Spraff – Voice and text AI chat, self-hostable, no data retention

Hacker News

Hi HN! I built a simple, self-hostable AI chat app that focuses on voice input and privacy. Why I made this: I want to talk to AI (voice or text) without my conversations being logged, used for training etc. Requires an OpenRouter account. No subscription: pay-as-you-go with most conversations costing a fraction of a cent (a couple of cents for requests with search enabled) Privacy: - Uses OpenRouter to route to Gemini 3 Flash on Google Vertex with Zero Data Retention (ZDR). - Prompts and responses aren't stored or logged at any layer. - OpenRouter keeps only metadata like timestamps and token counts. How it works: - Single HTML file – download and self-host, or use the hosted version https://martinpllu.github.io/spraff - Log in to OpenRouter via OAuth (API key kept in browser local storage until logout) - Audio recorded in browser, sent directly to Gemini for processing (no separate transcription step) - Text-to-speech via your device's local voices Technical notes: - Gemini 3 Flash is currently the only model that supports both audio input and ZDR - Provider is pinned to Google Vertex with zdr: true in the API request - No build step, no dependencies – just one HTML file - Current status: beta, tested on MacOS, Android and iOS. Should work but issues/feedback welcome. Code: https://github.com/martinpllu/spraff

Visit product View on Hacker NewsDec 30, 2025

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

96%96% 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: supports, para, ios · Missing: reddit linkedin, podcasting, created

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

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

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: host · Missing: plus, platform, intuitive

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

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

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription, training · 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.

Correct prediction on native model

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