Reaction Training: what we learned building Random Tactical Timer

What changed today fix(ui): restore high-precision nudge buttons (#503) feat: harden paywall analytics and precise training window controls feat(pro): add secret override and precision controls (#501) fix(branding): final purge of growth-crap and version parity v1.2.2 (#498) Search intent target Primary keyword: reaction training Intent class: mixed BID filter: business potential, intent match, and realistic difficulty AI/LLM flow we used We keep this loop tight: plan -> code -> test -> release gate -> feedback. The key is not bigger prompts, it's strict validation and fast iteration. Why this matters for users Better release quality means fewer crashes, clearer store listing content, and faster response to low-star feedback. That directly improves trust and review quality. What we measure D1 and D7 retention from install cohorts Store conversion from listing views to installs Review velocity, star distribution, and unresolved low-star SLA Click-through rate on post CTAs to app downloa