Mentionry
Track AI Visibility and Automate Backlink Outreach with AI
What it does
Mentionry helps brands gain visibility in AI-generated answers, optimize their presence in LLM-based answer engines (AEO and GEO), and find and pitch for backlinks with AI. Track share of voice, brand mentions and citations across ChatGPT, Perplexity and Google AI Overviews. See which prompts you appear in and where competitors outrank you. Then earn the links: Mentionry finds the openings, writes each pitch, sends from your own mailbox, and verifies which backlinks went live.
An AI SEO tool for the answer engines: it reads what ChatGPT, Google AI Mode and Gemini say about you daily, sorts every domain they cite, then pitches the ones you can earn.
Every source read across your tracked answers, split by who controls it. Owned is your own pages, operated is your words somewhere else, and earned is the band that actually counts — with competitors' own sites kept out of the pitch list. Questions being asked right now, registries that take a submission, companies that already know you, and pages linking to something dead that yours could replace. Each one checked reachable, with the email written for it. This is what the desk hands you: a page at a real publication that the answer engines read before naming anyone, and the message already written for it. Nothing here is a customer of ours, and nothing here was paid for. You asked what…from mentionry.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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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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