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Perfect your presentation with a panel of AI reviewers

Hacker News

Perfect your presentation with a panel of AI reviewers

Hi HN! We (Thorntale) are a startup building presentation tools, we noticed that while people disliked the tedium of making slide decks, a lot of people actively dread presenting those decks, especially if it's a high-stakes call or if they're inexperienced. Speaking from experience, practice helps a lot, but it can feel embarrassing to give the same pitch to your friend or cofounder for the tenth time. We built this prototype https://review.thorntale.com/ as a way to practice; upload a PDF deck, give your presentation, and the selected AI "personas" will analyze the slides and transcript and provide feedback. At the moment, we've tuned "personas" to give you feedback from the perspective of an investor, a teacher, or a marketing/branding lead (as well as a few entertaining extras.) If you have a particular use case, shoot us a comment, we're refining the persona system and planning what else to build there. The tool is totally free and doesn't require signup or login; we'd love any feedback if you check it out! https://review.thorntale.com/

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10points
7comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
70%70% 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, 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
34%34% 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
32%32% 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, active · Missing: mrr, revenue, profit
14%14% 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.

Incorrect prediction on native model

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