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.
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.
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.
Annotate Image is a JavaScript image annotation library that creates Flickr-style comment annotations on images. You can draw rectangular regions on any image and attach interactive hotspots and notes to those regions. Version 2.0 is a complete TypeScript rewrite that works standalone with vanilla JavaScript or integrates with jQuery, React, and Vue. It's ideal for building photo galleries, design review tools, or any application requiring collaborative image markup.
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.
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.
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.
In 2026, the designers who thrive won't be the ones who just create beautiful Figma files. They will be the ones who understand structure, logic, systems, interactions, and implementation. FigmaMake generates surprisingly coherent interface layouts that respect design patterns and component logic. It allows us to create coded interactive prototypes in minutes, allowing to test our experiences faster and see our ideas live. The best part - no need to leave Figma.
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:
