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IBM/Red Hat: Mainframes History, Fwupd 1.5 Released, and Lots of Stuff Outsourced to Microsoft (GitHub)

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  • The First Mainframe Computer: Harvard Mark I

    The mainframe computer, or ‘big iron’ in the computer industry, is the longest-running computer system in history. This technology has been substantially useful since the World War II era. In fact, the first mainframe computer was used mainly by the US Navy during the war. Like supercomputers, the mainframe computer addressed the need for an automatic, large-scale calculator as a more efficient and error-free way of computing. It was the invention of such machines that redefined the term ‘computer’ to refer to devices that can carry out automatic calculations of mathematical operations, a term that used to refer to humans who performed the manual calculations of such operations. Today, the importance of this technology in large-scale transaction processing remains unparalleled. Large industries in both the public and private sectors, from government and banking to aviation and healthcare, are in constant need of faster large-scale mainframes with higher stability and reliability. Consequently, big irons continue to evolve, as they remain at the core of every IT infrastructure.

    Inspired by Babbage

    Howard Aiken was a graduate student at Harvard when he came up with the concept of a device that can automatically calculate differential equations, after encountering difficulties in solving mathematical physics problems in his research. He envisioned a machine that could take in loads of mathematical inputs and produce precise and reliable results in a short time. After coming up with an initial design, he approached some manufacturers, but none were interested. Unabashed, Aiken explored other technological advances to improve his design. He eventually came upon Henry Babbage’s demonstration of his father’s Analytical Engine at Harvard, performed 70 years prior. Noticing the similarities between his design and that of Charles Babbage’s, Aiken studied Babbage’s work on the Analytical Engine and used his principles in the development of a new conceptual design. Aiken finished the design in 1937 and obtained the support of the Harvard faculty, who were impressed by his efforts. He presented his design to several manufacturers. Aiken eventually gained the nod from IBM in 1939 after Thomas Watson, then chairman of IBM, saw it as good publicity for the company and as an opportunity to showcase the company’s talents.

  • New fwupd 1.5.0 release – Technical Blog of Richard Hughes

    Today we tagged the 1.5.0 release of fwupd. Quite a bit has changed since the last release and I figured a blog post probably made sense to explain things.

    From a firmware engineer point of view, the most useful is the ability to build composite images, for instance building a firmware.dfuse file from different A.dfu and B.dfu images. At the moment there are commands in fwupdtool to convert one file format to another, but not to merge or alter them. Many firmware files are really just containers which can store multiple images, each with optional id, index and addresses. This new fwupd feature also allows us to create very small complicated container binaries for fuzzing.

  • Fwupd 1.5 Released With Expanded Hardware Support, New Capabilities

    Version 1.5 of the Fwupd utility is available for updating various component firmware/BIOS natively on Linux and integrating with the Linux Vendor Firmware Service (LVFS) for the easy distribution of said firmware images.

    [...]

    Some of the hardware plug-in additions were motivated by Lenovo's increased support for Linux on their systems, which is great to see continuing.

  • Red Hat Publishes Open Source Participation Guidelines

    Red Hat’s Brian Proffitt says the guidelines “reflect the values and culture of Red Hat in the most appropriate way possible: a collaboration of many associates working toward a common goal, documenting how Red Hat is committed to contributing to any free and open source project in the most collaborative ways possible.”

  • Linux in the enterprise as seen from IBM

    It’s hard to imagine now, but 20 years ago, enterprise support and use of Linux was a controversial choice. Executives had trouble seeing the value of investing resources in an unproven, open-source operating system when mainstream options achieved the same results with less perceived risk.

    With the benefit of hindsight, so many enterprise clouds are run on Kubernetes containers with Linux that the initial concerns seem silly. The changes gave developers the capabilities to produce more agile, robust and innovative work than ever before. The success of the modern Linux cloud is due partly to IBM’s decision to support Linux on its mainframes 20 years ago.
    We spoke to Javier Perez, open-source program leader at IBM, about why and how IBM made this decision and what trends he thinks are going to influence the next 20 years for developers.

  • Open Source AI and Data: Keep up with rapid advances with LF AI and Data – IBM Developer

    The Linux Foundation Artificial Intelligence Foundation (LF AI) is merging with ODPi, which has a focus on big data in the enterprise, including governance, business intelligence, and data science education. The merged foundation will be called LF AI and Data. IBM believes this move is great for the AI and data open source space and that the new LF AI and Data Foundation will pave the way for stronger, safer open source AI and data projects.

    Why is this important?

    The world’s technology increasingly runs on open source software and data. Open source AI software development has led to advances in AI pattern recognition, including image recognition, speech recognition, and entity extraction in text, that were only possible because researchers were able to use open data sets and open source software to benchmark and compare systems and approaches.

    The data you or your organization create influences and is influenced by AI. Increasingly, both productivity and quality of service depends on data-driven AI systems across business and society. And those AI systems are largely based on open source software and data sets at their core.

  • How IT consultants can build trust with clients

    As a consultant, I’ve learned that consulting requires forming a customer relationship built upon trust. The client and the consultant usually establish trust through shared experiences. The most common shared experience is working through contract delivery. However, only focusing on contract delivery may cause the clients or clients to miss additional opportunities.

    I’ve laid out some low-cost or free opportunities for building trust below. These are not just for one party or the other to initiate! If you are the client, I encourage you to engage your consultants to find mutual interests. Likewise, if you’re the consultant, look for ways to add value for your customer by sharing your expertise.

  • The Red Hat Accelerators Wear Many Fedoras

    With Red Hat Accelerators, there’s a strong community feel with benefits for practitioners to expand their expertise, offer a voice to influence offerings and a seat at the table during industry events. Check out a few examples of how Red Hat Accelerators have been involved since the program’s inception.

    [...]

    Red Hat Accelerators have the opportunity to join briefings and other sessions, hearing from and sharing with teams from across the company, including engineers, marketing, product managers, executives and more. These in-depth discussions and forums have included opportunities to engage with Red Hatters like Chris Wright (senior vice president and CTO), Stefanie Chiras (senior vice president and general manager of Red Hat Enterprise Linux), and Paul Cormier (Red Hat’s president and CEO).

    Forming deeper relationships with customers means engaging Red Hatters from every direction, so customers in the Accelerators program can pick their brains and access candid, unfiltered information on any topic, big or small (or hybrid).

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