438 karma · joined August 31, 2018
All my experiences were wonderful, especially its performance and powerful features like rule rewriting.
Also PostgREST is brilliant.
1. Go to the lectures, sit upfront (where you are definitely not going to use your phone or fall asleep)
2. Get the book or script that corresponds to your lecture. Almost all math lectures follow some sort of book. If unsure, ask your professor.
Just reading ahead for half an hour prior to the lecture will make _such_ a difference, as you won’t be lost and can focus more on contexts than on definitions.
3. Feel free to ask questions. If you know where you are (see 2.) it’s much easier to know what’s wrong. Professors are human and sometimes forget to give some theorem they are using. It’s really easy (and frankly surreal at first) to be that guy that points out errors to the professor (in a respectful way!).
If your shy or you have the feeling, that the problem is indeed on your end – take notes of things that are unclear and ask them after the lecture. Professors usually love to talk about their subject!
4. Last but not least: Keep calm and carry on. It’s completely normal to feel overwhelmed at first ;)
Most of the time I still end up reading the digital copy on my Laptop, as I personally find this more comfortable for textbooks.
One thing I stumbled upon the other day was that I (and google apparently, too) have trouble navigating the new website.
I find it much simpler to find relevant information on the old site.
Anyway, keep up the good work and stay healthy!
https://github.com/maxmunzel/talk-algorithms-for-np-hard-pro...
There is no way to transfer Messages between iOS and Android or to backup messages in any way really - except of storing them plaintext at Google/Apple.
I'd be really thankful, if anyone had some solution for this...
First test: A single 2.9GB file
time rg Result all.pgn | sort --radixsort | uniq -c 13 [Result ""] 1106547 [Result "0-1"] 1377248 [Result "1-0"] 1077663 [Result "1/2-1/2"] rg Result all.pgn 1.12s user 0.55s system 99% cpu 1.680 total sort --radixsort 3.87s user 0.37s system 71% cpu 5.911 total uniq -c 2.69s user 0.02s system 45% cpu 5.909 total
Using Apache Flink and a naive implementation It took 13.969 seconds.
Second test: same dataset, split between 4 files
time rg Result chessdata/ | awk -F ':' '{print $2}' - | sort --radixsort | uniq -c 13 [Result ""] 1106547 [Result "0-1"] 1377248 [Result "1-0"] 1077663 [Result "1/2-1/2"] rg Result chessdata/ 1.70s user 0.97s system 42% cpu 6.292 total awk -F ':' '{print $2}' - 5.47s user 0.07s system 88% cpu 6.289 total sort --radixsort 4.13s user 0.42s system 43% cpu 10.559 total uniq -c 2.73s user 0.03s system 26% cpu 10.559 total
Flink: 12.724s
Conclusion: For this kind of workload, both approaches have comparable runtimes, even tough taco bell programming has the upper hand (as is should for simply filtering a text file). It took me about equally long to implement both. I think both approaches have their use case.
I ran this locally on my Laptop with 4 logical cores.
Feel free to ask any questions.
Reductions are pretty easy to write and modern SAT solvers are crazy fast.
See #Controvery