CrawlHunt - Framer SEO Plugin
SEO Audits & Optimization for Framer Websites
What it does
CrawlHunt is an SEO optimization tool for Framer that audits your website for technical and on-page SEO issues, including meta tags, headings, image ALT text, broken links, and indexing problems. Get clear, actionable recommendations to improve your site's SEO and search visibility.
Crawl Hunt - SEO Optimization Tool
CrawlHunt is an AI-powered SEO tool for Framer websites that helps you find, understand, and fix technical SEO issues from one dashboard. Audit your Framer website for problems that can affect Google rankings, search visibility, indexing, crawlability, and website performance . Run a complete Framer SEO audit to identify missing meta titles and descriptions, image ALT text issues, broken links, duplicate metadata, heading problems, indexing issues, and other technical SEO errors. Framer SEO Audit: Scan your website and identify technical and on-page SEO issues. AI SEO Fixes: Get AI-powered recommendations and assistance for fixing common SEO problems. Meta Title & Description Analysis: Find…from framer.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