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Friday 14th June’s issue is presented by DataDog |
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Why Data-Driven Product Decisions Are Hard (Sometimes Impossible) — Andrew Chen
tl;dr: “We strive to be data-driven in our decision making. And barring that, data-informed, overlaying our intuition and thoughts on top of the data. We certainly don’t want to be ignorant, and just make decisions with our gut. And yet sometimes that is exactly what happens — and some argue, better than being data-driven.” Andrew shares the limits of decision making with data.
Leadership Management |
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Don't Try To Step-Function Everyone All At Once
tl;dr: Managers often try to step-function everyone's career growth at once, which is ineffective and usually impossible. A better approach is to intensely focus on 2-3 people at a time in 6-month cycles. Create specific goals, monitor progress monthly, and repeat with a new cohort. This targeted approach leads to meaningful growth. The same method applies for training - focus deeply on upleveling a few people vs. shallowly training everyone.
Management |
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The State Of DevSecOps
tl;dr: An analysis of tens of thousands of applications and container images across thousands of cloud environments. This report assesses the security posture of applications today and evaluates the adoption of best practices that are at the core of DevSecOps—infrastructure as code, automated cloud deployments, secure application development practices, and more.
Promoted by DataDog DevOps Management |
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What Do GenZ Software Engineers Really Think? — Gergely Orosz
tl;dr: “These days, most new grad software engineers belong to GenZ, having been born between 1997 and 2012... strap in as we dive into responses from the latest generation of tech talent, and find out what young professionals really think about modern workplaces and their more “experienced” colleagues!”
Leadership Management |
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"Before you are a leader, success is about growing yourself. When you become a leader, success is about growing others."
— Jack Welch
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Falsehoods Programmers Believe About Names — Patrick McKenzie
tl;dr: As a public service, I’m going to list assumptions your systems probably make about names. All of these assumptions are wrong. Try to make less of them next time you write a system which touches names. (1) People have exactly one canonical full name. (2) People have exactly one full name which they go by. (3) People have, at this point in time, exactly one canonical full name. (4) People have, at this point in time, one full name which they go by. (5) People have exactly N names, for any value of N.
Naming Antipattern |
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The State Of DevSecOps
tl;dr: An analysis of tens of thousands of applications and container images across thousands of cloud environments. This report assesses the security posture of applications today and evaluates the adoption of best practices that are at the core of DevSecOps—infrastructure as code, automated cloud deployments, secure application development practices, and more.
Promoted by DataDog DevOps Management |
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Don’t Refactor Like Uncle Bob. Please
tl;dr: The author critiques an example refactoring from "Clean Code" by Robert Martin, arguing that Martin's changes introduce unnecessary complexity and side effects. The original function is split into a stateful class with unclear naming and fragmented logic. The author provides alternative refactorings that aim for simplicity and clarity over dogmatic adherence to certain principles.
BestPractices Refactoring |
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Why Curl Closes PRs on GitHub — Daniel Stenberg
tl;dr: “I believe having good commit messages following a fixed style and syntax helps the project. It makes the git history better and easier to browse. It allows us to write tools and scripts around git and the git history. Like how we for example generate release notes and project stat graphs based on git log basically.” Daniel shares how GitHub’s UI doesn’t allow for this.
GitHub |
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Let's Reproduce GPT-2 (124M) — Andrej Karpathy
tl;dr: “We reproduce the GPT-2 (124M) from scratch. This video covers the whole process: First we build the GPT-2 network, then we optimize its training to be really fast, then we set up the training run following the GPT-2 and GPT-3 paper and their hyperparameters, then we hit run, and come back the next morning to see our results, and enjoy some amusing model generations.”
LLM GPT |
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Most Popular From Last Issue |
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Adobe Alternatives: A list of alternatives for Adobe software.
Ice: Powerful menu bar manager for macOS.
Lexbor: OS HTML renderer library.
Puter: OS internet operating system.
Restate: Building resilient applications.
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