The Landscape: Three Genuinely Different Approaches to Tutorial Video Generation

OBS Studio, Video Editing Fundamentals, and Audio Editing & Production Basics — this site's own existing Audio & Video Production courses — all cover genuinely manual workflows: you record, you edit, you narrate, all by hand, with software that does exactly what you tell it and nothing more. This course covers a different, newer category entirely: tools that automate part or all of that process using AI — auto-zooming as you click, generating a voiceover from your own screen actions, or turning a rough recording into a finished tutorial without you touching a timeline at all. Six tools sit at the center of this course, and the first thing worth understanding is that they don't all do the same job — they split into three genuinely different categories, and knowing which category a tool belongs to matters more than any single feature list.

Three Genuinely Different Categories

Every tool this course covers falls into exactly one of three categories, defined by when and how automation actually happens — not by marketing language, which uses "AI-powered" almost interchangeably across all three:

This split matters practically, not just conceptually: a capture-time recorder can't fix a recording you've already made (the polish happens live, during capture), while a post-hoc pipeline can't improve how you captured the footage in the first place — it only works with whatever you hand it. Knowing which category a tool sits in tells you immediately what kind of problem it can and can't solve for you.

The OS-Coverage Map

Two of the six tools in this course are Mac-only, which matters a great deal if you're not on a Mac — this course is written to be genuinely useful regardless of platform, so the map below is worth checking before investing time in any single chapter:

Notice the pattern: the two post-hoc pipelines are entirely web-based, so they never actually care what you recorded with or what OS you're on — the OS constraint only applies to the recorder you use before uploading. If you're on Windows or Linux, Chapter 5's Cap is the one capture-time recorder in this course that's actually usable natively; Chapters 3 and 4 are still worth reading even if you can't run either tool yourself, since the underlying category — and the readability problems it solves — applies just as much to whatever recorder you do end up using.

What Makes a Coding Tutorial Different From a Generic Product Demo

Every one of these six tools is marketed primarily at product demos and SaaS onboarding videos — not coding tutorials specifically. That distinction matters more than it might first appear: a product demo is usually short (60–90 seconds), shows a polished UI at a comfortable viewing size, and succeeds if it looks slick. A coding tutorial is often longer, shows small monospaced text that has to stay genuinely legible, and succeeds only if a viewer can actually follow and reproduce what happened — a real, different bar. Chapter 2 works through exactly what that bar requires in practice, and every tool-specific chapter after it measures its own subject against those same criteria, not against how well it demos a landing page.

How This Course Is Organized

• Chapter 2 — the evaluation criteria this whole course applies: legibility, pacing, and narrating intent rather than clicks
• Chapters 3–5 — the capture-time recorders: Screen Studio, Screenify Studio, and Cap
• Chapters 6–7 — the post-hoc AI-narration pipelines: DemoPolish and Trupeer
• Chapter 8 — Descript, the full manual editing suite
• Chapter 9 — a decision framework across all six tools, keyed to OS, budget, and how much manual control is actually wanted
• Chapter 10 — a capstone producing one real coding tutorial three different ways, comparing the actual results side by side

Hands-On Exercises