A/B testing, algorithmic optimization, and the small act of ignoring both — and what it means to make something by hand when a number can tell you what would have worked better.
Noam Brown on Twitter / X
And yes we did try other major problems without success. Sadly no Millennium Prize problems (yet).But also, we didn’t spend a lot on each problem. It’s possible to push test-time compute much further.— Noam Brown (@polynoamial) August 1, 2026
A result does not tell you how it was made - Sensemaker
A correct formula can have an unverified origin, and a successful AI answer can hide a forbidden route. Those claims need different evidence.
When benchmarks go bad - what I learned from measuring performance wrong - Holly Cummins
The world of performance analysis is littered with flawed claims, cognitive biases, dangerous intuitions, and beguiling fallacies. Sadly…
The Optimization Trap: Why Too Much Efficiency Makes Us Fragile with Olivier Hamant
AI Has Ruined the Job Market
Maybe flawed people were better than brute algorithms.
Narcissistic number
In number theory, a narcissistic number[1][2] (also known as a pluperfect digital invariant (PPDI),[3] an Armstrong number[4] (after Michael F. Armstrong)[5] or a plus perfect number)[6] in a given number base b {\displaystyle b} is a number that is the sum of its own digits each raised to the power of the number of digits.
OpenAI’s math breakthrough played to AI’s strengths
I tried to explain OpenAI’s solution more clearly than OpenAI did.
Algorithm appreciation: People prefer algorithmic to human judgment
Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this effect, what we call algorithm appreciation, when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic attraction (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when: people chose between an algorithm’s estimate and their own (versus an external advisor’s; Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of “big data” and algorithmic advice it generates.
Misperception of Exponential Growth: Are People Aware of Their Errors?
Previous research shows that individuals make systematic errors when judging exponential growth, which has harmful effects for their financial well-being. This study analyzes how far individuals are aware of their errors and how these errors are shaped by arithmetic and conceptual problems. Whereas arithmetic problems could be overcome using computational assistance like a pocket calculator, this is not the case for conceptual problems, a term we use to subsume other error drivers like a general misunderstanding of exponential growth or overwhelming task complexity. In an incentivized experiment, we find that participants strongly overestimate the accuracy of their intuitive judgment. At the same time, their willingness to pay for arithmetic assistance is too high on average, often much above the actual benefits a calculator provides. Using a multitier system of task complexity we can show that the willingness to pay for arithmetic assistance is hardly related to its benefits, indicating that participants do not really understand how the interplay of arithmetic and conceptual problems shape their errors in exponential growth tasks. Our findings are relevant for policymaking and financial advisory practice and can help to design effective approaches to mitigate the detrimental effects of misperceived exponential growth.
Mathematicians still don’t know the fastest way to multiply numbers
A 23-year-old student overturned an ancient conjecture about one of math’s simplest operations
Akin's Laws of Spacecraft Design
1. Engineering is done with numbers. Analysis without numbers is only an opinion.
Producing The Perfect Token
The unspoken inference quality gap and how numerics determine if the inference you're paying for is worth it.
Who earned the score? - Sensemaker
This week's AI claims blurred models, systems, simulations and people. The evidence becomes clearer when the tested subject comes first.
math.r — Brain Building Game
Mathr is a brain building game to improve your math calculation skills. Practice arithmetic and level up!
Time for another A/B test
NSFW in For You
spacecowboy17.leaflet.pubwhen everyone has a different explanation of why the bad numbers are not that bad or why the small number is still very important, the simplest explanation is that the bad number is actually just indeed very bad
Christopher Mims
So I guess either Bluesky becomes a nonprofit or we all start sharing our Threads handles on here…?
