PTE Monster
AI-powered PTE practice with real prediction questions
What it does
Practice PTE Academic & PTE Core with repeated real exam questions, prediction tiers, full mock tests and instant AI scoring that shows lost marks.
Repeated real exam questions, ranked into prediction tiers by how likely they are to appear next. Every answer — written and spoken — scored by AI in seconds, marked up to show you the exact words that cost you points. The passage argues that remote work have has reshaped productivity, and that occured occurred across most industries. Every question is sorted into a tier by how likely it is to appear on your exam. Study the top tier and you walk in having already seen the paper. The complete bank. Every question, every task type, both exams. Questions that have appeared in recent real exams — the proven repeats. The questions most likely to come up next. Short on time? Start here. Short on…from ptemonster.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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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.
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