Projects

You're going to build a real web app for one thing you wish existed, and put it live on the internet where actual people can use it. The skill is scope: deciding the ONE job it does well, cutting everything else, then shipping it and fixing what breaks when a real person tries it. That's the core of software engineering, turning an idea into a working thing and deciding what not to build, and doing one tells you fast whether shipping a live product is your kind of work.

You're going to build a bot that lives in a real Discord server and does one useful thing: a study buddy in your class server, a converter in a hobby server, a mood-check in your friend group. You'll get it replying fast, give it an AI brain, then decide what it should refuse to do, and the payoff is that it runs on its own and real people come to rely on it. That's what software engineers actually ship, a service that works without you watching it, and doing one tells you fast whether building things people depend on is your kind of work.

You're going to build a Chrome extension that fixes something on the web that annoys you every day (a cluttered feed, endless threads, a site that buries what you need) and get it running in real browsers. The skill is subtraction: testing on the real sites, then cutting it to the one behavior actually worth keeping and making that solid. That's real software engineering, shipping code that runs in other people's browsers and doing one thing well, and doing one tells you fast whether building tools that run in the wild is your kind of work.

You're going to build a single-feature web product, give it a landing page, and get one real person to actually use it. The skill is building for a real user: watching someone use your thing, then cutting everything that isn't what they needed. That's the founder instinct, finding someone with a problem and making them exactly what solves it, and doing one tells you fast whether building something people want is your kind of work.

You're going to build an AI app that does a real job for a real user, then make it reliable enough that they can actually depend on it. The skill is eval-hardening: writing a test set of normal and adversarial cases, finding where the AI breaks, deciding what 'reliable enough' means for your user, and fixing the worst failures. That's what AI engineers actually spend their time on and the part of the work that's becoming a real career, and doing one tells you fast whether making an unpredictable system trustworthy is your kind of work.