The new tracker features a simplified progress bar that shows just four stages of pizza creation. The new design was rolled out to all platforms, and there's also new Lock Screen widgets for iOS that bring the pizza chain's most famous tech feature to the Liquid Glass age.
AI Mode can use your previous conversations, along with places you've searched for or tapped on in Search and Maps to deliver more relevant options, personalized to you. So if AI Mode infers that you have a preference for Italian food, plant-based meals, and places that have outdoor seating, you may get results suggesting options like these.
Neo4j Aura Agent is an end-to-end platform for creating agents, connecting them to knowledge graphs, and deploying to production in minutes. In this post, we'll explore the features of Neo4j Aura Agent that make this all possible, along with links to coded examples to get hands-on with the platform.
The component also provides features for columns (sort, hide, resize), rows (select), cells (keyboard navigation, pointer interactions, custom rendering). Feel free to ask and look at the code if you're interested in knowing more. The <HighTable> component is developed at hyparam/hightable. It was created by Kenny Daniel for Hyperparam, and I've had the chance to contribute to its development for one year now.
Weather impacts sales. Every retailer knows it. But for most, the likelihood that it might rain, snow, or sleet on the third of March somewhere in the Midwest is rarely used. Vendors such as Weather Trends have offered accurate, long-range forecasts for more than 20 years. But the opportunity is not predicting the weather; it's knowing what to do with the data. AI might change that.
Good morning, programs! Today I'm sharing yet another example of Chrome's on-device AI features, this time to demonstrate a "Bluesky Sentiment Dashboard". In other words, a tool that lets you enter terms and then get a report on the average sentiment for posts using that word. I actually did this before (and yes, I forgot until about a minute ago) last year using Transformers.js: Building a Bluesky AI Sentiment Analysis Dashboard.
When we rolled out a custom-built company GPT to our 14,000 teammates several years ago, we saw three clear groups emerge. First, there was the 'jump-in-with-both-feet' crowd. These are the early adopters who treat anything new like a shiny toy. Next were the skeptics who wondered how much of an impact AI would have on their daily work lives. And finally, there was a big group that genuinely wanted to learn but didn't know where to start.
Imagine you're selecting an influencer to work with on your new campaign. You've narrowed it down to two, both in the right area, both creating the right sort of content. One has 24.6 million subscribers, the other 1.4 million. Which do you choose? Now imagine you could find out the first had 8.7 million unique viewers last month, while the second had 9.9 million. Do you want to change your mind?
Completely free and open source (view our licence here). data_object Supports export for integration with frameworks including React, Vue, and Angular. Fully configurable, featuring custom triggers and adjustable text to support multiple language locales. 60 languages supported by default (view the languages here). Includes multiple views, including Map, Line, Chart, Days, Months, and Color Ranges. export_notes Export data to multiple file formats (view the supported types here), with system clipboard setting support.
Most beginner data portfolios look similar. They include: A few cleaned datasets Some charts or dashboards A notebook with code and commentary Again, nothing here is wrong. But hiring teams don't review portfolios to check whether you can follow instructions. They review them to see whether you can think like a data analyst. When projects feel generic, reviewers are left guessing:
Every year, poor communication and siloed data bleed companies of productivity and profit. Research shows U.S. businesses lose up to $1.2 trillion annually to ineffective communication, that's about $12,506 per employee per year. This stems from breakdowns that waste an average of 7.47 hours per employee each week on miscommunications. The damage isn't only interpersonal; it's structural. Disconnected and fragmented data systems mean that employees spend around 12 hours per week just searching for information trapped in those silos.
When it comes to working with data in a tabular form, most people reach for a spreadsheet. That's not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for massaging tables of data. But what if you want more control, precision, and power than Excel alone delivers? In that case, the open source Pandas library for Python might be what you are looking for.
A traveler might search for a weekend getaway and still see travel ads weeks later, long after returning home. The data was right. The timing wasn't.AI-driven marketing has the potential to close that gap - but only if it understands context. Personalization built solely on identity or past behavior can reveal who someone is, but not when or why they're ready to act.As AI takes center stage in marketing strategy, context is emerging as the differentiator that turns reactive automation into predictive intelligence.
When we announced the pre-release version of Lumen AI, our goal was ambitious: build a fully open, extensible framework for conversational data exploration that always remains transparent, inspectable, and composable, rather than opaque, closed and non-extensible. Today, with the full release of Lumen 1.0, that vision has been realized while also significantly evolving. This release represents a substantial re-architecture of both the UI and the core execution model, along with major improvements in robustness, extensibility, and real-world applicability.
