Vignelli
On-brand marketing assets, in 3 clicks
What it does
Vignelli turns your website into a working brand kit in about two minutes; logo, colors, fonts, and voice, pulled from the site you already have. From there it generates on-brand social graphics in a single shot, then hands you a real editor: every layer stays manually editable, so you fix a headline by dragging it, not by re-prompting. Built for businesses that have a brand but no designer. Start free.
Vignelli builds your brand kit from your website and designs on-brand social graphics with you — your colors, your fonts, your voice.
You built the brand — Vignelli works with it. Graphics in your colors, your fonts, your voice, designed with you. Your logo, colors, fonts, and voice — pulled from your site and shown to you first. Approve, adjust, or overrule any of it. Say what you need, shape what comes back, and export when it's yours. Extraction isn't magic — sometimes a color is off or the wrong font wins. Every part of your kit is editable, and your corrections stick, even if you rebuild from your site later. Download your brand guidelines as a PDF and share them with anyone. Generate cohesive carousels — 2 to 10 slides that hang together — plus wide and link-preview formats for ads and blogs. Every graphic ships…from vignelli.io
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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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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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