Somewhere in the universe, there’s a place that lists every x86 operating system from scratch. Not just some bootloaders, or just a kernel stub, but documentation to build a fully functional, interrupt-handling, multitasking-capable OS. [Erik Helin and Adam Renberg] did just that by documenting every step in The Little Book About OS Development.

This is not your typical dry academic textbook. It’s a hands-on, step-by-step guide aimed at hackers, tinkerers, and developers who want to demystify kernel programming. The book walks you through setting up your environment, bootstrapping your OS, handling interrupts, implementing virtual memory, and even tackling system calls and multitasking. It provides just enough detail to get you started but leaves room for exploration – because, let’s be honest, half the fun is in figuring things out yourself.

Completeness and structure are two things that make this book stand out. Other OS dev guides may give you snippets and leave you to assemble the puzzle yourself. This book documents the entire process, including common pitfalls. If you’ve ever been lost in the weeds of segmentation, paging, or serial I/O, this is the map you need. You can read it online or fetch it as a single 75-page long PDF.

Mockup photo source: Matthieu Dixte

[ombates] shares a step-by-step method for making a conductive bio-string from scratch, no fancy equipment required. She demonstrates using it to create a decorative top with touch-sensitive parts, controlling animations on an RGB LED pendant. To top it off, it’s even biodegradable!

The string is an alginate-based bioplastic that can be made at home and is shaped in a way that it can be woven or knitted. Alginate comes primarily from seaweed, and it gels in the presence of calcium ions. [ombates] relies on this to make a goopy mixture that, once extruded into a calcium chloride bath, forms a thin rubbery length that can be dried into the strings you see here. By adding carbon to the mixture, the resulting string is darkened in color and also conductive.

There’s no details on what the actual resistance of a segment of this string can be expected to measure, but while it might not be suitable to use as wiring it is certainly conductive enough to act as a touch sensor in a manner similar to the banana synthesizer. It would similarly be compatible with a Makey Makey (the original and incredibly popular hardware board for turning household objects into touch sensors.)

What you see here is [ombates]’ wearable demonstration, using the white (non-conductive) string interwoven with dark (conductive) portions connected to an Adafruit Circuit Playground board mounted as an LED pendant, with the conductive parts used as touch sensors.

Alginate is sometimes used to make dental molds and while alginate molds lose their dimensional accuracy as they dry out, for this string that’s not really a concern. If you give it a try, visit our tip line to let us know how it turned out!

[Simon Willison] has put together a list of how, exactly, one goes about using a large language models (LLM) to help write code. If you have wondered just what the workflow and techniques look like, give it a read. It’s full of examples, strategies, and useful tips for effectively using AI assistants like ChatGPT, Claude, and others to do useful programming work.

It’s a very practical document, with [Simon] emphasizing realistic expectations and the importance of managing context (both in terms of giving the LLM direction, as well as the model’s context in terms of being mindful of how much the LLM can fit in its ‘head’ at once.) It is useful to picture an LLM as a capable and obedient but over-confident programming intern or assistant, albeit one that never gets bored or annoyed. Useful work can be done, but testing is crucial and human oversight simply cannot be automated away.

Even if one has no interest in using LLMs to help in writing production code, there’s still a lot of useful work they can do to speed up the process of software development in general, especially when learning. They can help research options, interactively explore unfamiliar codebases, or prototype ideas quickly. [Simon] provides useful strategies for all these, and more.

If you have wondered how exactly glorified chatbots can meaningfully help with software development, [Simon]’s writeup hopefully gives you some new ideas. And if this is is all leaving you curious about how exactly LLMs work, in the time it takes to enjoy a warm coffee you can learn how they do what they do, no math required.