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Soliciting post placement on Hacker News

Hacker News

Soliciting post placement on Hacker News

Hey, HN -- I just received the following email and I'm not sure what to do about it. Obviously I want to discourage requested-posting like this; is there some structured way that we can do that? One of the things I love about HN is how high the signal-to-noise ratio is relative to...pretty much the rest of the internet. I wouldn't ordinarily call someone out like this, but I want to innoculate HN against this sort of thing. ---- Hey doches, Not sure if it's conventional, but thought I'd ask anyway. Would you be open to posting about my product on Hacker News? I understand that similar to Product Hunt, HN gives higher priority to users with higher karma. I usually keep up with recent discussions via RSS feed on Feedly, but almost neve post. So I thought I'd reach out to someone that has authority. My product is called Popupular and it helps embed just about anything into a popup via a Google Chrome extension. [rest of email truncated]

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Actual performance

11points
12comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos
84%84% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, 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
53%53% 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 · Strong signals: users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
35%35% 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
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.

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