Zero Lines of Code: A GPU Scheduling System and a Paid Transcription API Built by AI
A working, paid SaaS product built entirely by AI — including a custom infrastructure scheduler — resets expectations for what a solo developer can ship in under two weeks. The deliberate choice to expose MCP alongside a REST API signals a practical bet that AI agents, not just human users, are becoming a paying audience.
PennyScribe converts audio and video into clean, multilingual text using Qwen ASR, deliberately skipping timestamp generation to keep the pipeline simple and costs low. The service is designed for AI agents and developer workflows that need to feed transcripts into downstream summarization, extraction, or retrieval tasks. It exposes CLI, API, and MCP interfaces rather than locking functionality behind a UI.
Under the hood, the infrastructure runs on cheap consumer GPUs rented from Vast.ai. Because those instances are inherently unstable, the builder used Codex to scrap the official Serverless option and write a custom GPU instance scheduler from scratch. The entire product, including the scheduling layer, was produced without writing a single line of code manually.
What would have been a three-month solo build was compressed to under two weeks. The current bottleneck is GPU scheduling throughput, and further architecture work aims to drive down the per-hour transcription price.
Treating AI agents as a paying customer segment — not just human users — is a product thesis that justifies exposing MCP alongside a traditional API.
Omitting timestamps is a deliberate scope cut that avoids a known weak point in the model pipeline, trading a common feature for higher reliability and lower cost.
Building a custom scheduler on top of spotty consumer GPUs, rather than paying for managed infrastructure, is a cost play that only becomes practical when AI handles the integration work.