Most businesses are collapsing under invisible labor. We gave ours a memory

Seven months ago, I shared Audioscrape - a podcast exploration tool built entirely in Rust, running on a $7/month VM. Since then, we've transformed it into a full-fledged podcast intelligence platform, serving over 1,000 users, including PR teams, researchers, and marketers.

What's New:

Real-Time Monitoring: Track mentions across the top 100 U.S. podcasts, covering over 80% of U.S. listenership.

Advanced Search: Filter by speaker, sentiment, timeframe, and topic using AI-powered search.

Custom Alerts: Receive notifications for brand, competitor, or topic mentions.

API Access: Integrate podcast monitoring data into your workflows.

Transcription Accuracy: Achieved 92.2% accuracy across 20,000+ episodes.

Technical Stack:

Backend: Axum (async web framework)

Database: SQLite with SQLx for type-safe queries

Authentication: OAuth2

HTML Templating: Askama

Async Runtime: Tokio

Our commitment to Rust has enabled us to maintain low operational costs while scaling effectively.

Try It Out: https://www.audioscrape.com

Discussion Points:

Has anyone else scaled a Rust-based MVP into a production platform?

What strategies have you employed for efficient scaling and user acquisition?

Looking forward to your insights and feedback!

Comments (2)

yehors · 1d ago

Are you using a local Whisper? If yes, what do you use for inference, candle/ort?

lukaesch · 1d ago

Not local. Inference is the only part not written in Rust so far.

I am using Replicate to run docker images with a pipeline based on faster-whipser, VAD, pyannote and a custom LLM enhancement flow.

Thanks for sharing candle/ort. Interesting to see the WASM in-browser opportunities

Most businesses are collapsing under invisible labor. We gave ours a memory

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Show HN: Audioscrape – From $7 Rust MVP to Podcast Intelligence Platform

Comments (2)