- Trusting Browser Code (Tim Bray) — on the fundamental weakness of the ‘net as manifest in the browser.
- Deep Learning in the Raspberry Pi (Pete Warden) — $30 now gets you a computer you can run deep learning algorithms on. Awesome.
- Announcing Docker Hub and Official Repositories — as Docker went 1.0 and people rave about how they use it, comes this. They’re thinking hard about “integrating into the build ship run loop”, which aligns well with DevOps-enabling tool use.
- Apple’s Secure Database for Users (Ian Waring) — excellent breakdown of how Apple have gone out of their way to make their cloud database product safe and robust. They may be slow to “the cloud” but they have decades of experience having users as customers instead of products.
ENTRIES TAGGED "raspberry pi"
Blowing open the doors to low-power, on-demand supercomputing
Packing impressive supercomputing power inside a small credit card-sized board running Ubuntu, Adapteva‘s $99 ARM-based Parallella system includes the unique Ephiphany numerical accelerator that promises to unleash industrial strength parallel processing on the desktop at a rock-bottom price. The Massachusetts-based startup recently ran a successfully funded Kickstarter campaign and gained widespread attention only to run into a few roadblocks along the way. Now, with their setbacks behind them, Adapteva is slated to deliver its first units mid-December 2013, with volume shipping in the following months.
What makes the Parallella board so exciting is that it breaks new ground: imagine an Open Source Hardware board, powered by just a few Watts of juice, delivering 90 GFLOPS of number crunching. Combine this with the possibility of clustering multiple boards, and suddenly the picture of an exceedingly affordable desktop supercomputer emerges.
This review looks in-depth at a pre-release prototype board (so-called Generation Zero, a development run of 50 units), giving you a pretty complete overview of what the finished board will look like.
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)
Insecure Hardware, Doc Database, Kids Programming, and Ad-Blocking AP
- Researchers Can Slip an Undetectable Trojan into Intel’s Ivy Bridge CPUs (Ars Technica) — The exploit works by severely reducing the amount of entropy the RNG normally uses, from 128 bits to 32 bits. The hack is similar to stacking a deck of cards during a game of Bridge. Keys generated with an altered chip would be so predictable an adversary could guess them with little time or effort required. The severely weakened RNG isn’t detected by any of the “Built-In Self-Tests” required for the P800-90 and FIPS 140-2 compliance certifications mandated by the National Institute of Standards and Technology.
- rethinkdb — open-source distributed JSON document database with a pleasant and powerful query language.
- Teach Kids Programming — a collection of resources. I start on Scratch much sooner, and 12+ definitely need the Arduino, but generally I agree with the things I recognise, and have a few to research …
- Raspberry Pi as Ad-Blocking Access Point (AdaFruit) — functionality sadly lacking from my off-the-shelf AP.
Remote Work, Raspberry Pi Code Machine, Low-Latency Data Processing, and Probabilistic Table Parsing
- Fog Creek’s Remote Work Policy — In the absence of new information, the assumption is that you’re producing. When you step outside the HQ work environment, you should flip that burden of proof. The burden is on you to show that you’re being productive. Is that because we don’t trust you? No. It’s because a few normal ways of staying involved (face time, informal chats, lunch) have been removed.
- MillWheel (PDF) — a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous ﬂow of records, all within the envelope of the framework’s fault-tolerance guarantees. From Google Research.
- Probabilistic Scraping of Plain Text Tables — the method leverages topological understanding of tables, encodes it declaratively into a mixed integer/linear program, and integrates weak probabilistic signals to classify the whole table in one go (at sub second speeds). This method can be used for any kind of classification where you have strong logical constraints but noisy data.
The Fundamental Interconnectedness of Things
A little over a week ago, I wrote about how the authentication model for an unpublished Tesla REST API was architecturally flawed because it failed to take basic precautions against the sharing of credentials with third-parties common to most REST-based services these days. Since its publication, the main criticism of the article centered around the fact that the API is neither a published API nor has it been advertised as being meant for third-party consumption.
The adding of value to devices and services with or without the knowledge/permission of their creators is an integral part of the Internet of Things. These days, people expect an API around their devices. They will discover any APIs and add value to the device/service—even if the task requires a little reverse engineering work. A responsible creator of a device or service in today’s world defined by the Internet of Things must therefore do the following things—always:
- Give it a public API
- Protect any internal communications so they can’t be reverse engineered
- Protect any public communications so that they don’t put end users at risk when they leverage third-party devices and services
Teaching Future Coders
Ever since PyCon 2013, the interest in the Young Coders class has been intensifying. Practically every Python conference since then has asked about doing one, and several have run their own. Classes outside of conferences have sprung up, as well, from one time workshops to after school clubs.
As more classes happen, more people have been asking about running their own. These classes do take quite a bit of effort to set up, but the payoff is enormous. Also, once you do one, doing subsequent ones gets easier and easier.