Queay - One QA Platform
Manual When You Want. Automated When You Need.
What it does
Queay brings software testing into one platform. Write BDD scenarios in plain English, run browser automation, manage test cases and plans, test APIs, track defects, and generate test evidence - all in one workspace. Built to help QA and engineering teams reduce manual testing effort and ship with confidence.
Queay is one QA platform for manual and automated testing — no-code and low-code test creation, evidence-linked defects, and AI only when it helps.
Test your way with Queay. Create and execute manual tests, build automated tests, and manage everything in one QA workflow. A real walkthrough of building and running tests with Queay — see exactly what you get before you sign up. Queay is an AI-powered software testing platform that combines manual testing, test management, UI test automation, API testing, CI/CD execution, and AI-assisted testing in one QA workflow. Queay is designed for QA engineers, software testers, developers, QA leads, and engineering teams that need to manage manual and automated testing in one platform — without switching tools for test case management, automation, and reporting. All of it lives in the same QA…from queay.com
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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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Life & fun · 10d ago · louisabraham.github.io
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
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.
AI · 17d ago · simedw.com