ReadStats– A Free, Client-Side Tool for Visualizing Your Goodreads Data

Hacker News

I built ReadStats a while ago to help me understand my reading habits better. It's a simple tool that takes your Goodreads data and generates visualizations of your reading history, all in your browser. Key features: - Easy import of data directly from your Goodreads CSV export - Completely free to use - 100% client-side processing - no data is collected or stored - Data exploration of your readings divided in 6 sections (Reading Journey, Ratings, Authors, Publication Date, Book Length, Controversial); each with many charts and highlights. It's not groundbreaking, but I think it's cool. The best part is that it respects your privacy - all data processing happens in your browser, so your reading history stays with you. Link: https://www.readstats.com PS: If you don't feel like using your data you can check it out with my some demo data here https://www.readstats.com/app?defaultData=true#overview I've a couple of other projects now but lately I was feeling like improving it, so I'm open to feedback and suggestions! Thanks for checking it out, hope you like it!

Visit product View on Hacker NewsAug 24, 2024

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

53%53% 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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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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