Always had plans to show off your amazing vacation on Facebook but never got around to picking which photos to share? Use Sifterr to pick the best and share them!
Order Prints with Sifterr
Just returned from vacation? Use Sifterr to decide which vacation pics are worthy of being committed to physical prints – then order your prints right in the app!
Also check out our “how to sift an album” post to find out how to sift through just your vacation photos.
Sift an album with Sifterr
Just got back from vacation and need to clean up your bad photos, or decide which ones to share? Sift through your “vacation” album! With Sifterr 2.0, you can sift through an album instead of sifting through your entire photo library.
Clean Up Your Photo Library with Sifterr
Have blurry, dark or otherwise forgettable photos clogging up your photo library and consuming space on your phone? Use Sifterr to clean up your library in your spare time – while you’re standing in line to get coffee or riding the subway! Download Sifterr now on the App Store®!
Announcing Sifterr 2.0
Making Sense of Apple’s Photo Syncing Ecosystem
As photos have grown in importance on mobile devices, Apple has introduced a variety of different ways to keep photo libraries synced across multiple devices. With so many options, it’s often difficult to know which makes the most sense for you. Here we give some of the major pros and cons of each of the three primary methods of syncing libraries across devices – iCloud Photo Library (the most recent addition), My Photo Stream, and syncing from iTunes. Tap a cell in the table to learn more about a particular feature of each syncing method.
FOLDING(IN)THECLOUD – AWS’s New C4 Instances
Late last year, we began running tests to see how efficient Amazon’s AWS cloud services are at protein folding, the computationally heavy physical simulations used in a variety of medical research. We discovered a remarkable result – under certain conditions, AWS is cheaper than the tiny additional cost of electricity you’d consume running the simulations at home.
Given this discovery, we were anxious to continue folding (and we still are – follow us on Twitter to learn about our findings on Google’s Compute platform and Microsoft’s Azure). We were especially excited about Amazon’s announcement of the c4 instance type, the successor to the c3 (the type of computer which showed the best performance in our previous tests). These machines offer a new, faster processor which we hoped would translate into even more folding efficiency.A Minor Update
After collecting the data, however, we were underwhelmed – the c4 instances barely edged out their c3 counterparts in most cases. Other benchmarks (not protein-folding specific) have found a larger performance increase, making our results even more surprising.
Compare the performance of each c3 machine with its c4 counterpart below. Mouse over each bar to see its exact value and select different headers to see the values that went into calculating folding per dollar.Folding points per dollar = Folding points per hour / Dollars per hour
See our original post to learn about our methodology.
Many Possible Explanations
There are many reasons the new c4 instances might not have delivered the performance boost we hoped for. Maybe folding@home is limited by memory more than by CPU (this seems unlikely, given that our original test showed the memory-optimized r3 instances underperforming the c3). Maybe folding@home is not taking full advantage of the c4's 36 vCPUs. Or perhaps a more statistically rigorous test would show larger gains.
Are you a folding@home aficionado, or do you live and breathe cloud computing? We'd love to hear your thoughts - did you expect larger improvements? Why do you think we saw the results we did? Leave a comment below, or tweet us!
FOLDING(IN)THECLOUD – Folding@Home’s Sweet Spot on Amazon’s AWS
At Maple Avenue Labs, we depend on the magic of cloud computing, the growing industry that makes it possible to rent, rather than buy, computer infrastructure. It powers our first product, huelab.me, and allows us to quickly and cheaply process photos to create personalized infographics / abstract works of art.
Earlier this year, we started investigating the effectiveness of using cloud computing for good by renting cloud computers to run folding@home. folding@home is a “distributed computing” project – anyone from across the globe can donate their computer’s processing power to run physical simulations of protein folding. By simulating how proteins take shape, folding@home is able to understand how diseases act and design drugs to fight them.A Remarkable Result
huelab.me runs in Amazon’s cloud, AWS. AWS offers a variety of different computers, and we asked – which is the most cost effective for protein folding? We spent weeks running tests across different computer types and discovered an amazing fact. AWS offers a processor so efficient at protein folding that it is cheaper than the cost of the additional electricity your home computer consumes in order to run folding.
That’s so remarkable it’s worth repeating.When a computer isn’t doing anything it consumes a certain amount of electricity.
When it is doing computation, it consumes slightly more electricity.
That tiny extra bit of electricity has some cost.We measured how much folding our home computer could accomplish with one dollar’s worth of extra electricity. With that same dollar, you could instead rent processing power from Amazon and achieve more protein folding.Does this mean you should shut down the folding@home program on your home computer? Absolutely not.First, we were paying New York City electricity rates, some of the highest in the country (about $0.30 / kWh). Second, we were running folding@home on a 2009 Mac Mini – a 2011 MacBook Pro accomplished more folding per dollar than our Amazon machine.folding@home is not expensive, and the 2009 Mac Mini is not inefficient. While folding, the Mini consumed only about half as much as electricity as a 60W light bulb. All this tells us is that cloud computing is – for some applications – insanely cheap.
How to Contribute
So if you’re not contributing to folding@home yet, you should. It’s quick and easy to get started. With just a few clicks you’ll be contributing to the fight against cancer, Alzheimer’s, Parkinson’s, and more. Go to folding.stanford.edu to learn more – or, if you use the Chrome web browser, you can contribute at folding.stanford.edu/nacl/ without installing any software.
After running these tests, we rented AWS machines for hundreds of hours of additional folding. We have completed more than 1,000 work units worth more than 10,000,000 points. Follow our team’s continued folding progress.Future Work
Amazon recently announced a new generation of machine types. These are not yet available, but we expect them to perform even better than the current machines. When they become available, we will run tests on these new machines and update our results.
[In a follow-up post, we ran these same tests on Amazon’s newest machine type, the c4, and saw only modest improvements. See the performance of c4.8xlarge against the original machines below, in the results section.]In addition, we plan to run similar tests on Microsoft’s Azure platform and compare the efficiency of AWS machines to Azure.We will update our results when those tests are complete – follow our Twitter account to stay updated.
Keep your photos organized in 2015
If you’re like most people, you’re probably making resolutions for the new year – and there’s a good chance those resolutions include the word “organize”. Here is a simple three step method to keep your digital photos organized using Sifterr!
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Pick your tags.
Think about who or what you take lots of pictures of – “food”, “outdoors”, “cats”, or “Erik”, for example. What do you wish you could find more easily? Select a handful of labels that you think will be most useful for YOUR photos.
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Sift your photos.
Throughout the month of January, sift your existing photos for each of your tags. Resolve to replace all the time you’d normally waste on your phone – on social media or games – with sifting time. In two minutes you can easily sift 100 photos, and soon you’ll have your entire existing collection organized.
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Keep your photos sifted.
Now it’s only a matter of maintenance. Each week, spend just a few minutes sifting any new photos you’ve taken. Now you can easily find the photos you’re looking for!
Sharing Holiday Photos with Sifterr
Sifterr users already know how to bring order to their photo collections one swipe at a time. Because Sifterr stores your sifted photos as albums, you can do much more than just organize – for example, Sifterr can help share photos to Facebook!
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When you share photos from the Facebook app, you’ll be shown a dialog allowing you to select which photos to share. Notice a drop down at the top of the screen – the default selection is “Camera Roll”.
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Tap this drop down to see a list of all your Sifterr tags. Select a tag.
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All photos with the selected tag are displayed. Select which tagged photos you’d like to share.
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After you’ve selected which photos to share, you’ll have the opportunity to add a description, captions, and change who you’d like to share with.This is just one example of how you can use the tags you create in Sifterr. What other ways do you use your sifted photos?