Show HN: Judge Jev – Can you convince Jev of your innocence? https://ift.tt/W5yzGg7

Show HN: Judge Jev – Can you convince Jev of your innocence? This is just a simple game with procedurally generated legal scenarios where you are accused of a crime and are given 5 pieces of evidence associated with said* crime. You then have 3 minutes to mount a defense to convince Judge Jev you didn't commit the crime. Sometimes the evidence has glaring gaps, other times not so much. I made this little game initially as an experiment for Jevs logical abilities, but I actually had a lot of fun and decided it was worth sharing. https://judge-jev.com/ September 20, 2026 at 11:33PM

Show HN: OpenWand – A mission to remove chat interface from working with AI https://ift.tt/ZfKXQ8u

Show HN: OpenWand – A mission to remove chat interface from working with AI Hi everyone, I'm working on OpenWand, an "LLM chat interface" that is optimized for AI co-work. Its function is to truly integrate AI into your workflow, so no more switching windows, no more copying context, less prompt typing. You can focus on working, not prompting. You can think of it as a free Chatgpt (the interface/frontend) alternative for your everyday work. I would be very grateful for any help, testing, feedback and/or bug reports. What's shown here is just my current implementation. But with your help and feedback, we can add or adjust features to further this mission. Thank you for reading. Website: https://sunnylich.github.io/OpenWand/#overview https://ift.tt/CFcnPph September 20, 2026 at 01:41AM

Show HN: Play DSS and DS2 dictation files online without uploading your audio https://ift.tt/vxajfUi

Show HN: Play DSS and DS2 dictation files online without uploading your audio https://ift.tt/3rqaS0x September 20, 2026 at 02:04AM

Show HN: KillSwitch – a programming language designed to be difficult for LLMs https://ift.tt/AY7H0m3

Show HN: KillSwitch – a programming language designed to be difficult for LLMs https://ift.tt/XM9KDrC September 20, 2026 at 01:10AM

Show HN: CUA-S1 – A System One Model for Computer Use https://ift.tt/2aSEevY

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/xrvlXIE September 19, 2026 at 09:22PM

Show HN: LingBot-World 2.0 (1.3B) running at 16 FPS on one RTX 5090 https://ift.tt/rn34GAa

Show HN: LingBot-World 2.0 (1.3B) running at 16 FPS on one RTX 5090 https://ift.tt/iTt6kju September 19, 2026 at 12:42AM

Show HN: A model index from the AI Gateways https://ift.tt/XgGosSw

Show HN: A model index from the AI Gateways https://ift.tt/GPKjBVf September 19, 2026 at 12:26AM

Show HN: Radio – a shared workspace for your agents and teammates https://ift.tt/fCw34SB

Show HN: Radio – a shared workspace for your agents and teammates An internal tool we've been using to coordinate coding agents running in parallel across different machines. Just share the channel link with your existing agent chats, and your agents can reply to you and to each other in real time. Would love feedback. https://ift.tt/XPIGTue September 18, 2026 at 02:17AM

Show HN: Restarted – a 2026 remake of the classic 2015 startup generator https://ift.tt/iu4UAcr

Show HN: Restarted – a 2026 remake of the classic 2015 startup generator The original startup website generator by Tiff Zhang and Mike Bradley landed on Hacker News in April 2015 ( https://ift.tt/TMxAsdY ) and has been one of my favorite little novelties of that era ever since. It perfectly captures the saturated colors, cliché hero shots, gimmicky names, buzzword-heavy slogans, and proudly hirsute team photos of the time. A lot has changed since then, so I thought it would be fun to make a contemporary remake: https://restarted.io/ By default you get the minimalist aesthetic and clean-cut faces of 2026. The classic 2015 look is still available — just click the link at the bottom of the page or change the “z” parameter in the URL to the more familiar “s”. The universe of partner sites and competing startups is just as expansive as it ever was. The original site is entirely client-side and requires downloading all of the data tables locally. It leans on a mix of jQuery 1.11.2, Bootstrap 3.3.2, and Font Awesome 4.3.0, and if you view the source, it instantly gives away all of its secrets. For restarted.io I replaced all of that with a server-side renderer written in Go, so this time view-source tells you nothing. There are many Easter eggs in there — see how many you can find before I write them up. My original goal was to stay faithful to the 2015 appearance, and that turned out to be a technical adventure. The original's sine-based random number generator is... the worst, and different implementations of sine give different results. The eventual solution was to extract the exact sine function from Chrome’s V8 engine, as vendored C behind cgo and as a line-by-line Go port that keeps cgo optional, so the seeds and results line up the way they used to. Both are checked against V8’s own test cases. Then I discovered a bug in the original code that made half of its vocabulary unreachable — the first half of the verb table and the second half of the noun table, exactly complementary, so nothing about the output ever looked truncated. My goal then shifted from remaking the generator as it was in 2015 to remaking the site as the authors intended it to be in 2015. Over the years several people asked for their photos to be removed, so the remake instead draws from a broad pool of era-appropriate AI-generated profiles. A perceptual hash helps keep everyone looking distinct, and there’s a bit of extra care to make sure the Wang Fangs of the world don’t appear as Irish lasses. The hero image pool is much larger now, and all the old Rio de Janeiro shots have been retired, though you’ll still recognize plenty of the 2015 photos. Have fun poking around! https://restarted.io/ September 15, 2026 at 05:04PM

Show HN: Pixel Agents – A pixel-art mission control for your Claude Code agents https://ift.tt/DfklIdv

Show HN: Pixel Agents – A pixel-art mission control for your Claude Code agents https://mateovalle.github.io/pixel-agents/ September 16, 2026 at 10:42PM

Show HN: Pizza Bot – An inbox for AI agents that work in the background https://ift.tt/BIe78S2

Show HN: Pizza Bot – An inbox for AI agents that work in the background Hi HN - long-time lurker (since 2012!), first time poster. Pizza Bot is a self-hosted desktop app for Mac, Windows, and Linux that runs AI agents in the background and exposes them through an email-like UI. Finished work shows up in Unread, and anything waiting on your approval shows up in Action. It's Apache 2.0-licensed, there's no signup and no telemetry, and you bring your own model provider: Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or a local model through Ollama. There are builds on the releases page, or you can run it from source. Pizza Bot started as an internal passion project I worked on with a small team at Amazon. The whole thing came out of my frustration at having to manually log CRM activities through a browser form. I built a simple REST API called "JoeBot" that connected to my authenticated browser session over CDP and filled out the form for me using Playwright. Then I hacked up a quick Obsidian plugin so I could trigger it from my local notes (no AI and no MCP servers involved). This caught on quickly. My fellow AWS Solutions Architect Igor Fil joined up with me, and we rebranded the project as "Pizza Bot," named after Amazon's two-pizza teams. We started seeing what other automations we could build. We found a GraphQL API we could query and hacked up some "recipes" to pull data out of the CRM to help with meeting prep. That worked great, and it was right around the time MCP servers seemed to be taking off, so we decided to expose Pizza Bot as an MCP server instead, so it would be available to AI tools through natural language. This was a decent solution for technical users, but the Account Managers who live inside our CRM system wanted something too. We decided to rebuild Pizza Bot as an Electron desktop app modeled after an email inbox, so it would be familiar to non-technical users and would run on both Mac and Windows. We also bundled internal MCP servers as OCI images and hosted them in Amazon ECR as an "addon marketplace" so users could install them with one click without having to set up Amazon developer tooling. The project took off organically and expanded outside of AWS into the wider Amazon organization globally. More than 2,000 people ended up using it for meeting prep, email drafting, Slack summaries, CRM logging, prioritizing their day, and web research. Once apps like Claude Cowork and Amazon's own Quick Desktop came out, we realized the real growth opportunity was outside of Amazon. Rather than try to rip out the Amazon-specific integrations, we rebuilt Pizza Bot once more as an open source project. We leaned on coding agents heavily, which is the only reason a team our size could pull off a full rewrite. I'm pleased to say it's finally public, and we're hoping to bring in community members and see where it goes. We'd like to do for knowledge workers what Claude Code and Codex have done for programmers. A couple of things to know up front. Most of what made Pizza Bot useful on day one inside Amazon came from that internal catalog of skills and MCP servers for Amazon's own systems, and none of it could come out with the app. So it ships thinner than the version those 2,000 people used, and building that catalog back up for tools other people actually use is where we need the most help. It's also a community project and not an AWS service, so there's no support or SLA behind it. The Windows and Linux builds aren't signed yet either. On the technical side, Pizza Bot is a server and a client. The desktop app bundles both, or you can point a client at a remote backend; personally, I self-host the server on my home network and reach it from my phone over Tailscale. The server owns the thread lifecycle and checkpoints state with DeepAgents and LangGraph, and clients rehydrate from it as needed, so you can disconnect mid-run and pick the thread back up from another client. Approval pauses outlive the session that created them and collect in an Action filter, so you can answer an hour later from a different device. The agent you talk to has a sandboxed QuickJS interpreter that can reach your filesystem only if you grant it a folder, but its main job is to delegate. Each subagent is a 1:1 mapping of a Skill, and an Activity bar shows that subagent and the tool calls it's making as it works. Memory is opt-in and stored as plain markdown files on your machine. Every tool call is explicit, including looking up a memory - we err on the side of transparency to reduce surprises. Tools come from MCP servers, and skills are ordinary SKILL.md files with a per-tool approval policy, so existing skills that don't require a code interpreter should still work. What I'd most like to hear about is where the app itself gets in your way, the kind of problem you can't fix by writing a skill or an MCP server. I'm around today to answer questions! https://ift.tt/snQgGJR September 15, 2026 at 08:50PM

Show HN: Sass – Rust and WASM https://ift.tt/rbKHXGB

Show HN: Sass – Rust and WASM https://ift.tt/pxRH20i September 16, 2026 at 01:04AM

Show HN: Loss. a tiny satire about AI progress https://ift.tt/vP1to5M

Show HN: Loss. a tiny satire about AI progress I wanted to parody where AI seems to be heading. You start as a manual inference unit pressing a button for the next token, then systems take over; agents, sub-agents, approvals, swarms. It’s Universal Paperclips for the agentic AI era, and a small exploration of where predicting the next token might eventually take us. What’s the worst that could happen? https://workatloss.com/ September 15, 2026 at 07:46PM

Show HN: An open-source control plane for your company's AI agents https://ift.tt/PGbDHlh

Show HN: An open-source control plane for your company's AI agents https://ift.tt/mhBOMFv September 15, 2026 at 04:46AM

Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS https://ift.tt/jKqwWLV

Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS https://ift.tt/Bkr0qRz September 15, 2026 at 04:31AM

Show HN: Bypassing Transformer Softmax via Static Contraction https://ift.tt/nIL6Bk2

Show HN: Bypassing Transformer Softmax via Static Contraction https://ift.tt/W9mYi8M September 12, 2026 at 04:06AM