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Hacker News Map

Hacker News

Hacker News Map

Hey Everyone! We made a hacker news map so you can hang out with other readers in the cities you're in: https://summerplaybook.com/g/HN-Map/e2e3d3bc5699417b8c54bbb252bebbf1?in=v6&rn=hn We run this website, summer playbook, which helps college students meet others near them over the summer Thought we'd toss up a hacker news map while we're at it. Catch us around the birthplace of YC, Cambridge, MA, if you sign up :) It could use a little polish on some of the edges but it gets the job done. The goal here is just to help people meet others around them. Though we wouldn't mind if a startup or two resulted from it (see: how the zapier founders met!) If it turns out a lot of people really like it, maybe we'll polish it up some more, but we're mostly focused on freight software right now and really just want this to help us meet other HN readers. How it works is you add your name, location, any travel dates, and a bio. You'll then see a map with other HN users and can message them. They'll then get an email with your email in the 'replyto' section. P.S. there's a part in the signup where you can add your gmail contacts -- feel free to skip that. It adds a "gmail contacts" map where you can see others who opted in on a map, if that's something interesting to you. It's a way to see friends and acquaintances using the site even when they're on other maps.

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: user, new, email · 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: hacker news, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
22%22% 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
13%13% 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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