Stella Foster – iMessage on Any Phone

Hacker News

Hey HN — I built Stella Foster (stellafoster.com). Stella lets you access your iMessages, WhatsApps, and Telegrams from any phone — flip phone, feature phone, anything. A Mac desktop app connects to your messaging channels natively. Once that's running, Stella has a phone number — you text or call her from any device and she gives you your messages. No apps or internet needed on the second phone. If it can call or text, it works. Free to try. Happy to get into the technical details in the comments.

Visit product View on Hacker NewsMar 25, 2026

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Launch Intel predictions

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

84%84% 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 · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% 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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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