lukaesch

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.

Hey HN! I built RemoteMCPList (https://remotemcplist.com) because I was frustrated by the lack of a proper registry for Model Context Protocol servers.

With Anthropic and OpenAI both pushing MCP as a way to extend AI capabilities, but neither providing a comprehensive directory of available servers, I decided to take matters into my own hands.

What is it? RemoteMCPList is a searchable directory of remote MCP servers. Each listing includes:

Server capabilities (read/write data, OAuth support) Categories (Development, Data Analysis, Payments, Communication, etc.) Quick descriptions of what each server does Integration details

Why I built this: I kept discovering amazing MCP servers scattered across GitHub, random blog posts, and Twitter threads. There was no central place to find them all. As someone building with MCP, I wanted a single source of truth for what's available.

Current features: 50+ servers cataloged and growing Filterable by category and capabilities Community submissions via GitHub Clean, fast interface (no JS framework bloat)

Technical details:

Static site for speed Open source contributions welcome Planning to add: installation guides, user reviews, and compatibility matrices

I'm actively adding new servers as I discover them. If you've built an MCP server or know of one that's missing, please submit it!

Would love to hear your thoughts on what features would make this more useful for the community. Are you using MCP? What servers would you like to see added?

I’ve been working on a way to connect Claude (and other Model Context Protocol clients like ChatGPT) to a large index of public and user-uploaded audio.

The result is a Remote MCP server for Audioscrape that lets an AI assistant query and return relevant segments from:

Popular podcasts

Court hearings

Earnings calls

Any audio you upload yourself

Example query:

"What is Joe Rogan saying about Germany on his podcasts?"

The MCP server handles the transcription index and exposes a search_audio_content tool. Claude then calls it and surfaces exact matching moments from the audio.

Tech notes:

Server: Remote MCP spec implementation

Search: Full-text index over transcripts

Client: Works with Claude Desktop, Mobile, Web MCP support

Data: Combination of public audio sources + user uploads

Docs & setup:

Claude setup: https://www.audioscrape.com/docs/claude-setup

MCP server details: https://www.audioscrape.com/docs/mcp

I’m curious how others might extend MCP for searching non-text data sources - especially if you’ve tried integrating AI assistants with large datasets outside the usual APIs.

I run Audioscrape — a search engine for podcast knowledge. It lets you search transcriptions and extract who said what across top podcasts.

What’s surprising: over the past few weeks, we’ve been seeing more referral traffic from AI assistants (like ChatGPT, Perplexity, etc.) than from traditional search engines like Google or Bing.

Is this a fluke, or the start of something new?

I'm wondering if we’re entering an era of "LLM SEO" where optimizing for visibility in AI-generated answers becomes just as important (or even more so) than ranking on search engines.

Would love to hear:

Has anyone else noticed a similar pattern?

Is there such a thing as “LLM optimization” yet?

Any tips or ideas on how to stay visible in this new kind of information flow?

Feels like the rules of discovery are changing quietly, but rapidly.

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!

I'm excited to share my journey of bootstrapping Audioscrape, a podcast exploration platform built entirely in Rust. While many would advise using Ruby on Rails, Python, or TypeScript for rapid MVP development, I chose Rust to challenge myself technically and to optimize for low operational costs. The result is a highly performant application running on just a $7/month virtual machine, proving you can launch lean and scale efficiently.

Rust was selected for several reasons. It offers cost-efficiency, using minimal resources that translate directly into lower hosting expenses, essential for a bootstrapper. Its performance in audio processing and web serving allows for growth without immediate infrastructure scaling. Additionally, investing in Rust has long-term benefits given its growing demand, offering valuable skills for the future. This project also aims to demonstrate Rust's viability for rapid web application development.

Audioscrape functions by aggregating podcast RSS feeds, transcribing episodes, and providing an interactive interface for content exploration and discussion—it's a full-stack Rust application, utilizing a range of crates from the ecosystem. The technical stack includes Axum for backend processing, SQLite with SQLx for database management, OAuth2 for authentication, Askama for HTML templating, and Tokio for async runtime, forming a robust and efficient system.

My development approach was lean yet focused. Approximately 4,000 lines of code are organized within a single main.rs file, along with HTML templates. This setup enabled rapid iteration, facilitated by Neovim, keeping all operations contained to a single virtual machine without relying on external services, thereby maintaining low operational costs.

Audioscrape's features include transcription browsing, allowing users to read along with podcasts or easily find topics of interest. Segment highlighting encourages sharing and discussion of specific episode moments. Community features such as upvotes, comments, and bookmarks enhance user interaction. An advanced search function helps locate relevant content across multiple podcasts, and person profiles allow exploration of guest or host appearances across various shows.

The project came with its share of challenges, like mastering Rust's ownership model, which eventually led to efficient transcription and speaker detection capabilities. Managing all facets alone—from development and design to operations—was both challenging and rewarding.

Looking forward, the plans for Audioscrape include developing an API for third-party integrations and using WebAssembly for client-side processing. Strategies for scaling larger datasets, particularly for search functionality, are on the agenda, along with personalized content delivery options.

In terms of metrics, Audioscrape currently boasts 500 active users over the past week, with monthly recurring revenue at $0. However, the cost per user is less than $0.01 due to efficient resource use. The next goal is to implement paid features to cover operating costs and reduce expenses per episode page.

I'm eager to engage with the Hacker News community. Has anyone else bootstrapped using Rust, and what were your experiences? How do you balance technical debt versus shipping features, especially in solo projects? What marketing strategies have worked for you with such technical products? As I continue to bootstrap this project, I welcome any feedback, particularly from those with similar experiences or who have utilized unconventional tech stacks for MVPs. I invite you to visit Audioscrape at www.audioscrape.com and share your thoughts!

I'm excited to share my journey of bootstrapping Audioscrape, a podcast exploration platform, built entirely in Rust. Despite conventional wisdom suggesting RoR, Python, or TypeScript for rapid MVP development, I chose Rust to challenge myself technically and optimize for low operational costs. The result? A performant application running on a $7/month VM, demonstrating that you can launch lean and scale efficiently.

Why Rust for a Bootstrapped MVP?

Cost-Efficiency: Minimal resource usage translates to lower hosting costs, crucial for bootstrapping.

Performance: Efficient handling of audio processing and web serving, allowing for growth without immediate infrastructure scaling.

Learning Investment: Deepening expertise in a language with growing demand, potentially opening future opportunities.

Proving a Point: Demonstrating Rust's viability for rapid development in web applications.

Project Overview:

Audioscrape aggregates podcast RSS feeds, transcribes episodes, and provides an interactive interface for exploring and discussing podcast content. It's a full-stack Rust application, leveraging various crates from the ecosystem.

Technical Stack:

Backend: Axum (async web framework)

Database: SQLite with SQLx for type-safe queries

Authentication: OAuth2

Image Processing: For social media previews

HTML Templating: Askama

Async Runtime: Tokio

Development Approach:

4k LoC in a single main.rs file (plus HTML templates)

Rapid iteration using Neovim for navigation

Lean development: No external services beyond the VM

Key Features:

Transcription Browsing: Read along with your favorite podcasts or quickly scan for topics of interest.

Segment Highlighting: Share and discuss specific moments from episodes.

Community Interaction: Upvote, comment on, and bookmark your favorite segments.

Advanced Search: Find relevant content across multiple podcasts and episodes.

Person Profiles: Explore appearances of specific guests or hosts across different shows.

Challenges and Learnings:

Overcoming the learning curve of Rust's ownership model

Efficient transcriptions and speaker detection

Managing all aspects solo: Development, design, and operations

Future Plans:

API for third-party integrations

WebAssembly for client-side processing

Scaling strategies for larger datasets (search)

Personalized content delivery

Metrics and Goals:

Current Users: 500 (last 7 days)

MRR: $0

Cost per User: Less than $0.01 (thanks to efficient resource use)

Next Milestone: Add paid features to pay the bills + reduce cost per episode page creation

Questions for the HN Community:

Has anyone else bootstrapped using Rust? What were your experiences?

How do you balance technical debt vs. shipping features in a solo project?

What strategies have worked for you in marketing such technical products?

I'm bootstrapping this project and would greatly appreciate any feedback, especially from those who've launched similar technical products or used unconventional tech stacks for MVPs. Check out Audioscrape at www.audioscrape.com and let me know your thoughts!