A reviewer looking at a stack of portfolios sees the same four projects over and over: chat with a PDF, a wrapper around a chat API, a fine-tune on a public dataset, an agent that browses. None of them is a bad idea. All of them are indistinguishable, because the version in the tutorial made every interesting decision already.
Why most AI portfolios read the same
A project demonstrates judgement only where judgement was required. Following a tutorial removes precisely the parts a reviewer is looking for: what you chose, what you rejected, what you measured, and what you did when it did not work. The artefact looks similar either way, which is why reviewers stop reading the artefact and start reading the write-up.
So the differentiator is not the topic. It is whether the project contains a decision that could have gone the other way, and whether you can show the evidence that made you choose. A modest project with a real evaluation and an honest account of a failure beats an ambitious one that was never measured, every time, and it beats it in about thirty seconds of a reviewer’s attention.
Two is the right number of projects. Three if one is genuinely small. A long list of half-finished repositories is a negative signal because the thing a reviewer most wants to know — can this person finish something — is answered badly by it.
