FastPitchDeck – AI to generate VC-ready pitch decks

Hacker News

For my last startup, I spent over 100 hours tweaking pitch decks in PowerPoint, Google Slides, and Figma. Every investor asked for something different, and I kept getting lost in formatting hell instead of focusing on storytelling. After talking to 50+ founders, I realized this was a universal problem: Wasting time on design instead of messaging. Struggling to structure a compelling narrative. No ‘source of truth’ for updates across versions. So I built FastPitchDeck: a tool that generates investor-ready decks in minutes, not days. It uses AI to suggest slide structures, auto-formats everything, and keeps decks synced with your startup’s metrics. Key features: AI-powered outline generator (based on YC/Techstars best practices) One-click styling (no more pixel-pushing) Live data integration (tie slides to your Stripe, Google Analytics, etc.) Collaborative editing (founder-friendly, no design skills needed) Try the free beta here: https://fastpitchdeckai.vercel.app/ (no VC required, just founder love).

Visit product View on Hacker NewsJun 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, stripe, 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 · Missing: supports, reddit linkedin, podcasting

78%78% 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: ide, io · Missing: https docs, excited, just released

42%42% 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: google · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

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

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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