You can think of them as basic, but for AI SEO, and especially for beginners, they are working quite well for us.

The number one is consistent NAP. This may seem like a simple SEO requirement that businesses have been doing for years, but in reality, this is one of the smartest strategies one can use. Use the same business name for every platform. It maximizes the chances of getting cited and indexed in AI answers.

The 2nd one is also related to this, and that is brand mentions. Mentioning your brand naturally creates trust signals for search engines. The best example is how famous brands use each other’s names as sources in their blogs. This is what brand mentions are all about.

You yourself mentioning your brand is good, but getting someone else to do it for you is the cherry on top. You can start by posting great content, such as blogs and posts on social forums.

Been testing a lot of AI website builders lately and I swear every AI-generated website now looks like the same SaaS startup with different logos.

Same giant headline. Same purple gradients. Same fake dashboard mockup. Same “built for modern teams” copy. Whether it’s Framer AI, Wix AI, Webflow templates, Lovable, or other AI web design tools, everything is starting to feel weirdly identical.

What’s funny is AI website builders were supposed to make web design more creative and accessible, but instead they seem to be flattening originality. A lot of modern startup websites look polished for 10 seconds, then instantly forgettable because users have already seen the exact same layout 50 times.

Even the website copy feels AI-generated now. Every company is either “streamlining workflows,” “transforming collaboration,” or “unlocking scalable growth.” Brother what does the product actually do?

Feels like AI web design is slowly turning the internet into one giant template library. Anybody else noticing this sameness across modern websites lately?

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars.

What I built

phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers):

1. Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes
2. Pulls star and fork events from the last 24 hours per repo
3. Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history)
4. Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%)
5. Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window
6. Files an issue directly on the targeted repo so the maintainer knows what's happening

Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended.

What the pattern actually looks like

It's remarkably consistent. A fake engagement campaign in the raw data:

• 40-200 accounts, all created within the same 1-2 week window
• Zero original repositories, or only forks they never touched
• No bio, no location, no followers, no following
• All of them starring the same repo within a 90-minute window
• The target repo usually has a name implying it's a tool, hack, executor, or generator

Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast.

Notifying the affected repo

When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first.

Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently.

Why I built this

Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected.

It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/

The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users.

All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq.

Repo: https://github.com/tg12/phantomstars

Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process.

Questions welcome on the detection approach, GraphQL batching, or campaign ID stability.

I've been comparing DR and AI Visibility lately and I find these two metrics are actually pretty similar. They're both third-party companies measuring Google and the models, and what they give you is a simulated value. DR is a bit more accurate, since links are easy to measure, they either exist or they don't. But at the end of the day, it measures backlink weight, which is one layer away from actual rankings. AI Visibility's numbers are even less accurate.

I've also lost confidence in DR, because I think external links are getting less important. Google uses mixed signals now. Even if your site has high DR or strong backlinks, if your content makes users unhappy and bounce rate or other metrics are bad, Google will keep pushing your pages down. I also have some pages with low DR but useful content. Especially the guide-style pages where users stay for a long time, those rankings keep going up.

As for AI Visibility, the only use it has for me now is letting me see which articles are being cited by AI consistently, and observing competitor content. The links it gives are more useful than the numbers. I've noticed the top citations are pretty stable, but the tail ones (especially after rank 4) actually fluctuate a lot. I can't help wondering if it only uses the top two or three pieces of content to build the answer, and the rest are just for reference. If Google only needs the top 10 links on page one to be solid, then for AI maybe it only needs the top two or three links to be solid.

I’ve noticed a new claim floating around lately: some people are starting to say that using screenshots instead of generic stock images will increase "AI trust" in your website.

We already know that Google encourages original images as part of your content. But what about AI? AI doesn't actually "see" or recognize an image the way a human does; it basically slices the image into tokens to process and read it.

Honestly, I’m highly skeptical of this whole "AI trust" narrative. From my perspective, I don't think AI models even possess a functional mechanism that equates to "trust" in the way these people are claiming.

When it comes to visuals, I strongly believe their primary role is to help humans better understand the context—which aligns perfectly with Google’s emphasis on "people-first content."

I really don't think Google (or any Generative Engine) is going to give your site a magical ranking boost just because you used an original screenshot instead of a stock photo.

What are your thoughts on this? Is "AI trust" an actual thing we should be optimizing for, or is this just another GEO superstition? Would love to hear your takes!

Lately I’ve been seeing Reddit threads show up for almost every search.

Sometimes I search something and the Reddit result feels more useful than the actual blog posts 😅

Makes me wonder if SEO is slowly shifting from:
“perfect optimization”

to

“real discussions + real opinions”

Even AI tools like ChatGPT and Perplexity seem to pick up Reddit conversations a lot.

Now I’m spending more time:

• reading communities
• checking what real users ask
• understanding language people actually use

instead of only staring at keyword volume tools.

Anyone else changing their SEO strategy because of this?

From u/lilray on X (via GlennGabe) - thanks for sharing

As of May 7, 2026, FAQ rich results are no longer appearing in Google Search. We will be dropping the FAQ search appearance, rich result report, and support in the Rich results test in June 2026. To allow time for adjusting your API calls, support for the FAQ rich result in the Search Console API will be removed in August 2026.

As this sub and many of our related experts that we share, like u/jakehundley - Mod of r/agency - a great sister sub to r/SEO and r/SEO_Digital_Marketing - this isn't surprising.

As we said - Google doesnt actually read FAQ Schema anyway - because less than 0.001% of site qualify

Mythbusting generative AI search: what you don't need to do

As generative AI search evolves, so have the theories and practices—and sometimes, the misconceptions—surrounding it. While terms like Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) are common online, many suggested "hacks" aren't effective or supported by how Google Search actually works.

To help you focus on what matters for your website's visibility, we've collected some of the most prominent topics circulating the internet around generative AI and Google Search. Here are a few things you can ignore for Google Search:

• LLMS.txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.
• "Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.
• Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.
• Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.
• Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

You might see people shouting that they’ve “cracked the algorithm” and are now offering AI SEO services that promise to get your brand into AI answers.

But honestly, is AI SEO really different from normal SEO?

We’ve been working with a client where the main focus was improving their local SEO presence first. As their local visibility improved, they also started appearing in AI Overviews without us specifically doing “AI SEO.”

From what I’ve seen so far, AI SEO is mostly a combination of strong SEO fundamentals and high-quality content.

No generic AI-written fluff, no broken links, no shortcuts, just solid industry-standard SEO, clear content structure, and content that genuinely helps users.

What do you think?

I’ve noticed that for a lot of searches now, I automatically add “reddit” at the end because normal search results increasingly feel like AI-generated SEO contests instead of actual human opinions. Whether it’s product reviews, SEO advice, hosting recommendations, or web design tools, Reddit threads usually end up being more useful than polished blog posts trying to rank for affiliate keywords.

What’s funny is websites spent years trying to sound more “professional,” and users responded by trusting anonymous Redditors with usernames like “keyboardwarrior428” more than brands with million-dollar content strategies. Most blog content now feels overly optimized, overly sanitized, and weirdly repetitive, while Reddit still sounds like people having actual experiences.

And honestly, Google seems to know this too. Reddit threads are ranking everywhere now because people are clearly searching for real opinions instead of another “Top 10 Best Tools in 2026” article written by someone who tested the product for 11 minutes. Anybody else adding “reddit” to searches way more than before?

Been thinking about this a lot lately. The shift from ranking pages to basically being cited by AI systems feels pretty significant. Like, even just a couple of years ago I was obsessing over position 1 rankings and click-through rates. Now I'm spending way more time thinking about whether a brand even shows up inside AI Overviews, Google AI Mode, or Perplexity answers at all. Traffic from informational queries has dropped noticeably for a lot of us, but the brands, getting mentioned in those AI answers are still building awareness somehow, even without the click. So the role isn't gone, it's just. different. Less about chasing keywords, more about entity signals, content structure, E-E-A-T, and making sure AI systems actually trust your source enough to pull from it. The part I can't fully figure out is how this plays out for generalist SEOs vs specialists. Someone who's solid at technical SEO and information architecture probably transitions fine because that stuff still feeds the AI systems directly. But someone who's mostly been doing keyword research and link building for local clients? Reckon that's a harder pivot. Job listings back this up too honestly, senior and director-level roles are dominating right now and, they're expecting way more cross-functional stuff, analytics, digital PR, experimentation, and yeah, actual AI visibility knowledge. So not cooked, but definitely not the same job it was. Curious where people here are actually focusing their energy right now, and whether you think the fundamentals still carry over or if this genuinely needs a full rethink.

I’ve noticed tutorial-style content with original screenshots and UI examples seems to get surfaced more in AI-generated answers

Feels like AI Overviews care less about traditional local landing page tactics and more about:

• reviews
• mentions
• entity trust
• local discussions
• multi-platform signals

Curious what’s actually working for local visibility in AI search right now.

Feels like once a brand becomes the default answer in AI systems, it keeps getting reinforced over and over.

Honestly didn't plan this. It happened gradually.

First AI replaced quick lookups.

Then comparisons.

Then tool research.

Then strategy questions.

Now I only open Google for:

— specific news articles

— local things

— images

The weird part? I don't miss it.

Anyone else in this position or am I just getting lazy with my research habits?

Search engine optimization has always been about adapting to change. First it was keyword stuffing, then quality content, then mobile‑first indexing. Now, with AI shaping how search engines work and how content is created, SEO feels like it’s entering a new era.

AI is already influencing search in big ways. Search engines use machine learning to understand intent better, which means they’re less focused on exact keywords and more on context. Tools like ChatGPT and other generative AI platforms are also changing how people search. Instead of typing short queries, users are asking full questions and expecting conversational answers. That shift could reshape how websites compete for visibility.

For marketers, this raises important questions. If AI tools summarize information directly in search results, will users still click through to websites? If content can be generated instantly, how do you make yours stand out? The answer seems to be focusing on trust, originality, and authority. Search engines are rewarding content that demonstrates expertise and credibility, not just volume.

Another big factor is personalization. AI allows search engines to tailor results based on user behavior, location, and preferences. That means SEO strategies may need to move away from one‑size‑fits‑all approaches and focus more on niche audiences.

At the same time, technical SEO isn’t going away. Site speed, mobile usability, structured data, and accessibility still matter. In fact, with AI analyzing sites more deeply, technical flaws could hurt rankings even more.

Looking ahead, I think SEO will be less about chasing algorithms and more about creating experiences that genuinely help users. AI can filter out low‑quality content, but it can’t replace human insight, creativity, and trust. The challenge for marketers is to use AI as a tool without losing the human touch that makes content valuable.

I wanted to share these thoughts because SEO feels like it’s at a turning point. What do you think the biggest change will be in the next few years? Will AI make SEO harder, or will it open new opportunities for those who adapt?

For the past 6 months, we've been actively structuring our news articles for what I call AI Discoverability, showing up in AI models recommendations & citations.

We're making great headway with it; we're showing up in 10% of the citations across models and since January, we've had over 4 million impressions on our articles. We're even in the top 10 of all AI newsletters, competing with very large popular newsletters that have 1M+ subscribers. Not too bad if I do say so myself. 😉

But the smaller businesses aren't aware this is even happening right now. The small businesses I've spoken with in my community didn't know that Google search has changed. Sure, they see the Google AI Overviews but because the AI sounds confident and there are a few links to sources, they take their answer and leave.

These small businesses are still trying to get traditional SEO to work and rank on Google, and wondering why their traffic has fallen off.

If AI can’t quickly figure out:
* what your business does
* who you help
* what topics you actually know about
* whether your content is credible

…you probably become less visible over time in AI-generated search answers.

That’s part of why I think AI discoverability is becoming different from traditional SEO.

Traditional SEO depended on back links, keywords, etc. but AI discoverability is more dependent on real human insights, experiences, and natural language that is clear & explicit.

For example, a website that says, “We offer the best service in town,” does not clearly explain what the business actually does, where it operates or what makes it different. A website that says, “We provide same-day plumbing repair in Ocean Beach, Point Loma and Pacific Beach for residential and small commercial properties,” gives AI systems much clearer information they can connect to customer questions.

Curious what other people here are seeing so far with AI search traffic/citations; are you seeing this too? What are you doing to stand out & get found in AI answers?

Has anyone else lost their AI Overview traffic tracking after Google's May 6 update?

We were using the #:~:text= (Scroll-To-Text Fragment) in referral URLs as a proxy to identify traffic coming from Google AI Overviews and AI Mode. When Google cited a page in AI Overview, the link included a text fragment like:

We used this as a signal in our analytics pipeline to segment AI-driven sessions separately from regular organic traffic. It wasn't perfect — Featured Snippets and PAA also generate STTF — but it was the closest thing we had to a deterministic marker for AI Overview clicks.

Since May 1 (officially announced May 6), we're seeing a ~90% drop in sessions with this fragment across both web and app. After digging in, it looks like Google completely changed the link architecture in AI Overviews and AI Mode — citations moved from a side panel (which used STTF anchors) to inline links woven directly into the AI response text. No more #:~:text= in the URLs.

The remaining 10% still showing STTF appear to be long-tail queries and some Featured Snippet/PAA traffic — not AI Overview specifically.

A few things we confirmed:

- Drop is simultaneous across platforms (not a tracking bug on our end)

- Step-change on May 1, not gradual — consistent with a feature flag rollout before the official announcement

- Google's share within STTF sessions dropped from ~70% to ~35%, meaning Google specifically stopped generating these fragments while other sources (browser highlight links, Bing) stayed stable

Now the AI traffic just looks like regular organic Google referrals

Has anyone found a reliable way to identify AI Overview / AI Mode clicks post-update? Are you combining multiple signals? Using any third-party tools that have adapted to the new link format?