15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T645,Hi HN,

I built EchoEntry (https://echoentry.ai) – a speech-to-text API optimized specifically for digits.

The problem: Generic STT APIs struggle with numbers. "One oh five" becomes "105" sometimes, "15" other times. For healthcare apps, warehouse systems, or IVR, this inconsistency breaks workflows.

My solution: Fine-tuned Whisper-small on 1-999 spoken numbers across 5 English accents. Gets 95% accuracy on 1-3 digit numbers.

Tech stack: - Custom Whisper model (1.7GB) - FastAPI backend - Deployed on 8GB Linode - FFmpeg for audio processing

Try it now (two commands, no signup):

# Download test audio curl -O https://echoentry.ai/test_audio.wav

# Test the API curl -X POST https://api.echoentry.ai/v1/transcribe \ -H "X-Api-Key: demo_key_12345" \ -F "file=@test_audio.wav;type=audio/wav"

Currently free beta (1,000 calls/month per key). Looking for feedback on: 1. What accuracy threshold makes this production-ready for you? 2. Are there other number-heavy use cases I'm missing? 3. Would you pay for this vs. using generic STT?

Docs: https://echoentry.ai/docs.html

Happy to answer technical questions about the fine-tuning process or deployment!17:T5f7,Hey everyone, I built llm-schema-guard because LLMs are amazing at spitting out JSON... until they suddenly aren't. Even with JSON mode or function calling, you still get missing fields, wrong types, or just plain broken syntax that kills your ag

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