The most honest test of technical judgment isn't how elegantly you can build something. It's whether you had the discipline to ask if you needed to build it at all. The Reddit user Odd-Donut-4388 captures something essential: the difference between useful research and impressive-looking research comes down to the problems you choose, not the complexity of your pipelines. We think that observation deserves more attention, because it applies far beyond machine learning, it applies to how anyone should think about adopting new tools in their work.
What Odd-Donut-4388 describes as "research taste" is really just a practical filter that keeps you honest. Start with a problem people actually care about. Then try the dumbest solution first. If a ten-line prompt solves it, you are done. That is not a failure of ambition. That is efficiency. Too many of us have built elaborate spreadsheets, automated workflows, or ML pipelines that could have been replaced by a simple lookup or a well-worded question. The mental model, find, test, scope down, is a direct challenge to the instinct that makes us reach for complexity first. It asks you to treat your own time and your users' attention as finite resources worth protecting.
For people working alone or in small teams, the hard part is staying honest without someone to push back. A good advisor or a collaborator who asks "why can't you just…" builds taste through that kind of friction. When you lack it, you have to build your own. You can set a rule: before you write a line of code or build a new model, write down the simplest possible solution. If you cannot articulate why that simple solution fails, you are probably over-engineering. You can also set a time limit. If you cannot solve the core problem with off-the-shelf tools in an hour, either the problem is too hard right now or you need to scope it down.
The practical takeaway is not a philosophy. It is a workflow. Choose a problem someone actually has. Test the simplest fix first. If it works, ship it. If it does not, then decide whether the hard path is worth walking. That is the discipline that separates tools people use from projects people admire. Odd-Donut-4388 gave you the framework. Now apply it to your next decision, whether it is a data pipeline, a spreadsheet, or a report. Complexity should be earned, not defaulted to.