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Breathe CLI – Paced resonance breathing in the macOS terminal

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Breathe CLI – Paced resonance breathing in the macOS terminal

I built a terminal app that paces slow breathing at 6 breaths per minute for vagal tone training. It's a single Python file, stdlib only, no dependencies — just run breathe and follow the bar. I'm a cardiology patient (HFrEF). Slow breathing at resonance frequency is one of the few non-pharmacological interventions shown to improve cardiac vagal tone and baroreflex sensitivity (Bernardi et al., Circulation 2002; Lancet 1998). I wanted a frictionless daily habit tool — no app store, no account, no subscription, just open terminal and go. Design constraints, all grounded in the clinical literature: - No breath retention — Valsalva risk in cardiac patients - No rapid breathing — minimum 8-second cycles - Exhale ≤ 2x inhale — no evidence for extreme ratios - Immediate exit, always — q or Ctrl+C restores the terminal even on crash The README includes a resonance frequency measurement protocol for anyone with a chest-strap HRV monitor who wants to find their individual optimum instead of using the 6 bpm default. macOS only (uses afplay for audio cues). MIT licensed. pip install breathe-cli or brew tap marekkowalczyk/breathe && brew install breathe.

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, single · Missing: agents, agent, cursor
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription, training · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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