Create intelligent shareable media libraries

Hacker News

Hey HN, Our team at Speak Ai is excited to share V1 of our shareable media library with you! As more marketers, researchers and every day people starting using our software we realized they were looking for better ways to collect, analyze and share information-rich language data. They needed to do this quickly and without code or a development team. They also wanted to be able to custom brand their libraries and update data in real-time. That is why we built this. The shareable media library is a culmination of years of work in speech recognition, NLP and media. We are evolving the function to include better insights, more inputs, and simple ways to get answers through natural language queries. If you work in research, marketing or are passionate about exploring personal insights, we would love to get feedback on how we can make this offering even more valuable for you.

Visit product View on Hacker NewsFeb 8, 2022

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

95%95% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

21%21% 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.

Correct prediction on native model

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