RepoRoulette: Randomly sample repositories from GitHub
(github.com/gojiplus)
Spin the wheel and see which GitHub repositories you get!
Spin the wheel and see which GitHub repositories you get!
Reservoir Sampling
(samwho.dev)
Reservoir sampling is a technique for selecting a fair random sample when you don't know the size of the set you're sampling from.
Reservoir sampling is a technique for selecting a fair random sample when you don't know the size of the set you're sampling from.
Can LLMs do randomness?
(rnikhil.com)
While LLMs theoretically understand “randomness,” their training data distributions may create unexpected patterns. In this article we will test different LLMs from OpenAI and Anthropic to see if they provide unbiased results. For the first experiment we will make it toss a fair coin and for the next, we will make it guess a number between 0-10 and see if its equally distributed between even and odd. I know the sample sizes are small and probably not very statistically significant.
While LLMs theoretically understand “randomness,” their training data distributions may create unexpected patterns. In this article we will test different LLMs from OpenAI and Anthropic to see if they provide unbiased results. For the first experiment we will make it toss a fair coin and for the next, we will make it guess a number between 0-10 and see if its equally distributed between even and odd. I know the sample sizes are small and probably not very statistically significant.
Show HN: Find a random wedding website
(randomweddingfinder.com)
Maybe alyssa and edward are getting married:
Maybe alyssa and edward are getting married:
A puzzle of two unreliable sensors
(wordpress.com)
Suppose you are trying to measure a value P and you have two unreliable sensors. Sensor A returns 0.5P + 0.5U, where U is uniform random noise over the same domain as P. Sensor B will return either P or U with 50% likelihood. In other words, sensor A is a noisy measurement of your variable, and B is sometimes the correct value and sometimes pure noise.
Suppose you are trying to measure a value P and you have two unreliable sensors. Sensor A returns 0.5P + 0.5U, where U is uniform random noise over the same domain as P. Sensor B will return either P or U with 50% likelihood. In other words, sensor A is a noisy measurement of your variable, and B is sometimes the correct value and sometimes pure noise.
The Forest – jump to a random website
(theforest.link)
Surfing the web is becoming increasingly boring.
Surfing the web is becoming increasingly boring.
Certified randomness using a trapped-ion quantum processor
(nature.com)
Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers1,2, realizing this potential remains a challenge.
Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers1,2, realizing this potential remains a challenge.
There's Math.random(), and then there's Math.random() (2015)
(v8.dev)
Math.random() is the most well-known and frequently-used source of randomness in Javascript.
Math.random() is the most well-known and frequently-used source of randomness in Javascript.
Randomness extractors: making fair coins out of biased coins
(bytepawn.com)
In a previous post titled Fair coin from biased coin, I looked at the problem of creating a uniform random coin given access to a biased coin. I looked at multiple approaches, and determined that they're actually all the same in some sense.
In a previous post titled Fair coin from biased coin, I looked at the problem of creating a uniform random coin given access to a biased coin. I looked at multiple approaches, and determined that they're actually all the same in some sense.