I Open-Sourced My AI Toy–ESP32, OpenAI Realtime API on Deno, Supabase

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This project is built with Vercel, Supabase, Deno, Arduino. It helps you run a secure websocket on the ESP32 using Deno edge functions on Arduino + Platform IO. It has built in use of Opus that gives you up to 10 minutes of smooth conversations globally. I built this because OpenAI's realtime WebRTC ESP-IDF project was too complex. This project is easy to get started and to deploy your AI ESP32 based toys in the real world. I appreciate any feedback

Visit product View on Hacker NewsApr 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, using, open · Missing: mac, agents, macos

93%93% 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.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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