ngould's comments

Not odd at all, actually. Numpy might implement certain functions differently from other libraries. With a background in algorithms, you could make an educated guess as to complexity, but without knowing the exact implementation it's still a guess.

This is a fairly brilliant referral advertising scheme.

In the U.S., the main reason prices go negative is because of the production tax credit. Wind producers get a subsidy per MWh produced. Since the unsubsidized marginal cost of production is zero, the result is a negative effective production cost. So if wind is the marginal energy producer, the clearing price goes negative.

I don't know how things work in Germany, but I'd be surprised if the negative prices there had much to do with the costs of starting/stopping conventional units. In most electricity markets, participants have to bid in their marginal cost. Even if you're an inflexible nuke, your marginal cost is positive. You usually need genuinely negative marginal cost units to drive the clearing price negative. That only really happens when subsidies are part of the picture.

A few other big ones IMO:

7. Energy density (as opposed to power density; i.e. c-rate matters)

8. Temperature sensitivity (many batteries lose voltage at low temps, e.g.)

9. Ability to hold a charge

This comment got my gears turning about good ways to split up Uber in an anti-trust case. One possibility is to have Uber A provide dispatch as a service, while Uber B collects fares, disburses driver payments, and provides the front-end UI. Uber B could also set up whatever driver incentives they wanted. Uber A would be a regulated monopoly, and would have to allow other companies to compete with Uber B on its platform.

In this scenario, dispatch would basically be a public utility, and drivers could trust the algorithm not to cheat. I.e. since Uber B and its competitors would be running any driver incentives, Uber A wouldn't even know about it. (And legally shall not know such things.)

I realize this is a half-baked pipe dream, but an interesting thought experiment.

Yes, it's worth investing the time. Grammar of graphics is life changing if data visualization is part of your day to day.

Gooulo, agreed insofar as we should reject hate in its various forms. It's ugly, terrifying, and has nothing to do with what has made this country what it is.

But even as a progressive democrat, I feel sympathy for djschnei's response here. I think we should embrace and celebrate free speech in all of its forms. And when racists speak publicly, we should publicly reject their views.

When you suggest that large swaths of people should be "systematically suppressed," I think you're driving a wedge between yourself and a lot of fairly moderate conservative folks who could be allies in protecting civil society from the likes of actual Nazi's, etc.

My understanding is the CUDA interface is for NVIDIA GPU's only. There are non-CUDA API's for other types of GPU's, but they don't necessarily work out of the box with neural network libraries like Tensorflow yet.

Cool!

Mortgage backed securities were not risky because they were diversified. They were risky because nobody fully understood how undiversified they were, especially at the lower grade tranches.

Meanwhile, LC was alive in '08, and you can look at how their notes performed. Investors lost single digit percentages. In other words, they beat S&P by a lot.

Historically people have made money on Lending Club, and lost it in Vegas. I'll stick with Lending Club.

The star schema is still very useful, even though it wasn't invented in the last six months.

If you're interested in defining dimensions using tabular data in JS, check out crossfilter.js.

https://github.com/square/crossfilter

I'm surprised you didn't mention the mortgage interest tax deduction as a contributor to sprawl. I.e. federally subsidized home ownership. Talk about reasons to ditch your downtown rental and borrow against a grassy slice of suburban land.

http://freakonomics.com/2012/06/29/homeownership-and-suburba...

Very cool, thanks for the update.

Hey pkrein, I am a Segment fan and startup-scale user of your warehouse service. The warehouse service is super convenient, but its main shortfall for us is that you don't offer historical backfilling of data more than 60 days before the warehouse was enabled. That is the data we need in our warehouse most.

There are other ways to load in Salesforce, Zendesk, or Stripe data. It is certainly nice to be able to do that all with Segment -- but it is not necessary. Sources is nice to have, but the core warehouse service is not really complete (for us, and I suspect others too) until you can seamlessly support data backfill at all price tiers. A one-time fee for backfill fee would be okay, but saying "no we don't support that" makes me sad.

Word on the street is Periscope costs $1000/mo for unlimited users, up to 1B rows.

Cool stuff. Curious to know what systems these containerized tasks are pulling data from. Does adroll consider let those containers access production database instances? Or are they backed by non-production systems? (EDIT: I understand that it's S3 for intermediate steps. But I'm curious where the data comes from initially.)