Adding Context: The Hidden Value in Every Chart

Adding Context: The Hidden Value in Every Chart A simple—and because of that, great—example of how to build a graph and how providing clear visualization, along with the right context, can guide users toward useful insights and better decisions. I really like this site because it consistently presents strong examples and ideas on how to communicate key messages through graphs. By going step by step, you can clearly see the added value that context and information bring to each version. (text revised by a llm) https://slowrevealgraphs.com/2026/01/09/highest-paid-athletes-in-the-world-comparing-salary-winnings-with-endorsements/

- Pedro

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Making Space for ‘I’m Sorry’ and owning up to your mistakes

Making Space for ‘I’m Sorry’ A must-see: a balanced, sensible, and well-structured statement on this subject — the best I’ve heard so far. “If he said something as bad as what he said, I would still hope there’s room for someone to apologize and say, ‘I’m sorry — I made a huge mistake.’” (text revised llm) https://youtu.be/fuVMzJBzLhI?si=bCJBKOWc_8cEygX_

- Pedro

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The Art of Noticing Everyday Life

The Art of Noticing Everyday Life The Map of Tiny Perfect Things A good movie to watch with your pre-teen kids. An easy, entertaining storyline — but when you finish it and reflect on it, you realize how many good messages it conveys. The allegory of living the same day is nothing more than an extreme replica of how we live our lives every day — the routine, the inability to see what’s around us, and even to notice the tiny, perfect things we should enjoy and cherish each and every day. We become so inward-looking that we are completely blind to them. Finally, it also touches on the pain and the path you need to go through when you lose someone dear and close to you. A good combo, I would say, to watch with your kids — or with the kid inside you 🙂 (text revised by a llm) https://www.primevideo.com/detail/0OJWTXRLI9TEU0I224FZAIHQBS/ref=atv_dp_share_cu_r

- Pedro

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"It’s hard to compete against someone who’s just there to have fun"

"It’s hard to compete against someone who’s just there to have fun" A great podcast with Peter Steinberger, the creator and mastermind of OpenClaw, that I strongly advise everyone to listen to (>3 hours at normal speed). It really nudged me to take a step forward and embrace agentic AI without looking back. I will invest a significant part of my leisure time (at least for now) in this topic and also try to have fun building tools/models/stuff leveraging my domain expertise and improving them, AND enjoying the journey. https://youtu.be/YFjfBk8HI5o?si=kqSMob780alfWEUW

- Pedro

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13/20… Would You Do Better?

13/20… Would You Do Better? AI videos… can you spot the difference? Are you sure? The latest models really make it hard — almost impossible — to identify what’s real and what isn’t. Have a read and take the test. I did, and I was only able to spot 13/20 (which seems to be better than average). That said, even the ones I got right were, in most cases, 50/50 or just slightly better — but not by much. Next time you see a video, activate your critical thinking. Validate the sources and double-check — what you think you saw might not be real. Imagine what can be done, for good and for bad, with this technology. https://runwayml.com/research/theturingreel

- Pedro

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“…AI didn’t kill analytics. It killed analytics middlemen. …”

“…AI didn’t kill analytics. It killed analytics middlemen. …” When the business user can seamlessly — or with very low friction — navigate data through an LLM, ask relevant questions in natural language, and quickly receive highly accurate answers (instead of waiting days or weeks for feedback from the Analytics team), several things will/are happening: Up to 80% of the analytical work as we know it becomes vulnerable. The traditional task of translating business questions into SQL (or other query languages) and present a BI tool to retrieve and provide answers will increasingly be absorbed by AI. Only the top 20% of analysts will truly thrive. These are the ones who deeply understand the business, grasp the causality behind key variables, and know how to ask the right questions — not just the technically correct ones. Decision-making speed will increase significantly. With fewer intermediaries validating data and generating insights, organizations will reduce the cost of making decisions (which is different from the value of the decision itself). The number of analysts will likely decrease substantially. Translator tasks will be automated by AI. However, the analysts who remain will generate multiple times more value than before. Why? Because they will focus less on querying and more on: Framing the right problems Making recommendations Validating assumptions Running scenarios Assigning probabilities to outcomes Connecting insights to business impact In short, they will move from reporting to providing Intelligence. For a deeper perspective on this shift, I strongly recommend reading the well-designed article by Tree Data Point Thursday.

- Pedro

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Football: Physical, Cognitive, Psychological

Football: Physical, Cognitive, Psychological Who doesn’t like a good confirmation bias coming their way—especially when it’s backed by a scientific study? For several years now, I’ve been convinced that to be a great football player, athleticism and coordination are just as important as cognitive abilities and personality traits. The paper in the link, “Decoding the elite soccer player’s psychological profile,” presents an extensive study using a robust set of validated psychological tests across several domains, combined with advanced statistical techniques. It shows that elite football players tend to have: 1-Heightened planning and memory capacities. 2-Enhanced executive functions—especially cognitive flexibility—as well as higher levels of conscientiousness, extraversion, and openness to experience. 3-Lower levels of neuroticism and agreeableness. Football is played across three equally important domains: physical, cognitive, and psychological. Of course, I haven’t looked for studies that might prove me wrong 😊—so for now, I’ll fully enjoy my confirmation bias. P.S. I plan to look more closely at how they applied neural network features to the psychological variables to obtain and interpret their results. The approach seems both interesting and potentially applicable to other fields of study. https://www.pnas.org/doi/10.1073/pnas.2415126122?sma=lanewsletterdekikollaneras_2026.02.13&utm_medium=email&utm_source=newsletter&utm_campaign=lanewsletterdekikollaneras_2026.02.13

- Pedro

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