CryDecoder
On-device ML for classifying baby cries (Swift, Core ML)
Details
- External ID
- 46466738
- Source
- HN
- Company
- —
- Product
- CryDecoder
- Website domain
- apple.com
- Launched
- Jan. 2, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.09617918313570488
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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 thisThanks for taking a look.
Enrichment
- Theme
- personal and family tracking tools
- Vertical
- Healthcare
- Function
- Analytics & BI
- Audience
- B2B
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- on-device machine learning for baby cry classification
- Manually corrected
- False
Could you build this?
Partial The iOS frontend and Core ML inference wrapper are easily vibe-coded, but collecting and labeling a proprietary, clinically validated dataset of infant cries is a distinct barrier.
What it would actually take: The solution requires an iOS app (Swift/SwiftUI) feeding audio buffers into a mel-spectrogram pipeline and a Core ML CNN/Transformer model. The main difficulty is acquiring thousands of verified, labeled infant audio samples (categorized into hunger, gas, pain, fatigue) with clinical backing, which cannot be generated purely through software prompting.
Discussion
2 comments analyzed.
Concerns raised: Accuracy of cry identification at first use
Competitors
Other products that read as similar to this one — 34 launches clear the similarity bar, closest 8 shown.
Attention rank: #33 of 35 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 64 days after the earliest competitor.
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- wavrail · github · 2026-09-26 · 31 upvotes · similarity 0.35
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a analytics & bi tool for Legal yet.