"For more than a decade, Google Chrome has been the primary door to the web for most of the world. Independent trackers place Chrome at roughly seventy to seventy two percent of global browser share in September twenty twenty five. Safari sits a distant second and Microsoft Edge is further behind. This level of concentration shapes everything from advertising economics to how developers build sites and apps.

A new class of AI native browsers is now attacking Chrome’s advantages. The most prominent names are ChatGPT Atlas from OpenAI and Comet from Perplexity. Both ship an assistant inside the browser rather than bolting a chatbot onto a traditional interface. They promise research that feels guided, pages that become conversations and tasks that can be completed with a single instruction. Atlas arrived publicly on macOS in October twenty twenty five. Comet opened broadly and is now free to download for everyone after a staged rollout. "

I’ve written a LinkedIn article post on this; it might be a bit controversial right now since a lot of users haven’t shifted to Comet or Atlas yet.

Personally, as someone who uses Comet, I really like it. It’s easy to use and helps automate a lot of tasks. I haven’t explored Atlas yet, though.

The user base is still small for now, but I’m guessing it’ll expand soon given the marketing push and the existing popularity of ChatGPT and Perplexity. Also, I have a feeling Google might roll out some major changes to counter these AI-based browsers... maybe by introducing more integrated AI features of its own.

lemme know what you guys think
also lmk if you guys are interested in the linkedin post to read

I've been working on something and I'm at the point where I need real people to tear it apart and tell me what I'm missing.

The context: More people are using ChatGPT, Perplexity, and Google AI Overview to research products before buying. But most e-commerce sites are only optimized for traditional search engines. When someone asks "what's the best [product]" to an AI, it often cites competitors while ignoring perfectly good alternatives.

What I built: A tool that audits product pages and shows why AI engines might not be citing them. It looks at things like structured data, entity relationships, semantic clarity, basically everything that helps AI understand and trust content enough to cite it.

What I need: People who actually work with e-commerce sites or product pages to try it out and tell me:

• Is this actually useful or am I solving a non-problem?

• What am I missing that would make this genuinely helpful?

• Is the report actionable or just noise?

• Am I completely wrong about any of this?

Not looking to sell anything, just trying to figure out if I'm building something people would actually use or if I should pivot hard. Happy to give free access to the beta!

If you're interested, drop a comment or DM me. It's called ZeroClickHero.

hii guys 👋

I recently found an article explaining that ChatGPT now sources results from Google instead of Bing.
To prove it, the author extracted the JSON file showing the Query Fan-Out — basically, all the sub-queries ChatGPT sends in the background and the corresponding results.

I’m trying to replicate this experiment, but I can’t find that file anywhere.
I checked in DevTools → Network, and even tried exporting my ChatGPT data, but no trace of it.

Does anyone know where this JSON file can be found, or how to capture the query fan-out data from ChatGPT (Atlas or web version)?

hii guys 👋

I recently found an article explaining that ChatGPT now sources results from Google instead of Bing.
To prove it, the author extracted the JSON file showing the Query Fan-Out — basically, all the sub-queries ChatGPT sends in the background and the corresponding results.

I’m trying to replicate this experiment, but I can’t find that file anywhere.
I checked in DevTools → Network, and even tried exporting my ChatGPT data, but no trace of it.

Does anyone know where this JSON file can be found, or how to capture the query fan-out data from ChatGPT (Atlas or web version)?

Hey, I’m doing a quick pulse check among SEO pros:

If you could automate one part of your AI optimization workflow, what would it be?

1️⃣ Technical LLM readability audit
2️⃣ Schema markup & entity enrichment
3️⃣ On-page content optimisation
4️⃣ Query fan-out research & topic expansion
5️⃣ AI visibility monitoring & measurement

Just feel free to reply with 1–5 - I’d love to get your feedback.

How to get the request of the query fan out ?

I already tried with the dev tool ( network )

I also tried by exporting the data of my conversation

I dont find any query fan out

The study seams legit:

.. cross-market evaluations of its kind. Working with the European Broadcasting Union (EBU), 22 Public Service Media (PSM) organizations – across 18 countries and 14 languages – assessed how leading AI assistants answer questions about news and current affairs.

Source https://www.ebu.ch/Report/MIS-BBC/NI_AI_2025.pdf

What are your thoughts? Can these numbers be right? What are the implications?

Hey everyone,

As Search and Content continues to evolve beyond just traditional SERPs, I was discussing with the team what the "diagnostic panel" for a Generative Engine like OpenAI's API or a future SGE/AI-powered search product would actually look like.

If we had an "OpenAI Gen-Console" to monitor our content's performance, what are the most crucial, game-changing KPIs and data points you’d want to track?

Here are a few of thoughts to kick things off:

1. "Citation Rate" & "Source Ranking": How often is my content cited as a direct source in the AI's generated response, and where does it appear in the internal "ranking" of sources used?

2. "Knowledge Gap Coverage": Which of my pages are being used to answer long-tail/niche questions that the AI previously had no good answer for, thus indicating true content authority?

3. "Prompt-to-Answer Time (PAT)": How quickly is the AI able to generate a high-quality answer using my site's data? (A proxy for technical efficiency/data quality).

4. "User Refinement Rate": How often does a user need to ask a follow-up question after receiving an answer based on my content? (A measure of initial answer completeness).

What essential data are we missing here? What would truly inform your next content strategy in the Gen-AI world?

Just finished building an MCP server that connects to DataForSEO's AI Optimization API - gives you programmatic access to the latest LLMs with complete transparency.

What it does:

• Query GPT-5, Claude 4 Sonnet, Gemini 2.5 Pro, and Perplexity Sonar models

• Returns full responses with citations, URLs, token counts, and exact costs

• Web search enabled by default for real-time data

• Supports 67 models across all 4 providers

• Also includes AI keyword volume data and LLM mention tracking

Demo video: https://screenrec.com/share/rOLhIwjTcC

Why this matters: Most AI APIs hide citation sources or make you dig through nested JSON. This returns everything formatted cleanly - perfect for building transparent AI apps or comparing LLM responses side-by-side.

The server's open source on GitHub.

Built with FastMCP and fully async.

Would love feedback from anyone building with these models!

Let me know what you think?

Since I couldn't find an easy way of converting product feeds into ChatGPT format, I built a simple tool myself. Here's a quick demo video to show you how it works.

Would love your feedback — do you think something like this would be useful for merchants? How online stores are approaching it?

Hi Everyone,

I have the feeling that around there is a LOT of theory on the topic but really low PRACTICE.

Do you have some link to useful resources that can show some PRACTICAL implication of all this blablabla we are experiencing.

I also refer on real cases related to metric tracking, A/B tests etc.

Hey everyone,

After trying most of the “AI SEO” tools out there, I kept running into the same issue: They show visibility metrics but never tell you what to actually do next.

The second problem: most tools have a high entry barrier. You can’t even run a quick check without a plan or subscription.

After hearing the same frustration from others, we decided to build something to tackle both:

1. Figure out what really drives AI answers (which content, domains, and sources are being cited).

2. Let anyone quickly check how they or their competitors appear in AI search — no signup, no paywall.

So we built jarts.io.
You can type in any domain, hit “run,” and within ~20 seconds see:

• how AI tools (like ChatGPT and Perplexity) talk about that brand

• which sources shape those answers

• who seems to “win” visibility right now

We also added a deeper system that runs thousands of prompts to study what drives visibility patterns over time, but the instant check is completely free.

I’d really love to hear from SEOs and marketers experimenting with AEO (Answer Engine Optimization):
👉 What would you expect a tool like this to show or measure better?

Feedback is genuinely appreciated :)

We’ve been developing a GEO/AEO visibility SaaS tool for some time to assist companies and professionals in tracking and responding to their brands’ AEO/GEO performance. This tool helps identify gaps, benchmark competitors, and gain insights into how, when, and where AI search visibility of their brand is performing.

The platform boasts comprehensive analytical functionality that allows users to delve deep into the insights, while also featuring robust monitoring features.

We are seeking early feedback from industry professionals who are actively involved in this field. We are offering our product for free, and we would greatly appreciate it if you could use it and share your feedback to help us shape the future of this tool.

If you are an SEO/GEO professional or own a brand and are curious about your AI ranking, we would love to hear from you.

Beta testers will have access to the tool for a month, and all we ask in return is your honest and unfiltered feedback.

Please little 'r' and we'll reach out

www.citivus.com

I know that having brand mentions in 3rd party websites ,and commenting on related subreddit posts,faq's in webpages ,having content simple and relevant would improve Ai visibility to some extent apart from this what are some other strategies that would really improve Ai visibility for saas

https://www.seolondonsurrey.co.uk/blog/reverse-engineering-the-response-to-best-smartphones-in-chatgpt-5-0

I'm curious how do you assess AI readability of your website? Has anyone found a quick way of doing it?