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OpenAI/reflect – Physical AI Assistant that illuminates your life

Hacker News

OpenAI/reflect – Physical AI Assistant that illuminates your life

I have been working on making WebRTC + Embedded Devices easier for a few years. This is a hackathon project that pulled some of that together. I hope others build on it/it inspires them to play with hardware. I worked on it with two other people and I had a lot of fun with some of the ideas that came out of it. * Extendable/hackable - I tried to keep the code as simple as possible so others can fork/modify easily. * Communicate with light. With function calling it changes the light bulb, so it can match your mood or feelings. * Populate info from clients you control. I wanted to experiment with having it guide you through yesterday/today. * Phone as control. Setting up new devices can be frustrating. I liked that this didn't require any WiFi setup, it just routed everything through your phone. Also cool then that they device doesn't actually have any sensitive data on it.

Visit product View on Hacker NewsAug 19, 2025

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92points

52comments

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

72%72% 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: ide, io · Missing: https docs, excited, just released

56%56% 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 · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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