Did GitHub Copilot Really Increase My Productivity?

- Yuxuan Shui tl;dr: “I had free access to GitHub Copilot for about a year, I used it, got used to it, and slowly started to take it for granted, until one day it was taken away. I had to re-adapt to a life without Copilot, but it also gave me a chance to look back at how I used Copilot, and reflect - had Copilot actually been helpful to me?”

featured in #513

How I Setup My Terminal For Max Productivity

- Jordan Cutler tl;dr: Jordan discusses his terminal setup and shares daily commands across the following categories: (1) Terminal app, shell, and plugin manager. (2) Theming. (3) Best terminal plugins. (4) Aliases and history config. (5) Command line utilities to install. 

featured in #506

Notes On How To Use LLMs In Your Product

- Will Larson tl;dr: “I’ve been working fairly directly on meaningful applicability of LLMs to existing products for the last year, and wanted to type up some semi-disorganized notes. These notes are in no particular order, with an intended audience of industry folks building products.” Will discusses opportunities re-configuration, combining LLMs with unsophisticated algorithms to retrieve data. And more.

featured in #505

Developing Rapidly With Generative AI

- Shannon Phu tl;dr: From the engineering team at Discord: “We break down the process of building with LLMs into a few stages. Starting with product ideation and defining requirements, we first need to figure out what we’re building and how it can benefit users. Next, we develop a prototype of our idea, learn from small-scale experiments, and repeat that process until our feature is in a good state. Finally, we fully launch and deploy our product at scale. In this post, we will dive deeper into each stage of this process.”

featured in #505

Using GitHub Copilot In Your IDE: Tips, Tricks, And Best Practices

tl;dr: 15 tips include: (1) Open your relevant files. (2) Provide a top-level comment. (3) Set includes and references. (4) Meaningful names matter. (5) Provide specific and well-scoped function comments. (6) Provide sample code. (7) Inline chat with GitHub Copilot. (8) Remove irrelevant requests. (9) Navigate through your conversation. (10) Use the @workspace agent. 

featured in #503

Claude And ChatGPT For Ad-Hoc Sidequests

- Simon Willison tl;dr: The author demonstrates a quick ”sidequest" task where he converted the shapefile of a largest park in NY to a GeoJSON polygon in just 6 minutes. “One of the greatest misconceptions concerning LLMs is that they’re easy to use. They aren’t: getting great results requires a great deal of experience and hard-fought intuition, combined with deep domain knowledge of the problem you are applying them to.”

featured in #501

How Uber Tripled Developer Productivity And What You Can Learn From That (Video)

- Debo Ray tl;dr: In 2016, as Uber’s engineering team grew beyond the hundreds, things started to break. Velocity averaged 1PR/developer / sprint. Production wasn’t doing well either. Over the next 5 years they introduced many changes and tools like Cerberus, SubmitQueue and DevPod to triple productivity. Many companies today are Uber in 2016 so Debo and Mihai, early Uber engineers, decided to share their story.

featured in #500

What Type Of Interruptions Impact Developer Productivity Most?

- Lizzie Matusov tl;dr: Research shows that: (1) Self-interruptions i.e. voluntary task switching is more disruptive than external interruptions. (2) Developers self-reported that external interruptions are more disruptive than self-interruptions. (3) Contextual factors such as time of day are a stronger determinant of how disruptive an interruption might be than task-specific factors. (4) Switching between programming and testing tasks, compared to other development tasks, makes developers more vulnerable to interruptions.

featured in #487

Measuring Developer Productivity: Real-World Examples

- Gergely Orosz Abi Noda tl;dr: In this issue, Abi outlines the developer productivity metrics used at 17 tech companies, such as Amplitude, Etsy, DoorDash. He then dives deep into several companoes of varying size, notably Google & LinkedIn, Peloton, scaleups and smaller companies. Abi’s advice on how to choose your metrics: start with the problem you want to solve. Is it shipping frictionless, retaining developers by keeping them happy and satisfied, raising the quality of software shipped, or something else? Then work backwards from there. 

featured in #481

Measuring Engineering Velocity On A Software Team

- Zach Zaro tl;dr: Zach Zaro, CEO and cofounder of Coherence, reviews the history and state of the art of velocity. Measuring engineering velocity is a valuable exercise for software teams aiming to improve their processes and deliver value consistently. By understanding its nuances and potential pitfalls, teams can use velocity as a tool for growth and continuous improvement rather than a blunt instrument of judgment.

featured in #479