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  • Google I/O 2012 - Building Android Applications that Use Web APIs

    Google I/O 2012 - Building Android Applications that Use Web APIs Yaniv Inbar Google offers a large and growing set of back-end services, from AdSense to Tasks to Calendar to Google+, that can enrich your app, and increasingly they have a uniform set of APIs. This session discusses how to use them efficiently and securely, including authenticating safely and with good user experience, and describes Android-specific app-level optimizations. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 563 12 ratings Time: 55:14 More in Science & Technology

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  • Is there a modern tutorial for setting up SSL on apache2?

    - by John Baber
    I've been running apache2 for ages on my ubuntu server without SSL. Now that I want to have some directories delivered by SSL, I can't find any straightforward tutorials that were written recently. The best I've found is http://vanemery.com/Linux/Apache/apache-SSL.html but it tells me to put stuff in /etc/httpd/conf I don't want to guess that that should translate to /etc/apache2/conf because guessing based on old tutorials has ruined my web serving before.

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  • Auto-generate Visual Studio Project Documentation with GhostDoc

    GhostDoc is a free Visual Studio extension that automates the process of writing code comments. Find out how you can use it to document your code automatically....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Google I/O 2012 - Google Play: Marketing 101 for Developers

    Google I/O 2012 - Google Play: Marketing 101 for Developers Patrick Mork, Kushagra Shrivastava As soon as you hit the "Publish" button on your app, you become (partly) a marketer; you might as well try to be a good one. We'll share everything we know about promoting apps on Google play: building a strategic marketing framework, making good use of media channels, taking advantage of the assets we've built for developers, and convincing the Play team to feature your app. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1522 15 ratings Time: 56:13 More in Science & Technology

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  • indexing and crawling

    - by ricky
    hello mate my site is dailytopup.com...earlier my site was indexed imediately i post anything but last month my website was crashed due to sever problem and i adont have back up at that time so i recover everything from cached copies but before doing that i remove old urls from the webmaster and then repost again.but after that my website is not indexed properly reaults in no optimsation.everytime i have to use fetch as google but this is not that effective..can you please tell where um lacking or what should i do now?

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  • Google I/O 2012 - Building Mobile App Engine Backends for Android, iOS and the Web

    Google I/O 2012 - Building Mobile App Engine Backends for Android, iOS and the Web Dan Holevoet, Christina Ilvento Mobile application development is growing at explosive rates and the best of those applications have a backend server. Find out how you can use App Engine's new feature to build powerful APIs to support mobile applications running on Android, iOS, and mobile browsers. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1783 43 ratings Time: 48:38 More in Science & Technology

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  • Google Top Geek E05

    Google Top Geek E05 In Spanish! Google Top Geek (GTG) es un show semanal que generamos desde México con noticias, las tendencias en búsquedas y YouTube en América Latina, así como referencias a apps y eventos interesantes. GTG se transmite los lunes al medio día, 12 pm, desde Google Developers Live. Guión del programa Esta semana 1. Geeks interactuando y socializando en el mundo real, eso justamente es lo que ha logrado el juego masivo Ingress que liberó Google recientemente. Tienen que escoger un bando: resistance o enlightened, el proyecto Niantic. Campos de energía, elementos, intriga, combate, ... Y lo mejor de todo: mucha diversión. Cuando obtengan su código, si están del lado correcto, pueden encontrarnos en Ingress Enlightened Latin America +page en Google+. 2. Reality show para desarrolladores en Argentina: +Next Level, 40 estudiantes y profesionales de TI trabajarán siete días con cámaras todo el tiempo, expertos de toda América Latina via Google Hangouts... Del 26 de noviembre al 2 de diciembre, en la ciudad de Tandil. 3. Google Apps for Business Un tema relativamente nuevo en el mundo empresarial en nuestra región es la nube y cómo aprovecharla mejor. Google Apps for Business es un servicio basado en la nube que provee Mensajería y Colaboración a través de los productos que todos conocemos de Google pero con el nivel de controles y auditoría que requieren las empresas. El enfoque de Google es y siempre ha sido la satisfacción de nuestros usuarios y Google Apps for Business le <b>...</b> From: GoogleDevelopers Views: 1 0 ratings Time: 15:39 More in Science & Technology

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  • Deploying website content via Subversion

    - by Johann
    we have recently set up a new development infrastructure and process for one of our clients. This involves the strict use of subversion as a central source code repository. The svn repositories contains a seperate branch for code on the live system (/branches/live/). The repositories are use for PHP content (mainly Wordpress Blogs), but in future they may hold other asp code as well. Bonus points for a solutions which more or less in the same way with ASP code on Windows Server 2008 R2. We have two servers: one staging system and one live system. The staging system is updated regularly with the code of the trunk. The live system is update manually. Each webroot on the servers are working copy of either the trunk (staging system) or the live branch (live system). The current workflow is: Developing on the dev's box - commit into the trunk - auto-deploy on staging system - testing on the staging system - merging into /branches/live/ - manual deployment on live system. This works for one-way changes very well, however we have some troubles on every wordpress (or plugin) update: The WP update process removes the directories and unpack the archive of the new version. This removes the svn admin area as well, which produces a lot of errors. We could switch to SVN 1.7 with a single, global admin area, but this would only solve on part of the problem. Finally, we have done the update via the WP Gui, restored the svn admin area, added/removed the files and committed the changes to the trunk. After testing, we had to do basically the same thing on the live server (except the commit, we just reverted the changes and merged the new files from the staging system to the live system). I'm currently thinking of the following: The htdocs of each website is a svn export Each website has a svn working copy beside the htdocs directory a script which "replays" the changes in the wc from htdocs after an update in WP (rsync'ing the changed files to the working copy, rsync'ing new files and svn add them and finally svn delete the deleted files). The script would have to exclude some files (like wp-config.php, uploads/temp directories, etc.). Are there better ways to do this? Unfortunaly, a complete CI server is out of scope due to time and budget limitations.

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  • Various issues linked to my CD drive, when it has a disc in it

    - by Voyagerfan5761
    When I go to the Desktop and click on a media icon (for my flash drive, a CD, whatever it is), the following problems occur, in this approximate sequence: Nautilus will close if it's open. the desktop icons disappear my Window List shows a button that says "Starting File Manager" the icons reappear the button in Window List disappears Because of this problem, I can no longer drag and drop media, nor can I right-click to perform actions such as "Eject" and "Safely Remove Drive". The same symptoms occur if I click a media icon (that is also present on the desktop) in Nautilus' Computer view, though notably not if I click in the places list on the left. I have confirmed that this problem happens only if there is a CD in the drive (Matshita UJDA360). Also, inserting a disc into the CD drive appears to kill all running programs and restart Nautilus (or X; I'm not sure). Applications like Brasero and Rhythmbox will not start while there is a disc in the drive. Removing the disc doesn't result in the list of media updating; it must be forced to update by clicking on one of the desktop icons and going through one of the above-described cycles. It doesn't seem to matter what type of disc is in the drive. This has happened with CD-RWs I burned years ago using Roxio on Windows XP, the Ubuntu disc I installed from (burned with InfraRecorder Portable under Windows XP), and the retail game disc for Star Trek Armada II. The first indication of a problem was Brasero dying when I tried to insert a disc for erasure and rewriting. Since then, I've drafted several different questions on various issues, finally combining them into this one when I realized that having a CD in the drive was the common link. Could this be a simple driver issue? If Ubuntu is dynamically detecting my hardware on boot, can I specify drivers for devices that I know will be a problem if the default files are used? I'm beginning to think that my laptop, an old Dell Inspiron 2650, is just too old or proprietary-driver-hungry (or something, maybe RAM-starved) for Ubuntu and Windows XP to play nicely alongside each other. Or maybe I just need to carefully take my wall-wart machine to a coffee shop for an afternoon so I can download updates and such from the Internet, as I lack a home connection.

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  • Apps Script Office Hours - September 13, 2012

    Apps Script Office Hours - September 13, 2012 In this week's episode of Google Apps Script office hours, Jan and Arun: - Introduce the Google Apps Script app that was recently published in the Chrome Web Store: chrome.google.com - Answer a variety of questions from the Google Moderator. - Answer live questions about UiApp, triggers, ScriptDb, and other topics. To find out when the next office hours will be held, visit developers.google.com From: GoogleDevelopers Views: 221 7 ratings Time: 17:26 More in Science & Technology

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  • Running C++ AMP kernels on the CPU

    - by Daniel Moth
    One of the FAQs we receive is whether C++ AMP can be used to target the CPU. For targeting multi-core we have a technology we released with VS2010 called PPL, which has had enhancements for VS 11 – that is what you should be using! FYI, it also has a Linux implementation via Intel's TBB which conforms to the same interface. When you choose to use C++ AMP, you choose to take advantage of massively parallel hardware, through accelerators like the GPU. Having said that, you can always use the accelerator class to check if you are running on a system where the is no hardware with a DirectX 11 driver, and decide what alternative code path you wish to follow.  In fact, if you do nothing in code, if the runtime does not find DX11 hardware to run your code on, it will choose the WARP accelerator which will run your code on the CPU, taking advantage of multi-core and SSE2 (depending on the CPU capabilities WARP also uses SSE3 and SSE 4.1 – it does not currently use AVX and on such systems you hopefully have a DX 11 GPU anyway). A few things to know about WARP It is our fallback CPU solution, not intended as a primary target of C++ AMP. WARP stands for Windows Advanced Rasterization Platform and you can read old info on this MSDN page on WARP. What is new in Windows 8 Developer Preview is that WARP now supports DirectCompute, which is what C++ AMP builds on. It is not currently clear if we will have a CPU fallback solution for non-Windows 8 platforms when we ship. When you create a WARP accelerator, its is_emulated property returns true. WARP does not currently support double precision.   BTW, when we refer to WARP, we refer to this accelerator described above. If we use lower case "warp", that refers to a bunch of threads that run concurrently in lock step and share the same instruction. In the VS 11 Developer Preview, the size of warp in our Ref emulator is 4 – Ref is another emulator that runs on the CPU, but it is extremely slow not intended for production, just for debugging. Comments about this post by Daniel Moth welcome at the original blog.

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  • parallel_for_each from amp.h – part 1

    - by Daniel Moth
    This posts assumes that you've read my other C++ AMP posts on index<N> and extent<N>, as well as about the restrict modifier. It also assumes you are familiar with C++ lambdas (if not, follow my links to C++ documentation). Basic structure and parameters Now we are ready for part 1 of the description of the new overload for the concurrency::parallel_for_each function. The basic new parallel_for_each method signature returns void and accepts two parameters: a grid<N> (think of it as an alias to extent) a restrict(direct3d) lambda, whose signature is such that it returns void and accepts an index of the same rank as the grid So it looks something like this (with generous returns for more palatable formatting) assuming we are dealing with a 2-dimensional space: // some_code_A parallel_for_each( g, // g is of type grid<2> [ ](index<2> idx) restrict(direct3d) { // kernel code } ); // some_code_B The parallel_for_each will execute the body of the lambda (which must have the restrict modifier), on the GPU. We also call the lambda body the "kernel". The kernel will be executed multiple times, once per scheduled GPU thread. The only difference in each execution is the value of the index object (aka as the GPU thread ID in this context) that gets passed to your kernel code. The number of GPU threads (and the values of each index) is determined by the grid object you pass, as described next. You know that grid is simply a wrapper on extent. In this context, one way to think about it is that the extent generates a number of index objects. So for the example above, if your grid was setup by some_code_A as follows: extent<2> e(2,3); grid<2> g(e); ...then given that: e.size()==6, e[0]==2, and e[1]=3 ...the six index<2> objects it generates (and hence the values that your lambda would receive) are:    (0,0) (1,0) (0,1) (1,1) (0,2) (1,2) So what the above means is that the lambda body with the algorithm that you wrote will get executed 6 times and the index<2> object you receive each time will have one of the values just listed above (of course, each one will only appear once, the order is indeterminate, and they are likely to call your code at the same exact time). Obviously, in real GPU programming, you'd typically be scheduling thousands if not millions of threads, not just 6. If you've been following along you should be thinking: "that is all fine and makes sense, but what can I do in the kernel since I passed nothing else meaningful to it, and it is not returning any values out to me?" Passing data in and out It is a good question, and in data parallel algorithms indeed you typically want to pass some data in, perform some operation, and then typically return some results out. The way you pass data into the kernel, is by capturing variables in the lambda (again, if you are not familiar with them, follow the links about C++ lambdas), and the way you use data after the kernel is done executing is simply by using those same variables. In the example above, the lambda was written in a fairly useless way with an empty capture list: [ ](index<2> idx) restrict(direct3d), where the empty square brackets means that no variables were captured. If instead I write it like this [&](index<2> idx) restrict(direct3d), then all variables in the some_code_A region are made available to the lambda by reference, but as soon as I try to use any of those variables in the lambda, I will receive a compiler error. This has to do with one of the direct3d restrictions, where only one type can be capture by reference: objects of the new concurrency::array class that I'll introduce in the next post (suffice for now to think of it as a container of data). If I write the lambda line like this [=](index<2> idx) restrict(direct3d), all variables in the some_code_A region are made available to the lambda by value. This works for some types (e.g. an integer), but not for all, as per the restrictions for direct3d. In particular, no useful data classes work except for one new type we introduce with C++ AMP: objects of the new concurrency::array_view class, that I'll introduce in the post after next. Also note that if you capture some variable by value, you could use it as input to your algorithm, but you wouldn’t be able to observe changes to it after the parallel_for_each call (e.g. in some_code_B region since it was passed by value) – the exception to this rule is the array_view since (as we'll see in a future post) it is a wrapper for data, not a container. Finally, for completeness, you can write your lambda, e.g. like this [av, &ar](index<2> idx) restrict(direct3d) where av is a variable of type array_view and ar is a variable of type array - the point being you can be very specific about what variables you capture and how. So it looks like from a large data perspective you can only capture array and array_view objects in the lambda (that is how you pass data to your kernel) and then use the many threads that call your code (each with a unique index) to perform some operation. You can also capture some limited types by value, as input only. When the last thread completes execution of your lambda, the data in the array_view or array are ready to be used in the some_code_B region. We'll talk more about all this in future posts… (a)synchronous Please note that the parallel_for_each executes as if synchronous to the calling code, but in reality, it is asynchronous. I.e. once the parallel_for_each call is made and the kernel has been passed to the runtime, the some_code_B region continues to execute immediately by the CPU thread, while in parallel the kernel is executed by the GPU threads. However, if you try to access the (array or array_view) data that you captured in the lambda in the some_code_B region, your code will block until the results become available. Hence the correct statement: the parallel_for_each is as-if synchronous in terms of visible side-effects, but asynchronous in reality.   That's all for now, we'll revisit the parallel_for_each description, once we introduce properly array and array_view – coming next. Comments about this post by Daniel Moth welcome at the original blog.

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