Anos – a hand-written ~100KiB microkernel for x86-64 and RISC-V
I pretty much always have a kernel project going on, and have been that way for decades. Over the past couple of years, that's been Anos, which has gotten further along than any of my previous hobby kernels, supporting IPC, multitasking, SMP (x86-64 only right now) and running on real hardware. LLMs (mostly Claude Code) have been used during development, but I learned early on that it's not _great_ at code at this level, so I've restricted its use to mostly documentation and tests. There's _a little_ AI code in the user space, but I have a strict "no AI code" rule in the kernel itself. I…
In plain words
Anos is a compact microkernel written in approximately 100 KiB for x86-64 and RISC-V architectures. It implements inter-process communication, multitasking, and symmetric multiprocessing on x86-64, running on real hardware. Developed as a long-term hobby project by an experienced kernel developer, it prioritizes hand-written code in the kernel itself while using AI tools selectively for documentation and testing. The project emphasizes learning through direct implementation rather than code generation.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I pretty much always have a kernel project going on, and have been that way for decades. Over the past couple of years, that's been Anos, which has gotten further along than any of my previous hobby kernels, supporting IPC, multitasking, SMP (x86-64 only right now) and running on real hardware. LLMs (mostly Claude Code) have been used during development, but I learned early on that it's not _great_ at code at this level, so I've restricted its use to mostly documentation and tests. There's _a little_ AI code in the user space, but I have a strict "no AI code" rule in the kernel itself. I find this helps not only with the quality / functionality of the code, but also with learning - for example, even though I've written multiple kernels in the past, it wasn't until Anos that I _truly_ grokked pagetable management and what was possible with a good VMM interface, and if I'd outsourced that implementation to an LLM I probably wouldn't have learned any of that. In terms of approach, Anos avoids legacy platform features and outdated wiki / tutorial resources, and instead tries to implement as much as possible from manuals and datasheets, and it's definitely worked out well so far. There's no support for legacy platform features or peripherals, with all IO being memory mapped and MSI/MSI-X interrupts (no PIC), for example, which has helped keep the codebase focused and easy to work on. The kernel compiles to about 100KiB on x86-64, with enough features to be able to support multitasking and device drivers in user space. As a hobby project, progress ebbs and flows with pressures of my day job etc, and the main branch has been quiet for the last few months. I have however been working on a USB stack as time allows, and hopefully will soon have at least basic HID support to allow me to take the next step and make Anos interactive. I don't know how useful projects like Anos are any more, given we now live in the age of AI coding, but it's a fun learning experience and helps keep me technically grounded, and I'll carry on with it for as long as those things remain true.
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Launched alongside, April 2026
the whole month →
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With social media and now AI, its important to keep the indie web alive. There are many people who write frequently. Blogosphere tries to highlight them by fetching the recent posts from personal blogs across many categories. There are two versions: Minimal (HN-inspired, fast, static): https://text.blogosphere.app/ Non-minimal: https://blogosphere.app/ If you don't find your blog (or your favorite ones), please add them. I will review and approve it.
AI · Apr 2026 · text.blogosphere.app