Painless Functional Specifications – Part 1: Why Bother? – Joel on Software

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

Three giant, important reasons why you need to write good specs – and the real reason why people don’t.

Source: Painless Functional Specifications – Part 1: Why Bother? – Joel on Software

Joel Spolsky explains why you really shouldn’t write even a single line of code before you wrote the specs (rudimentary as they may be). ’nuff said.

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The Art of Readable Code – a recomendation

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

The Art of Readable Code (Theory in Practice) , Dustin Boswell, Trevor Foucher

As programmers, we’ve all seen source code that’s so ugly and buggy it makes our brain ache. Over the past five years, authors Dustin Boswell and Trevor Foucher have analyzed hundreds of examples of “bad code” (much of it their own) to determine why they’re bad and how they could be improved. Their conclusion? You need to write code that minimizes the time it would take someone else to understand it—even if that someone else is you.

Continue reading “The Art of Readable Code – a recomendation”

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Matching Wildcards: An Empirical Way to Tame an Algorithm

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

Optimizing and testing a wildcard algorithm to get 5x performance improvement.

Source: Matching Wildcards: An Empirical Way to Tame an Algorithm | Dr Dobb’s

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Tracing Python memory leaks

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

… It’s not so easy for a Python application to leak memory. Usually there are three scenarios …

Source: LShift – Tracing Python memory leaks

Just used the method in this article to solve a stochastic app crush problem for a client. How would imagine setting some global variables is more than just typing global in random places around your code base.

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Fast Learner Tip: A Visual Introduction to Machine Learning

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

What is machine learning? See how it works with our animated data visualization.

Source: A Visual Introduction to Machine Learning

 

When approaching a performance problem this method of train and test is, sadly, not yet fully applicable. This is why we have to keep our software as scalable as possible, to enable it to fit on the go with increasing (or falling) demand.

The ability to train on real time data (or learn from mistakes) using a closed feedback loop is a common method to do adaptive machine learning, and is exactly the same as we use to determine how many instances of our software currently needs to be up.

In Storm, circles in topologies are usually looked upon as a perversion, but with a couple of simple tricks you can always move the model out of Storm to a datastore (Redis maybe?), and separate the training bolt from the predicting (or classifying, or testing, depending on what stage you are) bolt.

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Andrew Montalenti – streamparse: real-time streams with Python and Apache Storm – PyCon 2015 – YouTube

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

A nice introduction to one the backbone tools we use here at Tamar. Streamparse allows us to rapidly transform regular python code to a  apache storm runable  topology.

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“EPIC” fail—how OPM hackers tapped the mother lode of espionage data | Ars Technica

Adam Lev-Libfeld

A long distance runner, a software architect, an HPC nerd (order may change).

Latest posts by Adam Lev-Libfeld (see all)

The Obama administration has ordered a “30-day Cybersecurity Sprint.”

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In the meantime, two separate “penetrations” exposed 14 million people’s personal info..

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yea…

Source: “EPIC” fail—how OPM hackers tapped the mother lode of espionage data | Ars Technica

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