Friday 28th March’s issue is presented by QA Wolf | | | Manual testing on personal devices is too slow and limited. Teams cut releases a week early just to test before submitting them to app stores— and without broad device coverage, issues slip through. | QA Wolf’s AI-native service delivers 80% automated test coverage in weeks, with tests running on real iOS devices and Android emulators — all in 100% parallel with zero flakes. | Engineering teams move faster, releases stay on track, and testing runs automatically —freeing developers to build, not debug. | ⭐ Rated 4.8/5 on G2 | | | | | — Wes Kao | | tl;dr: “But” is a negating word. It cancels out whatever comes before it. Most people use a structure of saying, “The positive thing, but the negative thing,” which accidentally cancels out all the positive stuff. Wes shares a better approach. | CareerAdvice | | | — Paul Gross | | tl;dr: “Using AI in software development is not about writing more code faster; it's about building better software. It’s up to you as a leader to define what “better” means and help your team navigate how to achieve it. Treat AI as a junior team member that needs guidance. Train folks to not over-rely on AI; this can lead to skill erosion. Emphasize "trust but verify" as your mantra for AI-generated code. Leaders should upskill themselves and their teams to navigate this moment.” | Leadership Management | | | — Jon Perl | | tl;dr: Traditional outsourced QA relies on inefficient, costly tech stacks that fall short of QA engineers' needs. QA Wolf took a smarter approach. They built proprietary technology that aligns with customers’ needs, enabling their QA engineers to deliver 80%+ automated test coverage for their clients in just 4 months. In this free webinar, CEO Jon Perl reveals how QA Wolf is redefining QA automation. | Promoted by QA Wolf | Management Tests | | | — Ben Kuhn | | tl;dr: “I had an unusually hard time becoming a manager: I went back and forth three times before it stuck, mostly because I made lots of mistakes each time. Since then, as I had to grow my team and grow other folks into managing part of it, I’ve seen a lot of other people have varying degrees of a rough time as well—often in similar ways. Here’s a small, lovingly hand-curated selection of my prodigious oeuvre of mistakes, and strategies that helped me mitigate them.” | Leadership Management | “For every complex problem there is an answer that is clear, simple, and wrong.” | | | | H. L. Mencken |
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| | — Neil Macy | | tl;dr: “I had the morning off work today. So I did what anyone would do, and started playing around with my Terminal setup. There are some things that have bugged me for a while, and some things that I've meant to try out for a while, and this seemed like a good chance to play around.” | Terminal | | | | tl;dr: APIs are the backbone of modern applications, but security vulnerabilities, latency issues, integration complexities, and compliance risks can turn them into a nightmare. From preventing data breaches to optimizing performance and ensuring seamless scalability, tackling these challenges requires the right strategies. This article dives deep into the most common API pitfalls and provides technical solutions engineers can apply today. Read the full breakdown to strengthen your API architecture and build more resilient systems. | Promoted by CarsXE | BestPractices API | | | — Birgitta Böckeler | | tl;dr: “While the advancements of AI have been impressive, we’re still far away from AI writing code autonomously for non-trivial tasks. They also give ideas of the types of skills that developers will still have to apply for the foreseeable future. Those are the skills we have to preserve and train for.” | AI CareerAdvice | | | | tl;dr: A comprehensive guide to practical algorithms including: Selection Sort, Insertion Sort, Heap Sort, Quick Sort, Merge Sort, Tim Sort, Binary Search, DFS, BFS, Prim's, Kruskal's, Dijkstra's, Bellman-Ford, and more. | Algo | | | | tl;dr: Netflix’s personalized recommender system is a complex system, boasting a variety of specialized machine learned models each catering to distinct needs including “Continue Watching” and “Today’s Top Picks for You.” However, maintenance of the recommender system became quite costly and it was difficult to transfer innovations from one model to another. This scenario underscored the need for a new recommender system architecture where member preference learning is centralized, enhancing accessibility and utility across different models. | ML | | Most Popular From Last Issue | Categories Of Leadership On Technical Teams — Ben Kuhn | | Notable Links | Awesome MCP: Collection of MCP servers. | Folo: Follow everything in one place. | Typia: Super-fast type framework. | Usertour: OS onboarding platform for developers. | Yaak: Desktop API client. | |
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