11–20 of 28 posts
Gave me a smile to see the shout out to LISP in there. Reading this take on it, it feels like a JIT compiler could also accomplish a fair bit of this? I'm also reminded of the way a lot of older programs would generate tables during build time. I'm assuming that is still fairly common?
> Mojo, D, Nim, and Zig can do it, and C++ as of C++20. There are likely some other languages that can do it, but these are the only ones that can truly run normal run-time code at compile time Pretty sure Julia can do it.
Depending on the userbase of the site, simply checking for @gmail.com at the end, I'd bet, would result in a quick win, as well as restricting the username's alphabet to allowed Gmail characters. The other optimization I'd guess at would be to async/thread/process the checking before and after the @ symbol, so they can run in parallel (ish). Extra cpu time, but speed > CPU cycle count for this benchmark.
[Rehashing an old comment] In the math department, we had a Moodle the students in the first year of my university in Argentina. When we started like 15 years ago, the emails of the students and TA were evenly split in 30% Gmail, 30% Yahoo!, 30% Hotmail and 10% others (very aproxímate numbers). Now the students have like 80% Gmail, 10% Live/Outlook/Hotmail and 10% others/Yahoo. Some of the TA are much older, so perha…
Gave me a smile to see the shout out to LISP in there. Reading this take on it, it feels like a JIT compiler could also accomplish a fair bit of this? I'm also reminded of the way a lot of older programs would generate tables during build time. I'm assuming that is still fairly common?
Depending on the userbase of the site, simply checking for @gmail.com at the end, I'd bet, would result in a quick win, as well as restricting the username's alphabet to allowed Gmail characters. The other optimization I'd guess at would be to async/thread/process the checking before and after the @ symbol, so they can run in parallel (ish). Extra cpu time, but speed > CPU cycle count for this benchmark.
[Rehashing an old comment] In the math department, we had a Moodle the students in the first year of my university in Argentina. When we started like 15 years ago, the emails of the students and TA were evenly split in 30% Gmail, 30% Yahoo!, 30% Hotmail and 10% others (very aproxímate numbers). Now the students have like 80% Gmail, 10% Live/Outlook/Hotmail and 10% others/Yahoo. Some of the TA are much older, so perha…
Depending on the userbase of the site, simply checking for @gmail.com at the end, I'd bet, would result in a quick win, as well as restricting the username's alphabet to allowed Gmail characters. The other optimization I'd guess at would be to async/thread/process the checking before and after the @ symbol, so they can run in parallel (ish). Extra cpu time, but speed > CPU cycle count for this benchmark.
Tell us you used to work at Google, without telling us. "simply do X" is such a programmer fallacy at this point I'm surprised we don't have a catchy name for it yet, together with a XKCD for making the point extra clear.
The trope is "At Google we..." and then casually mention "violating" the CAP theorum with Spanner or something.
It is simple, and I really do hope any first year CS student could extract a substring from a string. Have LLMs so atrophied our programming ability that extraction of a substring is considered evidence of a superior programmer?
https://github.com/hanickadot/compile-time-regular-expressio...
A 54:47 presentation at CppCon 2018 is worth more than a thousand words...
see https://www.youtube.com/watch?v=QM3W36COnE4
followup CppCon 2019 video at https://www.youtube.com/watch?v=8dKWdJzPwHw
As the above github repo mentions, more info at https://www.compile-time.re/
Unfortunately, so far as I can tell:
- LMS has not been updated for years and never moved to scala 3. https://github.com/TiarkRompf/virtualization-lms-core
- LMS was written to also use "scala-virtualized" which is in a similar situation
There's a small project to attempt to support it with virtualization implemented in scala 3 macros, but it's missing some components: https://github.com/metareflection/scala3-lms?tab=readme-ov-f...
I'd love to see this fully working again.
Having never heard of mojo before, I found this article fascinating. It provides a great example of how a toy regex parser works and an excellent explanation of why vanilla regex tends to be slow. It also presents a novel solution: compiling the regex into regular code, which can then be optimized by the compiler.