Turn any Python function into a WebRTC/WebSocket stream

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I built FastRTC to solve the headaches of working with realtime audio/video. If you've ever tried building audio applications with LLMs or speech models, you know how frustrating it can be. WebRTC and Websockets are difficult to get right. Voice detection, turn-taking, and building test UIs all require significant effort that distracts from your core application logic. FastRTC addresses these issues by letting you convert any Python function into a realtime WebRTC/Websocket stream with minimal code. While it's built in Python, even non-Python developers can benefit through its API endpoints and ready-to-use interfaces. Key features: - Transform Python functions into streaming endpoints with a few lines of code - Built-in UI for immediate testing without frontend work - FastAPI integration for custom frontend deployment - Built-in utilities for on-device text-to-speech and speech-to-text to get started (Moonshine/Kokoro TTS) Example: Creating a voice assistant in under 20 lines of code link: https://fastrtc.org/#__tabbed_1_2 For JavaScript/TypeScript developers, you can easily set up a FastRTC server in Python and connect to it from your frontend application - no need to implement complex WebRTC logic on your end. Check out the cookbook for example applications built with Gemini Realtime API, OpenAI Realtime API, ElevenLabs and open source models: https://fastrtc.org/cookbook

Visit product View on Hacker NewsFeb 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, elevenlabs · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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