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David Aguilar davvid

  • Walt Disney Animation Studios
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@kristopolous
kristopolous / hn_seach.js
Last active July 24, 2023 04:12
hn job query search
// Usage:
// Copy and paste all of this into a debug console window of the "Who is Hiring?" comment thread
// then use as follows:
//
// query(term | [term, term, ...], term | [term, term, ...], ...)
//
// When arguments are in an array then that means an "or" and when they are seperate that means "and"
//
// Term is of the format:
// ((-)text/RegExp) ( '-' means negation )
@michael-k
michael-k / git-lfs-diff.sh
Created June 26, 2015 10:22
Git LFS Diff
#!/bin/sh
if [ $# -ne 3 ] ; then
echo "Usage: $0 <ref> <ref> <filename>"
exit 1
fi
RevA=$1
RevB=$2
File=$3
@jonhoo
jonhoo / README.md
Last active July 19, 2021 10:49
Distributed RWMutex in Go
@staltz
staltz / introrx.md
Last active May 6, 2024 01:44
The introduction to Reactive Programming you've been missing
@wrouesnel
wrouesnel / .remote-backup-excludes.txt
Last active September 5, 2021 23:32
A script for performing a remote backup to another server using rsync and bup for versioning.
# this is an rsync-excludes format list of files to exclude.
@debasishg
debasishg / gist:8172796
Last active March 15, 2024 15:05
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep=rep1&amp;t
@shawndumas
shawndumas / .gitconfig
Created August 5, 2013 19:08
Using WinMerge as the git Diff/Merge Tool on Windows 64bit
[mergetool]
prompt = false
keepBackup = false
keepTemporaries = false
[merge]
tool = winmerge
[mergetool "winmerge"]
name = WinMerge
@ssylvan
ssylvan / rh_hash_table.hpp
Last active January 12, 2023 04:52
Quick'n'dirty Robin Hood hash table implementation. Note, I have implemented this algorithm before, with tons of tests etc. But *this* code was written specifically for the blog post at http://sebastiansylvan.com/post/robin-hood-hashing-should-be-your-default-hash-table-implementation/, it has not been extensively tested so there may be bugs (an…
#define USE_ROBIN_HOOD_HASH 1
#define USE_SEPARATE_HASH_ARRAY 1
template<class Key, class Value>
class hash_table
{
static const int INITIAL_SIZE = 256;
static const int LOAD_FACTOR_PERCENT = 90;
struct elem
@1wErt3r
1wErt3r / SMBDIS.ASM
Created November 9, 2012 22:27
A Comprehensive Super Mario Bros. Disassembly
;SMBDIS.ASM - A COMPREHENSIVE SUPER MARIO BROS. DISASSEMBLY
;by doppelganger (doppelheathen@gmail.com)
;This file is provided for your own use as-is. It will require the character rom data
;and an iNES file header to get it to work.
;There are so many people I have to thank for this, that taking all the credit for
;myself would be an unforgivable act of arrogance. Without their help this would
;probably not be possible. So I thank all the peeps in the nesdev scene whose insight into
;the 6502 and the NES helped me learn how it works (you guys know who you are, there's no
@hellerbarde
hellerbarde / latency.markdown
Created May 31, 2012 13:16 — forked from jboner/latency.txt
Latency numbers every programmer should know

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs