AnswerShaper
AI Search Visibility, M2M Infrastructure & S2S Attribution
What it does
Track your brand presence across ChatGPT, Perplexity, and Gemini. Inject machine-readable M2M structured data (JSON-LD, llms.txt), and attribute real revenue from AI search using cookieless Server-to-Server tracking.
AnswerShaper (Answer Shaper) helps brands track, analyze, and optimize their visibility across AI search engines like ChatGPT, Perplexity, Gemini, and Claude.
Official data from Microsoft Advertising reveals the Zero-Click reality. 99.73% of visibility happens inside the AI. The few who click convert 2x better. ChatGPT, Claude, and Perplexity are reshaping the buyer's journey right now. Your competitors are already being cited. If you don't optimize your Generative Engine Presence today, you vanish tomorrow. On Google, there were 10 blue links. On ChatGPT and Perplexity, there is only ONE recommended answer. If it's not yours, your competitors capture 100% of high-intent buyers. Your prospects no longer scroll Google: they ask ChatGPT or Gemini directly 'What is the best solution for...?' AI doesn't show 10 blue links. It analyzes real-time data…from answershaper.com
Does the same job
all alternatives →More growth this month
the category →
AstraPixels▲267A pixel-art solar system at its real current positions.
Growth · 29d ago · astrapixels.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