How insights from cognitive science and social psychology explain why software knowledge is so hard to preserve
The Economics of Software Teams: Why Most Engineering Organizations Are Flying Blind
A breakdown of what software development teams actually cost, what they need to generate to be financially viable, and why most organizations have no visibility into either number.
Laws of Software Engineering
A collection of principles and patterns that shape software systems, teams, and decisions.
The Social Physics of Conversation: Why Communication Patterns Matter
Discover how communication patterns shape team performance, innovation, and collective intelligence. Learn why idea flow matters more than talent alone.
How teams build – Linear
AI usage patterns in software teams: who is adopting AI, how it reshapes where teams spend their time, and how much more they ship.
Peopleware: productive projects and teams
Demarco and Lister demonstrate that the major issues of…
Value Pairs: A New Way of Approaching Product Development
How AI-enabled teams can change how we think about product development
Why Is Everyone In Tech So Sad?
A lot of people seem to be realizing that knowledge work is mostly pointless. AI might give us the pleasure of finding out what happens if an entire class of workers loses faith in their careers.
How cognitive elaboration fosters knowledge acquisition on social media—a field experiment
Abstract. Social media technologies have been criticized as ineffective sources of information because users seem to increase their subjective but not thei
Commons Computer
A modern team knowledge base for your internal documentation, product specs, support answers, meeting notes, onboarding, & more…
The Missing Discipline in Computer Science
On Teaching Computer Scientists to Ask “Why”
Knowledge sharing: Why smart creatives build in public
Knowledge sharing: Why smart creatives build in public
What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & knowledge to be a successful AI-enhanced developer are organized into four domains (using Generative AI effectively, core software engineering, adjacent engineering, and adjacent non-engineering) deployed at critical junctures throughout a 6-step task workflow. In order to "future proof" developers for this age of AI, on-the-job learning initiatives and computer science degree programs will need to target both "soft" skills and the technical skills & knowledge in all four domains to reskill, upskill and safeguard against deskilling.
What We Know We Don't Know: Empirical Software Engineering
Empirical Software Engineering is the study of what actually works in programming. Instead of trusting our instincts we collect data, run studies, and peer-review our results. This talk is all about how we empirically find the facts in software and some of the challenges we face, with a particular focus on software defects and productivity. Talk doesn’t seem to be online yet; in the meantime, you can see a recording of an older version of the talk here.
Why AI Makes Things Worse for Enterprise Teams, by Paul Ford
Why are so few engineering teams reaping the benefits of AI? On this week’s episode, Paul presents Rich with the findings from a recent report from CircleCI and
