A small tool to view real-world ActivityPub objects as JSON! Enter a URL
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Accept
header
to the server to view the underlying object.
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"content": "<p>Real coin flips are ~49-51 not 50-50 <a href=\"https://www.scientificamerican.com/article/scientists-destroy-illusion-that-coin-toss-flips-are-50-50/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">scientificamerican.com/article</span><span class=\"invisible\">/scientists-destroy-illusion-that-coin-toss-flips-are-50-50/</span></a></p><p>But you can guarantee equal probability with a simple trick! Flip 2x in a row starting with the same side up.</p><p>HT->call it H<br />TH->call it T<br />HH,TT->try again</p><p>(due to von Neumann <a href=\"https://en.wikipedia.org/wiki/Randomness_extractor#Von_Neumann_extractor\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">en.wikipedia.org/wiki/Randomne</span><span class=\"invisible\">ss_extractor#Von_Neumann_extractor</span></a>)</p><p>This leads to randomness extractors: from a given random process, what's the biggest uniform distribution you can get efficiently?</p><p>Randomness extractors give another interpretation of <a href=\"https://mathstodon.xyz/tags/entropy\" class=\"mention hashtag\" rel=\"tag\">#<span>entropy</span></a>:</p><p>avg # bits needed to *describe* the outcome<br />=<br /># uniformly random bits you can *extract* from the outcome</p><p><a href=\"https://mathstodon.xyz/tags/math\" class=\"mention hashtag\" rel=\"tag\">#<span>math</span></a> <a href=\"https://mathstodon.xyz/tags/probability\" class=\"mention hashtag\" rel=\"tag\">#<span>probability</span></a> <a href=\"https://mathstodon.xyz/tags/ComputationalComplexity\" class=\"mention hashtag\" rel=\"tag\">#<span>ComputationalComplexity</span></a> <a href=\"https://mathstodon.xyz/tags/TCS\" class=\"mention hashtag\" rel=\"tag\">#<span>TCS</span></a> <a href=\"https://mathstodon.xyz/tags/InformationTheory\" class=\"mention hashtag\" rel=\"tag\">#<span>InformationTheory</span></a></p>",
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"en": "<p>Real coin flips are ~49-51 not 50-50 <a href=\"https://www.scientificamerican.com/article/scientists-destroy-illusion-that-coin-toss-flips-are-50-50/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">scientificamerican.com/article</span><span class=\"invisible\">/scientists-destroy-illusion-that-coin-toss-flips-are-50-50/</span></a></p><p>But you can guarantee equal probability with a simple trick! Flip 2x in a row starting with the same side up.</p><p>HT->call it H<br />TH->call it T<br />HH,TT->try again</p><p>(due to von Neumann <a href=\"https://en.wikipedia.org/wiki/Randomness_extractor#Von_Neumann_extractor\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">en.wikipedia.org/wiki/Randomne</span><span class=\"invisible\">ss_extractor#Von_Neumann_extractor</span></a>)</p><p>This leads to randomness extractors: from a given random process, what's the biggest uniform distribution you can get efficiently?</p><p>Randomness extractors give another interpretation of <a href=\"https://mathstodon.xyz/tags/entropy\" class=\"mention hashtag\" rel=\"tag\">#<span>entropy</span></a>:</p><p>avg # bits needed to *describe* the outcome<br />=<br /># uniformly random bits you can *extract* from the outcome</p><p><a href=\"https://mathstodon.xyz/tags/math\" class=\"mention hashtag\" rel=\"tag\">#<span>math</span></a> <a href=\"https://mathstodon.xyz/tags/probability\" class=\"mention hashtag\" rel=\"tag\">#<span>probability</span></a> <a href=\"https://mathstodon.xyz/tags/ComputationalComplexity\" class=\"mention hashtag\" rel=\"tag\">#<span>ComputationalComplexity</span></a> <a href=\"https://mathstodon.xyz/tags/TCS\" class=\"mention hashtag\" rel=\"tag\">#<span>TCS</span></a> <a href=\"https://mathstodon.xyz/tags/InformationTheory\" class=\"mention hashtag\" rel=\"tag\">#<span>InformationTheory</span></a></p>"
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