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IBM/Red Hat Leftovers

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Red Hat
  • Ken Hess (Red Hat): Cyber Week 2020: 13 ideas for what to buy the sysadmin in your life | Enable Sysadmin

    It's that special time of year when you can get great discounts on tech for your favorite sysadmin.

  • [IBM Emeritus] Irving Wladawsky-Berger: Are There Limits to the Predictability of Elections?

    The elegant mathematical models of classical mechanics depict a world in which objects exhibit deterministic behaviors. These models make perfect predictions within the accuracy of their human-scale measurements.

    But, once you start dealing with atoms, molecules and exotic subatomic particles, you find yourself in a very different world, one with somewhat counter-intuitive behaviors governed by the laws of quantum mechanics. The orderly, predictable models of classical physics have now given way to wave functions, uncertainty principles, quantum tunneling and wave-particle dualities.

    But, the world of the very small is not the only one with non-deterministic behaviors. So are highly complex systems, especially those systems whose components and interrelationships are themselves quite complex. This is the case with social systems, which are based on individuals, groups, and institutions. It’s quite a challenge to make accurate predictions in such systems due to the the dynamic nature of human behaviors. Terms, like emergence, long tails, and butterfly effects - every bit as fanciful as quarks, charm and strangeness, - are part of the social systems lexicon.

    Which brings us to the 2020 US election. “The polls were wrong again, and much of America wants to know why,” wrote NY Times journalist David Leonhardt in a recent article. “This is a disaster for the polling industry and for media outlets and analysts that package and interpret the polls for public consumption, such as FiveThirtyEight, The New York Times’ Upshot, and The Economist’s election unit,” said David Graham in The Atlantic.

  • [Red Hat] Why failure should be normalized and how to do it | Opensource.com

    All of your heroes have failures under their belts—from minor mistakes to major disasters. Nobody knows how to do everything automatically, and the process of learning is usually a messy one. So why is the perception that everyone but you knows what they’re doing so common? Why do we externalize our successes but internalize our failures?

    How does it make you feel when you struggle to learn something new, then see another person take their Jira card away and return at the end of the sprint with something fully fleshed out and working, gushing about it at the demo? Sure, you closed your card too, but it was really hard! There was a new algorithm, a new programming language, a new system all to be learned. How did she make it look so effortless?

    The truth is, she might have struggled with the same issues you did and wondered how you made it look so effortless!

    [...]

    It could be very easy to title this section "my mistakes" and then rattle off all the times I’ve made mistakes, but that doesn’t quite illustrate the point. I recognize these mistakes, but they’re also events that expanded the understanding of my craft. While I didn’t set out to intentionally do any of these things, I certainly learned from them.

    I have accidentally dropped (deleted) a customer’s database. It was lucky for everyone that it was a beta-phase database and no further harm was done. I learned a valuable lesson that day: be very watchful of what code is doing, and be careful about what environment you are working in.

    One day, while performing routine maintenance with an odd DNS setup, I accidentally broke the ability for customers to provide credit card information to the secure site. We had two "payments" DNS records that served to override a wildcard DNS record, and I assumed that the second "payments" record was still present. It wasn’t. And then the wildcard record took over, and the DNS started behaving like "payments" wasn’t special at all anymore. Of course, I had no idea this was happening at all—it wasn’t until my maintenance was over that I learned of the folly.

    Customers weren’t able to provide payment information for almost two hours! I learned my lesson, though: when there is something special about a particular configuration, be sure to make sure it stays special throughout its lifetime. When DNS gets involved, all kinds of things can break.

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4 big lessons from my internship with open source

If you happened to have a time machine and chose to shoot yourself back a year to ask me what I thought about making open source contributions (of all the things you could've done), you may have guessed that I would just shrug and say something along the lines of, "I don't know, isn't that reserved for all the hard-core devs with the insane GitHub stats and decked-out macros and stuff? I'd have no idea what I was doing, and who would even care what some random college student had to say about their code?" And you'd probably be correct. But this was all before I stumbled onto the incredible opportunity to intern at Red Hat within the OpenShift Engineering division for the better part of 2020. I came into the internship like any fresh-faced computer science student, skirting through life writing untested, barely readable but somehow still functional code, and feeling proud of it. But this internship brought the opportunity to get my hands dirty with the open source culture and finally see what all the hype is about. Read more

Voltage Measurement HAT for Raspberry Pi offers 16-bit analog inputs

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VPN Providers with Custom Clients for Linux

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After Parler, Google Targets Blockchain-based ‘Free Speech’ Social Network Minds

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