Ma

Mail-a-Day (a weekend project)

Hacker News

Mail-a-Day (a weekend project)

My dog Mel will send you an email for the next 7 days! This is a fun weekend project that took two extra days! I just wanted to make something that I could call "finished", although I still would like to include timezones (and maybe payments, analytics, marketing campaigns, load balancers, etc., all those things that would look good on my CV). So, you write the subject line, the message, set the time, and you'll get that email at that time for the next 7 days. I'm using it to remind me every morning to take life easy. There's no registration, your email address will be deleted when you stop receiving the emails. Everything worth mentioning is in the FAQs. Tech-wise, it's a plain-simple "boring" server-side rendered django app with a sqlite3 database, and a bootstrap frontend. There's no backups, so if the database gets deleted, you'll stop receiving the emails (just create them again). Have fun! http://mailaday.com/

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Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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, using, plain · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% 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 · 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 · 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
17%17% 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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