OpenAI unveils screenless hardware: how it will impact data, UX, and SEO.

OpenAI and Jony Ive’s recent announcement has made one thing clear: we’re about to cross a new threshold in the history of technology.
Not because of what was shown — but precisely because of what wasn’t: a device with no screen, no keyboard, and unlike anything we’ve used before.

This is the first physical device developed by IO — the new company formed by OpenAI and legendary designer Jony Ive — and it promises to take ChatGPT off the screen and into the real world.

An assistant that’s no longer seen, but always present

OpenAI hasn’t unveiled a phone, glasses, or a smartwatch. They’ve introduced a new hardware category, which Sam Altman calls the “third essential object” on your desk — next to your laptop and phone.

But unlike those two, this one doesn’t need to be looked at. It’s always there, listening, understanding the context, and responding precisely. No tapping, no icons, no scrolling. Just natural interaction.

This is the ultimate leap for the digital assistant: from something you consult, to something that accompanies you.

And now… how do we measure the invisible?

With this revolution come inevitable questions for data, product, and marketing teams. If there are no clicks, no screens, no user sessions… how do we know if something is working?

Here are a few key ideas:

Voice interaction

Engagement is measured by activation frequency, usage context, and type of query. It becomes more conversational — but also more situational.

Contextual sensors

Devices like IO’s capture proximity, movement, temperature, or even behavioral patterns to determine when and how to step in.

Edge computing

Processing happens on the device itself — not in the cloud. That means faster responses, greater privacy, and measurement based on contextual events, not visual interactions.

SEO without results, AEO without clicks

Another front is changing: positioning.

When queries are no longer typed, but spoken — and no longer go through search engines, but through assistants — we fully enter the era of Answer Engine Optimization (AEO).

We no longer optimize to be seen in Google. We optimize to be selected by an AI response.

AEO checklist for the screenless era:

  • Use conversational language — think how your customer speaks, not how they search

  • Focus on user intent, not isolated keywords

  • Create concise, structured content that language models can easily interpret

  • Implement Schema Markup and microdata

  • And most importantly: think in terms of useful answers, not pretty results

What does all this teach us?

The IO device from OpenAI isn’t just a new gadget. It’s a paradigm shift.

It’s no longer about seeing more, but interacting better.
No longer about counting views, but understanding context.
No longer about designing screens, but designing moments.

This is the future in front of us: invisible interfaces, assistants embedded in our environment, and experiences where AI isn’t across the screen… it’s by your side.

Are we ready to measure what can’t be seen?
To optimize what’s not being searched?
To design for what doesn’t exist yet?

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OpenAI unveils screenless hardware: how it will impact data, UX, and SEO.

OpenAI and Jony Ive’s recent announcement has made one thing clear: we’re about to cross a new threshold in the history of technology.
Not because of what was shown — but precisely because of what wasn’t: a device with no screen, no keyboard, and unlike anything we’ve used before.

Advanced Research with the New ChatGPT Integration in HubSpot

In the competitive world of Customer Relationship Management (CRM), HubSpot has taken a decisive step by integrating ChatGPT through its new feature: the Deep Research Connector. This tool allows companies to perform advanced analysis on their own data—without the need for a dedicated data team or specialized analysts.

Group your data like a pro: clustering with K-Means and BigQuery ML

Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

But what if you could automatically discover patterns and create data groups—without manual rules, endless scripts, or leaving your BigQuery analysis environment?

That’s exactly what K-Means with BigQuery ML allows you to do.

What is K-Means and why should you care?

K-Means is a clustering algorithm—a technique for grouping similar items. Imagine you have a table with thousands of URLs, users, or products. Instead of going through each one manually, K-Means can automatically find groups with common patterns: pages with similar performance, campaigns with similar outcomes, or users with shared behaviors.

And the best part? With BigQuery ML, you can apply K-Means using plain SQL—no need for Python scripts or external tools.

How does it actually work?

The process behind K-Means is surprisingly simple:

  1. You choose how many groups you want (the well-known “K”).

  2. The algorithm picks initial points called centroids.

  3. Each row in your data is assigned to the nearest centroid.

  4. The centroids are recalculated using the assigned data.

  5. This process repeats until the groups stabilize.

The result? Every row in your table is tagged with the cluster it belongs to. Now you can analyze the patterns of each group and make better-informed decisions.

How to apply it in BigQuery ML

BigQuery ML simplifies the entire process. With just a few lines of SQL, you can:

  • Train a K-Means model on your data

  • Retrieve the generated centroids

  • Classify each row with its corresponding cluster

This opens up a wide range of possibilities to enrich your dashboards and marketing analysis:

  • Group pages by performance (visits, conversions, revenue)

  • Detect behaviors of returning, new, or inactive users

  • Identify products often bought together or with similar buyer profiles

  • Spot keywords with unusual performance

How many clusters do I need?

Choosing the right number of clusters (“K”) is critical. Here are a few strategies:

  • Business knowledge: If you already know you have 3 customer types or 4 product categories, start there.

  • Elbow Method: Run models with different K values and watch for the point where segmentation no longer improves significantly.

  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

Real-world examples

With K-Means in BigQuery, you can answer questions like:

  • What types of users visit my site, and how do they differ?

  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

Grouping data this way not only saves time—it reveals opportunities and issues that might otherwise go unnoticed.

Conclusion

If you're handling large data sets and need to identify patterns fast, clustering with K-Means and BigQuery ML can be a game-changer. You don’t need to be a data scientist or build complex solutions from scratch. You just need to understand your business and ask the right questions—BigQuery can handle the rest.

Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 

Claude 4.0: Advances and Challenges in Conversational AI

Artificial Intelligence (AI) continues to progress at an accelerated pace, and Claude 4.0, developed by Anthropic, marks a major milestone in this journey. This next-generation language model stands out for its ability to comprehend complex contexts, deliver accurate responses, and adapt to a wide range of business needs.

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