Lizard Optimization

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Lizard Optimization

Hey HN, I wrote a new book, called Lizard Optimization; Grab it for free for the next 48 hours using this coupon link: https://leanpub.com/lizardoptimization/c/OU26BerIXIt5 The coupon link allows you to download the ebook for free (epub/PDF) from LeanPub, and it's valid until 2024-08-26 15:50 UTC. The book is about a bunch of experiments that unlocked exponential growth for Narakeet, a SaaS tool I'm working on, and helped usage grow by 500 times in 12 months. I'm very grateful for all the insights from the HN community over the last few years, and as a small thanks I'd like to give you early access to the book for free (it officially comes out on 1st September). The book is called Lizard Optimization; and the key topic is a process of engaging long-tail users that seem to follow some unexplainable "lizard" logic. This helped us reduce churn and keep customers engaged for longer, uncover new feature ideas, engage overlooked categories of users, and address larger markets. Hopefully it helps you do the same. For a bit more info about the book, check out https://lizardoptimization.org/ For people who prefer paper books, it's also on Amazon ( https://www.amazon.com/Lizard-Optimization-Product-Engaging-... ), it will start shipping on September 1st.

Visit product View on Hacker NewsAug 24, 2024

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best fitHighest predicted score across all platforms for this description.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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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