Made a tool to find pitch deck examples, Make and share pitch deck

Hacker News

Hello HN, Really glad to share what I have been working on for the past 3 months now. This is a journey of solving one of my personal core problems when designing pitch deck for my clients , I am always having to look out for places where I can find pitch deck examples, collaborate with my clients on a pitch deck and get pitch deck designs done that converts investors Meet pitchdeck design, Your all in one place for pitch deck, You can find inspiration from 1k+ pitch deck , share a pitch deck example to anyone , Make a pitch deck and also purchase pitch deck templates. All the features we have built has been based on user feedback and we listen to our users a lot Would love to know how we can help your fundraising better and please give us feedback to take our product to the next level

Visit product View on Hacker NewsApr 15, 2024

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Actual performance

3points

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Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

85%85% 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.

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

46%46% 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: users · 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: personal, month, users · Missing: mobile apps, ios, entrepreneurs

34%34% 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

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: collaborate · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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