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There is a saying in the enterprise software business.

You are a Walmart or becoming one. There is no middle ground.

The enterprise executives do not have the luxury of time or risk appetite to keep doing multi-dollars deals and review MSAs.

In that respective, IBM just extended their life by another 10-15 years. It is a brilliant move by IBM.

No serious enterprise uses an operating system or any piece of enterprise software without costly support and maintenance.

Once they are in, the support and maintenance agreements disappear only if the purchaser goes out of business.


This is provable more easily places in hub cities like Bangalore, Bay Area and Seattle. The benefits are trickling down to other cities slowly.

Most people who are smart enough to jump ships now have 2-3 offers. In cities like Bangalore, a jump can fetch anywhere from 20-200%. 20% is minimum irrespective of the current salary.

Perfect time for anybody who hasn't tried checking this in a couple of years.


Even those of us who jumped recently can't keep recruiters off them if my LinkedIn messaging is any indication.


>> For a problem to be, in a statistical sense, causally identified there must be some random or as-if random manipulation of treatment.

It would be good to see your ideal example of a casual, for non statistics people to understand your long note better.


You take a random sample of 1,000 white men between the ages of 45 and 55, who have lived in New England for at least 10 years, with no known history of heart disease. Your randomly split them in half. You give half of them a supplement to take every day for 12 months, and you give the other half a placebo. If the number of heart attacks in the placebo sample is greater than in the treatment sample, you have some believable evidence that the supplement can help prevent unexpected heart attacks, at least in white men in their 40s and 50s.

The idea is that you've controlled for just about every factor that could affect the rate of unexpected heart attacks, or those factors are evenly distributed throughout both samples because you were careful to sample randomly. Therefore, if there is a difference between the groups, on average, it must be because of the treatment that you introduced to one group and not the other.

I'm hand-waving, of course, and I'm sure there are medical researchers out there who will read my study design and laugh at how badly controlled it is. But that should give you the general picture of one comon method used to perform "causal" analysis.


Great example. In the design you propose, in expectation, you would have an unbiased causal inference. We would probably want to check for pre-treatment balance between groups to make sure that stochastic (chance) imbalance did not emerge even though the process itself is good. I don't know anything about heart attacks so I don't have the subject matter knowledge here, but imagine that smoking causes heart attacks. If that's the case, although your design should not cause the presence of smokers among treated and control units to systematically vary, maybe it did by chance. We'd want to assess balance. Same with any other potential confounders.

Another technique we might use is a blocked (or stratified) random sample. Knowing that there will be both smokers and non-smokers, we recruit two separate samples, and randomize treatment assignment within each. This ensures that smoking status does not predict treatment assignment and guards against some potential threat from overall randomization.

We could also mitigate the imbalance that does exist by doing a matched analysis, where each treated unit is paired with a control unit that looks most like him (some control units are reused). Or we could match on propensity scores. Or we could weight on inverse propensity weights. Or we could weight using covariate balancing. Or...

My point in doing this info dump is to a) back up nerdponx's example, which is great and b) illustrate how there's a lot to learn about how statisticians have taken the problem of causal analysis seriously and developed techniques appropriate for answering causal questions.

People in the CS side of things tend to use Pearl's DAGS for conceptualizing this stuff. I'm in the stats/econ side of things so I use Neyman-Rubin. They're equivalent. Allow me to suggest Rubin and Imbens - Causal Inference for Statistics, Social and Biomedical Sciences as a good textbook that we assign to graduate students learning this stuff. Some of my students tell me the "Causal Inference Mixtape" is popular among people who want less statistical theory and more "what should I do as a practitioner". A virtue of both the resources I just mentioned is that they discuss not just experimental designs but also observational data studies, like the one the original post would have wanted to conduct.


Your comment is a great follow up to the article. I am copying(with slight modifications) a few actions highlighted in the article.

>> Staring isn't staring if you're smiling. Or waving. Or if you say hi. Adults- do stare but do it smilingly or say hi

>> Just tell your child to wave.

>> And don't worry if he(your child) asks an awkward question, like, "Can't she talk?" That's a welcome chance for us to introduce Esprit.

>> Just ask a disabled child's parents whether the planned activity will work for their son or daughter. If an adjustment is needed we can figure it out together.

>> A nuisance like leaving a picnic early is normal (for parents with disabilities), so don't make a big deal out of an annoyance with a portent-filled comment like, "I don't know how you do it."

>> They desire the human contact that most of us take for granted. So increase your awareness, by reaching out to one of them.


And like most things, the cure for fear is exposure. The more time you spend with disabled/diverse/different people, the more natural it will feel to you, and the less weirdly you will act. This goes for your encounters with people with visible disabilities and differences, mental disabilities and differences, racial differences, linguistic differences, religious differences. . .


For same reason one of the founders mentioned >> most people are not the best at advocating for themselves

Is there a service to help negotiate a better raise? (Of course, working behind the scenes)

I presume this is a struggle for every employee everywhere, every year throughout the career.

I managed to do better a few times by writing a nice email but many a times I just lacked the energy to compose such emails and settled for whatever is offered.


>> S&P/Nasdaqs/NYSE won't list you Whats the cut off?. Is there a link you can suggest to read more about % of cash raised/valuation?


They all have minimum listing criteria that you can find, a private industry solution to curb what they considered abuses.


The company is taken over by the VCs. Perhaps the new executive thinks it is a waste of time/money to engage in VCPs.


Doesn't really matter what they think of it, new management has to honor existing commitments.


--- From the slide on list of Open Source tools and Algorithms

• Kaggle(www.kaggle.com) is a good start for this - start with “somewhat real” problems • Use higher level tools - Keras(https://keras.io/), otherwise easy to get lost in weeds • Consider having a real world goal - eg: if you’re in real estate figure out how to use a simple CNN (not the latest algorithm) for image search • Depending on need consider integration with hadoop/Spark(http://spark.apache.org/)


False analogy. Creating a file upload service with no authentication or no resources to keep it safe is like installing a fully loaded AK 47 at an intersection and hoping people would just use it to learn how it works or use in an emergency to shoot at criminals passing that intersection.

There are free file upload services and there are ways to upload from command line(1).

[1] https://github.com/andreafabrizi/Dropbox-Uploader


You cannot compare bytes on a disk with a gun. People don't die from transferring data.


It is yammer all over again http://i.imgur.com/DKODqy3.png

I can't help but post this based on my experience pitching to vendors to join an app store.

Slack CEO: Yammer made $1.2B. We need to make $12B. For that I need to make a hit song with 10,000 background dancers with me on the stage.

Board: How much can you pay each dancer?.

CEO: $10/hr

Board: Ok. Announce an App Store.

You are already a hero and there are hundreds of them to jump on stage to dance with you in that 5 minute song.

CEO: Now you are talking!


We were looking at a company communication channel to replace Yammer and Google Chat a while back, and briefly played with Slack. In the end trading in one cloud service for another seemed unwise.

Because we already used self-hosted GitLab when they announced the inclusion of MatterMost (FOSS self-hosted alternative to Slack) we simply activated that and have been quite satisfied with it.


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