I built a voice-only AI language tutor using OpenAI's Realtime API

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I'm an expat in Bangkok who can read Thai menus but freezes the moment someone speaks to me. Duolingo didn't help, I needed actual speaking practice, but tutors are expensive and scheduling is a pain. So I built speaklanguageonline.com - a voice call with an AI that speaks Thai (or Vietnamese), listens to your attempts, and gives you one gentle correction at a time. No typing, no flashcards, just talking. Tech: OpenAI Realtime API (WebRTC) for speech-to-speech Next.js 14 + Vercel No transcription step - the model processes audio directly, which preserves tone (critical for Thai's 5 tones) What makes it different from ChatGPT voice: - Tuned for slow, patient corrections (not conversational chat) - One correction per turn (anxiety-inducing to get 5 things wrong at once) - Corrections explained in your native language - 3-minute session cap to keep it focused Pricing: Credits, not subscriptions. You pay only for minutes used. I hate subscription guilt as much as you do. Current state: Thai and Vietnamese work well. Adding Spanish, Hindi, Mandarin soon — OpenAI's model handles them but quality varies. What I learned: Realtime API latency is ~300-500ms which feels natural for conversation Prompting for "one correction only" took way more iteration than expected Tonal languages need explicit instruction to focus on tone mistakes Would love feedback, especially from anyone who's built voice-first apps or is learning a language.

Visit product View on Hacker NewsJan 27, 2026

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

92%92% 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: mistakes · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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