Overpass API/Overpass QL – OpenStreetMap Wiki
Sweet idea! Unfortunately looks like yet another unwieldy query language…
7 weeks ago
HOWTO: Sony Vaio Pro 13 DualBoot (Win 8 + Ubuntu Trusty 14.04) SecureBoot Wifi LVM - Page 2
Just in case you know poor owners of some Sony notebooks who try to run Linux but are booted back into Windows by Sony’s silly bootloader. (A recurring theme I see is: a) ugh, the hardware of my [Windows] computer is broken, b) fix it by installing Linux)
ubuntu  linux  windows  sony 
11 weeks ago
Bad Data - Dilbert Comic Strip on 2018-04-03
»Does the bad data suggest we should do what we wanted to do anyway? – Well Yes. – That’s called good data.«
april 2018
Meeting Rooms Are Booked - Dilbert Comic Strip on 2018-03-12
My project stalled because all of our meeting rooms have been reserved by managers just in case they need them
march 2018
A talk by Gary Bernhardt from Strange Loop 2015
programming  video 
january 2018
John Edmark - Sculptures
Cool sculptures with a mathematical twist.
34c3  sculpture 
january 2018
Computer latency: 1977-2017
Interesting analysis and nice qualifiers. » If you’ve been to a non-academic non-enterprise tech conference in the past decade, there’s a good chance that there was at least one talk on how complexity is the root of all evil and we should aspire to reduce complexity.«.
december 2017
[1712.01208] The Case for Learned Index Structures
»Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible.«
index  machinelearning 
december 2017
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