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

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/6ZpxmdP October 26, 2026 at 11:12PM

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

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/5Mgs89T October 27, 2026 at 12:29AM

Show HN: HealthViewer – A local-first desktop app for tracking lab results https://ift.tt/V3ApQEi

Show HN: HealthViewer – A local-first desktop app for tracking lab results https://ift.tt/cEw7K0e October 26, 2026 at 08:56PM

Show HN: Hurston-1 open source TTS that speak Southern American and Afro-English https://ift.tt/RlUu8KJ

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/9wegXTx October 25, 2026 at 08:30AM

Show HN: Duls – A bit of du, a bit of ls. Best of both https://ift.tt/U2eCIxh

Show HN: Duls – A bit of du, a bit of ls. Best of both https://ift.tt/Ddw0s1G October 25, 2026 at 03:14AM

Show HN: A phone lock I can't undo on impulse because I never learn the code https://ift.tt/Zke9BVH

Show HN: A phone lock I can't undo on impulse because I never learn the code https://ift.tt/x64tFDJ October 24, 2026 at 09:22PM

Show HN: Agentsh – A Decentralized, Scalable Multiagent Harness https://ift.tt/oD3BEm5

Show HN: Agentsh – A Decentralized, Scalable Multiagent Harness https://ift.tt/sd6KxP8 October 25, 2026 at 01:19AM

Show HN: Swurf – Swipe through the real web https://ift.tt/vwEklb4

Show HN: Swurf – Swipe through the real web Hello HN, Swurf allows you to swipe through the real web one page after another. It learns what you like and doesn’t show you the same page twice. The idea came naturally: there’s a seemingly infinite amount of high quality blog posts, random topic rabbit holes, and trending or cool sites that exist which the average person simply will never load on their browser. Swurf solves that by allowing you to rapidly traverse a constantly growing and learning feed of sites that it thinks you’d want to view. Quickly sift through endless pages and find things you like - effortlessly. One of the core drivers for making this project was the desire for real content. The project started as a simple tool for myself to find new and interesting things to learn and read about. Hours spent scrolling on my phone - while it still has its place - is increasingly losing its sense of reward. It’s genuinely hard to put into words exactly how swurfin’ feels but it’s almost refreshing in a sense. I encourage you to try it out and provide feedback. At this time there are no accounts and you can jump in and start swurfin’ immediately. There is a three dot menu button on the bottom right which allows you to submit links, save links, control adult content, and search similar topics. One of the things that’s great about Swurf is that its almost like a group browsing session (though entirely private) where everyone else who is spending time hunting down great reads or solid sites is helping massage the feed for you and you for them - all within your own set of interests. Enjoy and please ask any questions you have as theres plenty of room to grow. https://swurf.org/ October 23, 2026 at 01:54PM

Show HN: Vericto – Check AI agents' SQL before Postgres or MySQL runs it https://ift.tt/zD4Kd2t

Show HN: Vericto – Check AI agents' SQL before Postgres or MySQL runs it https://ift.tt/t2vMi6Q October 23, 2026 at 09:16PM

Show HN: Fire hazard zone, power line risk and fire history for any CA address https://ift.tt/5hRbAUX

Show HN: Fire hazard zone, power line risk and fire history for any CA address https://ift.tt/t4IMnKN October 22, 2026 at 10:52PM

Show HN: OnKey – I'm a bad singer, so I built an app to show where I'm off-key https://ift.tt/d5x8sFE

Show HN: OnKey – I'm a bad singer, so I built an app to show where I'm off-key I'm not a good singer, but strangely enough I still like singing. I've improved a little over time with singing apps, but their song lists are limited by licensing, so I'd often open a song expecting the original and find a cover I didn't want to sing to. So I built OnKey: a free, open-source (MIT) Mac app that listens to whatever you're playing in Spotify, Apple Music or YouTube, learns the singer's melody, and shows you live where you're sharp or flat, with a score at the end. Nightingale did something similar years ago for your own audio files. OnKey works with the music you already stream. Demo (1:35, sound on): https://singonkey.app The first time you play a song, you can sing along while it listens, or play a whole playlist and let OnKey learn the songs in the background before you come back to sing them. When a song finishes it learns the notes on your Mac (a couple of minutes), and from the second play the notes scroll towards you before you sing them. Learning the song and scoring you both happen on your Mac. No account, no analytics, and the music is never saved or sent anywhere. Your takes stay there too. To check the score means something, I ran 20 recordings of singers, from poor to professional, that music experts had already scored. OnKey ranked them in nearly the same order as the experts did. It's still rough in places: busy mixes and harmonies can confuse which voice is the tune, and now and then a note lands an octave off. Scoring is forgiving on purpose for now, set for beginners like me, so stricter modes for better singers are next. And it's Apple Silicon and macOS 14.2+ only. I'd love to hear where it scores you unfairly or the notes go wrong, and I'm happy to answer questions about how it hears the music and learns a song. https://ift.tt/CP3YbDK October 22, 2026 at 09:04PM

Show HN: Gembop, a block-placement puzzle game in 2D and 3D https://ift.tt/erBcEhM

Show HN: Gembop, a block-placement puzzle game in 2D and 3D https://gembop.games/play October 21, 2026 at 10:04PM

Show HN: ULDA – Client-side encryption with a simple JavaScript API https://ift.tt/DXrnjql

Show HN: ULDA – Client-side encryption with a simple JavaScript API Hi there! We have designed client side encrypted storage that sync on multiple devices and allows to hold that data on your server. We have started as chat application in late 2022 that has completly open-readable database. The idea was simple - server holds zero secrets, like non at all. But we ran into the problem of authentication - there were no auth method what could make quick, synced authorization with completely zero secrets. So in early 2023 we came to an idea of authentication via proof of ownership by providing proof that next step is made by the same user. Now you can see our job as ulda core https://ift.tt/KNeFJsH . Ulda was called as universal liniar data authentication. But a year later we faced that “None need another extremely secure chat application” and we decided to export our ideas and share ulda as a method in order to gather some feedback from some smarter people. In 2025 we tried to publish our white papers at REKS, CMIS and in the end in Journal of Cryptology. Ulda was never published and then we decided that it would probable a good idea to go and publish at least something somewhere to bring some attraction and hopefully some feedback. So, we assembled an sdk, that worked via ulda core and gave developers an ability to work in with encrypted and synced storage directly by editing json. We tried to make is simple as possible. We hope to bring some attention not to a sdk itself, but rather to authentication method itself. You can read our white paper here: https://ift.tt/NfdVuZa Also, if you would like to see our sdk, you can also try it here: https://docs.ulda.io , https://ulda.io . Thank you very much for your time and we hope for your feedback about authentication method. Best regards, Mark Shaposhnik Vitalii Radko https://demo.ulda.io/ October 20, 2026 at 12:16AM