SiteCheck Pro
Scan your website. AutoFix what’s safe. Verify the results.
What it does
SiteCheck Pro starts with a FREE website scan of up to 50 pages for SEO, AI visibility, SEM & PPC readiness, accessibility, security, crawlability and indexability. It then shows what can be safely repaired with AutoFix, automating supported fixes that could otherwise require hours of manual work. SiteCheck Pro verifies the website again and provides a Completion Guide for anything requiring human attention. No email, registration or credit card required for the free scan.
SiteCheck Pro audits websites for SEO, AI visibility, accessibility, security, crawlability, indexability, and SEM readiness, safely repairs supported findings with AutoFix, and verifies the results.
Website audits. AI Visibility. SEM Readiness. Safe AutoFix repairs. SiteCheck Pro audits your website for SEO, AI Visibility, SEM & PPC Readiness, Accessibility, Security, Crawlability, and technical health — then helps repair eligible issues with safe AutoFix workflows. AutoFix is designed to repair supported website issues safely, verify the result, and show measurable improvement after completion. Identify the issues limiting visibility, discoverability, trust, performance, and why website referrals have fallen. AutoFix safely repairs supported issues, improving AI Visibility, search discoverability, and SEM Readiness. See measurable score improvements and receive a completion…from sitecheckpro.app
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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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Life & fun · 10d ago · louisabraham.github.io
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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.
AI · 16d ago · simedw.com