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 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 take a business idea, build a landing page, put it in front of real people, and read the signal: is there real demand, or should you kill it? The skill is reading that signal honestly, making the pursue-or-drop call without talking yourself into the answer you wanted. That's a founder superpower, finding out early whether an idea has real demand before you pour months into building it, and doing one shows you whether making that honest call is your kind of work.

You're going to write a real Product Requirements Document for a feature you care about, then have AI play three people who tear into it from their own angles: an engineer, a designer, an exec. You decide which objections to take and which to push back on, then revise. That's the actual product-manager muscle, deciding what to build and defending the call when smart people disagree, and doing one tells you fast whether owning that kind of decision is your kind of work.

You're going to build a real AI assistant for one narrow job, write the instructions that make it nail that job every time, and ship it to 10 real people who actually use it. The surprise is what's hard: not the tech, but scoping the problem tightly enough that the AI gets it right every time, which is the core skill of building with AI. This is the lightest way in to a whole career, and doing one tells you fast whether shaping a tool around a real problem 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.