cydrobolt

haha :)

Also useful for making easy to remember links to often-used resources

Minimalist in regards to its design, perhaps not the code structure :)

In contrast to other projects such as YOURLS, Polr tries to be minimalist by avoiding heavy graphs or unnecessary bloat.

Agreed. Using Bitcoin would definitely help make it resemble cash, however, this could potentially be less convenient. Venmo is more popular among students, especially on university campuses, so the idea was to make it easy to adopt among friend groups, not specifically to guarantee the transfer of funds.

Agreed. Although it has none of the guarantees of cash, it would also be more difficult to steal (as the authorization can be revoked). This is more similar to a bank check than actual legal tender.

(potential drunken party game: throw 10 paypyruses and the first to cash in gets the money)

Paypyrus being based on Venmo, we cannot guarantee payment if the bill creator decides to revoke our authorisation or delete the bill. This is due to Venmo's friend-to-friend model, which we are based on. No bot accounts are allowed, so the money is never placed in our hands. It is a direct P2P payment between friends, presumably.

This would be analogous to a bank check rather than a paper bill. Since it is not legal tender and there is no middleman in the transaction, it could not be spent (or stolen) as easily as a dollar bill.

Yeah. Right now, it is largely based on trust. We're hoping to implement some kind of one-time "seal" and new token that a user can verify after receiving a bill to ensure that someone else can't activate it with a picture or by saving the token. That obviously comes with the downside of making it more difficult to transfer.

As a proof of concept, and being based on Venmo, I think it is and will be largely based on user trust as long as the bill creator keeps the funds (Venmo allows no bot accounts).

Yeah. The speech recognition is really dependent on the placement of your mic, background noise, etc. We're using wit.ai's recognition engine for this, but we could also base it on other libraries or APIs. Perhaps we can figure out a way to optimise the detection in the future.