How Do AI Coding Tools Actually Change Developer Work?
- Lizzie Matusov tl;dr: “Researchers from Microsoft and the Institute for Work Life ran a three-week randomized controlled trial of GitHub Copilot with 228 engineers at a large global software company. Engineers were randomly assigned to one of three groups: those newly given access to GitHub Copilot and instructed to use it (treatment), those asked not to use any AI tools (control), and those who were already using Copilot (continuing). Over three weeks, participants in all groups completed daily diary entries. Researchers also collected telemetry data to observe behavioral patterns alongside shifts in beliefs and attitudes.”featured in #606
featured in #605
Vertical Integration For Superior QA
- 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.featured in #605
Crafting a Standout Leadership CV: A Comprehensive Guide
- Lena Reinhard tl;dr: “Your CV should tell a compelling story about your leadership journey, highlighting your ownership mindset, adaptability, and impact. Focus on quantifiable achievements rather than vague responsibilities. Include often-overlooked elements like detailed context about companies, team structure, cross-functional initiatives, and technical expertise. Avoid passive language that diminishes your sense of ownership. Structure matters: use clear formatting, include contact information on every page, and ensure your CV is ATS-friendly.”featured in #605
Senior Developer Skills In The AI Age: Leveraging Experience For Better Results
- Manuel Kießling tl;dr: “I’m now convinced that AI-assisted software development has the potential to elevate our craft to the next level in terms of productivity. This is why I believe our community should embrace it sooner rather than later — but like all tools and practices, with the right perspective and a measured approach. My motivation for sharing these experiences and the best practices I’ve identified is to help move the needle forward in terms of AI adoption within the broader software development community — even if realistically, it’s only by some micrometers.”featured in #605
Google’s Principles For Measuring Developer Productivity
- Abi Noda tl;dr: Abi discusses the following: (1) Avoid single-metric models. (2) Measure all outcomes you care about, and capture multiple metrics for each outcome. (3) Be mindful of incentives created by measurement. (4) Measure different facets of productivity. (5) Use system-based and self-reported data together.featured in #604
In Defense Of Ruthless Managers
- Sean Goedecke tl;dr: “Empathetic managers care. They are emotionally invested in their employees as human beings, and actively campaign to support their employees’ needs. Ruthless managers are there to do their job. They aren’t necessarily assholes, but they see their main role as communicating the company’s needs to their engineers and vice versa. They will almost never go out on a limb on an employee’s behalf.”featured in #604
featured in #603
featured in #603
You Make Your Evals, Then Your Evals Make You.
- Tongfei Chen Yury Zemlyanskiy tl;dr: The post introduces AugmentQA, a benchmark for evaluating code retrieval systems using real-world software development scenarios rather than synthetic problems. AugmentQA uses codebases, developer questions, and keyword-based evaluation outperforming open-source models that excel on synthetic benchmarks but struggle with realistic tasks.featured in #603