Show HN: Matcha – fresh job postings pulled directly from company career pages https://ift.tt/GQmO5Wb

Show HN: Matcha – fresh job postings pulled directly from company career pages https://matchajobs.co/ September 22, 2026 at 06:44PM

Show HN: Vellum, the best diagram editor you'll ever use https://ift.tt/rRDmhM4

Show HN: Vellum, the best diagram editor you'll ever use I love Excalidraw, I use it all the time for work to quickly draw up a proposal for a customer. But its annoying I have to re-draw the same thing, but better in Draw.io / Visio when it comes to writing the formal documentation later. Vellum is designed to give you the speed of a whiteboard, but the architecture-diagram-finish in quality. It uses YAML to store everything, and even has a 'graph' section for those of you who like the diagram-as-code elements of mermaid... but vellum allows full authoring of layout without the restrictions of Mermaid. I love it to bits. I hope you will too. Been working on it a while, but decided to make it source available here: https://ift.tt/w8NZjYC Proud to say its not purely vibe coded slop, but I used Claude to help with some of the more challenging geometry based elements and some housekeeping stuff. https://ift.tt/3BKkfET September 21, 2026 at 11:05PM

Show HN: Distributed SQLite on Modal https://ift.tt/z8u0Mro

Show HN: Distributed SQLite on Modal https://ift.tt/oEmDzLp September 21, 2026 at 11:05PM

Show HN: Judge HN Threads with Jev https://ift.tt/qGeYbNm

Show HN: Judge HN Threads with Jev https://hnjudge.vercel.app September 21, 2026 at 10:54PM

Show HN: Swarm, an offline family tree app for macOS https://ift.tt/i17ujgZ

Show HN: Swarm, an offline family tree app for macOS Hi HN! My wife and I got deep into our family genealogy and wanted to map out all our relatives - but keep the data on our own Macs instead of uploading it to a genealogy service. Nothing we found did that, so we built Swarm, a native macOS app in Swift. It's built on GEDCOM 5.5.1, the universal genealogy file standard, so your tree exports cleanly to other ancestry apps if you ever want out. Example trees of famous historical families are included to play with. Features: multiple ancestry views (tree, fan, world map), media files, GEDCOM import/export, PDF export, tree merging, and kinship naming. Fully open source, no AI in the app, no internet connection required, no account required. Would appreciate any feedback! https://ift.tt/2TS7hrs September 20, 2026 at 11:01PM

Show HN: AI Hack Watch - timeline and dataset of hacking incidents(JSON/RSS) https://ift.tt/VMFGYKE

Show HN: AI Hack Watch - timeline and dataset of hacking incidents(JSON/RSS) https://aihackwatch.com September 20, 2026 at 10:33PM

Show HN: KillSwitch – a programming language designed to be difficult for LLMs https://ift.tt/dSmC7EP

Show HN: KillSwitch – a programming language designed to be difficult for LLMs https://ift.tt/1DO9iT5 September 20, 2026 at 02:40AM

Show HN: CUA-S1 – A System One Model for Computer Use https://ift.tt/eP14pvK

Show HN: CUA-S1 – A System One Model for Computer Use Hello HN! We're Dillon and Francesco from Cua. We were wondering how many computer use tasks actually need a full general purpose LLM (e.g. gpt-6-astra, claude-opus-5 etc.) to think through all their decisions and steps. Some tasks require thinking about a plan, exploring different paths, recovering from failure. Other tasks are a question of making local decisions, like this value should go in this box, or should I check this box, or this element should be ignored. We wondered how far we could go with a small model trained to only make these kinds of decisions. Our inspiration was Typesafe's Jev and its System One Model framing. This is a nod to the dichotomy between thinking quickly, automatically, and intuitively (system 1) vs. thinking slowly, analytically (system 2), as described by Daniel Kahneman. The interesting question for us was: what happens if you give a model an interface of current context, and a set of possible choices, and you ask it to return a probability for each choice? This kind of model does not generate output token by token like most LLMs do, but rather scores the options you give it, which you can check, trust, and use to drive your app's behavior. CUA-S1 is our answer for narrow, specialized decision models for computer use. Our first release is CUA-S1-FORMS. We built this from ideas and code in jevlike, and then trained a second model just to handle form interactions. It has 706k parameters, and the original checkpoint is 2.8 MB. The first training iteration took less than 30 minutes on synthetic data. Given a set of structured elements and values extracted from a document, it predicts whether to use the given value, CHECK, CLICK, or SKIP for each element. It does not predict new values for text fields, and does not consider screenshots. Element decisions are scored together, and your code can order the actions, and Cua Driver will execute them one at a time. A first evaluation of this specialist vs. hosted Jev on our form task: - For the whole decision set: 99.7% correct vs 83.6%. - For the subset of steps that require an action: 100% correct vs 96%. - For the subset of steps that are just leaving already-filled fields alone: 100% correct vs 74%. The specialist was trained specifically for this task and convention (just press skip for already filled boxes), while hosted Jev has not been fine-tuned for it, so this is an experiment in scoped specialization. We measured 7-9 ms to score a form locally vs. 260-280 ms per call to hosted Jev including network latency, though those samples measure different things and are not end-to-end form completion times. Our interest here is in the space between a brittle script and a general agent loop. The content and layout of form fields vary enough that scripts get unwieldy, but the set of available decisions can remain narrow and well scoped. We want to explore the possibility of a general agent encountering something novel, and passing well understood decisions over to specialists like this. That is a direction we are looking into. The current release is for forms only. We're open sourced the synthetic data generation, training, evaluation, and Driver integration under libs/cua-s1 with an MIT license. Comments welcome! Especially if you are building computer-use agents and have run into a recurring decision that is too variable to script but is too narrow to call another LLM for. https://ift.tt/SdDUK5P September 19, 2026 at 10:52PM

Show HN: BreachScanner – Check if your email has been in data breaches https://ift.tt/0pXyeiJ

Show HN: BreachScanner – Check if your email has been in data breaches https://ift.tt/2JKb9rP September 20, 2026 at 12:30AM

Show HN: Learn divergently/chaotically with tree-learn https://ift.tt/rQm0tUn

Show HN: Learn divergently/chaotically with tree-learn Chat interface is linear, I never quite liked that for when I would have too many questions, I would end up asking questions in a numbered list and not really have independent branches of conversation. On cli, the pi agent gives you this same functionality. On web, I made Fable make this. BYOK Openrouter - all client side - you can check the network console! https://ift.tt/rTy7dm6 https://tree-learn.srijanshukla18.workers.dev September 18, 2026 at 11:21PM

Show HN: Jev helps you to not run malicous code https://ift.tt/qby5It2

Show HN: Jev helps you to not run malicous code https://ift.tt/hTRxrdK September 18, 2026 at 11:44PM

Show HN: App Launch: GrabThat (NLP Launcher for Windows) https://ift.tt/50NSBvc

Show HN: App Launch: GrabThat (NLP Launcher for Windows) https://grabthat.ai/ September 18, 2026 at 11:15PM

Show HN: Dishlist – my favorite things on the menu https://ift.tt/voWbaQw

Show HN: Dishlist – my favorite things on the menu Hi HN! My friend and I wanted to rank our favorite breakfast burritos. Yelp and Beli allow users to rate or rank restaurants, but not individual dishes. So I built dishlist, an iPhone app around the things on restaurant menus. I schlepped around Dogpatch, Potrero Hill, and Mission Bay taking photos of restaurant menus. Then, I used Claude with my menu schema to turn the photos into structured data. Garry Tan likes to say, "The rocks can talk." Also, "The rocks can see." I represent the menu hierarchy in the database. LLMs can determine what type of thing each menu item is. This lets me index things like breakfast burritos across restaurants rather than just indexing the restaurants themselves. The app suggests possible tags for a menu item. You decide which ones you want to use on your profile. You can build a list of your favorite breakfast burritos, pizzas, burgers, or whatever else you care about. I value my privacy, so you can use the app without logging in at all. If you want to save menu items, you can sign in with Apple. I don't collect names, emails, or phone numbers. If you want to share with friends, you can add a handle. You have to follow another user to see their dishlist, and they have to follow you to see yours. Follow requests are approved or denied by the user. The app is currently available through TestFlight while the App Store submission is being reviewed. https://ift.tt/HzJgIRE I'd love feedback on the idea, the search experience, and especially whether organizing things around individual menu items feels useful. https://ift.tt/HzJgIRE September 17, 2026 at 11:53PM