CryDecoder – On-device ML for classifying baby cries (Swift, Core ML)
Hi HN, I’m the developer behind CryDecoder. I built this after too many nights at 3am staring at a crying infant, completely exhausted, trying to guess whether it was hunger, gas, or just general fussiness. I realized I was essentially running a mental decision tree on very little sleep, so I decided to see if I could automate some of that signal processing. What it does: CryDecoder analyzes short audio clips of a baby’s cry and classifies them into categories like hunger, discomfort/gas, tiredness, or general fussiness. How it works: • Tech: On-device audio feature extraction paired…
What it does
In the maker’s words, at launch
Hi HN, I’m the developer behind CryDecoder. I built this after too many nights at 3am staring at a crying infant, completely exhausted, trying to guess whether it was hunger, gas, or just general fussiness. I realized I was essentially running a mental decision tree on very little sleep, so I decided to see if I could automate some of that signal processing. What it does: CryDecoder analyzes short audio clips of a baby’s cry and classifies them into categories like hunger, discomfort/gas, tiredness, or general fussiness. How it works: • Tech: On-device audio feature extraction paired with a lightweight ML model trained on labeled cry patterns. • Performance: Inference runs locally on the phone, which keeps latency low and avoids sending audio off-device. Results come back quickly enough to feel near real-time. • Philosophy: This isn’t meant to replace parental judgment. It’s intended as an extra data point — a sanity check when you’re tired and not sure what to try next. The business side: The app currently uses a paid model with a preview. I’m an engineer first and still iterating on pricing and paywall placement. I’d appreciate feedback on: 1. The technical approach and responsiveness 2. Whether the paywall timing feels reasonable for a utility like this Thanks for taking a look.
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