Bite-Sized Summaries of Patents

Hacker News

Hi everyone For the past several months I have been building a platform to publish bite-sized summaries of latest patents. The purpose is to help engineers and product managers keep track of changing technology landscape and learn faster. Recently, we published summaries of 22 Apple patents related to augmented & virtual reality. Follow this link to read: https://www.fastscience.tv/collections/k1ntwsy7lJLihhL5iA89 Use cases described in summaries: Move around furniture, make travel less boring, a better Google Cardboard, enhanced interaction with maps, annotate real world objects & more. Please follow the collection if you like the idea :)

Visit product View on Hacker NewsJul 24, 2020

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% 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: apple, google · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, 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

58%58% 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: platform · Missing: plus, intuitive, reviews

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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