Sokuji – speech real-time translation powered by Gemini Live APIs

Hacker News

Sokuji Now Supports Gemini 2.5 Flash & 2.0 Flash Live APIs (Beta) Real-time AI translation powered by Google's latest Gemini models - now in beta testing! We're excited to announce that Sokuji now supports Google's cutting-edge Gemini APIs in beta: New Beta Features: - Gemini 2.5 Flash Live API Native Audio Preview - Advanced audio processing, optimized for Asian languages (Japanese, Korean, etc.) - Gemini 2.0 Flash Live API Preview - Cost-effective multimodal AI, excellent for major languages (English, French, Portuguese, etc.) Note: This is currently in experimental phase as we integrate these powerful new APIs. Free Tier Available: Gemini 2.0 Flash (More cost-effective): - 15 requests/minute, 1M tokens/minute, 1,500 requests/day - Great for: English, French, Portuguese, Spanish, German Gemini 2.5 Flash (Enhanced language support): - 10 requests/minute, 250K tokens/minute, 500 requests/day - Optimized for: Japanese, Korean, Chinese, and other Asian languages Live API Native Audio: - 1-3 concurrent sessions, 25K-50K tokens/minute Source: Google AI Rate Limits Documentation https://ai.google.dev/gemini-api/docs/rate-limits Choose Your Model: Budget-conscious + Major languages → Gemini 2.0 Flash Asian languages + Premium features → Gemini 2.5 Flash Both available for free during beta testing! Beta Status: We're actively testing and refining the integration. Some advanced features are still being implemented. Your feedback helps us improve!

Visit product View on Hacker NewsJun 20, 2025

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

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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