A curated collection of open-source TUI apps for developers, operators, and power users.

Reverse.horse is my newest project, an attempt to understand and explain the new AI model Jev, by using your feeble human brain as the model. Place yourself in-request and respond as best as you can to incoming questions.

If you want to jump right in, go try it. The horse will put you through your training on how to be the AI model and then cut you loose to do the job of the AI model. You may come back with a new appreciation of what these models are doing.

The UEFI Shell command set, rewritten from scratch

Files, disks, UEFI variables, drivers and devices, PCI/SMBIOS/ACPI, memory, network, editors. UEFI Shell options are accepted too. More than 80 commands are documented in the user manual.

cosh doesn't impose a philosophy: attach or detach its built-in tools and choose your model to shape the agent you want. Works with Claude, OpenAI, Gemini and many more providers. Boots in milliseconds.

I have been using DeepSeek 4.1 Flash for about a month, heavily, across a dozen projects. It is super capable, and orders of magnitude cheaper than the "frontier" models. When I'm mid-session, if I don't look at the model name, I honestly could not tell you if I'm using DeepSeek or Opus. Whether it's our conversations, the work, or the speed, I don't notice a difference. I don't care that there is no 4.1 "Pro". I treat this like a frontier model because it behaves like one. I'm coming at this from my subjective usage experience, but you can see more complete benchmarks here if that floats your boat.

Flyline: a Bash plugin to replace readline for a modern line editing experience: syntax highlighting, agent integration, rich prompts, tooltips, fuzzy history search, and more!

Securing and modernizing software is a top priority today. Tech debt and memory-safety bugs remain the largest sources of vulnerabilities, which is why teams increasingly turn to Rust for its reliability and memory safety. But rewrites are hard, scary, and carry a lot of risk. Mainmatter assembled our experience from helping clients successfully modernize some of the worlds most important software into this book.

When we made our strategic bet on Rust back in 2022, we focused on Rust for backends and cloud systems. At the time, that was not an obvious choice. Rust was mostly seen as a systems language only, a safer replacement for C and C++ in operating systems, embedded devices, or browser engines. Suggesting to use it also for the kind of web backends that teams would typically build with Rails, Django, or Node.js required some explaining.

Let’s debunk the myth of Vim and learn how it’s possible to remember all the shortcuts using the specific Vim language. We’ll see how to move with vim motions, and I’ll share what I’ve learned so far, and why you might give Vim a try as well.

The internet allowed us to publish and link-together an enourmous amount of information, how to find anything in this vast ocean of interlinked documents is still a problem. To build a better internet we need to work on its content's discoverability.

500 things you can make with JavaScript and a single canvas element. No WebGL, no libraries, just a 2D drawing context and a loop. Open any demo to play with it, then copy a prompt that gets your coding agent started on your own version.

Every image a computer shows you, whether it's a game, a film effect, a map or the text you're reading right now, ends up as a grid of colored dots. This book is about how those dots get their colors.

We start with the simplest thing there is: setting one dot. From there we draw lines, fill shapes, mix colors, and then step into 3D, building a complete renderer from scratch in plain JavaScript, followed by a ray tracer and a path tracer that follows light the way physics says it really behaves. Only when every stage is familiar do we hand the work to the GPU, the specialized chip built to do exactly what you've just written, millions of times faster. The next parts chase the path tracer's realism in real time and bring things to life with animation and simulation. The last part covers the techniques behind today's real-time renderers, from temporal antialiasing and clustered lights to volumes, ray tracing in compute shaders, and scenes learned from photographs.

This past year, OpenAI, Anthropic, and other labs have announced breakthroughs on numerous long-standing mathematical problems, in some cases pushing well beyond what researchers expected current systems to be capable of — including resolving one of the famous Millennium Prize problems. But in classic Silicon Valley style, AI labs are moving fast and breaking things, barreling through the discipline with all the grace of a runaway bulldozer. Results that might normally have been celebrated have instead sparked backlash. AI labs say they are learning from earlier mistakes. Whether those promises bear fruit remains to be seen.

Something shifted over the last year or so, and it's been hard to put into words.

Open a PR on a popular repo today and observe what happens. Within seconds, a bot leaves an AI review summarizing your own change back to you. Another bot posts a preview deployment. Another posts a "walkthrough" with a sequence diagram nobody asked for. Sometimes a fourth shows up to respond to the third. By the time a human actually looks at it, the thread is a wall of generated text, and the real conversation (the human part) is buried somewhere in the middle.

The details vary, but the shape is the same: maintainers are no longer just deciding whether a contribution is good, they’re deciding whether they can afford to find out.

There are not many computer file formats I would trust to still be readable fifty years from now, but plain text is one of them. That may sound like faint praise. A text file cannot embed a spreadsheet, preserve elaborate page layouts, run a presentation, or provide many of the conveniences we expect from modern applications. What it does instead is store text in one of the simplest and most widely understood forms in computing, and that simplicity is a large part of why it has lasted.

Ansi.md is my brain dump for creating modern command line apps. My intent is to gradually expand this guide with hard-won tricks, recommended apps and homegrown tools.