Sun.Mar 07, 2021

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Don’t Believe Everything You Think

Digital Tonto

“The first principle is that you must not fool yourself—and you are the easiest person to fool.” We need to have a sense of humility. It’s far too easy to be impressed with ourselves and far too. [[ This is a content summary only. Visit my website for full links, other content, and more! ]].

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Your Job Is to Attend Meetings

Innovation Architecture

People distinguish between attending meetings and doing their jobs: “all these meetings are getting in the way of my real work.". Implicit in this complaint—"I don't have time to get my job done"—is that meetings are an unwelcome imposition upon our more life-affirming endeavors. What does that mean, exactly? Are organizations hamstringing the business by keeping people from more productive, albeit solitary pursuits?

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Episode 033: Creativity? Make It Someone Else’s Problem

Innovation in Practice

Are you struggling with creativity? Just make it someone else’s problem. Now, it doesn’t mean you should just stop being creative and outsource your creative thinking to someone else. . But there’s a great yet simple way to boost your creative output by imagining you’re being creative for someone else’s problem. . In other words, you’re playing a trick on your mind.

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It's Not So Easy to Walk in Someone Else's Shoes

Michael Roberto

Source: [link] Leaders can be much more effective if they demonstrate empathy for their team members. However, many of us struggle at times to empathize. In fact, quite surprisingly, we may empathize LESS if we have been in another person's shoes in the past, and we have faced simliar obstacles. Several years ago, Rachel Ruttan, Mary-Hunter McDonnell, and Loran Nordgren reported on some fascinating research they had conducted concerning our capacity to empathize.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.