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I made an open-source webapp to create memorial pages easily

Hacker News

I made an open-source webapp to create memorial pages easily

Recently, one of my family members passed away, and as a software engineer, I felt compelled to use my Python/Django skills to create something meaningful in his honor. I discovered nice platforms for creating memorial pages, like Cake, Forever Missed, Much Loved, and so forth. However, given my mental state at the time, I felt there were too many steps and overwhelming options to get this done quickly. So, I turned to Carrd.com and created a very basic page with a link to send emails to my wife and me, where you could share condolences, support messages, book recommendations, and so forth. After that, I thought, "why not create a platform to build these kinds of pages in just one step and start receiving support from friends and family without feeling pressured to answer the messages?" And after two weeks of coding, I created EasyTribute.com Source code https://github.com/mariorojas/easytribute So, feel free to take a look, and thank you all for your support and feedback.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, wife · Missing: supports, reddit linkedin, podcasting
87%87% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, coding, code · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% 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.

Correct prediction on native model

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