I wrote a book about remodeling old houses

Hacker News

Heya HN! Over the summer I took a much needed break from working on software all day (I'm a designer who codes) and wrote a book about remodeling old houses instead. Over the last 8 years or so I've found working with my hands – building things, flipping houses, learning new skills – to be incredibly fulfilling. There's just something about ending your day physically tired vs mentally tired that's just so different. In 2020 in particular I decided renovate our primary bath top to bottom, learning a ton about tiling, plumbing, electrical, and so much more. After three massive house projects – and a ton of mistakes – I decided to write down everything I've learned about remodeling a house. If you've ever wanted to try some DIY projects around the house or make bigger changes to your space you might enjoy the book. I'd love to know what you think or hear about your projects!

Visit product View on Hacker NewsSep 1, 2022

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

83%83% 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: ide · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, 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

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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