The Tiny Book of Great Joys

Hacker News

Hey, I want to share a personal project: I made a tiny pen-plotted book for my wife. I did everything myself—drawings (with some help from Midjourney), plotting, cutting, and binding. I even used a 3D printer to make a helper tool. It's absolutely over-engineered, but I enjoyed it a lot. Multi-disciplinary projects, especially those with a physical output, are a lot of fun for me. The post covers the process in detail, but if you're interested in anything specific, let me know. Cheers!

Visit product View on Hacker NewsFeb 13, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: tiny, physical · Missing: mac, agents, macos

73%73% 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 · Strong signals: wife · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

13%13% 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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