TLDR your video-meetings with GPT-3

Hacker News

We recently plugged in GPT-3 to our software which can record / transcribe / search and share meetings. And the quality of the meeting minutes outputted is just baffling. GPT-3 understands what a discussion revolves around - even if the subject matter is only mentionned once - and summarizes it really efficiently. It lays out the different topics and always puts forward the next steps. Linked is a quick example on one of my conversations with a company in the Philippines. An amusing bug was that if for whatever reason no to little transcription was outputted, GPT-3 would insult the participants (is it trained on Reddit??). The danger would be if GPT-3 misinterprets something and this then gets into the formal decision process of a company. However I haven't seen it yet after 1 month of live production and a little more than a hundred of internal calls tried out on. Check it out it's free to try out: https://cutt.ly/welcome_to_spoke :)

Visit product View on Hacker NewsJul 19, 2022

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

89%89% 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: efficiently · Missing: supports, reddit linkedin, podcasting

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AppSumoStrong fit for a featured deal · Strong signals: efficient, calls · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, 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

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% 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

16%16% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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