Introduction: Most people treat protein intake as a fixed number: “1 gram per pound of body weight” or “1.6 grams per kilogram.” That’s fine as a starting point, but in reality your protein needs fluctuate day to day. Recovery, sleep, stress, and activity levels all affect how much protein your...
Most people treat protein intake as a fixed number: “1 gram per pound of body weight” or “1.6 grams per kilogram.” That’s fine as a starting point, but in reality your protein needs fluctuate day to day. Recovery, sleep, stress, and activity levels all affect how much protein your body actually requires. Instead of blindly following a static rule, you can think of protein intake as a hypothesis about what your body needs, and use Bayes’ Theorem to update that hypothesis as your body gives feedback. Over time, this approach lets you estimate the protein amount that works best for you, rather than relying on generic recommendations.
Let’s imagine a simple scenario: John... (read 511 more words →)
Are you a swimmer? If so, you might be a part of a majority who claim they have pool allergies with the root cause of this claim being chlorine. This is false. Pool allergies are rather about the chemical byproducts that form when chlorine reacts with organic matter like sweat, sunscreen(omitted if indoors), and urine. The key is that different pools produce different chemical mixtures depending on organic load, pH, temperature, and circulation.
You can model it as a chain: pool composition => reactions with chlorine => irritant concentration => individual sensitivity => observed allergic response.
In this view, “allergic” and “not allergic” are generally from the external chemistry of a person, not the chemistry of the pool. This means that you can prevent swimming allergy via changes to your external chemistry.
Suggestion:
Change your skincare/hygiene routines & products one-by-one to see if you can isolate a specific factor causing this, rather than messing with your pool chemistry.
Caffeine is the standard stimulant for focus, but theobromine, found in chocolate, may offer a more stable alternative. Both act on adenosine receptors, yet theobromine binds more weakly and has a longer half-life, leading to a slower and less disruptive stimulation curve. Additionally, lead is also present in chocolate therefore it would make sense to use artificial theobromine not just pure "dark chocolate".
If we model stimulant use as a feedback loop between receptor activation and adaptation, caffeine’s strong, fast effect increases the rate of tolerance formation, while theobromine’s mild, steady profile minimizes it.
Theobromine is to caffeine as caffeine is to coca-caffeine based stimulants.
I predict that after a month of daily use, individuals relying on theobromine will experience more consistent cognitive performance and fewer withdrawal symptoms than caffeine users, even if their peak alertness is lower. This would suggest that for long-term productivity, the optimal stimulant may be the one that causes the least disruption to baseline equilibrium.
My model for the supplement space is a high-noise, low-signal environment where the vast majority of products are marketing artifacts with negligible effect sizes. It seems correct to mentally partition protein powder from 'supplements' entirely. This is a tool of convenience for hitting protein targets that is otherwise undifferentiated from chicken breast or lentils.
Obsessing over the marginal gains from exotic pills before your sleep, training, and whole-food diet are 95% optimized which is a classic failure mode of misallocating attention and resources. The rational approach seems to be nailing the fundamentals for months, using protein powder only if it solves a genuine dietary logistics problem, and then, maybe, adding the one or two compounds with a high burden of evidence behind them, like creatine.
Hey Dalmert,
Yeah, the 0.2 and 0.7 aren’t meant to be special, they’re just encoding how plausible the “poor recovery” evidence is under each protein hypothesis. In a real-life scenario, you’d have no idea what those numbers should be at first, so starting both hypotheses at 0.5 and updating as you collect experience would make more sense. Over time, you could even adjust the likelihoods themselves (empirical Bayes style) if you notice your model consistently over- or under-predicts fatigue.
You’re also right that the setup’s under-constrained, real protein needs depend on stress, sleep, calorie intake, etc., and those should all be treated as separate evidence streams. You would need more diverse and semi-independent evidence... (read more)