I fixed journaling for myself

Hacker News

Journaling is a great practice to learn more about yourself - if done daily. I tried it multiple times and couldn't stick to it. This project is how I finally fixed it for myself. It works by eliminating my excuses. There is only one question a day that I'll answer - no time spent on "finding the perfect topic". I only need to go to the page - not find my notebook or create a new note/paragraph in another app. I only left myself a relatively small input area, less than a page in a small notebook - that way the commitment doesn't feel too big. I really enjoy the process and it has become something that I do early in the morning - a little bit of time for myself. It's now public because I am sure it could work for you too. Bonus: The data is all local, the input will be saved in the browser (IndexedDB) while I type, no login necessary - the full journal is accessible as a CSV (bottom right). https://dailyprompt.org

Visit product View on Hacker NewsJul 18, 2022

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

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best fitHighest predicted score across all platforms for this description.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%50% 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

28%28% 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

18%18% 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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