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Shortwhale – Get fewer, shorter, and better emails

Hacker News

Shortwhale – Get fewer, shorter, and better emails

Dan Ariely and I are looking for ways to help people with high email load. One idea was Shortwhale, which we've tested for a few months now. The idea is super simple: It's a place to tell people how you prefer email and it offers a simple contact form that adds the necessary structure to email. Although technically trivial, it works extremely well for Dan. We have ideas on how to expand this but first we'd like to know if someone else might find this useful too (we know that this is only a solution for people who get tons of (unsolicited) email). Underlying to all of this is the idea to put more demands on the sender (there are things to pick from a drop-down and people can create multiple-choice emails, which allows the receiver to answer with one click). However, what we found is that it can also make it easier for people to write Dan because it actually removes some demands from them: they know that he doesn't expect any formalities and the structure helps them too. And, above all, senders are more likely to get a response (and quicker). Please have a look: http://shortwhale.com As an example, here's Dan's Shortwhale page (he links to it from his website and in his email signature): http://shortwhale.com/danariely HN, we'd love to hear what you think. Many thanks, Dan Ariely and Dominik Grolimund PS: If you're interested, Lifehacker published an interview with Dan where he talks about "how he works": lifehacker.com/im-dan-ariely-author-and-professor-and-this-is-how-i-1615748781

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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 · Missing: mac, agents, macos
81%81% 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
57%57% 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
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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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