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Willow Voice (YC X25) – Personalized Dictation You Can Use Anywhere

Hacker News

Willow Voice (YC X25) – Personalized Dictation You Can Use Anywhere

Hi HN, we're Lawrence and Allan, and we're building Willow Voice, a voice dictation tool for people who type a lot and want to move faster. We started out building for healthcare—first assisted living, then SNFs, then outpatient clinics—but the idea that stuck wasn’t the vertical. It was voice. We watched elderly patients use dictation to stay connected, and doctors reclaim hours with AI scribes. That’s when we realized dictation shouldn’t be limited to healthcare. If it were fast, accurate, and personal enough, everyone could benefit. Willow is not basic speech-to-text. It runs in the cloud for low-latency, supports technical terms and custom dictionaries, and learns your formatting and syntax preferences over time. Our users say it feels like it “just works,” even if they’ve never used dictation before. Some things we’ve already built: 1. Real-time dictation with extremely fast output 2. Context-aware text generation 3. Automatic formatting and structure detection Privacy is our absolute top priority - we do not store or retain voice data by default. Here are a few simple demos: 1. https://youtu.be/yFxH5HY-72Y?si=krI9WNFKBQE1Hvph 2. https://youtu.be/l2zlECbQQcU?si=fJLX-oYrflOI02kj 3. https://youtu.be/yKTIVCZbwHY?si=ohRMpuAUS8eqceAQ We’d love feedback from the HN community, technical questions, product ideas, and more! You can try it at https://willowvoice.com . Happy to answer any questions in the comments!

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3points
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, context · Missing: mac, agents, macos
90%90% 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, started · Missing: reddit linkedin, podcasting, created
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
65%65% 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 · Strong signals: users · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, users · Missing: mobile apps, ios, entrepreneurs
42%42% 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
18%18% 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.

Incorrect prediction on native model

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