#visualizations

#visualizations

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.

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.

In his graphic design work, Aldon transforms periodic tables and dense masses of information into maximalist pieces of design, expressing information whilst also challenging the impossibility of taking it all in. Data sprawls across screens and pages, overlapping in overloads and feedback loops, communicating more the aesthetic of information rather than its substance, playing with images we have all seen in science classes or colour palettes. These are exploded infographics.

Good morning, programs! Today I'm sharing yet another example of Chrome's on-device AI features, this time to demonstrate a &quot;Bluesky Sentiment Dashboard&quot;. 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.

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.

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.

To learn how to query, visualize, and export live warehouse data from Streamlit, just keep reading. Now that we've prepared our helper modules and configured Snowflake credentials, it's time to bring everything together into one cohesive Streamlit app. The main driver script, lesson3_main.py, acts as the command center - defining layout, navigation, and page logic. It connects Streamlit's interactive UI to the Snowflake data warehouse and orchestrates how users query, explore, visualize, and export results.

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:

One skill separates good designers: the ability to clearly articulate their intention. No matter what tool you use, whether it's a traditional UI design tool like Figma or Sketch or AI tools like Figma Make, your ability to explain what you want to see accounts for 50% of your design success. The other 50% comes from your hard and soft skills. When it comes to AI-powered design, your ability to write decent prompts will have a direct impact on the quality of your design. In this guide, I want to share some specific tips and tricks that you can use for Figma Make to maximize the output.

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.

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.