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What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

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##Perks of Microsoft

####Salary

• Can't go into details, but can't complain at all.

####Health and Wellness Care

• Medical and hospitalization: Two choices for medical insurance. Both with no premium but different deductibles.
  • The two choices on medical plans are a high-deductible health plan (Microsoft puts a chunk of money into the Health Savings Account for you, which covers most of the deductible) and an HMO.
• With the HMO, you pay basically nothing as long as you only go to Group Health doctors. With the high-deductible plan, you're covered under the local Blue Cross provider which means you can go to just about any doctor in the country. For

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System Design Cheatsheet

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

Basic Steps

1. Clarify and agree on the scope of the system

• 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?

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def binary_crossentropy_with_ranking(y_true, y_pred):
    """ Trying to combine ranking loss with numeric precision"""
    # first get the log loss like normal
    logloss = K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
    
    # next, build a rank loss
    
    # clip the probabilities to keep stability
    y_pred_clipped = K.clip(y_pred, K.epsilon(), 1-K.epsilon())

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"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

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#!/usr/bin/env python
#
# Extracts email addresses from one or more plain text files.
#
# Notes:
# - Does not save to file (pipe the output to a file if you want it saved).
# - Does not check for duplicates (which can easily be done in the terminal).
#
# (c) 2013  Dennis Ideler <ideler.dennis@gmail.com>

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import numpy.lib
import numpy as np
import pandas as pd
import cPickle as pickle

def save_pandas(fname, data):
    '''Save DataFrame or Series

    Parameters
    ----------

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Originally:
https://gist.github.com/7565976a89d5da1511ce

Hi Donald (and Martin),

Thanks for pinging me; it's nice to know Typesafe is keeping tabs on this, and I
appreciate the tone. This is a Yegge-long response, but given that you and
Martin are the two people best-situated to do anything about this, I'd rather
err on the side of giving you too much to think about. I realize I'm being very
critical of something in which you've invested a great deal (both financially