Sharing my simple Telegram gratitude bot

Hacker News

I built this bot two years ago while learning to code. It is very crude but does the job. bot: https://t.me/gratitude_buddy_bot github: https://github.com/ikraamg/gratitude-buddy-bot After failing to get myself to do gratitude journaling through other means, I got the idea of a simple chatbot. For me, the 'killer feature' that kept me using it was a daily reminder of a past entry that got me to smile on a pleasant memory. I thought it might be helpful to more than my friends, family, and me. Feel free to use the same instance I use, or run it on your server. I know this will not scale, so if many people find it useful, I will rewrite the bot in rails, add a web interface and migrate the data. P.S. I had not dealt with databases at the time I built this, so please forgive the "DB" I used :)

Visit product View on Hacker NewsFeb 2, 2022

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

59%59% 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 NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

49%49% 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: using, code · Missing: mac, agents, macos

47%47% 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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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