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Dave Megginson (who drove the development of the SAX API that will be familiar to many XML developers who use Java) recently wrote Java is dead. Java stood out as a programming language (though not as a platform) in that Sun had refused to standardize it through an independent and reputable standards organization (a lot of the hard work had been done in one attempt to put it through ECMA and one to put it through ISO, both times Sun pulled out and eventually made their highly unsatisfactory JCP Java Community Process system.) Without the ability to alter Java significantly in ways that might go against their druthers, Java suffered two major forks (Microsoft's J++ then its C#, and IBM's SWT) where significant players disagreed with a major component (the graphics library). Java succeeded in middleware, and but failed to take advantage of the rise of browsers on the deskop: their HTML parser was great for the middle 1990s but was deliberately neglected to the point of being unusable: it is hard not to see this as a deliberate attempt by Sun to leave the browser market to its friends and enemies. I really liked Java, and bet my company on it (in a sense): I would not do that today.

As many long time readers know, I am an enthusiast of the F# programming language.  I make no apologies for the fact that, if you are developing software on the .NET platform, F# is one of the better choices you can make for numerous reasons.  It is one of the reasons I proudly contributed as a co-author to the book, Professional F# 2.0, which is being published by Wrox in October. Some of the oft cited benefits of F# are that, to distill them quickly, it is good at doing intensely mathematical operations, it is built for parallelism, and it is good at helping define domain specific languages.  Those benefits are so often cited by speakers on the F# speaker circuit that they pretty much seem cliche to me at this point (note, yours truly is proud to call himself a member of said circuit, and often gives this talk!)  As great as these features are, there are a couple features, that in my more mundane F# experiences, seem to stand out as things that “save my ass”, for lack of a better phrase, more often than not.

The latest episode of the Robots Podcast looks at the following scenario: Imagine being able to throw a hand-full of smart matter in a tank full of liquid and then pulling out a ready-to-use wrench once the matter has assembled. This is the vision of this episode's guests Michael Tolley and Jonas Neubert from the Computational Synthesis Laboratory run by Hod Lipson at Cornell University, NY. Tolley and Neubert give an introduction into Programmable Matter and then present their research on stochastic assembly of matter in fluid, including both simulation (see video above) and real-world implementation. Read on or tune in!

We had to cover monads eventually, and there are many great monad tutorials out there (see, for example, here: http://www.haskell.org/haskellwiki/Tutorials#Using_monads). In fact, there are web resources concerned solely with organizing the many monad tutorials available in the wild, and developing new monad tutorials seems to be a popular sport in the Haskell community.Today, Ralf Lämmel's lecture goes back to the roots, essentially revisiting Wadler's "The essence of functional programming"—the 1992 paper that discovered monads and popularized their use in functional programming. Ralf Lämmel's lecture and accompanying code distribution show Wadler's seminal insight: those original scenarios and observations still make sense today. Indeed, Simon Marlow (a Haskell/GHC high priest @ MSR Cambridge) recently noted: "it's still the best monad tutorial" (see http://twitter.com/simonmar/status/21397398061).

Microsoft Technical Fellow and C# creator Anders Hejlsberg explains the new C# and VB.NET asynchronous programming model, available as Async CTP now, which makes async programming much easier for .NET developers.

One of the common discussions that I've had with several colleagues when we're making use of some of the higher order functions that can be applied on collections is whether to use the LINQ style syntax or to chain the different methods together. I tend to prefer the latter approach although when asked the question after my talk at Developer Developer Developer I didn't really have a good answer other than to suggest that it seemed to just be a personal preference thing. Damian Marshall suggested that he preferred the method chaining approach because it more clearly describes the idea of passing a collection through a pipeline where we can apply different operations to that collection.

Flare is an ActionScript library for creating visualizations that run in the Adobe Flash Player. From basic charts and graphs to complex interactive graphics, the toolkit supports data management, visual encoding, animation, and interaction techniques. Even better, flare features a modular design that lets developers create customized visualization techniques without having to reinvent the wheel. View the demos and sample applications to see a few of the visualizations that flare makes it easy to build. To begin making your own visualizations, download flare and work through the tutorial. You should also get familiar with the API documentation. Need more help? Visit the help forum (you'll need a SourceForge login to post). Flare is open-source software released under a BSD license, meaning it can be freely deployed and modified (and even sold for $$). Flare's design was adapted from its predecessor prefuse, a visualization toolkit for Java

The Control Gallery is a directory of over 900 controls and components to use in your own applications. You will find everything from simple controls to full e-commerce components.

Piotr Zalewa has created a really great playground, jsFiddle, for testing sample code and playing with the Web. With an area for the holy trinity of the Web (HTML, CSS, JS) and an output region, you can get right to hacking. It goes beyond this though. You can also add resources, an Ajax echo backend, and auto load from a slew of JavaScript frameworks. You can also check out the examples and see great stuff such as Processing in action. And the finishing touch, share and embed. Piotr wrote all of this using CodeMirror and MooTools. Nice! Having worked on Bespin, and developed a playground like this (looking forward to show a new mobile one soon!) I appreciate the work!

TenAsys Corporation, a provider of real-time OS and virtualization software, has announced that all of its embedded virtualization software products, including the INtime real-time OS for Windows, provide full support for new 2010 Intel Core processors and companion chipsets for the embedded market.

OpenGL 3.1 introduced two new sources from where shaders can retrieve their data, namely uniform buffers and texture buffers. These can be used to accelerate rendering when heavy usage of application provided data happens like in case of skeletal animation, especially when combined with geometry instancing. However, even if the functionality is in the core specification for about a year now, there are few demos out there to show their usage, as so, there is a big confusion around when to use them and which one is more suitable for a particular use case. Both AMD and NVIDIA have updated their GPU programming guides to present the latest facilities provided by both OpenGL and DirectX, however I still see that people don’t really understand how they work and that prevents them from effectively taking advantage of these features. Once, at some online forum, I found somebody arguing why is this whole confusion introduced by the Khronos Group and why there is no general buffer type to use instead and the decision whether to use uniform or texture buffers should be a decision made by the driver. This particular post motivated me to write this article.

F# is Microsoft's first functional programming language to be included as one of Visual Studio's official set of languages. F# is a succinct, efficient, expressive functional/object-oriented programming language under joint development by Microsoft Developer Division and Microsoft Research. During the course of Erik Meijer's fantastic lecture series on functional programming fundamentals several of you asked for examples of specific topics in F#. Well, we listened. Dr. Don Syme is a principal researcher in MSR Cambridge. He has a rich history in programming language research, design, and implementation (C# generics being one of his most recognized implementations), and is the principle creator of F#. Who better to lecture on the topic than Don? This three part series will serve as an introduction to F#, including insights into the rationale behind the history and creation of Microsoft's newest language.

Verification of algorithm-intensive systems is a long, costly process. Studies show that the majority of flaws in embedded systems are introduced at the specification stage, but are not detected until late in the development process. These flaws are the dominant cause of project delays and a major contributor to engineering costs. For algorithm-intensive systems —including systems with communications, audio, video, imaging, and navigation functions— these delays and costs are exploding as system complexity increases. It doesn't have to be this way. Many designers of algorithm-intensive systems already have the tools they need to get verification under control. Engineers can use these same tools to build system models that help them find and correct problems earlier in the development process. This can not only reduce verification time, but also improves the performance of their designs. In this article, we'll explain three practical approaches to early verification that make this possible. First, let's examine why the current algorithm verification process is inefficient and error-prone. In a typical workflow, designs start with algorithm developers, who pass the design to hardware and software teams using specification documents.

Today I'm excited to share the project a small team of amazing people and I have been working on for the past two years; HipHop for PHP. With HipHop we've reduced the CPU usage on our Web servers on average by about fifty percent, depending on the page. Less CPU means fewer servers, which means less overhead. This project has had a tremendous impact on Facebook. We feel the Web at large can benefit from HipHop, so we are releasing it as open source this evening in hope that it brings a new focus toward scaling large complex websites with PHP. While HipHop has shown us incredible results, it's certainly not complete and you should be comfortable with beta software before trying it out. HipHop for PHP isn't technically a compiler itself. Rather it is a source code transformer. HipHop programmatically transforms your PHP source code into highly optimized C++ and then uses g++ to compile it. HipHop executes the source code in a semantically equivalent manner and sacrifices some rarely used features — such as eval() — in exchange for improved performance. HipHop includes a code transformer, a reimplementation of PHP's runtime system, and a rewrite of many common PHP Extensions to take advantage of these performance optimizations.

The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

From Nic:   We're piloting a new show idea on Wednesday called Help Desk where we put together a group of Windows trouble shooting gurus and get them to answer your Windows PC issues live on the air...   Any Niners out there have Windows PC issues/questions they need some help with?   Send to ch9live(AT)microsoft(DOT)com or tweet us @ch9live and make sure to watch the show live or check out the broadcast afterwards on demand.

Running languages on .NET is ultra-powerful. Using managed COBOL (from Micro Focus), it is possible to use F# code to work with COBOL code. Imagine a Cloud based F# map reduce system consuming legacy COBOL - yes, that really is on the horizon.

Andrew Phillips holds the title of Scientist with Microsoft Research Cambridge, and he's working on a method of programming that compiles into DNA. Part of this involves a visual programming language called Stochastic Pi Machine, or SPiM. This system models biological processes to help give researchers feedback on how organisms will react to modifications. The hope is that this can be used to help scientists program for large biological systems using modular components compiled to DNA. Yes, I’m in way over my head here, but I do my best to ask Andrew about the role this will play in medical treatment going forward, what it means to DNA computing, and the ability of back-engineering the genetic code we don’t use now

This API provides users with a standard FPGA communication interface from C++ code. It is intended to encourage more widespread adoption of FPGAs and reconfigurable computing platforms—particularly among Windows application developers