A slipping cap on our ceiling created a camera obscura with our television.
With the ever increasing amount of open data, I’m having a surprising difficulty in tracking down best practices for anonymizing data. I’ve started collecting some, and I’m sure there are lot of people out there with brilliant expertise to share.
Content after the break, or drop some knowledge here.
Chris Whong got a hold of a lot of GPS data for NYC taxis, using the cleverest hack of all: He asked nicely.
Not sure what I want to do with this yet, but spend an evening putting the basics together, and drew this from a small subset of the taxi dropoff locations. I love how the slightly fuzzy data lines up with street locations, and looks like the skeletons of pressed leaves.
[ hat tip to: @j0el ]
It’s not surprising to receive an unsolicited job offer, but the personalization and craftsmanship that went into the language of this one takes the cronut. Maybe an autonomous car was transferred to HR?
(1) NEW OPENING WORKING WITH GOOGLE
emorisse you were chosen as a candidate for this position
Anyone ever tell you that fundraising is a small world? Drawing the graph of connections between funders and NYC companies from 2001 through the present, we see a highly connected core with most outliers representing single deals. That level of connectedness is why reputation matters so very much.
(Also thrilled to see the NYC funding ecosystem grow over the years!)
[data from CrunchBase]
There are a lot of threads around asking, “Is cloud computing less expensive than on-premise?” The short answer is: it depends. Here’s a quick outline of the why the better question is which provides more value.
In economics, there are two types of costs in production: fixed costs and variable costs. Fixed costs are independent of the level of production. Buying a server is a fixed cost independent of the number of VMs run on that server. Variable costs are the per unit costs of production. The cost of applying operating system patches is a variable cost, it is dependent upon the number of VMs. Investing in fixed costs, servers supporting higher VM density, creates an economy of scale by reducing the variable cost of providing another OS instance to your customers. This is the production side of cloud economics.
On the consumption side, moving to cloud changes the fixed Capital Expense cost of hardware to a variable Operating Expense cost. Tying the cost of the infrastructure to the lifetime of the project reduces upfront costs.
Spreading the costs over the life of the project does increase the terminal costs of the project, however this is not the problem it seems. With the cloud model, it is also simpler close down when the project is no longer providing the necessary value.
Combining the lower upfront costs and risk incurred of starting a new project by spreading the cost across the life of the project, it is easier and lower cost to begin more new projects. With the simplicity of exiting unprofitable projects, these new projects carry less organizational risk by lowering incurred legacy. More projects, at lower risk = more value creation, faster. Or, as we like to say in marketing: value creation through innovation.
With the deliberate abstraction of hardware from software in the Joint Strike Fighter (JSF) , the US and development partners are creating the potential for realizing the customization and training powers of commoditization we’ve seen in the datacenter server world.
In Boston this week planning for the new fiscal year, I stayed in a brand new boutique hotel. So brand new that the price was well below any of the local chains, and they hadn’t worked out all of their kinks.
The look and comforts of the room was excellent: clean design furniture, Lavazza in-room coffee, great shower, fireplace, big fluffy towels, the works. It was a beautiful room, and no expense was spared in making guests comfortable in this small but luxurious place. But, then I had to use the room.
Person of Interest is filming in my neighborhood today, and the crew has changed some of the street signs.
I probably would not have noticed, except this happens to be the “famous” corner where Waverley intersects with itself. Remember Kramer getting lost in the West Village and stumbling upon intersection of 1st and 1st?