I’m introducing support for GPT-5.6 in Profound, bringing OpenAI’s newest flagship model family directly into the workflows I rely on for advanced AI performance.
With GPT-5.6, I can work across the new Sol, Terra, and Luna tiers, giving me the flexibility to support everything from frontier reasoning to high-throughput production workloads.
I’m especially focused on what this means for agentic workflows, coding, research, and enterprise knowledge work. GPT-5.6 delivers meaningful improvements in capability, reliability, and efficiency, making it easier for me to apply AI across a wide range of business use cases with more confidence.
I see Profound’s MCP evolution as a meaningful shift for Marketing Engineers. It now connects agents to a knowledge graph and adds 15 new capabilities built around how marketing teams actually work.
For retailers, I believe this demands a serious reframe. Answer engines are already shortlisting products and shaping purchase decisions long before shoppers ever land on retail or ecommerce websites. That compresses the shopping funnel and makes traditional search less reliable as the primary channel for customer acquisition.
Instead of waiting for shoppers to arrive through search, I need to think about how retailers can be recommended throughout the entire shopping journey. That means understanding how people use answer engines for Christmas gifting, how brands earn mentions and citations in relevant AI responses, and how visibility can be maximized across AI search experiences.
I see this report as a practical edge for retailers preparing for the next holiday cycle. It uses real shopper behavior from Christmas 2025, analyzed through Profound’s AI visibility lens, to show how people are using AI to shop for the holidays.
Most importantly, it turns those insights into actionable takeaways. By understanding where answer engines influence discovery, comparison, and purchase decisions, I can see how ecommerce teams should optimize product visibility before the 2026 season ramps up and compete more effectively for the AI shelf this Christmas.
I am introducing support for Grok 4.5 in Profound, bringing SpaceXAI’s newest flagship model into workflows built for deeper, more capable AI analysis.
Grok 4.5 is designed for agentic workflows and knowledge work, which makes it a strong fit for teams and operators who need AI systems that can reason, assist, and move complex tasks forward with more context.
With this support now available in Profound, I can use Grok 4.5 as part of a broader AI workflow and explore how its capabilities help with research, strategy, automation, and day-to-day knowledge work.
I often get asked why I “only” run each prompt one time per day.
For me, the answer comes down to signal quality. Running a prompt once daily gives me enough consistent data to understand performance without overloading the process with unnecessary repetition.
The statistics show that a single daily run is plenty. It gives me a reliable view of how prompts behave over time, while keeping the workflow focused, efficient, and easier to interpret.
I’m measuring downstream web browsing after AI brand mentions, focusing on what happens once a brand shows up in an AI-generated answer or recommendation.
For me, the AI mention effect is about connecting visibility inside AI experiences with real user behavior afterward, especially whether those mentions lead people to search, click, browse, and engage beyond the original AI response.
I’m seeing a major shift in how people plan trips: 40% of travelers now use AI to research, compare, and organize their travel decisions.
That changes how I think about travel content. It is no longer enough to write only for traditional search results. I also need to make content clear, useful, and easy for AI systems and large language models to understand, summarize, and cite.
In this guide, I focus on practical travel AI optimization strategies, including stronger FAQs, schema markup, topical authority, and a content strategy built around the questions real travelers ask.
My goal is simple: create travel content that answers intent directly, builds trust, and gives AI platforms the structured context they need to reference my brand when travelers are planning their next trip.
On June 11, 2026, I saw more than 1,000 marketing leaders come together in New York for Zero Click New York, Profound’s largest AI Marketing summit to date.
What stood out to me was the range of leaders and brands shaping the conversation. Speakers from Coca-Cola, LinkedIn, Delta Air Lines, U.S. Bank, and CVS Health shared how they are rethinking marketing strategy, team design, and measurement as AI changes the way audiences discover and trust information.
I also found the research sessions especially important. The summit explored Claude’s citation mechanics, ChatGPT’s emerging ads business, and the data behind the kinds of content AI systems are most likely to trust. Together, these conversations made Zero Click New York 2026 feel like a clear marker for where AI Marketing is heading next.
For the past two years, I have heard marketers ask the same urgent question: How do I show up in AI search?
I have seen plenty of conversation around AI optimization, visibility, and the way large language models decide which businesses to recommend. But I believe the more practical question is now becoming harder to ignore: How do I measure whether AI search is actually sending customers my way?
That is the challenge I wanted to understand more clearly.
After analyzing nearly 30 million inbound leads, I found that AI platforms are already shaping how customers discover businesses and decide to make contact. AI-generated leads still represent a small share of total volume, but they are growing steadily enough that I think marketers should start watching this channel closely.
In other words, the conversation is moving from visibility to measurement.
AI search is becoming a new attribution challenge
Traditional attribution models were built for channels like organic search, paid search, direct traffic, and referrals. AI search introduces a different discovery path, and I do not think most reporting systems are fully prepared for it yet.
A customer might ask ChatGPT for the best local HVAC company, use Perplexity to compare law firms, or ask Gemini to recommend a nearby dentist before picking up the phone.
From a marketer’s perspective, those customers may show up as direct traffic, or they may not be attributed at all. That creates a real blind spot.
If AI platforms are influencing customer discovery, I need a way to measure whether those recommendations are turning into real business outcomes.
What 30 million leads tell me
The data shows me that AI platforms are already generating measurable inbound leads for businesses. It also shows that this activity is growing over time and appearing across multiple industries, not just one category or use case.
One platform currently accounts for most AI-attributed calls, while other platforms contribute smaller shares that continue to change as customer behavior evolves. The data also reveals which industries are receiving more AI-driven calls than others.
At the same time, I have to be clear about what this dataset can and cannot measure. It does not explain why customers chose one AI platform over another, what prompts they used, or why a specific business was recommended. What it does measure is more concrete: when customers identify an AI platform as part of the journey that led them to contact a business.
That distinction matters. There is no shortage of opinion about AI search. What I need now is evidence that it is influencing customer acquisition.
Measurement should come before optimization
I understand why marketers are eager to optimize for AI search. But before investing in new tactics, I think it is worth answering a simpler question first: Is AI already driving customers to my business?
Without measurement, it is difficult to know whether greater visibility is translating into meaningful business results.
As AI search becomes another customer acquisition channel, I want to measure it the same way I measure other demand sources, including paid search, organic search, referrals, and social.
The goal is not to replace existing attribution models. The goal is to make sure those models evolve as customer behavior changes.
From visibility to measurement
The first wave of AI search focused on visibility. I believe the next wave will focus on proving business impact.
For marketers, that means moving beyond questions like, “Can customers find us?” and toward more outcome-focused questions like, “How many leads did AI actually generate?”
The businesses that answer those questions first will be better positioned to understand how AI fits into their marketing mix and where to invest as customer discovery continues to evolve.
Don’t just watch the shift. Start measuring it.
As AI search keeps evolving, I am focused on giving marketers the attribution they need to connect AI discovery with real customer conversations.
I see vibe coding as one of the most accessible ways to create small pieces of software with AI tools like ChatGPT, Cursor, Replit, and Gemini. Instead of writing code line by line, I describe what I want in plain language, receive working code in return, paste it into an environment like Google Colab, run it, and test the result.
In this guide, I’ll explain how I approach vibe coding, where I think it works well, where it breaks down, and which SEO examples can inspire practical projects of your own.
Vibe coding variations
I use “vibe coding” as a broad term, but it helps to separate it from nearby approaches:
Type
Description
Tools
AI-assisted coding
AI helps write, refactor, explain, or debug code. I usually associate this with developers or engineers who already understand the systems they are building.
GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding
AI handles most of the work after I provide the idea or prompt.
ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms
Platforms handle what I ask for through visual interfaces, drag-and-drop workflows, or background automation. Many now use AI, but they existed before AI became mainstream.
Notion, Zapier, Wix
For this guide, I’m focusing only on vibe coding.
The barrier to entry is low. In most cases, I only need a ChatGPT account, free or paid, and access to a Google account. Depending on the project, I may also need API access or subscriptions to SEO tools such as Semrush or Screaming Frog.
I also like to set expectations early: by the end of this kind of workflow, I’m usually aiming to run a small program in the cloud. If I want to build a SaaS product or software I plan to sell, I treat AI-assisted coding as the more realistic path because it usually requires more technical knowledge, more testing, and more budget.
Vibe coding use cases
I find vibe coding most useful when I’m working with clear buckets of data and need a helpful outcome, not a perfect one. That might mean finding related internal links, adding pre-selected tags to articles, comparing groups of URLs, or building something playful where the output does not need to be exact.
For example, I built an app that creates a daily drawing for my daughter. I type a short phrase about something she told me, such as “I had carrot cake at daycare.” The app uses examples of drawing styles I like and a few pictures of her, then generates a drawing as the final output.
When I ask for precise changes, the tool often gets worse. I once asked it to remove a mustache, and instead it recolored the image. That is exactly the kind of limitation I expect with this approach.
If my daughter were a client reviewing every detail, I would need someone with Photoshop or similar skills to make exact edits. For this use case, though, the result is good enough, and that is where vibe coding shines.
I would be cautious about building commercial applications solely through vibe coding. Some companies may even need vibe coding cleaners to clean up AI-generated work. But for demos, MVPs, internal tools, and quick experiments, I see vibe coding as a useful shortcut.
How I create SEO tools with vibe coding
When I create an SEO tool with vibe coding, I usually follow three steps:
I write a prompt describing the code I need.
I paste the code into a tool such as Google Colab.
I run the code and check whether the results match what I expected.
Here’s a real prompt example from a tool I built to map related links at scale. After crawling a website with Screaming Frog and extracting vector embeddings through the crawler’s OpenAI integration, I vibe coded a tool to compare topical distance between the vectors for each URL.
This is exactly what I wrote in ChatGPT:
I need a Google Colab code that will use OpenAI to:
Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A).
The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.
I’ll upload a CSV with these columns and expect a CSV in return with the answers.
After ChatGPT generated the code, I pasted it into Google Colab, which is a free Jupyter Notebook environment for running Python in a browser. I then used “Run all” to test whether the program produced the output I wanted.
That is the clean version of the process. In practice, AI can make the workflow look perfect while still producing code that does not behave the way I need.
I expect issues along the way, and most of them are simple to troubleshoot if I keep the prompt and testing process clear.
First, I always state the platform I’m using. If I want code for Google Colab, I say that directly in the prompt.
Sometimes I still get code that depends on packages that are not installed. When that happens, I paste the error back into ChatGPT and ask it to fix the code or suggest an alternative. I do not need to fully understand the missing package to move forward. I can also ask Gemini inside Google Colab to identify the problem and update the code directly.
I also check outputs carefully because AI can sound confident while inventing data. One time, I forgot to specify that the source data would come from a CSV file, so the tool created fake URLs, traffic, and graphs. “It looks good” is not the same as “it is correct.”
If I connect to an API, especially a paid API from a provider such as Semrush, OpenAI, Google Cloud, or another platform, I need to request my own API key and keep usage costs in mind.
A Semrush subscription screen highlights 2 million Standard API units and a masked API key, underscoring the setup step needed for SEO automation and vibe-coded tools.
If I want an even lower execution barrier than Google Colab, I can use Replit.
With Replit, I can prompt what I want, and the platform can generate the code, design the interface, and let me test everything in one place. That reduces copy-and-paste work and gives me a shareable URL quickly. I still need to review poor outputs and keep iterating until the app behaves properly.
The tradeoff is cost. Google Colab is free unless I use paid API keys, while Replit charges a monthly subscription and usage-based API fees. The more the app runs, the more expensive it can become.
SEO vibe-coded tools that inspire me
Google Colab is the easiest place for me to start, but SEOs are taking vibe coding much further. I’ve seen people create Chrome extensions, Google Sheets automations, and even browser games.
I’m sharing these examples because they show what is possible when useful SEO ideas meet practical AI tooling. If I see a tool and wish it had a different feature, that is often a sign that I could try building a version for myself.
A Replit vibe-coding session turns a simple prompt for toddler-friendly daily jokes into a published web app, illustrating how AI tools can quickly prototype playful ideas.
GBP Reviews Sentiment Analyzer by Celeste Gonzalez
After vibe coding SEO tools in Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, pushed the idea further by creating a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.
Her extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis from reviews over the last 30 days and shows review velocity. It also exports the information to CSV and works on Google Maps and Google Business Profile pages.
Instead of relying only on ChatGPT, Celeste used Claude to create stronger prompts and Cursor to turn those prompts into code.
AI tools used: Claude (Sunner 4.5 model) and Cursor
APIs used: Google Business Profile API (free)
A vibe-coded GBP Sentiment Analyzer turns review data into a quick snapshot, showing negative sentiment trends, key topics, and an export option for SEO workflows.
Platform hosting: Chrome Extension
Knowledge Panel Tracker by Gus Pelogia
I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Later, I discovered that Google’s Knowledge Graph Search API lets me check the confidence score for any entity.
That led me to build a vibe-coded tracker that checks entity scores daily, or at any frequency I choose, and returns the results in a Google Sheet. I can track multiple entities at once and add new ones whenever I need to.
The Knowledge Panel Tracker runs entirely in Google Sheets, and the Knowledge Graph Search API is free to use. This guide explains how to create and run it in your own Google account, or you can see the spreadsheet here and update the API key under Extensions > App Scripts.
AI models used: ChatGPT 5.1
A spreadsheet-based Knowledge Panel Tracker turns entity searches into structured SEO data, comparing names, entity types, descriptions, and confidence scores at a glance.
APIs used: Google Knowledge Graph API (free)
Platform hosting: Google Sheets
Inbox Hero Game by Vince Nero
I also like the idea of vibe coding a link building asset. That is what Vince Nero from BuzzStream did with the Inbox Hero Game. The game asks players to use the keyboard to accept or reject a pitch within seconds, and it ends if they accept too many bad pitches.
Inbox Hero Game is more complex than running a small script in Google Colab, and it took Vince about 20 hours to build from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.
The game was built with HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.
A retro arcade-style Inbox Hero screen turns PR pitch triage into a fast keyboard game, challenging players to accept or reject emails before time runs out.
He also found that longer prompt threads became less useful over time, “to the point where [he’d] have to restart in a new chat.”
That became one of the hardest parts of the project. Vince would add a feature, such as a score, and ChatGPT would “guarantee” it had found the error, update the file, and still return the same problem.
In the end, Inbox Hero Game shows that it is possible to create a simple game without coding knowledge. It also shows where a developer becomes valuable when the goal shifts from “working prototype” to polished product.
AI models used: ChatGPT
APIs used: None
Platform hosting: Webpage
How I think about vibe coding with intent
I do not expect vibe coding to replace developers, and I do not think it should. What I do see is a practical way for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without a heavy technical lift.
The key is realism. I use vibe coding where precision is not mission-critical, I validate outputs carefully, and I stay alert for the moment when a project grows beyond “good enough” and needs stronger technical support.
When I approach vibe coding thoughtfully, it becomes less about shipping perfect software and more about expanding what I can test. For internal tools, proofs of concept, and SEO side projects, the best results come from pairing curiosity with restraint.
With Profound’s Agent Template Marketplace, I can start from pre-built AI agent workflows instead of building every process from scratch.
It gives me ready-to-clone templates designed for marketing, SEO, and AEO teams, so I can move from idea to live workflow in minutes.
For me, the biggest advantage is speed: I can choose a proven workflow, clone it, customize it for my team, and start using AI agents faster with less setup.
