Map of all M2.5+ earthquakes of the last 24h.
Tectonic plate boundaries extracted from arcgis.com
/* | |
* This script goes through your Gmail Inbox and finds recent emails where you | |
* were the last respondent. It applies a nice label to them, so you can | |
* see them in Priority Inbox or do something else. | |
* | |
* To remove and ignore an email thread, just remove the unrespondedLabel and | |
* apply the ignoreLabel. | |
* | |
* This is most effective when paired with a time-based script trigger. | |
* |
# -*- coding: utf-8 -*- | |
# Form implementation generated from reading ui file 'design.ui' | |
# | |
# Created: Wed May 27 16:39:17 2015 | |
# by: PyQt4 UI code generator 4.11.3 | |
# | |
# WARNING! All changes made in this file will be lost! | |
from PyQt4 import QtCore, QtGui |
license: mit |
Map of all M2.5+ earthquakes of the last 24h.
Tectonic plate boundaries extracted from arcgis.com
Install Homebrew:
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Download Java 6 on this page and install it
Install Gephi:
brew cask install gephi
JD Maturen, 2016/07/05, San Francisco, CA
As has been much discussed, stock options as used today are not a practical or reliable way of compensating employees of fast growing startups. With an often high strike price, a large tax burden on execution due to AMT, and a 90 day execution window after leaving the company many share options are left unexecuted.
There have been a variety of proposed modifications to how equity is distributed to address these issues for individual employees. However, there hasn't been much discussion of how these modifications will change overall ownership dynamics of startups. In this post we'll dive into the situation as it stands today where there is very near 100% equity loss when employees leave companies pre-exit and then we'll look at what would happen if there were instead a 0% loss rate.
What we'll see is that employees gain nearly 3-fold, while both founders and investors – particularly early investors – get dilute
#!/bin/bash | |
# install CUDA Toolkit v9.0 | |
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb) | |
CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb" | |
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG} | |
sudo dpkg -i ${CUDA_REPO_PKG} | |
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub | |
sudo apt-get update | |
sudo apt-get -y install cuda-9-0 |
# Props to user brechmos for sharing the code here: https://www.raspberrypi.org/forums/viewtopic.php?t=55100 | |
# Tested with the following barcode scanner | |
# macbook# ioreg -p IOUSB | |
# <snip> | |
# | +-o WIT 122-UFS V2.03@14200000 <class AppleUSBDevice, id 0x10000c3c4, registered, matched, active, busy 0 (6 ms), retain 14> | |
# WIT 122-UFS V2.03: | |
# Product ID: 0x1010 | |
# Vendor ID: 0x05fe (CHIC TECHNOLOGY CORP) |
#!/usr/bin/env python | |
""" | |
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction. | |
""" | |
from __future__ import print_function, division | |
import numpy as np | |
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten | |
from keras.models import Sequential |
I was drawn to programming, science, technology and science fiction | |
ever since I was a little kid. I can't say it's because I wanted to | |
make the world a better place. Not really. I was simply drawn to it | |
because I was drawn to it. Writing programs was fun. Figuring out how | |
nature works was fascinating. Science fiction felt like a grand | |
adventure. | |
Then I started a software company and poured every ounce of energy | |
into it. It failed. That hurt, but that part is ok. I made a lot of | |
mistakes and learned from them. This experience made me much, much |