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:T6a3,A year ago, I shared my journey bootstrapping Audioscrape in Rust. Back then: 500 users, SQLite, 4k LoC in main.rs, running on a $7/month VM.

Today: 25,000+ transcribed episodes, knowledge graph with AI-extracted entities, and still running lean.

What changed:

Tech evolution: SQLite → PostgreSQL (scale). Added OpenSearch for full-text + semantic search. Self-hosted WhisperX on 2 GPUs (~100 episodes/hour). OpenAI for entity extraction (people, companies, topics). Still Rust/Axum, now ~15k LoC.

New features: Speaker diarization (who said what) using voice fingerprinting. Entity pages linking mentions across episodes. Timestamp-based sharing and deep linking. MCP server for AI agents to search podcasts.

What stayed the same:

Solo developer. Bootstrapped, no VC. Rust for everything backend. Obsessive cost optimization.

Current stats:

25,000+ transcribed episodes. Top podcasts: JRE, Lex Fridman, Huberman Lab, etc. Pipeline processes 100+ episodes/hour. Still under $100/month infra (excluding GPUs).

Learnings from year one:

Rust's async ecosystem is production-ready. SQLx migrations saved me during the PostgreSQL switch. Entity extraction is harder than transcription. SEO matters more than I expected for discovery.

2025 goals:

API access for developers. Real-time transcription for live podcasts. Improved semantic search with custom embeddings.

Try it: https://www.audioscrape.com

Search is free, no account needed. Would love feedback on the search UX and what features would make this useful for your workflow.17:T549,We built a small open-source benchmark to test how well vision-enabled LLMs handle pixel-level pointing on screens. Instead of complex UI screenshots, we use

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