Did the CEO of Google really say that AI only increased productivity by 10%?
Attention pendulum (goal setting)
Tim Ferriss explains how identifying “super tasks” in his to-do list helps plan his day
Comparing Task Dependency Features Among Tier-1 To-do List Apps (Todoist, TickTick, Amplenote)
Interesting Podcast Idea? Habits of World-Class Programmers
Business suggestion: Play games with an unfair advantage
Function detection: the pain & triumph of tackling hard projects on a small team
Ruby/Rails performance comparison of Intel Mac, M1 Max, and M2 Max
Measuring developer productivity in 2019
Until I spent the better part of 50 hours writing this guide about measuring developer productivity, I had little appreciation for how far we’ve come in the past four years. If you’re an engineering manager, there’s a growing body of data that suggests you can make a blanket increase in engineering efficacy. If you’re a great developer, you now stand to earn your due. These are exciting times to be programming.
Additional topics covered in the (admittedly a bit too epic) guide:
- Better data, better policies. Examples of how measurement can improve retention, morale, and amount of work completed.
- Best tools for measuring developers in 2019. Three tools available to measure developer performance, and how they differ.
- What makes code measurement possible? What changed that made it possible to measure developer output, when it was long assumed impossible?
- On securing developer buy-in. Addresses the first question asked by many companies pursuing developer measurement: how do I get the team on board?
- Transparency always wins on a long enough time horizon. What can be learned from the past about those who embrace and resist increased transparency?
If you manage developers, would love to hear what you think of this effort? Is it relevant to you? Anything that could make it better?
