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Combat Conditioning: what we learned building Random Tactical Timer
How-ToMachine Learning

Combat Conditioning: what we learned building Random Tactical Timer

via Dev.toIgor Ganapolsky4h ago

What changed today fix: voice defaults OFF, no-repeat, pause jump fix, sound workflow, voice AB test chore: merge release/v1.3.15 back into develop (#952) fix(ci): run release branch checks automatically fix(ios): attach subscriptions to review + subscription disclosures Search intent target Primary keyword: combat conditioning 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 download

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