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.
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.
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.
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?
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.
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.
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:
AI was everywhere, but I wasn't focused on product launches. I was looking at how companies think about data itself: how it's shared, governed and ultimately turned into decisions. And across conversations with executives and sessions on security and compliance, a pattern emerged: the technical limitations that once justified locking data down have largely been solved. What remains difficult is human. Alignment, trust and confidence inside organizations are now the true barriers.
You just finished a design project. And it was a mess. Timelines constantly shifted. Stakeholders disagreed, going back and forth. You made calls without enough data to support them. Maybe the final design wasn't what you wanted. Now comes the hard part: thinking about how you're going to talk about it in your portfolio or case study. Most designers have one basic instinct in this scenario: clean it up. Tell the story as if there was no conflict, no missteps, and a smooth experience.
At some point, every UX learner realizes that having a portfolio isn't the same as having a convincing portfolio. You may have screens, wireframes, and prototypes. You may even have multiple projects. But when your work is reviewed, the feedback feels vague. "Tell me more about your process." "Why did you make this decision?" "What was the impact?" That's because a strong UX case study isn't a gallery of designs. It's an argument.
