I'm 17 and built a voice assistant that networks for you

Hacker News

Hey HN, I'm Dris! I'm 17, and I built Walker, a voice assistant that connects college students with other ambitious people in under 5 minutes. Here's the (demo?) video: https://youtu.be/jldOWJ6AMdg Just call Walker, share your story & get matched with someone on-campus worth connecting with. No downloads, awkward intros, or onboarding. Plenty of cold DMs & conversations lead nowhere. Walker fixes that. Currently invite-only @ a few biz/finance schools. Feedback & critiques would be super helpful!

Visit product View on Hacker NewsApr 30, 2025

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Launch Intel predictions

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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.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, 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

36%36% 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 · Missing: arr, mrr, revenue

23%23% 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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