Avowd
AI visibility across ten engines, including Chinese ones
What it does
Avowd measures whether AI engines actually recommend your brand. It asks ten engines — ChatGPT, Perplexity, Claude, Gemini, Grok, plus Doubao, Qwen, DeepSeek, Kimi and Yuanbao — your buyers' real questions, records every answer, and reports mention rate, share of voice and citations monthly. Everything measured, nothing invented: every percentage traces back to a raw engine answer, and the methodology is open on GitHub.
Get your business recommended by AI. We measure and improve how ChatGPT, Perplexity, Claude, Grok and Gemini — and 豆包 / 通义千问 / DeepSeek / Kimi / 元宝 — talk about your brand. 让 AI 青眼相看。
Your customers stopped searching — they ask an AI. We measure what ten engines actually say about you, including the Chinese ones most tools skip , then make your name the answer. Your brand isn’t in that answer. The customer never sees you, never bounces off your site, never shows up in your analytics. The loss is invisible — that’s what makes it expensive. Four moments that cost real money, none of which show up anywhere you look. A family two blocks away asks an assistant where to eat tonight. It names three restaurants. You are not one of them, and you will never learn that the question was asked. Half your customers search in Chinese. Doubao and Qwen read Chinese sources, find nothing…from avowd.ai
Does the same job
all alternatives →
More ai this month
the category →
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.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
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.
AI · 16d ago · simedw.com