Leo Toff zaaath

Ideas For Startups

Written by Paul Graham and copied from http://www.paulgraham.com/ideas.html

October 2005

(This essay is derived from a talk at the 2005 Startup School.)

Details

People think it's hard to come up with good ideas for startups because they don't actually try to do it.

Using perf:

$ perf record -g binary
$ perf script | stackcollapse-perf.pl | rust-unmangle | flamegraph.pl > flame.svg

NOTE: See @GabrielMajeri's comments below about the -g option.

Picking the right architecture = Picking the right battles + Managing trade-offs

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

-E: will describe the underlaying queries of the \ commands (cool for learning!)
-l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

	!apt-get --purge remove cuda nvidia* libnvidia-*
	!dpkg -l \| grep cuda- \| awk '{print $2}' \| xargs -n1 dpkg --purge
	!apt-get remove cuda-*
	!apt autoremove
	!apt-get update

	!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
	!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
	!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
	!apt-get update

	This "cheat sheet" is designed to be a super-high level view of common and/or important computer science concepts. Good for test reviews, preparing for job interviews, brushing up on the basics, becoming a better person, or light reading for putting your significant other to sleep. I will continue to update this so if you have any suggestions or corrections please send a pull request or contact me. Licensed under http://creativecommons.org/licenses/by-sa/3.0/.

	Algorithms
	+ Limiting behavior (all the second sections are as limit(n)->infinity)
	* Big O - f is bounded by g :: f(n) <= g(n)*c (for some constant c) :: an upper bound, can be much higher than actual performance because this is not a tight bound, for example a log(n) algorithm technically is O(n!) but is obviously way better than something that actually gets n! performance
	* Small or Little o - f dominated by g :: f(n) < g(n)*c; f(n)/g(n) = 0 :: g gets much larger
	* Big Omega - f is bounded below by g :: \|f(n)\| >= g(n)*c :: a lower bound, bu