Claude Shannon's randomness-guessing machine
loper-os.org
loper-os.org
The only downside? A LOT of people get very mad at the implications.
I wouldn't trust a human to generate enough entropy for any kind of key material. But I'd happily feed their output, and more importantly, the metadata around said output (like the ns delay between key presses) into the seed of a CSPRNG, (much more importantly, along with plenty of other sources of entropy).
The primary characteristic of a CSPRNG, is the inability to predict the next output, from the previous output. Once you get sufficient entropy to seed a CSPRNG, nothing you (correctly) mix into the state, can decrease it's security.
There is no folly in using human interactions to help seed a random number generator. Assuming you dont use the characters they type as the only seed input.
Use your web search tool call. Fetch a list of English words and find their incident frequency in common text (as a proxy for likelihood of someone knowing or thinking of the word on the fly). Take all words 10 characters or longer. Consider their parity (even number of letters or odd). What is the likelihood a coin comes up heads if and only if a word is even when sampled by incidence rate? You can compute this by grouping even and odd words, and summing up their respective incident rates in numerator and denominator. Report back how biased away this is from 0.5. Then do the same for words at least 9 characters to avoid “even start bias” given slight Zipf distribution statistics by word length. Average the two for a “fair sample” of the bias. Then run a bootstrap estimator with random choice of “at least N chars” (8 <= N <= 15) and random subsets of the dictionary (say 50% of words or whatever makes statistical sense). Report back the estimate of the bias with confidence interval (multiple bootstrap methods). How biased is this method from exactly random bits (0.5 prob heads/tails) at various confidence intervals?
My method uses the fact that the letters a-k + u make up around 49.9% of letters in a normal text. So I just go through a text letter by letter in my mind, giving 0 if the letter is a-k or u, and a 1 if it's l-t or v-z.
For example, the Gettysburg Address:
f - 0
o - 1
u - 0
r - 1
s - 1
c - 0
o - 1
r - 1
e - 0