05 how we do developer productivity panel
summary
Panel explains why teams stop tracking estimation accuracy and instead quantify investment profile across deployment, tech discovery, KTLO and new features; they adopt Tara AI to replace ad hoc Python scripts. Concrete metrics include PR cycle time and the submetric pickup time, observability of critical user flows, and a composite feature health score tied to MTTR, customer impact and cost of doing business. Real examples: two low-git-activity cases (one led to a PIP, one was data-work) and benchmarks that flag high-performing teams.
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chapters (10)
- 0:00Panel introduction
- 1:51Beyond story points
- 3:56Ad-hoc scripts to tracking
- 6:02Predictability vs velocity
- 8:08Transparency and safety
- 12:27Perfect metrics danger
- 14:36Developer productivity to value
- 18:28Beyond Dora metrics
- 21:59Budget and experience
- 26:16Closing remarks
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