2026W31
- Elevators — The counterintuitive result is that destination dispatch, the kiosk where you punch your floor before boarding, performs worse than plain up/down buttons. Locking in the assignment early strips the system of its ability to re-optimize, while Otis’s RSR rescores every car every 5 seconds and quietly reroutes you mid-trip if a better one frees up. The other bit I liked is that the dumb LOOK algorithm beats RSR under heavy traffic, because once every car runs full there’s nothing left to optimize. More information isn’t the win here, keeping the assignment revisable is.
- 99 percent of my website traffic is bots — The gap that stuck with me is the measurement one. Tools like Plausible or Fathom only count visitors who run JavaScript, which almost no bot does, so his dashboard showed a healthy trickle of humans while the server logs told a completely different story, 5,977 human pageviews against 1.28 million pages served. The ratio I can’t get past is Anthropic’s crawler hitting the site 35,000 times for every single visitor it sends back. A 1.5 million page database built from IRS 990 forms is exactly the kind of thing crawlers feast on, and the analytics most of us trust are precisely the ones blind to it.
- Mario meets Pareto — Using Mario Kart 8 character stats to teach the Pareto frontier is the kind of framing that actually makes the idea stick. Koopa Troopa is dominated because Cat Peach gives more speed at the same acceleration and Toadette more acceleration at the same speed, so there’s never a reason to pick him. What I took from it is the split between the objective and the subjective, the frontier objectively drops the choices nobody should make, but where you land on it is still your call and your preference weights. It’s a cleaner way to reason about any trade-off than pretending one number captures everything.
- Don’t be a meat proxy — The phrase names something I’ve caught myself doing, passing along a model’s output as if forwarding it counted as contributing. His NATS example, “stream leader election / R3 quorum re-form during pod churn” relayed to a colleague without understanding a word of it, is the exact failure mode. The code review case is the sharper one though, where the developer pastes the ticket into Claude and iterates on reviewer feedback until it passes, which quietly turns the reviewer into the real engineer. The bar he sets is understanding the output well enough to say it in your own words, and that’s a bar worth holding myself to.
- Andrew Ho (@andrewho03) on X — His starting point is the one I keep circling too, that LLM capabilities are “spiky” rather than general, and that even coding, after tens of billions in investment, doesn’t generalize the way you’d expect. The bet he’s leaving OpenAI to make is that the fix isn’t more scale but better reinforcement learning data, specifically the messy scientific and biology tasks where a model has to explore, make judgment calls, and correct its own mistakes. Whether $100 billion really flows into training data is the part I’d want to watch play out, but the diagnosis that we’ve under invested in data relative to compute feels right to me. Leaving to sell that data back to his former employer is a clarifying kind of conviction.