Programming Leftovers

posted by Roy Schestowitz on Feb 14, 2026,
updated Feb 14, 2026

• Wouter Groeneveld ☛ Why Parenting Is Similar To JavaScript Development

  To be perfectly frank, in those moments, I often wonder if Crockford had been lying to us. Are there even any good parts at all? We all know JS was cobbled together overnight because Netscape needed “some” language to make static languages a bit more dynamic. A language for the masses! What a monster it has become—in both positive and negative sense.

• Vincent Delft ☛ Vincent's blog: How I've transferred my projects from git to got while keeping all history

  In this blog, I explain what I did to transfer my development projects from git on my brand new got server. Since got is based on git repository, there is nearly nothing specific to do.

  Just migrate your .git folder in your got repository and it will work.

• Andrew Nesbitt ☛ Respectful Open Source

  Even a perfect PR with a note saying “no rush” creates a low-grade obligation the moment it appears. The maintainer now knows it exists, unanswered. Someone in the thread suggested framing it as a gift with no expectations, and another person put it well: it doesn’t matter how carefully you word it, it still lands as a thing that needs a decision.

  The fix exists on my fork. If discovery were good, anyone hitting the same bug could find it there, but nobody will because fork discovery is effectively broken.

• Zig ☛ Devlog ⚡ Zig Programming Language

  As we approach the end of the 0.16.0 release cycle, Jacob has been hard at work, bringing std.Io.Evented up to speed with all the latest API changes: [...]

• R / R-Script

  • Rlang ☛ How Posit’s Public Package Manager manylinux_2_28 repository can help you if your R project is stuck on Ubuntu Focal Fossa

    I am a massive fan of repositories making binary R packages available. This includes the canonical CRAN repositories, r-universe, r2u, and the Posit (Public) Package Manager, and there are others. R-universe is outstanding because it builds binaries of GitHub only packages. The Posit Public Package Manager is outstanding due to its incredible breadth (it makes binaries for 14 Linux distros) and also its depth (its almost daily snapshotting service is remarkably useful for quickly making reproducible R environments).

  • [Old] Geocompx ☛ Spatial machine learning with R: caret, tidymodels, and mlr3

    The R language has a variety of packages for machine learning, and many of them can be used for machine learning tasks in a spatial context (spatial machine learning). Spatial machine learning is generally different from traditional machine learning, as variables located closer to each other are often more similar than those located further apart. Thus, we need to consider that when building machine learning models.

    In this blog post, we compare three of the most popular machine learning frameworks in R: caret, tidymodels, and mlr3. We use a simple example to demonstrate how to use these frameworks for a spatial machine learning task and how their workflows differ. The goal here is to provide a general sense of how the spatial machine learning workflow looks like, and how different frameworks can be used to achieve the same goal.

  • [Old] Geocompx ☛ Spatial machine learning with caret

    This document shows the application of caret for spatial modelling at the example of predicting air temperature in Spain. Hereby, we use measurements of air temperature available only at specific locations in Spain to create a spatially continuous map of air temperature. Therefore, machine-learning models are trained to learn the relationship between spatially continuous predictors and air temperature.

    When using machine-learning methods with spatial data, we need to take care of, e.g., spatial autocorrelation, as well as extrapolation when predicting to regions that are far away from the training data. To deal with these issues, several methods have been developed. In this document, we will show how to combine the machine-learning workflow of caret with packages designed to deal with machine-learning with spatial data. Hereby, we use blockCV::cv_spatial() and CAST::knndm() for spatial cross-validation, and CAST::aoa() to mask areas of extrapolation. We use sf and terra for processing vector and raster data, respectively.

  • [Old] Geocompx ☛ Spatial machine learning with the tidymodels framework

    In this blog post, we will show how to use the tidymodels framework for spatial machine learning. The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles.

  • [Old] Geocompx ☛ Spatial machine learning with mlr3

    This post aims to give a minimal example on how to use mlr3 for a spatial prediction task. We want to get from measurements of temperature at specific locations in Spain to a spatially continuous map of temperature for all of Spain.

    Such a spatial prediction task is often done by applying machine learning algorithms that are not necessarily developed for spatial tasks specifically and hence do not consider problems we might encounter in the spatial world, e.g., spatial autocorrelation or map extrapolation. In the last decade, a lot of methodological developments were made by various research groups to consider and deal with such specialties of spatial mapping. Many of which found their way in software packages such as mlr3.

  • [Old] Geocompx ☛ Specialized R packages for spatial machine learning: An introduction to RandomForestsGLS, spatialRF, and meteo

    This document provides an overview of three R packages, RandomForestsGLS, spatialRF, and meteo, that implement spatial machine learning methods, but are outside of standard machine learning frameworks like caret, tidymodels, or mlr3.1

    All of the examples below use the same dataset, which includes the temperature measurements in Spain, a set of covariates, and the spatial coordinates of the temperature measurements.

  • Geocompx ☛ Specialized R packages for spatial cross-validation: sperrorest and blockCV

    This document provides an overview of two R packages, sperrorest and blockCV, that can be used for spatial cross validation, but are outside of standard machine learning frameworks like caret, tidymodels, or mlr3.

• Java/Golang

  • Frank Delporte ☛ I Got Java 25 Running on the RISC-V BeagleBoard BeagleV-Fire

    After my initial struggles with the BeagleV-Fire in a previous video, I succeeded in getting Java 25 running on RISC-V-powered BeagleV-Fire! Let me walk you through the journey and the steps I took to make it work.

• Open Hardware

  • Hackaday ☛ R2D2 Gets New Brains

    While it is fun to get toys that look like your favorite science fiction props, it is less fun when the electronics in them don’t measure up to the physical design. [Steve Gibbs] took a Hasbro R2D2 toy robot and decided to give it a brain upgrade along with enhanced sensors. You can see a video of the robot doing its thing and some build details below.

  • Martijn Braam ☛ Building a FOSS live streaming camera

    I have used a lot of cameras but never made one myself. Specifically one optimized for live streaming so it just outputs the camera feed over HDMI.

  • Olimex ☛ SMT components are getting smaller — but your precision doesn’t have to

    We now offer a professional range of magnifying tools designed specifically for PCB inspection and electronics work

  • Maury ☛ Inside an alpha-beta scintillator:

    I've recently acquired this tiny contamination monitor: [...]

  • [Old] Scott Shambaugh ☛ An 8-bit Breadboard Computer

    I built a computer! A very primitive one, made out of breadboards, bare wire, and logic gates. But it’s fully functioning, it looks awesome, and unlike the staggering complexity of modern processors, it’s actually possible for one person to understand the whole thing.