What3words is recommended for use cases such as logistics and delivery services needing precise locations, outdoor activities and adventure travel where traditional addresses are impractical, emergency services requiring quick and exact location data, and individuals or organizations operating in developing regions with poor address infrastructure.
Based on our record, what3words seems to be a lot more popular than Bootstrap 4 Cheat Sheet. While we know about 125 links to what3words, we've tracked only 2 mentions of Bootstrap 4 Cheat Sheet. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Bootstrap cheat sheet. Obviously most classes I know, but occasionally you just need to see what else is out there. https://hackerthemes.com/bootstrap-cheatsheet/. Source: about 3 years ago
Enjoy an interactive list of Bootstrap classes. Chose to copy Code snippet of CSS class. BOOTSTRAP CHEAT SHEET. - Source: dev.to / over 3 years ago
IBAN for home addresses. Yawn. Nothing beats the What Three Words system and would be much more fun if not routeable at scale. https://what3words.com/. - Source: Hacker News / 11 days ago
What 3 words (https://what3words.com/) solves this problem, but it doesn't seem to be popular. If anyone has experience, I would be curious to know why. - Source: Hacker News / about 1 month ago
Or we can just start using https://what3words.com/ and geolocation. I disagree with the report, I think it's feasible with a bit of creativity. The government also has this: https://www.data.gov.uk/dataset/091feb1c-aea6-45c9-82bf-768a15c65307/open-postcode-geo We could also start with an imperfect solution, offer it as a free API (maybe even self-hosted and communicating with other services p2p) and wait for users... - Source: Hacker News / 10 months ago
Something to add to their list of common passwords is the What3Words database of locations https://what3words.com It's something like 50trillion sets of looks-random strings. That's quite a lot, but if the list could be narrowed very significantly to get some likely results by selecting locations in: 1) cities where a company is physically located 2) large capital & global cities 3) significant landmarks I see... - Source: Hacker News / over 1 year ago
I’m waiting for these guys to make a breakthrough here. Source: over 1 year ago
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