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OfficeHours – A Card Deck for Startups

Hacker News

OfficeHours – A Card Deck for Startups

I've been working on my first physical product recently and wanted to get some feedback at this early stage. It's a card deck of questions that you can use as a way of practicing for an elevator pitch, accelerator interview or even as an ice breaker at a meetup. I created it based on my experiences in an accelerator program a few years ago. I found the most valuable part of Office Hours was the back and forth discussion with a mentor, having to vocalise my ideas and fielding all kinds of questions. Really this deck is meant to be a pocket version of that interaction. Going forward custom decks are also a possibility too. They could be good promotional tool for trainers, businesses and event organisers. Would love to hear any other suggestions or improvements for the site or the deck. Thanks.

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Actual performance

5points
3comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
85%85% 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: physical · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
38%38% 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 · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
26%26% 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
11%11% 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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