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Audkite – Fastest way to discover giveaways across universe

Hacker News

Audkite – Fastest way to discover giveaways across universe

Hi Gilbert here, co-founder of Audkite(https://audkite-app.firebaseapp.com). Audkite is an app that helps users find massive one-time giveaways through 15-second audio, play and directly connect to the brand through a call or text all in one app. Cold calling is a painful and annoying experience we consumers face when we are sold to something we don't want or never intended to purchase. And what happens when you need to purchase anything and want to have an actual feel with the brand or seller? Of course, you would use craigslist but often you may fall into trap of a scam. In other scenarios, you receive a call from a product you signed for e.g Front.app and you're from Cape Town, the brand is from Paris, you can't easily get back to the brand because its costly to make international calls. Audkite removes all those barriers by letting consumers decide for themselves through AI-powered recommendations and later scheduling direct calls to the brands. Brands have central channels just like slack or a chatroom and consumers can reach out back and forth through texts or phone call. We also protect users' privacy by making giveaways last only 24hrs plus anonymize user's identity by making calls or texts through the app. For any questions or feedback, feel free to reach out!

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
83%83% 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: slack, user · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, users, way · Missing: mobile apps, personal, entrepreneurs
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users, calls · Missing: platform, intuitive, reviews
44%44% 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
41%41% 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: arr · Missing: mrr, revenue, profit
15%15% 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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