Java 11 released

Java 11 has arrived. The new release is the first planned appearance of Oracle’s long-term support (LTS) releases, although Oracle have also grandfathered in Java 8 as an LTS release to help bridge the gap between the old release model and the new approach.

The feature list for the new version has only evolved modestly since InfoQ reported on this earlier in the year, and the major new features in Java 11 are:

Nest-based access controls (aka “Nestmates”): revisits the implementation of inner classes and eliminates the need for compilers to insert bridge methods.
Dynamic class-file constants (aka “condy”): reduces the cost and disruption of creating new forms of materializable class-file constants and opens the door to new performance and platform approaches.
ZGC (Experimental): a brand-new garbage collector designed for sub-10ms pause times (even on large heaps) with an aim of no more than a 15% performance penalty.
Flight Recorder: low overhead data collection framework


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Linux, Git, MetaEdit+: how 3 Finns brought versioning, models and code together

Coders are often accused of being allergic to modeling, but I’m not sure that’s true. I think we’re just allergic to wasting time, to going through the motions to satisfy some meaningless rule, without actually producing anything worthwhile. And most of all we hate having to do the same thing twice. So management-mandated post hoc UML for documentation was never going to fly.
We love our language, our frameworks and our tools, and it takes something pretty big to get us to break away from them. Modeling may be great for non-programmers, who like the visual format, but it’s no substitute for code. Usually.
The most common case I’ve seen that persuades programmers to use modeling is state machines. If…elseif or switch…case just cannot make things clear, and neither do textual DSLs, whereas graphics work well. Even Linus’s kernel GitHub contains a state machine diagram — albeit in ASCII art!
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Building your own deep learning computer is cheaper than AWS

Gorgeous interiors of your Deep Learning ComputerIf you’ve used, or are considering, AWS/Azure/GCloud for Machine Learning, you know how crazy expensive GPU time is. And turning machines on and off is a major disruption to your workflow. There’s a better way. Just build your own Deep Learning Computer. It’s 10x cheaper and also easier to use. Let’s take a closer look below.This is part 1 of 3 in the Deep Learning Computer Series. Part 2 is ‘How to build one’ and Part 3 is ‘How to benchmark performance’. Follow me on Instagram and Twitter to get the new articles. Leave questions and thoughts in comments below!Building an expandable Deep Learning Computer w/ 1 top-end GPU only costs $3kThe machine I built costs $3k and has the parts shown below. There’s one 1080 Ti GPU to start (you can just as easily use the new 2080 Ti for Machine Learning at $500 more — just


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TCPflow – Analyze and Debug Network Traffic in Linux

TCPflow is a free, open source, powerful command line based tool for analyzing network traffic on Unix-like systems such as Linux. It captures data received or transferred over TCP connections, and stores it in…
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