The Hypermail Experiment: Social Note-Taking for Lurkers

Hacker News

There's no sign-up required. Just start sending emails to me@hypermail.sh. Every 24hrs you'll receive a report from you@hypermail.sh. The report contains a summary of all your notes within the time range. The report also contains anonymous summaries of other users based on a similarity score. This was originally meant to be a demo for another blog post but after building it I thought it was interesting enough to be it's own thing. I wanted to make something for people who don't contribute heavily to social platforms (like myself!). The blog post contains information about how and why it's designed this way. I'd love to know what you think :) NOTE: You'll receive a Welcome email when you first send a message. If it arrives in your Spam folder, it'd be really kind if you could mark it not spam. NOTE: You won't get spammed by Hypermail. You receive a report only if you sent a email in the 24hr time range.

Visit product View on Hacker NewsFeb 18, 2023

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3points

Did not reach leaderboard

Launch Intel predictions

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

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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