Show HN: AirFlick Drop – AirDrop for Windows with zero mobile app installs https://ift.tt/lPnmdET

Show HN: AirFlick Drop – AirDrop for Windows with zero mobile app installs https://ift.tt/4eKT7Gw November 2, 2026 at 02:08AM

Show HN: Jik – a self-hosted language using region-based memory management https://ift.tt/h5aEZcp

Show HN: Jik – a self-hosted language using region-based memory management https://ift.tt/V08CeR9 November 2, 2026 at 01:21AM

Show HN: A language model that lives in a boot sector https://ift.tt/NAodxgW

Show HN: A language model that lives in a boot sector All the ideas came from the boot sector demo scene where people had been writing chess and BASIC games in 512 bytes, even a C compiler. I wondered what one can do using a neural network instead of a program and generating boot logs for non-existent hardware was something fun to try. Entire inference engine is 510 bytes of MBR, weights, vocab, and strings reside in the sectors following MBR. Small char-level neural network was trained on dmesg generated by machine, so machine produces Linux boot messages infinitely. train.py performs precisely the same integer arithmetic operations as the asm code does and thus produces an output identical character-by-character to the one produced by QEMU. The difference analysis revealed my favorite bug. The characters at the beginning matched those generated by the simulator, but then the output diverged. I retrieved the hidden layer via QEMU’s debug port and compared the neurons' values with those of the simulator, one neuron at a time. Only the value of the first neuron was correct. The activation involves idiv, and idiv stores its remainder in edx. The pointer to the biases table was stored in edx, which resulted in the first neuron being biased correctly and the following ones using garbage. Now the bias' address is determined from the loop counter, and no pointers have to survive the division. Another bug I found simply by looking at the code before I did my first run-through: the BIOS print routine zeros out bx, but bx contains the predicted character number. This explains the push and pop of bx surrounding the print. sectorllm is the most similar to my project, as it runs an actual 5-layer llama2 with attention mechanism in just 1.2K of assembly code. It is the trade-off in the opposite direction, as my engine needs to fit into one sector and therefore my network has just one hidden layer of int8 operations for dreaming the dmesg. https://ift.tt/mHl3yrB October 31, 2026 at 06:28PM

Show HN: I rebuilt Kubernetes for ESP32 microcontrollers https://ift.tt/Uo9h2fB

Show HN: I rebuilt Kubernetes for ESP32 microcontrollers https://ift.tt/JeHsK6m October 29, 2026 at 10:05PM

Show HN: I built a map of billboards and banners in Poland (gpy.pl) https://ift.tt/n1A8YB6

Show HN: I built a map of billboards and banners in Poland (gpy.pl) https://gpy.pl/ October 29, 2026 at 01:24AM

Show HN: A visual map of related food dishes from around the world https://ift.tt/2j38e06

Show HN: A visual map of related food dishes from around the world A fun, personal project - AI did a pretty good job. https://ift.tt/t2aPqrW October 29, 2026 at 01:35AM

Show HN: A proof-of-concept formalization of the IRS federal tax forms https://ift.tt/XbKf9o8

Show HN: A proof-of-concept formalization of the IRS federal tax forms The linked repo is 100% vibe coded, but it was built on a handful of principles: - Most tax software derives trust from the party who maintains it (e.g. Intuit, the IRS, etc.) - Vibe coded software lacks this trustworthiness. - Therefore any formalization should build in verifiability and mechanisms that make it possible for users/maintainers to trace claims/interpretations back to authoritative source documents. It's very much a back-burner passive project right now (do not use this for anything real!), but I feel strongly that something like this ought to exist for the public good. I built it to get a sense of what the shape of what such a thing might be. https://ift.tt/A6UN91k October 29, 2026 at 12:00AM

Show HN: CleanCode – Claude Code and Codex collaborating on one canvas https://ift.tt/zqTSZtR

Show HN: CleanCode – Claude Code and Codex collaborating on one canvas https://ift.tt/Yu9En5o October 28, 2026 at 02:27AM

Show HN: Tar-minefield shows where tar implementations disagree https://ift.tt/yWem6dk

Show HN: Tar-minefield shows where tar implementations disagree https://ift.tt/ZnEblt7 October 28, 2026 at 12:51AM

Show HN: Jog – GitHub runners for 80% less https://ift.tt/74JKCsO

Show HN: Jog – GitHub runners for 80% less https://usejog.com/ October 28, 2026 at 12:21AM

Show HN: Hob - Open-source, on device alternative to Claude Cowork and ChatGPT https://ift.tt/s5phXU2

Show HN: Hob - Open-source, on device alternative to Claude Cowork and ChatGPT Hey HN, I'm Christian and this is a new OSS project I've been noodling on for the last couple of weeks. Hob is a free, MIT-licensed desktop app for Mac, Windows and Linux that works on your files the way the Claude and ChatGPT desktop apps do, except the model runs on your own computer. It uses open-weight models (currently Gemma 4 and gpt-oss). All your chats and files never leave your system. The only things that go online are web search (can be turned off in settings) and any MCP connectors you add yourself. Main features: - Automatically finds the best open-weight model for your hardware on first install (Mac/Linux/Windows) - Writes sandboxed Python code for ad hoc tools (data processing, document conversion, graphs etc.) - Built-in web search (powered by SearXNG) - MCP support - Image recognition - CRUD to your local filesystem - Works fully offline with no internet Before building this I tried every local AI app I could find, but it seemed each one had different problems. The main thing I noticed is that none of them could do what the ChatGPT and Claude apps do with your files, e.g. read and change files in a folder, look at images, run code in a sandbox, draw a chart. The most advanced thing any of them did was paste a plain text file into a small context window, which basically failed as soon as you had a file larger than a few KB. I couldn't find anything that could easily parse images and bigger PDFs. The OSS solutions that were more advanced all seemed to default to hosted models (either OSS or closed), which defeats the entire point of the privacy that on-device OSS offers. They also seemed to push towards an account/open-core style business. I build it using Electron bridged with llama.cpp in its own process. On initial load, the system will try and figure out if you have a compatible graphics card and if not default to CPU-only (I ran into a lot of issues of graphics card beeing seemingly compatible but then crashing on first query, there's a whole saga to this lol). Code execution happens in a sandbox that can only write to the chat's folder and has no network unless asked (sandbox-exec on macOS, Landlock on Linux and a low-integrity process on Windows). Images go to a small Qwen3-VL model. Dictation is a local Whisper model. The hardest part was definitely to get the smaller models to use tools reliably. Ran into a ton of issues of 4B models happily saying "here's your chart" without ever calling the chart tool or get stuck in an infinite call loop. I spent a fair amount of the work building guards vs tweaking prompts (that didn't really work for small models). I understand the pushback that local models are weaker than frontier cloud models. This is true, but for most tasks for most normal people, I'd argue you rarely need the newest frontier model. From my own evals, systems with 16+ GB of RAM do really well, and if you have a GPU or more RAM, you get very close to what you'll get out of the box with a frontier hosted model. Excited to hear what you think and see how it runs on other systems! https://ift.tt/oRc83Xn October 26, 2026 at 09:42PM

Show HN: RPI Pico (RP2040) PIO Emulator Package for Go https://ift.tt/AadQnMv

Show HN: RPI Pico (RP2040) PIO Emulator Package for Go I have been developing a bus communication protocol for a while using the Raspberry Pi Pico (RP2040)'s programmable IO (PIO) hardware (basically- 8 tiny processors that run assembly programs to manipulate GPIO pins) and I needed some way of testing my programs, as the real hardware is undebuggable. Unfortunately, none of the existing emulators I could find supported connecting multiple RP2040s together, and they each had a few different missing bits of functionality, so I decided to make my own! My emulator supports running multiple RP2040s electrically connected together, and emulates the whole of their PIO blocks (all 8 processors each), along with all of the more niche inter-processor interactions and GPIO functionality I could possibly find in the datasheet. I built it as a Go package so that it can be used inside of other programs; I intend on using it myself inside a test suite against my in-development protocol. I've spent quite a long time checking and double-checking most of the logic so it is as complete and accurate as I can make it, but if you spot anything amiss, I'd really appreciate the feedback. Thanks for checking it out! Hope it can be useful to other RP2040 users. https://ift.tt/v6JUrhc October 26, 2026 at 10:59PM

Show HN: Hurston-1 open source TTS that speak Southern American and Afro-English https://ift.tt/719rZzO

Show HN: Hurston-1 open source TTS that speak Southern American and Afro-English Hey HN, I’m open sourcing my Hurston-1 narrowband TTS model (Apache-2.0). It is very good with American English and does especially well with Southern American and Regional African English accents. Destined AI is based in Atlanta, and I built this because I’m amazed by what AI can do, and I wanted to help introduce and welcome more people to feel comfortable using and interacting with AI. Since 2021, I’ve been collecting fully consented voice data from over 20,000 people, 1M audio recordings, and over 1,800 hrs of audio captured to date. For this model, I used a small part of the data, 136 hrs, to finetune Maya1 and expand it’s capabilities. Hurston-1 is a narrowband TTS model that focuses the output on telephone use because that is still where most of these voices will be heard. I’ve used it to support customers such as Amazon and Salesforce and generated over half-mill revenue with it. Now I’m open sourcing it to help unlock opportunities for others as well. Limitations: this model doesn’t maintain the exact same voice across prompts and doesn’t support voice cloning. For many use cases, this is fine but maybe not so - if you’re creating podcasts. This is the first release - I’m already working on the next version with improvements. I would appreciate, if anyone is interested in this space, download the model from huggingface, check it out and let me know any feedback. Also, open to connecting with others in this space as well. https://ift.tt/bNuF8pQ October 25, 2026 at 07:00AM