- 12 Predictions About the Future of Programming (Infoworld) — not a bad set of predictions, except for the inane “squeezing” view of open source.
- Conceal (Github) — Facebook Android tool for apps to encrypt data and large files stored in public locations, for example SD cards.
- Dreamliner Software — all three of the jet’s navigation computers failed at the same time. “The cockpit software system went blank,” IBN Live, an Indian television station, reported. The Internet of Rebooting Things.
- Contiki — open source connective OS for IoT.
"future of programming" entries
AI Lecture, Programming Provocation, Packet Laws, and Infrared Photography
- Analogy as the Core of Cognition (YouTube) — a Douglas Hofstadter lecture at Stanford.
- Why Isn’t Programming Futuristic? (Ian Bicking) — delicious provocations for the future of programming languages.
- Border Check — visualisation of where your packet go, and the laws they pass through to get there.
- Pi Noir — infrared Raspberry Pi camera board. (via DIY Drones)
Mobile Image Cache, Google on Net Neutrality, Future of Programming, and PSD Files in Ruby
- How to Easily Resize and Cache Images for the Mobile Web (Pete Warden) — I set up a server running the excellent ImageProxy open-source project, and then I placed a Cloudfront CDN in front of it to cache the results. (a how-to covering the tricksy bits)
- Google’s Position on Net Neutrality Changes? (Wired) — At issue is Google Fiber’s Terms of Service, which contains a broad prohibition against customers attaching “servers” to its ultrafast 1 Gbps network in Kansas City. Google wants to ban the use of servers because it plans to offer a business class offering in the future. […] In its response [to a complaint], Google defended its sweeping ban by citing the very ISPs it opposed through the years-long fight for rules that require broadband providers to treat all packets equally.
- The Future of Programming (Bret Victor) — gorgeous slides, fascinating talk, and this advice from Alan Kay: I think the trick with knowledge is to “acquire it, and forget all except the perfume” — because it is noisy and sometimes drowns out one’s own “brain voices”. The perfume part is important because it will help find the knowledge again to help get to the destinations the inner urges pick.
- psd.rb — Ruby code for reading PSD files (MIT licensed).
Microvideos for MIcrohelp, Organic Search, Probabilistic Programming, and Cluster Management
- How to Make Help Microvideos For Your Site (Alex Holovaty) — Instead of one monolithic video, we decided to make dozens of tiny, five-second videos separately demonstrating features.
- How Google is Killing Organic Search — 13% of the real estate is organic results in a search for “auto mechanic”, 7% for “italian restaurant”, 0% if searching on an iPhone where organic results are four page scrolls away. SEO Book did an extensive analysis of just how important the top left of the page, previously occupied by organic results actually is to visitors. That portion of the page is now all Google. (via Alex Dong)
- Church — probabilistic programming language from MIT, with tutorials. (via Edd Dumbill)
- mesos — a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, MPI, Hypertable, Spark (a new framework for low-latency interactive and iterative jobs), and other applications. Mesos is open source in the Apache Incubator. (via Ben Lorica)
Probabilistic languages can free developers from the complexities of high-performance probabilistic inference.
Probabilistic programming languages are in the spotlight. This is due to the announcement of a new DARPA program to support their fundamental research. But what is probabilistic programming? What can we expect from this research? Will this effort pay off? How long will it take?
A probabilistic programming language is a high-level language that makes it easy for a developer to define probability models and then “solve” these models automatically. These languages incorporate random events as primitives and their runtime environment handles inference. Now, it is a matter of programming that enables a clean separation between modeling and inference. This can vastly reduce the time and effort associated with implementing new models and understanding data. Just as high-level programming languages transformed developer productivity by abstracting away the details of the processor and memory architecture, probabilistic languages promise to free the developer from the complexities of high-performance probabilistic inference. Read more…
Unraveling what programming will need for the next 10 years.
Programming is changing. The PC era is coming to an end, and software developers now work with an explosion of devices, job functions, and problems that need different approaches from the single machine era. In our age of exploding data, the ability to do some kind of programming is increasingly important to every job, and programming is no longer the sole preserve of an engineering priesthood.Over the course of the next few months, I’m looking to chart the ways in which programming is evolving, and the factors that are affecting it. This article captures a few of those forces, and I welcome comment and collaboration on how you think things are changing.
Where am I headed with this line of inquiry? The goal is to be able to describe the essential skills that programmers need for the coming decade, the places they should focus their learning, and differentiating between short term trends and long term shifts. Read more…