linkedinpost.ai
Turn any idea into a LinkedIn post in seconds
What it does
linkedinpost.ai turns your raw ideas into publish-ready LinkedIn posts using AI. Pick a topic, generate a draft in your voice, edit if you want, then post. No blank page, no generic AI phrasing, no hours spent planning content. Built for founders, consultants, and creators who need to stay visible on LinkedIn without becoming a full-time writer. Start free, see your first draft in under a minute.
AI LinkedIn content for founders and creators. Generate authentic posts, plan a 30-day calendar, and schedule without sounding generic. Start free.
linkedinpost.ai writes, reviews, designs, and schedules LinkedIn posts through an AI content council, with your approval before anything goes live. Staring at an empty box kills momentum. Ideas feel obvious in your head but vanish the moment you start typing. A single good post can eat an hour. Doing it three times a week alongside real work is the first thing to slip. Clichés like "in today's fast-paced world" and hollow hooks. Your audience can smell autopilot, and it costs you credibility. Prefer a playbook? Read the LinkedIn guides , compare Taplio vs linkedinpost.ai , or browse AuthoredUp alternatives . Four steps to a repeatable LinkedIn workflow that actually sounds like you. See the…from linkedinpost.ai
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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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