Seofable
SEO articles on autopilot, without the AI slop
What it does
Connect your site once. Seofable finds keywords you can actually rank for, reads the live top 10 for consensus and gaps, writes a fact-checked article (visible verification note on every post) and publishes it daily — structured to get cited by ChatGPT & Google AI Overviews. First article free.
Connect your site. Seofable finds keywords you can actually rank for, interprets the search results, writes fact-checked articles and publishes them daily. First article free.
Seofable finds the keywords your site can actually rank for, writes fact-checked articles — no AI slop — and publishes them daily, structured to get cited in ChatGPT, Perplexity and Google AI Overviews. Most AI writers ask for a topic and start typing. Seofable works like an SEO strategist — the topic is the output of research, not the input. For every keyword Seofable reads the live top 10: the consensus you must cover, the questions nobody answers well, and the weak results that signal your opening. That information gain is the reason an article ranks. Every number, date and policy claim verified against live sources before publishing — with a visible fact-check note on the article. Live…from seofable.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 · 17d ago · simedw.com