The deepest thinker. Best for complex reasoning, nuanced writing, and hard problems where quality matters more than speed.
The everyday workhorse. Fast, smart, and capable — handles the vast majority of tasks beautifully. What you'll use most.
Lightning quick for simple tasks. Great when you need a fast answer, a short summary, or a quick rewrite.
These same three models power every way you access Claude — the website, the mobile app, the desktop app, and the terminal. The interface changes; the intelligence underneath doesn't.
Opus 4.6, released February 5th 2026, is state-of-the-art on several evaluations including agentic coding, multi-discipline reasoning, knowledge work, and agentic search.
For a brief moment, "prompt engineering" was a career. People sold courses. Companies posted job listings. There were frameworks, formulas, tricks.
Then the models got smarter — and all of that became unnecessary.
Memorize syntax. Use the right incantations. Format your prompt correctly or it won't work.
Be clear. Give context. Say what you actually want. Push back when it's wrong. That's it.
The best interface for AI is the one
you've been using your whole life —
plain English.
Web apps run in a browser — on any phone, laptop, or tablet. Claude Code's Desktop App can build these with zero technical setup on your end.
You can even make them feel like a real app on your phone — add to your home screen, works offline — these are called Progressive Web Apps (PWAs). No App Store, no Xcode, no $99/year developer fee.
Native iOS apps — the kind that live in the App Store — require a separate set of tools that even Claude Code can't skip.
A Note on the Design Process
The calendar app you just saw wasn't built in one prompt — it was shaped over many. Each round of feedback made it sharper. That back-and-forth is the skill.
In the next 30 minutes, we're going to build a real, working app — together, using Claude Code. No coding experience needed. You just need to know how to talk.
The school calendar PDF has everything — but it's a wall of text. This app turns it into a filterable, color-coded calendar so you can see exactly what matters to your daughter's grade.
No more scrolling through dates that don't apply. Filter by grade, filter by type, and see only what's relevant — at a glance.
Source: Brearley School Calendar PDF ↗
This is a real app you'll actually use. When the audience sees something built from their own school's data, it stops feeling like a demo and starts feeling like a superpower.
Curiosity, taste, and knowing what you need — that's the real skill. AI just gives you a way to act on it, faster and bigger than ever before.
ReginaFloresMir.ai