Saving a few clicks for readers: Project page: https://sql.ophir.dev/. - Source: Hacker News / 3 months ago

I am currently looking for a solution to run automated tests on a sql website generator I am working on ( https://sql.ophir.dev ) I wanted to use hurl (https://hurl.dev/), but Bruno's UI seems to be useful while developing the tests... Has someone tried both ? Which is better for automated testing, including when the response type is html and not json? - Source: Hacker News / 3 months ago

Full fledged BI tools like Superset and Metabase are amazing for their intended use cases. But they may be an overkill if your primary use case is to infrequently build semi-interactive reports for non-technical end-users and your use cases are are mostly covered by standard graphs & tables. Esp. So if you are familiar with SQL and have access to the underlying data source. Two nifty utilities I have found to be... - Source: Hacker News / 3 months ago

I feel obligated to add a shameless plug here. The idea is very close to a project I presented at pgconf.eu last week: SQLPage https://sql.ophir.dev/ SQLPage has the same goal as postgrest+htmx, but is a little bit higher level. It let's you build your application using prepackaged components you can invoke directly from SQL, without having to write any HTML, CSS, or JS. - Source: Hacker News / 5 months ago

I have been thinking about that a lot recently. Where I work, we spend a very small fraction of our time on building things that are unique to our business. Maybe we are doing something very wrong, but I am under the impression that most of the code that gets written is extremely low-entropy. This low-entropy, repetitive coding is not limited to the user interfaces. We do tend to describe the same structures and... - Source: Hacker News / 6 months ago

Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago

Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago

It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: about 2 years ago

You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago

To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago