Advice is given: supplement creatine for loading benefits and quick muscle gains; take retinol for anti-aging results; use X programming language; protein supplements help you look big (even though they might help kill you).

There is no solid evidence these treatments won’t harm you in the long term. Perhaps the study measures lifted weight, but not hundreds of other variables, like estrogen levels (I’m being hypothetical here), leading to long-term health impacts. In light of lifelong studies with large sample sizes, you are taking silent risks, especially when the gains are small (looking slightly bigger for your upcoming trip to Uzbekistan).

This kind of advice does not meet the “would this make sense for the rest of my life?” test.

If something is going to kill you, do everything you can. But if you’re just trying to fill up the arm sleeves (commendable aesthetic goal), creatine probably isn’t worth the risks.

Use X programming language? Why? Write tests? This stuff isn’t important in most cases. Unfortunately, many (all?) cultures penalize participants for not slavishly following convention/status markers (say “phenomenological” and not simply “pattern” in research communities). Your customers don’t care if your code is “bad.” The Monty Python cast has a nice anecdote here: basically, John Cleese asked the video editor why he cut the funniest clip out. The editor said it was because someone left an object in the scene that shouldn’t have been there. The editor was trying to impress other film editors, not make a funny movie, which is the actual goal.

Framing advice as “do X for the rest of your life” helps you determine whether doing X really makes sense at all. Is the knowledge robust to change, or are these people changing what they do every month? If you integrate a habit that is not time-tested or robust to change, you may very well end up being the sucker filling the bank accounts of charlatan influencers while whatever they sold you kills you.