How to Turn a Liberal Hipster into a Global Capitalist (The Guardian) — In Zoe Svendsen’s play “World Factory at the Young Vic,” the audience becomes the cast. Sixteen teams sit around factory desks playing out a carefully constructed game that requires you to run a clothing factory in China. How to deal with a troublemaker? How to dupe the buyers from ethical retail brands? What to do about the ever-present problem of clients that do not pay? […] And because the theatre captures data on every choice by every team, for every performance, I know we were not alone. The aggregated flowchart reveals that every audience, on every night, veers toward money and away from ethics. I’m a firm believer that games can give you visceral experience, not merely intellectual knowledge, of an activity. Interesting to see it applied so effectively to business.
Oblique Strategies: Prompts for Programmers — Do it both ways. Very often doing it both ways is faster than analyzing which is best. Now you also have experimental data instead of just theoretical. Add a toggle if possible. This will let you choose later. Some mistakes are cheaper to make than to avoid.
The Responsibility We Have as Software Engineers — Where’s our Hippocratic Oath, our “First, Do No Harm?” Remember that moment when Google went from “amazing wonderful thing we didn’t have before, which makes our lives so much better” to “another big scary company and holy shit it knows a lot about us!”? That’s coming for our industry and the software engineering profession in particular.
Why Are Eight Bits Enough for Deep Neural Networks? (Pete Warden) — It turns out that neural networks are different. You can run them with eight-bit parameters and intermediate buffers, and suffer no noticeable loss in the final results. This was astonishing to me, but it’s something that’s been re-discovered over and over again.
The Great Decoupling (HBR) — The Second Machine Age is playing out differently than the First Machine Age, continuing the long-term trend of material abundance but not of ever-greater labor demand.
OpenMP Support in LLVM — OpenMP enables Clang users to harness full power of modern multi-core processors with vector units. Pragmas from OpenMP 3.1 provide an industry standard way to employ task parallelism, while ‘#pragma omp simd’ is a simple yet flexible way to enable data parallelism (aka vectorization).
GM: That Car You Bought, We’re Really the Ones Who Own It — GM’s claim is all about copyright and software code, and it’s the same claim John Deere is making about their tractors. The TL;DR version of the argument goes something like this: cars work because software tells all the parts how to operate; the software that tells all the parts to operate is customized code; that code is subject to copyright; GM owns the copyright on that code and that software; a modern car cannot run without that software; it is integral to all systems; therefore, the purchase or use of that car is a licensing agreement; and since it is subject to a licensing agreement, GM is the owner and can allow/disallow certain uses or access. In the future, manufacturers own the secondary market.
Architectural Robots (Robohub) — The concept is named ‘Minibuilders.’ This is a group of robots each performing a specific task. The first robot layers a 15 cm (6 in) footprint or foundation, while a second and a third robot print the rest of the building by climbing over the structures they already printed and laying more material over them. This design is only possible at construction scale where printed layers are solid enough to support a robotic print head.
gigo — Fetching packages in golang can be difficult, especially when juggling multiple packages and private repositories. GIGO (Gigo Installer for Go) tries to solve that problem, effectively being the golang equivalent of Python’s pip.
On Font Design (Kris Sowersby) — The many pairs of hands and eyes involved have made this typeface special for me. For the first time I don’t feel I have ownership of a typeface I have ostensibly “created.” Lovely to read about the design journey for a font.
Why We Use Go — We use Go because it’s boring. Previously, we worked almost exclusively with Python, and after a certain point, it becomes a nightmare. You can bend Python to your will. You can hack it, you can monkey patch it, and you can write remarkably expressive, terse code. It’s also remarkably difficult to maintain and slow.
Unfortunately We Have Renewed Our ICANN Accreditation — You can thank ICANN for this policy, because if it were up to us, and you tasked us with coming up with the most idiotic, damaging, phish-friendly, disaster-prone policy that accomplishes less than nothing and is utterly pointless, I question whether we would have been able to pull it off at this level. We’re simply out of our league here.
Rise of the Robots and Shadow Work (NY Times) — In “Rise of the Robots,” Ford argues that a society based on luxury consumption by a tiny elite is not economically viable. More to the point, it is not biologically viable. Humans, unlike robots, need food, health care and the sense of usefulness often supplied by jobs or other forms of work. Two thought-provoking and related books about the potential futures as a result of technology-driven change.
Time Lapse Mining from Internet Photos (PDF) — First, we cluster 86 million photos into landmarks and popular viewpoints. Then, we sort the photos by date and warp each photo onto a common viewpoint. Finally, we stabilize the appearance of the sequence to compensate for lighting effects and minimize flicker. Our resulting time-lapses show diverse changes in the world’s most popular sites, like glaciers shrinking, skyscrapers being constructed, and waterfalls changing course.
Git Repository of CS Papers — The intention here is to both provide myself with backups and easy access to papers, while also collecting a repository of links so that people can always find the paper they are looking for. Pull the repo and you’ll never be short of airplane/bedtime reading.
Software For Reproducible Science — This quality is indeed central to doing science with code. What good is a data analysis pipeline if it crashes when I fiddle with the data? How can I draw conclusions from simulations if I cannot change their parameters? As soon as I need trust in code supporting a scientific finding, I find myself tinkering with its input, and often breaking it. Good scientific code is code that can be reused, that can lead to large-scale experiments validating its underlying assumptions.