Programming Leftovers

posted by Roy Schestowitz on Oct 15, 2025

• DJ Adams ☛ Modelling contained-in relationships with compositions in CDS

  There's a classic structure often found representing business data in enterprise software, and that's the "document". Purchase requisition, sales order, goods receipt, and so on. Header and items. Most of the time the items don't make sense existing on their own, independent of their header parent. Such documents are typically modelled using contained-in relationships.

• University of Toronto ☛ Staring at code can change what I see (a story from long ago)

  Many years ago, I was recruited to be a TA for the university's upper year Operating Systems course, despite being an undergraduate at the time. One of the jobs of TAs was to mark assignments, which we did entirely by hand back in those days; any sort of automated testing was far in the future, and for these assignments I don't think we even ran the programs by hand. Instead, marking was mostly done by having students hand in printouts of their modifications to the course's toy operating system and we three TAs collectively scoured the result to see if they'd made the necessary changes and spot errors.

• Daniel Hooper ☛ Hot Reloading SwiftUI Apps

  Did you know you can change the code of a SwiftUI app while it’s running? The technique is called “hot reloading”, and it’s a huge productivity boost. It’s better than Xcode Previews because it’s your whole app, you don’t have to create mock preview data, and it maintains state between reloads. In fact, you don’t even need Xcode. I’ll show you how by making a Todo app: [...]

• The New Stack ☛ A Strategic Playbook for a Shift-Left Transformation

  As engineering organizations scale their product lines, a common pattern emerges: Cycle times start to increase. In many cases, after analyzing the software development life cycle, a common culprit is identified: A sequential model where code development, manual validation/testing and test automation happen in distinct phases. This approach inherently delays the creation of regression suites and slows the overall release cadence.

  Adopting a “shift-left” philosophy addresses this challenge directly. It enables the parallelization of development and testing. This reduces cycle times and gives teams greater ownership of quality.

• Hackaday ☛ C Project Turns Into Full-Fledged OS

  While some of us may have learned C in order to interact with embedded electronics or deep with computing hardware of some sort, others learn C for the challenge alone. Compared to newer languages like Python there’s a lot that C leaves up to the programmer that can be incredibly daunting. At the beginning of the year [Ethan] set out with a goal of learning C for its own sake and ended up with a working operating system from scratch programmed in not only C but Assembly as well.

• Dirk Eddelbuettel ☛ Dirk Eddelbuettel: qlcal 0.0.17 on CRAN: Regular Update

  The seventeenth release of the qlcal package again following a QuantLib release as 1.40 came out this morning.

  the QuantLib release 1.40. Only one country calendar got updated; the diffstat looks larger as the URL part of the copyright monopoly got updated throughout. We also updated the URL for the GPL-2 badge: when CRAN checks this, they always hit a timeout as the FSF server possibly keeps track of incoming requests; [...]

• Perl / Raku

  • Noel Rappin ☛ Ruby And Its Neighbors: Perl

    Ruby takes a large part of its inspiration from two older languages: [...]

• Python

  • The New Stack ☛ SQL vs. Python: Frenemies of the Data World

    Every year, the IEEE Spectrum Top Programming Languages ranking provides a snapshot of what truly matters in the global software ecosystem. In the 2025 list, SQL jumped from No. 9 in 2024 to hold the No. 4 spot, just behind Python, Java and C++. That placement is remarkable, given SQL’s age and its specialized scope.

    In this context, the data tells a deeper story: Across decades of innovation, SQL endures as the backbone of enterprise analytics, unmatched in efficiency for representing and querying data at scale. I’ve written about why SQL is an ideal match for AI and believe the spectacular jump in SQL’s popularity further confirms the complementary nature of SQL and Python, which is often misunderstood when in fact they are both necessary for today’s data-intensive AI workloads.

  • DataGeeek ☛ Integrating Python Forecasting with R’s Tidyverse

  • Rlang ☛ Integrating Python Forecasting with R’s Tidyverse

    In this article, we executed a successful integration of a non-standard Python forecasting model into the R Tidyverse/Tidymodels framework, primarily leveraging the reticulate package.

• Rust

  • Bilal Elmoussaoui: Testing a Rust library - Code Coverage

    It has been a couple of years since I started working on a Rust library called oo7 as a Secret Service client implementation. The library ended up also having support for per-sandboxed app keyring using the Secret portal with a seamless API for end-users that makes usage from the application side straightforward.

    The project, with time, grew support for various components: [...]