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I did a words lat/long system as a couple day project at Christmas. https://wherewords.id/ https://wherewords.id/ I used the google S2 mapping, and spent quit

I did a words lat/long system as a couple day project at Christmas. https://wherewords.id/ https://wherewords.id/ I used the google S2 mapping, and spent quite a bit of time on my own wordlist. I like the S2 mapping - it lets you use fewer words to refer to a bigger area. I write a little bit about it here: https://wherewords.id/+about https://wherewords.id/+about I would have really liked to have found some good research on 'words that are hard to mistake when spoken aloud across various accents', but in the end I just spent ages going through my word list with various libraries and manually. And having friends point me to locations that had incredibly rude or offensive wherewords! Really there should be a free, open source word<->location system that we all just standardise on and build into GPS systems and maps, because a common, free system would be genuinely useful.

rozab 6y ago
How many times has the wheel been reinvented with these phonetic word list schemes? Loads of crypto apps use them, e.g. for bitcoin wallet recovery. Do they all roll their own scheme? Someone should attempt to make a standard if none exists. Interoperability isn't even that important, I just want to pull something into my project.
- kybernetikos 6y ago
  I originally planned to use the crypto bip39 wordlist but ultimately it proved not to be suitable. I talk a little about it https://wherewords.id/+about https://wherewords.id/+about
  - rozab 6y ago
    Great summary, thanks
- snypher 6y ago
  Crypto wallets may use BIP39 protocol, it might be close to a 'standard'? https://github.com/bitcoin/bips/blob/master/bip-0039.mediawiki https://github.com/bitcoin/bips/blob/master/bip-0039.mediawi... Edit: 2048 English wordlist is here https://github.com/bitcoin/bips/blob/master/bip-0039/english.txt https://github.com/bitcoin/bips/blob/master/bip-0039/english...
- ryebit 6y ago
  In addition to BIP39 cited below, the EFF also published some useful wordlists a few years ago... https://www.eff.org/deeplinks/2016/07/new-wordlists-random-passphrases https://www.eff.org/deeplinks/2016/07/new-wordlists-random-p... One has the nice property that all words have unique 3 letter prefix. But not as many in prefix list (1296) as their "long" list (7776). That said I'm kinda partial to BIP39... first four letters are unique, and words are more uniform than EFF prefix list. But it looks like GPS addressing schemes like w3w need a MUCH larger list by an order of magnitude.
  - kybernetikos 6y ago
    > But it looks like GPS addressing schemes like w3w need a MUCH larger list by an order of magnitude. If you're trying to get down to 3 words yes, but if you're happy with 4 words (and I am), then the long list would be more than enough. One problem I found was that I think people don't want strongly negative phrases being used to describe where they live (rural poverty assault, evil disease island). The BIP39 wordlist can create word groups that are very offensive, or that would feel rascist if applied to particular parts of the world. It also has words that are easily confused for other words like era/error, son/sun, alter/altar, aisle/I'll, floor/flaw, . I did use words from BIP39, but had to remove quite a few in the end for my wordlist (e.g. blast, load, black, finger, female etc.) because of the unfortunate clusters it could create. Ultimately, I think coming up with a good wordlist is still a bit of an unsolved problem. The ideal wordlist for something like this 1. can't form obviously rascist or overtly sexual word clusters 2. can be easily distinguished in spoken communication 3. can be easily distinguished in written communication 4. doesn't have words that sound similar to other words in any of the most common accents 5. doesn't have geographic words (it'd be confusing) 6. is mainly positive or neutral words 7. consists of words that are common and easy to spell, avoiding words that are commonly misspelled and where there are different standard ways of spelling the words depending on region 8. has words that are not too long, a small number of syllables 9. doesn't contain words that are concatenations of other words in the wordlist Obviously not easy if you need a significant number of them.
    - jasonwatkinspdx 6y ago
- kybernetikos 6y ago
  > I just want to pull something into my project My experimentation mostly lives here: https://github.com/kybernetikos/wherewords/tree/main/lib/wordlist https://github.com/kybernetikos/wherewords/tree/main/lib/wor... If you're coding in JS, I'd use https://www.npmjs.com/package/verbal-id https://www.npmjs.com/package/verbal-id for 10bits, bip39 for 11 bits, and maybe my wordlist for 12 bits.
MzHN 6y ago
Wow, I love this! It is such a great application of S2, which is an awesome concept itself. A drilldown of the words would be a neat way to explore the mapping. In other words: first you see the largest areas, and you can click on them, then you see areas/words within that area, and as you drill down you form the wherewords location.
- kybernetikos 6y ago
  You're right that would be cool. I haven't done anything on it since the beginning of the year. There is a small easter egg though - wherewords.id works with fewer than the four words, so for example, if you go to https://wherewords.id/system/festival/ https://wherewords.id/system/festival/ you'll see it highlight a particular part of New York City, while https://wherewords.id/system https://wherewords.id/system covers a big part of New York State and Pennsylvania.

rozab 6y ago

How many times has the wheel been reinvented with these phonetic word list schemes? Loads of crypto apps use them, e.g. for bitcoin wallet recovery. Do they all roll their own scheme? Someone should attempt to make a standard if none exists. Interoperability isn't even that important, I just want to pull something into my project.

kybernetikos 6y ago
I originally planned to use the crypto bip39 wordlist but ultimately it proved not to be suitable. I talk a little about it https://wherewords.id/+about https://wherewords.id/+about
- rozab 6y ago
  Great summary, thanks
snypher 6y ago
Crypto wallets may use BIP39 protocol, it might be close to a 'standard'? https://github.com/bitcoin/bips/blob/master/bip-0039.mediawiki https://github.com/bitcoin/bips/blob/master/bip-0039.mediawi... Edit: 2048 English wordlist is here https://github.com/bitcoin/bips/blob/master/bip-0039/english.txt https://github.com/bitcoin/bips/blob/master/bip-0039/english...
ryebit 6y ago
In addition to BIP39 cited below, the EFF also published some useful wordlists a few years ago... https://www.eff.org/deeplinks/2016/07/new-wordlists-random-passphrases https://www.eff.org/deeplinks/2016/07/new-wordlists-random-p... One has the nice property that all words have unique 3 letter prefix. But not as many in prefix list (1296) as their "long" list (7776). That said I'm kinda partial to BIP39... first four letters are unique, and words are more uniform than EFF prefix list. But it looks like GPS addressing schemes like w3w need a MUCH larger list by an order of magnitude.
- kybernetikos 6y ago
  > But it looks like GPS addressing schemes like w3w need a MUCH larger list by an order of magnitude. If you're trying to get down to 3 words yes, but if you're happy with 4 words (and I am), then the long list would be more than enough. One problem I found was that I think people don't want strongly negative phrases being used to describe where they live (rural poverty assault, evil disease island). The BIP39 wordlist can create word groups that are very offensive, or that would feel rascist if applied to particular parts of the world. It also has words that are easily confused for other words like era/error, son/sun, alter/altar, aisle/I'll, floor/flaw, . I did use words from BIP39, but had to remove quite a few in the end for my wordlist (e.g. blast, load, black, finger, female etc.) because of the unfortunate clusters it could create. Ultimately, I think coming up with a good wordlist is still a bit of an unsolved problem. The ideal wordlist for something like this 1. can't form obviously rascist or overtly sexual word clusters 2. can be easily distinguished in spoken communication 3. can be easily distinguished in written communication 4. doesn't have words that sound similar to other words in any of the most common accents 5. doesn't have geographic words (it'd be confusing) 6. is mainly positive or neutral words 7. consists of words that are common and easy to spell, avoiding words that are commonly misspelled and where there are different standard ways of spelling the words depending on region 8. has words that are not too long, a small number of syllables 9. doesn't contain words that are concatenations of other words in the wordlist Obviously not easy if you need a significant number of them.
  - jasonwatkinspdx 6y ago
kybernetikos 6y ago
> I just want to pull something into my project My experimentation mostly lives here: https://github.com/kybernetikos/wherewords/tree/main/lib/wordlist https://github.com/kybernetikos/wherewords/tree/main/lib/wor... If you're coding in JS, I'd use https://www.npmjs.com/package/verbal-id https://www.npmjs.com/package/verbal-id for 10bits, bip39 for 11 bits, and maybe my wordlist for 12 bits.

MzHN 6y ago

Wow, I love this! It is such a great application of S2, which is an awesome concept itself. A drilldown of the words would be a neat way to explore the mapping. In other words: first you see the largest areas, and you can click on them, then you see areas/words within that area, and as you drill down you form the wherewords location.

kybernetikos 6y ago
You're right that would be cool. I haven't done anything on it since the beginning of the year. There is a small easter egg though - wherewords.id works with fewer than the four words, so for example, if you go to https://wherewords.id/system/festival/ https://wherewords.id/system/festival/ you'll see it highlight a particular part of New York City, while https://wherewords.id/system https://wherewords.id/system covers a big part of New York State and Pennsylvania.