Prepaid talk+text+data or data-only mobile SIM cards, delivered to your home or hotel, prior to visiting the US. great service for temporary US business visits
The better solution is never to pipe untrusted data streams into bash. If you still want to run untrusted bash scripts a better approach is to pipe the contents of URL into a file, review the contents on disk and only then execute it.
Fascinating article on lullabies:
One way a mother might bond with a newborn is by sharing her joy; another way is by sharing her grief or frustration. We see this in songs across time. A 200-year-old Arabic lullaby still sung today goes: I am a stranger, and my neighbors are strangers; I have no friends in this world. Winter night and the husband is absent. And an old Spanish lullaby from Asturias, written down by the poet Federico García Lorca, goes: This little boy clinging so Is from a lover, Vitorio, May God, who gave, end my woe, Take this Vitorio clinging so. We assume the sound of these songs is sweet, as no lullaby endures without being effective at putting babies to sleep. Think of ‘‘Rock-a-bye Baby,’’ the way it tenderly describes an infant and its cradle falling to the ground: The singer gets to speak a fear, the baby gets to rest; the singer tries to accommodate herself to a possible loss that has for most of human history been relatively common, and the baby gets attentive care. In the Arabic and Spanish lullabies, the singers get to say something to the one being — their new burden, their new love — who can’t and won’t judge or discipline them for saying it. When even relatively happy, well-supported people become the primary caretaker of a very small person, they tend to find themselves eddied out from the world of adults. They are never alone — there is always that tiny person — and yet they are often lonely. Old songs let us feel the fellowship of these other people, across space and time, also holding babies in dark rooms.
lovely art via This Is Colossal
The AWS edge network has points of presence in more than 50 locations. Today, it is used to distribute content via Amazon CloudFront and to provide rapid responses to DNS queries made to Amazon Route 53. With today’s announcement, the edge network also helps to accelerate data transfers in to and out of Amazon S3. It will be of particular benefit to you if you are transferring data across or between continents, have a fast Internet connection, use large objects, or have a lot of content to upload. You can think of the edge network as a bridge between your upload point (your desktop or your on-premises data center) and the target bucket. After you enable this feature for a bucket (by checking a checkbox in the AWS Management Console), you simply change the bucket’s endpoint to the form BUCKET_NAME.s3-accelerate.amazonaws.com. No other configuration changes are necessary! After you do this, your TCP connections will be routed to the best AWS edge location based on latency. Transfer Acceleration will then send your uploads back to S3 over the AWS-managed backbone network using optimized network protocols, persistent connections from edge to origin, fully-open send and receive windows, and so forth.
Earlier this year, I asked a question on Stack Overflow about a data structure for loaded dice. Specifically, I was interested in answering this question: “You are given an n-sided die where side i has probability pi of being rolled. What is the most efficient data structure for simulating rolls of the die?” This data structure could be used for many purposes. For starters, you could use it to simulate rolls of a fair, six-sided die by assigning probability 1616 to each of the sides of the die, or a to simulate a fair coin by simulating a two-sided die where each side has probability 1212 of coming up. You could also use this data structure to directly simulate the total of two fair six-sided dice being thrown by having an 11-sided die (whose faces were 2, 3, 4, …, 12), where each side was appropriately weighted with the probability that this total would show if you used two fair dice. However, you could also use this data structure to simulate loaded dice. For example, if you were playing craps with dice that you knew weren’t perfectly fair, you might use the data structure to simulate many rolls of the dice to see what the optimal strategy would be. You could also consider simulating an imperfect roulette wheel in the same way. Outside the domain of game-playing, you could also use this data structure in robotics simulations where sensors have known failure rates. For example, if a range sensor has a 95% chance of giving the right value back, a 4% chance of giving back a value that’s too small, and a 1% chance of handing back a value that’s too large, you could use this data structure to simulate readings from the sensor by generating a random outcome and simulating the sensor reading in that case. The answer I received on Stack Overflow impressed me for two reasons. First, the solution pointed me at a powerful technique called the alias method that, under certain reasonable assumptions about the machine model, is capable of simulating rolls of the die in O(1)O(1) time after a simple preprocessing step. Second, and perhaps more surprisingly, this algorithm has been known for decades, but I had not once encountered it! Considering how much processing time is dedicated to simulation, I would have expected this technique to be better- known. A few quick Google searches turned up a wealth of information on the technique, but I couldn’t find a single site that compiled together the intuition and explanation behind the technique.(via Marc Brooker)