Tearline – wrap any HTML in one tag, get a thermal receipt and a PNG
One custom element that renders anything you wrap in it as a thermal receipt, then exports it as a PNG. Zero dependencies, no build step, MIT.
What it does
One custom element renders whatever you wrap in it as a thermal receipt — then hands your users a PNG of it. No canvas API, no template language, no image service. Markup goes in, paper comes out. Headings, rules, tables, lists, images. If it renders in HTML it prints on paper — you are not learning a receipt DSL. One call turns the receipt into an image, at any scale, ready to post. The paper is styling wrapped around your markup, so everything a browser does with text still works. The torn edge and the barcode are generated from a seed, so a receipt renders identically every time — and the export matches what your user actually saw. It is a custom element. React, Vue, Svelte, Astro, or a…from tearline.kynth.studio
Does the same job
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Hi HN, I built Eigendrum, a web tool that solves the 2D wave equation for arbitrary shapes so you can hear what they sound like as drums. How it works: * Solves -∇²u = λu using finite element analysis (Kφ = λMφ) on a triangle mesh. * Validated to <0.1% error against closed-form solutions for circles (Bessel zeros) and rectangles. * Sound model factors in strike location, Rayleigh damping, and mallet width. * Includes Kac drums I & II to demonstrate identical sound spectra from different geometries. * No frameworks, build steps, or dependencies. Repo and tests:…
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Launched alongside, August 2026
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Life & fun · 10d ago · louisabraham.github.io
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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.
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