/Management

Layers Of Context

- Will Larson tl;dr: “All interesting problems operate across a number of context layers. For a concrete example, let’s think about a problem I’ve run into twice: what are the layers of context for evaluating a team that wants to introduce a new programming language like Erlang or Elixir to your company’s technology stack?" Will shares some layers of context and how to see across them.

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15 Best Leadership Books

- Dr Milan Milanović tl;dr: “This is a reading guide for you as a leader to navigate and upgrade your leadership skills. It is a curated list of the best books on leadership that you can check and read during the holiday season. But it is not just a numbered list of books to read; it is a guide created by considering my selection from many books I’ve read and talked about with many leaders in the field.”

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Are Your Standards Too Low? In Defense Of Raising The Bar

- Wes Kao tl;dr: Wes discusses why you should consider raising your standards and why this has the potential to dramatically improve your team’s chances of getting what you want: (1) Why every leader should set higher standards. (2) Challenges when raising the bar. (3) How to normalize a culture of excellence. 

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Thinking About Risk: Mitigation

- Jacob Kaplan-Moss tl;dr: Jacob presents a simple framework to help frame discussions about risk mitigation. “It’s intentionally very simple, a basic starting point. I’ll present a more complex framework later in this series, but I want to lay more of a foundation before I get there, so we’ll start here.”

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Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

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Tying Engineering Metrics To Business Metrics

- Iccha Sethi tl;dr: “Most engineering organizations I’ve worked in or led have tracked some form of engineering metrics. These range from simple metrics like uptime and incident count to more complex frameworks like DORA. As an engineering leader, you’ve probably been asked, either by someone within or outside of engineering: Why do these metrics matter? or How do they align with our business goals?”

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The 6 Mistakes You’re Going To Make As A New Manager

- Matheus Lima tl;dr: “Reflecting on my first couple of years as an Engineering Manager, I realized that the lessons I learned are not unique to me; many new managers face similar experiences. That’s why I want to share these insights with you. My goal is to support and connect with other new managers who are going through this exciting yet demanding transition.”

featured in #573


Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

featured in #573


Product Management Is Broken. Engineers Can Fix It.

- James Hawkins tl;dr: “When Tim and I first started PostHog in 2020, I was adamant we would never hire a product manager. I wanted engineers to wrestle with hard product problems. Product managers, I believed, would just get in the way. Four years on, I admit I was (partially) wrong. We need product managers. But I was right about one thing: there is a better way. Over the past two years, we've redefined how PMs and engineers work together, and optimized everything we do for speed and autonomy. Here's our exact playbook.”

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Grifters, Believers, Grinders, And Coasters

- Sean Goedecke tl;dr: “Why do engineers get mad at each other so often? I think a lot of programmer arguments bottom out in a cultural clash between different kinds of engineers: believers vs grifters, or coasters vs grinders. I’m going to argue that good companies actually have a healthy mix of all four types of engineer, so it’s probably sensible to figure out how to work with them.”

featured in #573