2026W32
- Abdominal Fat Predicts Heart Disease Risk Better Than BMI - American College of Cardiology — The number that stuck with me is that people classified as normal weight by BMI still carried 15 to 50% higher risk across most outcomes if their waist-to-hip ratio was high. BMI collapses your whole body into one scalar and throws away where the fat actually sits, and it turns out where it sits is the part that matters. What I keep thinking about is how much medicine leaned on a single cheap number for so long precisely because it was cheap to measure, not because it was the right thing to measure. A tape measure around the waist would have told us more, and it was always sitting in the drawer.
- Finding the shortest binary string in a given interval — The reframing that made it click for me is that “shortest binary string” is really “simplest dyadic rational in the interval”, the fraction with the smallest power-of-two denominator that fits. So [0.1, 0.3] answers 0.01, meaning 0.25, because you can’t do it in fewer bits. The bit I liked is why the correction works, once you start above the target and only ever adjust by halving, the total wiggle you can still make is a convergent 1/2 + 1/4 + 1/8 series, so you’re guaranteed to land inside any interval with room in it. It’s the kind of problem where the naive first attempt looks obviously right and is quietly wrong, which is the whole reason Raymond’s posts are worth reading.
- Compression is prediction — I knew the compression and language modeling objectives were the same thing in the abstract, but seeing the numbers side by side made it concrete, an order-1 model needs 434 bits for a Dickens quote and GPT-2 needs 176. A better predictor literally is a better compressor, because the entropy coder just spends fewer bits on the tokens the model already expected. The honest part is where the author admits it’s useless in practice, shipping a multi-gigabyte model to shave a few kilobytes off an HTTP response is absurd. The idea is beautiful and the deployment story is a joke, and I appreciate a post that says both.
- Ordinary Abundance — Walking through an apartment object by object is a smart trick, because the awe only lands when it’s attached to something you’d otherwise ignore, the light switch, the tap, the fridge. The detail I can’t shake is Bellamy in 1888 imagining music on demand as the outer limit of human happiness, a thing I now dismiss a hundred times a day without noticing. Jefferson’s ink freezing mid-letter did the same work, it made “heated rooms” feel like an achievement rather than a baseline. I’m a little wary of nostalgia pieces, but this one earns it by staying concrete instead of sermonizing about gratitude.
- Blog about things you don’t understand yet — The test he sets is the one I want to steal, if your conclusion at the end of a draft is basically your introduction, you didn’t learn enough to publish. That flips the usual worry, the point of writing about something you don’t fully grasp isn’t to teach, it’s to force the “wait, that can’t be right” moment that only shows up when you try to write the claim down. I’ve had that experience often enough to trust it, thinking feels complete right up until you have to commit it to a sentence. The note about LLMs being unusually good and kind at flagging what you’ve genuinely misunderstood is a fair point, and a low-friction way to get the pushback that used to require a brave commenter.
- Concurrency vs. Throughput: why more parallelism can make databases slower — PlanetScale — The counterintuitive move is that when errors spiked they lowered the concurrency limit instead of raising it, dropping the transaction pool from 10,000 to around 1,000 and going from 1,500 queries/second back up to 60,000. The mechanism is the part I’ll remember, a long transaction holding locks makes every other query walk longer and longer InnoDB version chains to reconstruct its snapshot, so the cost per query grows with how many are in flight. That’s the N(N-1) coherency term in the Universal Scalability Law showing up in a real incident, and once it dominates, admitting more work just buys you more coordination overhead. It’s a clean argument against the instinct to treat a queue backing up as a signal to add capacity, sometimes the fix is admission control, not more workers.
- LLMs reward expertise — The Terence Tao example is what sells it, he asks short pointed questions, pushes back with his own suggestions rather than just contradicting, and can only do that because he knows the math well enough to spot what looks off. That reframes the skill entirely, it isn’t prompt phrasing, it’s having the taste to reject a mediocre answer with specifics like “can we express this simpler”. The line that the human is the bottleneck, not the model, matches my own experience, on code I know deeply I can push the model far harder than on code I’m just passing through. The author names the obvious objection, that this conclusion is suspiciously flattering to experts, and I’m not fully sure I’ve separated the true part from the comforting part yet.
- Almost No Skill Required to Cook a Steak (Though You Probably Can’t Make a Decent One) — Pairs naturally with the expertise piece above, the steak is just the more vivid version of the same claim, anyone can get to edible and almost nobody gets to good without judgment the tool can’t supply. The framing I liked is that the AI can’t see the picture in your head unless you translate it into requirements, constraints, examples, and tests, which is exactly the work people hope the tool removes and it doesn’t. Where it turns dark is the observation that quality collapses when every restaurant hires the same AI cook and optimizes for cost, so the burnt steak arrives everywhere at once. That’s the part I’m still chewing on, individual mastery might protect you, but it doesn’t protect you from a market that’s decided mediocre-and-cheap is the equilibrium.