Programming Leftovers
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Bootlin toolchains updated, edition 2020.02
Bootlin provides a large number of ready-to-use pre-built cross-compilation toolchains at toolchains.bootlin.com. We announced the service in June 2017, and released multiple versions of the toolchains up to 2018.11.
After a long pause, we are happy to announce that we have released a new set of toolchains, built using Buildroot 2020.02, and therefore labelled as 2020.02, even though they have been published in April. They are available for 38 CPU architectures or architecture variants, supporting the glibc, uclibc-ng and musl C libraries when possible.
For each toolchain, we offer two variants: one called stable which uses “proven” versions of gcc, binutils and gdb, and one called bleeding edge which uses the latest version of gcc, binutils and gdb.
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Squeezing the most out of the server: Erlang Profiling
An obvious way to reduce costs is to make the system more efficient and this means entering the hazardous land of software optimization. Even for experienced programmers, identifying bottlenecks is a hard enough problem when using the right tools; trying to guess what could make the code run faster will not only waste time but is likely to introduce unnecessary complexity that can cause problems down the line. The cousin of premature optimization is necessary optimization without profiling first
While Erlang is famously known for its concurrency model and fault-tolerant design, one of its biggest strengths is the level of live inspection and tuning it offers, often with little or no setup and runtime cost. In this article, we outline how we leverage those features to profile our system, driving the optimizations that can lead to cost reductions.
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S. Lott: Why Isn't COBOL Dead? Or Why Didn't It Evolve?
In short, why is FORTRAN still OK? Why is COBOL not still OK?
Actually, I'd venture to say the stories of these languages are essentially identical. They're both used because they have significant legacy implementations.
There's a distinction, that I think might be relevant to the "revulsion factor."
Folks don't find Fortran quite so revolting because it's sequestered into libraries where we don't really have to look at it. It's often wrapped into SciPy. The GCC compiler system handles it and we're happy.
COBOL, however, isn't sequestered into libraries with tidy Python wrappers and Conda installers. COBOL is the engine of enterprise applications.
Also. COBOL is used by organizations that suffer from high amounts of technical inertia, which makes the language a kind of bellwether for the rest of the organization. The organization changes slowly (or not at all) and the language changes at an even more tectonic pace.
This is a consequence of very large organizations with regulatory advantages. Governments, for example, regulate themselves into permanence. Other highly-regulated industries like banks and insurance companies can move slowly and tolerate the stickiness of COBOL.
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Google's Propeller Is Beginning To Be Upstreamed For Spinning Faster Program Binaries
We have begun seeing the start of upstreaming on Google's Propeller Framework for offering post-link-time binary optimizations in the LLVM compiler stack to offer measurably faster (re)generated binaries.
Propeller was developed by Google engineers as a result of Facebook's BOLT post-link optimizer for speeding up applications by optimizing the generated binary after being linked.
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5 tips for working from home from a veteran remotee
Due to the outbreak of COVID-19 and its rapid development, we are all being called to take protective and preventative measures, including avoiding social contact as much as possible. Events are canceled, trips are postponed, and companies are asking their employees to work from home. It's an exceptional situation for everyone, as remote work cultures with distributed teams are being introduced overnight. Many companies are being challenged to quickly organize a team that works completely remotely.
Many articles and recommendations on remote work, home offices, and teleworking are circulating. For example, GitLab, a pioneer in remote work, has recently published a detailed manual on remote working. I highly recommend it to anyone who is facing the challenge of setting up and managing a remote team. At OpenProject, we have been working in distributed teams for over 10 years.
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Love or hate chat? 4 best practices for remote teams
I encourage you to explore open source alternatives to chat like Mattermost, Rocket.Chat, and Riot.
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Create web tutorials with Reveal.js and Git
Whether you're a learner or a teacher, you probably recognize the value of online workshops set up like slideshows for communicating knowledge. If you've ever stumbled upon one of these well-organized tutorials that are set up page by page, chapter by chapter, you may have wondered how hard it was to create such a website.
Well, I'm here to show you how easy it is to generate this type of workshop using a fully automated process.
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Three Comics For Understanding Unix Shell
I just optimized Oil's runtime by reducing the number of processes that it starts. Surprisingly, you can implement shell features like pipelines and subshells with more than one "process topology".
I described these optimizations on Zulip, and I want to write a post called Oil Starts Fewer Processes Than Other Shells.
That post feels dense, so let's first review some background knowledge, with the help of several great drawings from Julia Evans.
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Targeted string replacements with sed and AWK
Global replacement of A with B with sed or AWK might be a mistake unless you're 100% sure that you really, truly want to replace every instance of A with B in the data file. Even more risky (says he, who has done it more than once to his regret) is globally replacing over a whole set of files:
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Robotic Process Automation (RPA): 6 open source tools
As with many new software implementations, there’s a build-or-buy choice when getting started with Robotic Process Automation (RPA).
On the build side, you can write your own bots from scratch, provided you’ve got the right people and budget in place. On the buy side, there’s a burgeoning marketplace of commercial software vendors offering RPA in various flavors, as well as overlapping technologies. (Some market themselves under different but related terms like “intelligent automation.”)
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Things that are called ML/AI that really aren’t
Artificial Intelligence (AI) is a genuine technical term to describe something that doesn’t quite yet exist in a truly cognitive form. However, AI is also a marketing buzzword used to distinguish items with extra usability or computing-power oompfh. The acronym often attempts to differentiate ordinary things, such as phones, from extraordinary things of the same ilk, such smartphones.
Because there’s no legal governance over the use of AI in marketing, the label is abundantly applied to hardware or software use traditional algorithms as well as to things that actually learn. Calling all these things “smart” muddies the waters even more – and makes it difficult to make rational decisions.
“Many times companies use the term ‘artificial intelligence’ to describe technology that operates without human interaction, but most times it’s just a sophisticated algorithm,” says Scott George, CEO of U.S. Consumer Healthcare Advocacy Group (USCHAG), a consortium of healthcare professionals, institutions, and organizations. He cites website chatbots as an example, which some consider AI – but usually don’t meet the technical criteria.
“The confusion here is that for something to qualify as AI doesn’t actually require it to have an advanced form of cognition,” says Benjamin Nussbaum, AI/ML advisor to the Greystones Group, a technical support provider for the Department of Defense (DoD) and commercial clients. So many companies can tout what they do as AI is because the definition of AI was established back in the 1950s and only requires that a machine can do as well or better that which a human can do. “This opens the door for basic automation, analysis algorithms, etc. to all be categorized as AI,” adds Nussbaum.
Naturally, that is extremely confusing for anyone who wants to assess any system’s value. The average algorithm is so sophisticated today that spotting the difference can be nearly impossible for the average buyer.
The solution is to look at the system’s value without regard to how it’s built. If it genuinely uses AI or machine learning, great; but what matters is whether it makes life better.
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An existential threat (that isn't COVID-19)
Many of you will know my good friend Peter Scott as a Perl luminary. More recently he has turned his attention and his considerable talents to focus on the future of AI, both as an unprecedented opportunity for our society...and as an unprecedented threat to our species.
A few years back, he released an excellent book on the subject, and just recently he was invited to speak on the subject at TEDx. His talk brilliantly sums up both the extraordinary possibilities and the terrible risks inherent in turning over our decision-making to systems whose capacities are increasingly growing beyond our own abilities, and perhaps soon beyond even our own understanding.
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