I'll start.

I believed that "keyword density" mattered. I spent hours making sure our target keyword appeared exactly 3-4 times per 500 words. I used tools that highlighted which words were "under-optimized." I even re-wrote paragraphs to squeeze in one more mention.

Turns out that hasn't been a real ranking factor for over a decade. Google's RankBrain (2015) and BERT (2019) made keyword density obsolete. These models understand context, synonyms, and user intent. They don't need you to say "best CRM for small business" five times. They know that "top CRM for startups" means the same thing.

What actually matters is topic coverage. Does your page answer the question completely? Do you cover related subtopics that a user would expect to see? Do you use natural language that matches how people actually ask questions?

Everyone is panicking about the March 2026 Core Update.
It started rolling out on March 27 and will take up to two weeks to complete .
The spam update hit just three days earlier and finished in 19.5 hours, the fastest spam update on record .

But here's what the data actually says.

JetDigitalPro analyzed 600,000 web pages across the update period. The correlation between AI usage and ranking penalties was 0.011, effectively zero . Google isn't penalizing AI content. It's penalizing low-value content that happens to be AI-generated.

Websites relying on mass-produced AI output without human oversight saw traffic drops of 60-80% . Affiliate sites were hit hardest 71% saw negative impacts .

Two days ago, I shared the 10k mistake product owners make with AI search. The response was overwhelming.

Since then, we've more than doubled our dataset at Rankfender. And many found they were invisible.

But here's what scared me more:

Of those who WERE visible, 43% had incorrect information in AI answers.

Hey PH Community !
We've been heads down building. Four new things in the works. I want to know which one matters most to you.

RASE v1.0 App Store Intelligence

Tracks how your mobile app appears in AI answers (ChatGPT, Perplexity) and in store search. If you build apps, this tells you where you're visible and where you're invisible.

Yesterday I showed you how to audit your AI visibility. Today I'm going to show you exactly what to do with those findings.

After analyzing 50,000+ AI answers at Rankfender, we've identified clear patterns. Certain content types get cited 3x more often than others.

Here are the 7 content types that win AI citations with real examples you can steal.

First, the data:

You built something remarkable.

The code works. The design sings. Customers who find you, love you.

But here's the problem AI will never just know.

Unlike Google, which crawls everything and figures it out eventually, AI learns from patterns. And if your product doesn't fit those patterns, you simply don't exist.

Over the past three months, I've spoken with 50+ agencies using Rankfender.

Most started the same way: a client asked "are we showing up in ChatGPT?" and they had no answer.

Now? Many have built entire new service lines around AI visibility adding 2k, 5k, even 10k in monthly recurring revenue.

Here's exactly how they're doing it.

For 20 years, SEO was a human game.
You wrote for people, optimized for Google's crawlers, and built backlinks by convincing other humans to link to you.
The inputs were human. The outputs were human.

GEO is different. You're optimizing for language models that extract and synthesize. The inputs are structured data, schema markup, comparison tables. The outputs are citations, not clicks.

So where does the human fit now?

What the data says about AI's performance:

We built a model to generate 1,000 questions that people actually ask.
Not random prompts.
We scraped 50,000 real user queries from search logs, forum threads, and support tickets across 12 industries.
We clustered them by intent and generated 1,000 representative questions.

We asked those same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3).
We ran the experiment daily for 30 days. We tracked every citation at the source level.

The goal: measure citation overlap.
How often do these models cite the same source for the same question?

The dataset:

Hey Product Hunt,

I'm Imed, founder of Rankfender.

We help brands track and improve how they appear in AI answers across ChatGPT, Perplexity, Gemini, and more. In 120+ languages.

We've now tracked over 3,000 brands, analyzed 75,000+ AI answers, and helped founders recover millions in lost revenue from AI errors.