E

E is for ENSHITTIFICATION – An illustrated children's book on big tech

Hacker News

E is for ENSHITTIFICATION – An illustrated children's book on big tech

My team wrote and illustrated a satirical toddler board book on the topic of Enshittification — perfect for tech professionals, savvy toddlers, and tired adults alike. A is for Algorithms. B is for Billionaire. C/D is for Cory Doctorow. Thought y'all might be interested. There's a free PDF or you can request a copy for future print runs. Would love to hear what you think. https://www.evanshunt.com/enshittification

Visit product View on Hacker NewsMar 25, 2026

Actual performance

26points

1comments

Made the leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% 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.

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

30%30% 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

25%25% 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.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

8%8% predicted probability of success on Product Hunt, 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.

Correct prediction on native model

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