Krisi, report forecasting performance over time

Hacker News

Hi HN, We realised that tracking the performance of our predictions over time was crucial to choose, debug and fine-tune our forecasting model. So we built Krisi ( https://github.com/dream-faster/krisi ) to help you with tracking metrics over time and generate informative reports. We'll be here answering questions - would love to hear your feedback! Here is the related blog post: https://www.appliedexploration.com/p/say-goodbye-to-collapse...

Visit product View on Hacker NewsMay 8, 2023

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Actual performance

4points

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: model · 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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% 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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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