Predicting how the big personal choices play out — careers, moves, money, relationships.
The decisions that shape a life rarely come with a spreadsheet attached. Should you take the new job? Move across the country? Make the career switch you keep circling back to? These are the questions where the stakes are highest and the data is thinnest — which is exactly why a structured prediction beats a gut call. MiroFish takes the scenario you describe and runs it forward into a set of weighted outcomes, so you can see the shape of the futures you're choosing between before you commit to one.
A personal prediction isn't a fortune-teller's verdict. It's a map. You describe your situation in plain language — the offer on the table, the savings you have, the partner whose career also matters — and the AI surfaces the assumptions it has to make, branches into the likely ways things unfold, and tells you which single factor your outcome is most sensitive to. That last part is usually the revelation: the move doesn't hinge on the salary bump, it hinges on whether you can rebuild a social circle in eight months.
The posts in this cluster work through the personal scenarios people most often want to predict. Each one shows how to frame the question so the prediction is actually useful, what the AI does with incomplete information, and how to read the branches without mistaking a probability for a promise. They're written to be used: read the one closest to your situation, then run your own version through the predictor.
Predictions about your own life carry a trap the business ones don't — you already have a preferred answer, and it's tempting to read the branches as confirmation. The articles here are deliberate about that. A good prediction earns its keep by naming the future you'd rather not look at and attaching a real probability to it, so you walk into the decision with both eyes open rather than one.
Describe your scenario and MiroFish predicts the likely outcomes — with probabilities and the reasoning behind each one.
Your financial future isn't one number — it's a spread of outcomes. Here's how to use scenario analysis to predict where you actually land, and which variable controls the spread.
A career switch is a multi-year bet with a delayed payoff. Here's how to predict whether yours pays off — the realistic timelines, the branches, and the factor that decides it.
Moving cities or countries is a prediction problem disguised as a logistics problem. Here's how to predict whether a relocation actually works out — and the variable it really hinges on.
A job change is a high-stakes scenario with thin data. Here's how to turn it into a prediction you can act on — what to assume, how the outcomes branch, and the one factor it usually turns on.