Leadership in Tech
Leadership in Tech
#308 – May 03, 2026
using fewer, higher-quality metrics to capture what matters
Measure less to learn more
8 minutes by Jake Mainwaring
Tracking too many metrics in experiments creates a statistical problem: the more metrics you measure, the harder it becomes to detect real effects without also generating false alarms. Discord cut their default experiment metrics from about 50 to 15 by identifying redundant measurements using correlation analysis and PCA. This improved their ability to catch genuine, moderate effects by roughly 45%.
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Managing a team that didn't choose you
8 minutes by manager.dev
Starting a new manager role means ditching rigid plans fast. After joining a team that had gone six months without real leadership, the first priority became people, not code. Pushing too hard on deadlines then created burnout, while ignoring a growing support backlog nearly caused real damage. The only thing that consistently worked was staying alert, adjusting quickly, and listening when the team pushed back.
How to one-on-one
5 minutes by Ben Balter
Most one-on-one meetings waste time on status updates that should live in writing. The best ones focus on things that only work out loud: career growth, coaching, feedback, and clearing tension. Good preparation means a shared agenda with real context added throughout the week. Ben suggests to ask more questions than you answer, follow the energy of the conversation, and leave with clear next steps written down.
Building influence within the team
8 minutes by Roman Nikolaev
Sacrificing yourself for the team and giving pep talks won't build influence. Roman argues real influence comes from driving meaningful change: modeling the behavior you want to see, finding allies who reinforce the same message, and making even small progress visible and celebrated. What destroys influence fastest is lacking your own point of view or failing to follow through on commitments.
Claude dispatch and the power of interfaces
7 minutes by Ethan Mollick
AI systems are more capable than most people realize, but chatbot interfaces make them hard to use. Long, messy conversations increase mental effort and limit productivity. New tools show better approaches: specialized apps, personal agents that work across your files, and AI that builds interfaces on demand. Ethan points out that the future of AI depends less on smarter models and more on designing simple, effective ways for people to interact with them.
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And the most popular article from the last issue was: