Pitchleague.ai – Perfect Your Pitch Deck

Hacker News

Hey HN, We're excited to share that we've already assisted over 800 founders in refining their pitch decks with our recently launched Pitch League AI. Created during a one-day hackathon, our tool harnesses the power of GPT-4 to help founders iterate and enhance their pitch decks. We've observed founders uploading their decks multiple times and making improvements based on the AI-generated feedback. Our mission is to support as many founders as possible in raising funds for their startups, starting with perfecting their pitch decks. While Pitch League AI is still a work in progress, users have found it genuinely helpful thus far. The platform extracts text from PDFs and employs GPT-4 prompts to evaluate and ensure the completeness of pitch decks across various categories, including deck length, grammar & spelling, and the inclusion of vital information (e.g., problem, solution, team, etc.). We'd love to hear your thoughts and suggestions on how we can enhance Pitch League AI and any additional features you believe would be valuable. Looking forward to your feedback! Cheers.

Visit product View on Hacker NewsApr 15, 2023

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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 · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting

79%79% 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: user · Missing: mac, agents, macos

79%79% 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: excited, lua, io · Missing: https docs, just released, exist

40%40% 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: users · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

27%27% predicted probability of success on AppSumo, 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 · 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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